From 189171d9acda79be444606548a1d34a0548c6ce4 Mon Sep 17 00:00:00 2001 From: ankitat Date: Thu, 14 May 2026 14:14:32 +0530 Subject: [PATCH 1/7] migrate: add nvds_logger and tracker_ReID model docs from mono-repo src/utils/README.md (already migrated) referenced these two paths under its "Tooling & Model Setup" section, but the files themselves were not brought over by PR #2 (DS OSS reference apps + tools migration). This fills in the dangling references. - src/utils/nvds_logger/: setup_nvds_logger.sh + upstream README. The script configures rsyslog to capture DeepStream DSLOG messages to /var/log/nvds/. - src/utils/nvmultiobjecttracker/model/tracker_ReID/: upstream README covering SAM2 segmentation, TAO ReidentificationNet, and TAO BodyPose3DNet model prep for the multi-object tracker. Source: GitLab DeepStream mono-repo, !89 (commit 0b544bc on main). Committed-By: ankitat's Claude Agent Codex Review: Approved: --- src/utils/nvds_logger/README | 43 +++++ src/utils/nvds_logger/setup_nvds_logger.sh | 73 +++++++++ .../model/tracker_ReID/README | 153 ++++++++++++++++++ 3 files changed, 269 insertions(+) create mode 100644 src/utils/nvds_logger/README create mode 100755 src/utils/nvds_logger/setup_nvds_logger.sh create mode 100644 src/utils/nvmultiobjecttracker/model/tracker_ReID/README diff --git a/src/utils/nvds_logger/README b/src/utils/nvds_logger/README new file mode 100644 index 00000000..ffde1436 --- /dev/null +++ b/src/utils/nvds_logger/README @@ -0,0 +1,43 @@ +#################################################################################################### +# SPDX-FileCopyrightText: Copyright (c) 2018-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +#################################################################################################### +nvds_logger is a basic logging library accompanying DeepStream and related software such as adaptors, apps etc. +It is based on syslog and offers many of the related functionality, including: +* different priorities (log levels) +* log filtering and redirection +* shared logging across different deepstream instances executing simultanously +* log retirement and management (using logrotate) +* cross platform support + +See setup_dslog.sh script to setup nvds_logger + +Usage: +1) Setup nvds_logger (one time basis across system reboots): +chmod u+x ./setup_nvds_logger.sh +sudo ./setup_nvds_logger.sh + +Note that the script enables various levels that help filter logs. +Please edit the script to set the log level, as mentioned in the comments within. + +Programmatic usage: +For emitting logs from within your source code, follow these three steps + +1) Before emitting log messages, application needs to call nvds_log_open function + +2) Call nvds_log to emit log messages + +3) Prepend the log messages with a keyword (ex: DSLOG) +and match the prepended keyword in setup_nvds_logger.sh + +4) Finally close log by calling nvds_log_close upon completion (to flush logs) + +NOTE: The log file is now found at /var/log/nvds/ds.log by default. Logs must be in /var/log/ for apparmor to +allow rsyslog access. The setup script can be used to choose a filepath but it will always be prepended by /var/log/nvds/. \ No newline at end of file diff --git a/src/utils/nvds_logger/setup_nvds_logger.sh b/src/utils/nvds_logger/setup_nvds_logger.sh new file mode 100755 index 00000000..fb1628a8 --- /dev/null +++ b/src/utils/nvds_logger/setup_nvds_logger.sh @@ -0,0 +1,73 @@ +#! /bin/bash + +#################################################################################################### +# SPDX-FileCopyrightText: Copyright (c) 2018-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +#################################################################################################### + +# usage: sudo ./setup_nvds_logger.sh [log name] +# eg: sudo ./setup_nvds_logger.sh ds.log +# log name is optional, if not provided, it will default to ds.log +# Log file will be found in /var/log/nvds/[log_name] +# Note: user can set logging severity level to enable log filtering as mentioned below + +if [[ $EUID -ne 0 ]]; then + echo "This script must be run as root" + exit 1 +fi + +nvdslogpath="/var/log/nvds/" +nvdslogfilepath="/var/log/nvds/ds.log" + +if [ "$1" != "" ]; then + nvdslogfilepath="$nvdslogpath$1" +fi + +echo "Using logging location: $nvdslogfilepath" +rm -rf /run/rsyslogd.pid + +if [ ! -d $nvdslogpath ]; then + echo "Creating logging location: $nvdslogpath" + mkdir $nvdslogpath + if [ ! -d $nvdslogpath ]; then + echo "Unable to create directory at the given path; please check permissions" + exit 1 + fi +fi + +chown root $nvdslogpath +chgrp syslog $nvdslogpath +chmod g+w $nvdslogpath +touch 11-nvds.conf + +# Modify log severity level as required and rerun this script +# 0 Emergency: system is unusable +# 1 Alert: action must be taken immediately +# 2 Critical: critical conditions +# 3 Error: error conditions +# 4 Warning: warning conditions +# 5 Notice: normal but significant condition +# 6 Informational: informational messages +# 7 Debug: debug-level messages +# refer https://tools.ietf.org/html/rfc5424.html for more information + +# Currently log level is set at INFO (6). +echo "if (\$msg contains 'DSLOG') and (\$syslogseverity <= 6) then $nvdslogfilepath" >> 11-nvds.conf +echo ":msg, contains, \"DSLOG\" ~" >> 11-nvds.conf +echo "& ~" >> 11-nvds.conf +rm -rf /etc/rsyslog.g/*-nvds.conf + +cp 11-nvds.conf /etc/rsyslog.d/ +rm 11-nvds.conf +# sudo touch /etc/rsyslog.d/10-mwx.conf +chgrp syslog $nvdslogpath +service rsyslog restart +echo "nvds logging setup. Logging to $nvdslogfilepath" + diff --git a/src/utils/nvmultiobjecttracker/model/tracker_ReID/README b/src/utils/nvmultiobjecttracker/model/tracker_ReID/README new file mode 100644 index 00000000..fd7e1c71 --- /dev/null +++ b/src/utils/nvmultiobjecttracker/model/tracker_ReID/README @@ -0,0 +1,153 @@ +################################################################################ +# SPDX-FileCopyrightText: Copyright (c) 2021-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +################################################################################ + +# Deep Learning Models for Multi-Object Tracking + +## Overview + +The DeepStream SDK's multi-object tracker supports multiple deep learning models for enhanced tracking capabilities: + +- **Segmentation**: Meta Segment Anything Model 2 (SAM2) for multi-object tracking and segmentation +- **ReID**: NVIDIA TAO ReidentificationNet for re-identification and re-association +- **Pose Estimation**: NVIDIA TAO BodyPose3DNet for 3D tracking and human height estimation + +All models utilize TensorRT for accelerated inference. This guide demonstrates how to prepare these models for both dGPU and Jetson platforms. + +## Table of Contents + +- [Segmentation](#segmentation) +- [ReID](#reid) +- [Pose Estimation](#pose-estimation) + +## Segmentation + +The `config_tracker_MaskTracker.yml` configuration enables high-accuracy multi-object tracking and segmentation using SAM2 with memory bank support. This creates a segmentation module based on `config_tracker_module_Segmenter.yml`. + +### Model Source + +- **Model**: Meta SAM2 +- **URL**: [SAM2 GitHub Repository](https://github.com/facebookresearch/sam2) + +### Setup Instructions + +1. **Prepare Model Files** + ```bash + mkdir -p ../../samples/models/Tracker/ + git clone https://github.com/NVIDIA-AI-IOT/sam2-onnx-tensorrt.git + cd sam2-onnx-tensorrt + python3 -m venv sam2_env + source sam2_env/bin/activate + bash run.sh + ``` + + Visit [SAM2 ONNX TensorRT Conversion](https://github.com/NVIDIA-AI-IOT/sam2-onnx-tensorrt) to convert SAM2 models to ONNX. + + Place the following four ONNX files in the `Tracker` directory: + - `image_encoder.onnx` + - `mask_decoder.onnx` + - `memory_attention.onnx` + - `memory_encoder.onnx` + + > **Note**: TensorRT engine files will be automatically generated when you launch deepstream-app. + +2. **Configure DeepStream App** + - Navigate to `samples/configs/deepstream-app/` + - Modify the `[tracker]` configuration in your deepstream-app config file: + ```bash + ll-config-file=config_tracker_MaskTracker.yml + ``` + +3. **Run DeepStream App** + ```bash + deepstream-app -c + ``` + +## ReID + +The `config_tracker_NvDCF_accuracy.yml` and `config_tracker_NvDeepSORT.yml` configurations support ReID functionality with two options available. + +> **Warning**: UFF models have been deprecated by TensorRT. We recommend switching to ONNX or NVIDIA TAO models. + +### Option 1: NVIDIA Pretrained TAO ReID Model (Recommended) + +#### Model Source + +- **Model**: NVIDIA TAO ReidentificationNet +- **URL**: [NVIDIA NGC Catalog](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/reidentificationnet) + +#### Setup Instructions + +1. **Prepare Model Files** + ```bash + mkdir -p ../../samples/models/Tracker/ + wget 'https://api.ngc.nvidia.com/v2/models/nvidia/tao/reidentificationnet/versions/deployable_v1.0/files/resnet50_market1501.etlt' -P ../../samples/models/Tracker/ + ``` + +2. **Configure DeepStream App** + - Navigate to `samples/configs/deepstream-app/` + - Modify the `[tracker]` configuration in your deepstream-app config file: + ```bash + ll-config-file=config_tracker_NvDCF_accuracy.yml + # or ll-config-file=config_tracker_NvDeepSORT.yml + ``` + +3. **Run DeepStream App** + ```bash + deepstream-app -c + ``` + +### Option 2: Custom ONNX Model + +#### Prerequisites +- Convert your custom model to ONNX format + +#### Configuration Parameters +Modify the following parameters in your tracker config file based on your custom model architecture: + +```yaml +reidFeatureSize: 128 +inferDims: [128, 64, 3] +networkMode: 0 +inputOrder: 1 +colorFormat: 0 +offsets: [0.0, 0.0, 0.0] +netScaleFactor: 1.0000 +addFeatureNormalization: 1 +onnxFile: "change-to-model-name.onnx" +``` + +## Pose Estimation + +For 3D tracking examples, refer to the [DeepStream Reference Apps](https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps). Currently, only NVIDIA TAO BodyPose3DNet is supported. + +### Model Source +- **Model**: NVIDIA TAO BodyPose3DNet +- **URL**: [NVIDIA NGC Catalog](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/bodypose3dnet) + +### Setup Instructions + +1. **Prepare Model Files** + ```bash + mkdir -p ../../samples/models/Tracker/ + wget 'https://api.ngc.nvidia.com/v2/models/nvidia/tao/bodypose3dnet/versions/deployable_accuracy_v1.0/files/bodypose3dnet_accuracy.etlt' -P ../../samples/models/Tracker/ + ``` + +2. **Configure and Run DeepStream App** + + Visit [DeepStream Reference Apps](https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps) for how to setup and use single and multi-view 3D tracking. + +## Additional Resources + +- [DeepStream Documentation](https://docs.nvidia.com/metropolis/deepstream/) +- [NVIDIA NGC Catalog](https://catalog.ngc.nvidia.com/) +- [TensorRT Documentation](https://docs.nvidia.com/deeplearning/tensorrt/) + From 9e530a72c18d831c7a85327c08e68a076a7652ab Mon Sep 17 00:00:00 2001 From: ankitat Date: Thu, 14 May 2026 14:18:41 +0530 Subject: [PATCH 2/7] migrate: copy build.sh and BUILD.md from mono-repo MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Migrates the top-level build infrastructure from the GitLab DeepStream mono-repo (post-MR-87 main): - build.sh: bash driver that builds all utils, gst-utils, gst-plugins, sample apps, and tao apps with platform-aware CUDA_VER defaults (x86=13.1, aarch64/sbsa=13.0). Includes the nvds_msgapi adaptor loop (originally MR !87 fix #4 — descends one level deeper than the general src/utils loop and handles azure_protocol_adaptor's device_client/module_client split). - BUILD.md: end-user build documentation covering prerequisites, platform support, the build flow, and the post-build install layout. Source: GitLab DeepStream mono-repo main (build.sh from MR !87, docs/sbsa/build-md updates from later MRs already folded in). Committed-By: ankitat's Claude Agent Codex Review: Approved: --- BUILD.md | 173 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ build.sh | 138 ++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 311 insertions(+) create mode 100644 BUILD.md create mode 100755 build.sh diff --git a/BUILD.md b/BUILD.md new file mode 100644 index 00000000..da86c41a --- /dev/null +++ b/BUILD.md @@ -0,0 +1,173 @@ +# DeepStream Mono-Repo — Build & Run Guide + +## Prerequisites + +### NVIDIA Compute Stack + +Before building from source, the NVIDIA compute stack must be installed on the host. Required versions: + +| Platform | OS | Driver | CUDA | cuDNN | TensorRT | +|---|---|---|---|---|---| +| x86 dGPU | Ubuntu 24.04 | 590.48.01+ | 13.1 | 9.18.0 | 10.14.x | +| Jetson (aarch64) | JetPack 7.1 GA | — (bundled) | 13.0 | 9.12.0 | 10.13.x | +| SBSA / DGX Spark | Ubuntu 24.04 | 590.48.01+ | 13.0 | 9.18.0 | 10.14.x | + +For installation instructions, refer to the official [DeepStream SDK Installation Guide](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Installation.html). + +> **Docker users:** NVIDIA NGC provides pre-built DeepStream 9.0 containers with the full compute stack pre-installed. If using a container, skip this section and proceed to Git LFS setup below. + +--- + +### Clone the Repository + +```bash +sudo apt-get install cmake +``` + +### x86 (dGPU) + +1. Download the DS 9.0 public released Debian package, install it, and add the + DeepStream lib directory to `LD_LIBRARY_PATH` so the runtime can locate the + shared libraries: + ```bash + sudo apt install ./deepstream-9.0_9.0.0-1_amd64.deb + sudo /opt/nvidia/deepstream/deepstream-9.0/install.sh + export LD_LIBRARY_PATH=/opt/nvidia/deepstream/deepstream-9.0/lib:$LD_LIBRARY_PATH + ``` + Append the `export` line to `~/.bashrc` to persist it across shells. + +2. Install system dependencies: + ```bash + sudo apt install \ + libssl3 \ + libssl-dev \ + libcurl4-openssl-dev \ + libgles2-mesa-dev \ + libgstreamer1.0-0 \ + gstreamer1.0-tools \ + gstreamer1.0-plugins-good \ + gstreamer1.0-plugins-bad \ + gstreamer1.0-plugins-ugly \ + gstreamer1.0-libav \ + libgstreamer-plugins-base1.0-dev \ + libgstrtspserver-1.0-0 \ + libjansson4 \ + libjsoncpp-dev \ + libyaml-cpp-dev \ + libmosquitto1 + ``` + + See also the [DeepStream Installation Guide — Install Prerequisite Packages](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Installation.html#id2). + +### aarch64 (Jetson) + +1. Download the DS 9.0 public released Debian package, install it, and add the + DeepStream lib directory to `LD_LIBRARY_PATH` so the runtime can locate the + shared libraries: + ```bash + sudo apt install ./deepstream-9.0_9.0.0-1_arm64.deb + sudo /opt/nvidia/deepstream/deepstream-9.0/install.sh + export LD_LIBRARY_PATH=/opt/nvidia/deepstream/deepstream-9.0/lib:$LD_LIBRARY_PATH + ``` + Append the `export` line to `~/.bashrc` to persist it across shells. + +2. Install system dependencies: + ```bash + sudo apt install \ + libssl3 \ + libssl-dev \ + libcurl4-openssl-dev \ + libgstreamer1.0-0 \ + gstreamer1.0-tools \ + gstreamer1.0-plugins-good \ + gstreamer1.0-plugins-bad \ + gstreamer1.0-plugins-ugly \ + gstreamer1.0-libav \ + libgstreamer-plugins-base1.0-dev \ + libgstrtspserver-1.0-0 \ + libjansson4 \ + libjsoncpp-dev \ + libyaml-cpp-dev \ + libmosquitto1 + ``` + + See also the [DeepStream Installation Guide — Install Prerequisite Packages](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Installation.html#install-prerequisite-packages). + +### SBSA / DGX Spark (Server Base System Architecture) + +SBSA and DGX Spark share the same code path. There is no DS tar/deb package for this platform. The validated environment is the SBSA Docker image. +Build and validation must be done **inside the Docker container**. + +1. Start the SBSA Docker container (with GPU access): + ```bash + docker run -it --gpus all bash + ``` + +2. Install build prerequisites inside the container: + ```bash + apt-get update && apt-get install -y git cmake make build-essential + ``` + +3. `sudo` is not present in the container by default. Create a passthrough stub since you are already root: + ```bash + printf '#!/bin/sh\nexec env "$@"\n' > /usr/local/bin/sudo && chmod +x /usr/local/bin/sudo + ``` + +4. Clone the mono-repo: + ```bash + git clone https://gitlab-master.nvidia.com/DeepStreamSDK/deepstream -b main deepstream + cd deepstream + ``` + +--- + +## Build and Install + +`build.sh` auto-detects the host platform and: +- On SBSA / DGX Spark: passes `AARCH64_IS_SBSA=1` to all Makefiles (both `aarch64` and `sbsa` report `uname -m` as `aarch64`; SBSA is identified by the absence of `/etc/nv_tegra_release`) +- Builds and installs open-source dependencies (opentelemetry, civetweb, prometheus-cpp, azure-iot-sdk) +- Builds all source components and installs them directly to `/opt/nvidia/deepstream/deepstream-/` + +```bash +sudo bash build.sh +``` + +Default CUDA version is architecture-dependent (`13.1` for x86_64, `13.0` for aarch64/sbsa/DGX Spark). To override: + +```bash +sudo CUDA_VER=13.1 bash build.sh # x86 +sudo CUDA_VER=13.0 bash build.sh # aarch64 +``` + +To override the target DeepStream version (default: 9.0): + +```bash +sudo NVDS_VERSION=9.0 bash build.sh +``` + +After first run, clear the GStreamer plugin cache so it picks up newly installed plugins: + +```bash +rm -rf ~/.cache/gstreamer-1.0/ +``` + +**Build outputs:** +- GStreamer plugins → `/opt/nvidia/deepstream/deepstream-/lib/gst-plugins/` +- Utility libs → `/opt/nvidia/deepstream/deepstream-/lib/` +- Sample app binaries → `/opt/nvidia/deepstream/deepstream-/bin/` +- service-maker app bins → `/opt/nvidia/deepstream/deepstream-/bin/service-maker-` +- service-maker modules → `/opt/nvidia/deepstream/deepstream-/service-maker/modules/` + +--- + +## service-maker — Local Compilation + +Individual service-maker apps can be compiled locally without running `build.sh`. +From within any app directory (e.g. `src/service-maker/sources/apps/cpp/deepstream_test1_app/`): + +```bash +mkdir build && cd build && cmake .. && make +``` + +The binary is placed in the local `build/` directory and nothing is installed to `/opt`. +See each app's `README` for usage instructions. diff --git a/build.sh b/build.sh new file mode 100755 index 00000000..c192c1b0 --- /dev/null +++ b/build.sh @@ -0,0 +1,138 @@ +#!/usr/bin/env bash +# Build and install all DeepStream mono-repo components. +# Usage: bash build.sh [CUDA_VER=] +set -e + +ARCH=$(uname -m) +case "$ARCH" in + x86_64) PLATFORM=x86; CUDA_VER_DEFAULT=13.1 ;; + aarch64) + # Distinguish Jetson (has /etc/nv_tegra_release) from SBSA server hardware + if [ -f /etc/nv_tegra_release ]; then + PLATFORM=aarch64 + else + PLATFORM=sbsa + fi + CUDA_VER_DEFAULT=13.0 + ;; + *) echo "Unsupported architecture: $ARCH"; exit 1 ;; +esac + +CUDA_VER=${CUDA_VER:-$CUDA_VER_DEFAULT} +NVDS_VERSION=${NVDS_VERSION:-9.0} +SM_APP_BUILD=/tmp/ds-sm-apps +SM_MOD_BUILD=/tmp/ds-sm-modules +SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd) + +# Extra make flags for SBSA — passes AARCH64_IS_SBSA=1 to all Makefiles +PLATFORM_MAKE_FLAGS= +[ "$PLATFORM" = "sbsa" ] && PLATFORM_MAKE_FLAGS="AARCH64_IS_SBSA=1" + +# --------------------------------------------------------------------------- +# Prerequisites: install open-source dependencies +# (Proprietary prebuilt libs are provided by the DS 9.0 deb install at +# /opt/nvidia/deepstream/deepstream-${NVDS_VERSION}/lib/.) +# --------------------------------------------------------------------------- +echo "==> Installing open-source dependencies" +sudo NVDS_VERSION=$NVDS_VERSION bash "$SCRIPT_DIR/scripts/install_opensource_deps.sh" + +echo "==> Building PLATFORM=$PLATFORM CUDA_VER=$CUDA_VER (outputs directly to /opt/nvidia/deepstream/deepstream-${NVDS_VERSION}/)" + +MK="CUDA_VER=$CUDA_VER NVDS_VERSION=$NVDS_VERSION $PLATFORM_MAKE_FLAGS" + +# nvds_rest_server must be built before gstnvdscustomhelper (it links against it) +make -C src/utils/nvds_rest_server $MK + +# gst-utils (order matters — gstnvcustomhelper must come first) +make -C src/gst-utils/gstnvcustomhelper $MK +make -C src/gst-utils/gst-nvdssr $MK +make -C src/gst-utils/gstnvdscustomhelper $MK + +# Utils (no top-level Makefile — build each individually) +for dir in src/utils/*/; do + make -C "$dir" $MK 2>/dev/null || true +done + +# nvds_msgapi message broker adaptors (Makefiles live one level deeper than +# the rest of src/utils, and azure_protocol_adaptor splits further into +# device_client/ and module_client/). +for dir in src/utils/nvds_msgapi/*/; do + if [ -f "$dir/Makefile" ]; then + make -C "$dir" $MK 2>/dev/null || true + else + for sub in "$dir"*/; do + [ -f "$sub/Makefile" ] && make -C "$sub" $MK 2>/dev/null || true + done + fi +done + +# GStreamer plugins (no top-level Makefile — build each individually) +for dir in src/gst-plugins/*/; do + make -C "$dir" $MK 2>/dev/null || true +done + +# Sample apps (no top-level Makefile — build each individually) +# Build to a staging dir first so a failed link step does not delete the +# deb-installed binary from the DS bin dir (make removes its output on error). +SA_BIN_STAGE=/tmp/ds-sample-apps-bin +DS_BIN=/opt/nvidia/deepstream/deepstream-${NVDS_VERSION}/bin +mkdir -p "$SA_BIN_STAGE" +for dir in src/apps/sample_apps/*/; do + make -C "$dir" $MK BIN_DIR="$SA_BIN_STAGE" 2>/dev/null || true + for bin in "$SA_BIN_STAGE"/deepstream-*; do + [ -f "$bin" ] && [ -x "$bin" ] && cp -v "$bin" "$DS_BIN/" || true + done + rm -f "$SA_BIN_STAGE"/deepstream-* +done +rm -rf "$SA_BIN_STAGE" + +# Prepare Eigen lib +sudo apt update || true +sudo apt install -y libeigen3-dev +sudo ln -sf /usr/include/eigen3/Eigen /usr/include/Eigen + +#Customized Yolo postprocessing lib +make -C tools/yolo_deepstream/deepstream_yolo/nvdsinfer_custom_impl_Yolo/ $MK 2>/dev/null || true + +# tao-apps — build and install via top-level Makefile +git submodule update --init --recursive || true +make -C src/apps/tao_apps $MK 2>/dev/null || true + +# Reference apps (no top-level Makefile — build each individually) +RA_BIN_STAGE=/tmp/ds-reference-apps-bin +mkdir -p "$RA_BIN_STAGE" +for dir in src/apps/reference_apps/*/; do + make -C "$dir" $MK BIN_DIR="$RA_BIN_STAGE" 2>/dev/null || true + for bin in "$RA_BIN_STAGE"/deepstream-*; do + [ -f "$bin" ] && [ -x "$bin" ] && cp -v "$bin" "$DS_BIN/" || true + done + rm -f "$RA_BIN_STAGE"/deepstream-* +done +rm -rf "$RA_BIN_STAGE" + +# service-maker apps (cmake-based) — build and install +SM_BIN_INSTALL=/opt/nvidia/deepstream/deepstream-${NVDS_VERSION}/bin +for dir in src/service-maker/sources/apps/cpp/*/; do + app=$(basename "$dir") + cmake -S "$dir" -B "$SM_APP_BUILD/$app" -DCMAKE_BUILD_TYPE=Release + cmake --build "$SM_APP_BUILD/$app" -j$(nproc) + for bin in "$SM_APP_BUILD/$app"/deepstream-*; do + [ -f "$bin" ] && [ -x "$bin" ] || continue + name=$(basename "$bin" | sed 's/^deepstream-/service-maker-/') + cp -v "$bin" "$SM_BIN_INSTALL/$name" + done +done +rm -rf "$SM_APP_BUILD" + +# service-maker modules (cmake-based) — build and install +SM_MODULES_INSTALL=/opt/nvidia/deepstream/deepstream-${NVDS_VERSION}/service-maker/modules +mkdir -p "$SM_MODULES_INSTALL" +for dir in src/service-maker/sources/modules/*/; do + mod=$(basename "$dir") + cmake -S "$dir" -B "$SM_MOD_BUILD/$mod" -DCMAKE_BUILD_TYPE=Release + cmake --build "$SM_MOD_BUILD/$mod" -j$(nproc) + cp -v "$SM_MOD_BUILD/$mod/lib${mod}.so" "$SM_MODULES_INSTALL/" +done +rm -rf "$SM_MOD_BUILD" + +echo "==> Done" From 8bd3b97549126f2b3a573791ab5c5e27469c5d26 Mon Sep 17 00:00:00 2001 From: ankitat Date: Thu, 14 May 2026 14:32:53 +0530 Subject: [PATCH 3/7] migrate: port MR 89 Triton samples + README section additions Brings the remaining MR !89 content over from the mono-repo: - src/apps/sample_apps/TritonBackendEnsemble: Triton ensemble + custom C++ backend sample. Top-level delegating Makefile wires the nvdsinferserver_custom_impl_ensemble library into build.sh's sample_apps loop. nvds_triton_cpp_custom_backend is cmake-based (external Triton repos) and is intentionally not wired into build.sh -- README documents the manual build. - src/apps/sample_apps/TritonOnnxYolo: ONNX YoloV3 dynamic-tensor custom-process sample. Same delegating-Makefile pattern. - src/apps/sample_apps/README.md: add Triton Inference Server section describing the two library-only samples and their run flow. - src/utils/README.md: add Tooling & Model Setup section indexing nvds_logger and nvmultiobjecttracker/model (which were ported in the earlier commit on this branch and were previously dangling references). Source: GitLab DeepStream mono-repo main (MR !89 + follow-up commits). Committed-By: ankitat's Claude Agent Codex Review: Approved: --- src/apps/sample_apps/README.md | 19 + .../TritonBackendEnsemble/Makefile | 22 + .../sample_apps/TritonBackendEnsemble/README | 169 ++++ .../config_infer_primary.txt | 84 ++ .../config_infer_secondary_ensemble.txt | 76 ++ ...eam_app_config_triton_backend_ensemble.txt | 173 ++++ .../CMakeLists.txt | 208 +++++ .../TutorialRecommendedBackendConfig.cmake.in | 67 ++ .../src/libtriton_recommended.ldscript | 57 ++ .../src/recommended.cc | 813 ++++++++++++++++++ .../Makefile | 49 ++ ...vdsinferserver_custom_process_ensemble.cpp | 566 ++++++++++++ .../1/placeholder.txt | 1 + .../config.pbtxt | 47 + .../1/placeholder.txt | 1 + .../config.pbtxt | 97 +++ .../triton_custom_backend_ensemble/labels.txt | 3 + src/apps/sample_apps/TritonOnnxYolo/Makefile | 20 + src/apps/sample_apps/TritonOnnxYolo/README | 105 +++ ...inferserver_primary_yolov3_onnx_custom.txt | 89 ++ .../deepstream_app_triton_config_yolov3.txt | 79 ++ .../nvdsinferserver_custom_impl_yolo/Makefile | 45 + .../nvdsinferserver_custom_process_yolo.cpp | 506 +++++++++++ .../yolov3-10_onnx/config.pbtxt | 56 ++ src/utils/README.md | 12 + 25 files changed, 3364 insertions(+) create mode 100644 src/apps/sample_apps/TritonBackendEnsemble/Makefile create mode 100644 src/apps/sample_apps/TritonBackendEnsemble/README create mode 100644 src/apps/sample_apps/TritonBackendEnsemble/config_infer_primary.txt create mode 100644 src/apps/sample_apps/TritonBackendEnsemble/config_infer_secondary_ensemble.txt create mode 100644 src/apps/sample_apps/TritonBackendEnsemble/deepstream_app_config_triton_backend_ensemble.txt create mode 100644 src/apps/sample_apps/TritonBackendEnsemble/nvds_triton_cpp_custom_backend/CMakeLists.txt create mode 100644 src/apps/sample_apps/TritonBackendEnsemble/nvds_triton_cpp_custom_backend/cmake/TutorialRecommendedBackendConfig.cmake.in create mode 100644 src/apps/sample_apps/TritonBackendEnsemble/nvds_triton_cpp_custom_backend/src/libtriton_recommended.ldscript create mode 100644 src/apps/sample_apps/TritonBackendEnsemble/nvds_triton_cpp_custom_backend/src/recommended.cc create mode 100644 src/apps/sample_apps/TritonBackendEnsemble/nvdsinferserver_custom_impl_ensemble/Makefile create mode 100644 src/apps/sample_apps/TritonBackendEnsemble/nvdsinferserver_custom_impl_ensemble/nvdsinferserver_custom_process_ensemble.cpp create mode 100644 src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/deepstream_triton_custom_cpp_backend/1/placeholder.txt create mode 100644 src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/deepstream_triton_custom_cpp_backend/config.pbtxt create mode 100644 src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/triton_custom_backend_ensemble/1/placeholder.txt create mode 100644 src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/triton_custom_backend_ensemble/config.pbtxt create mode 100644 src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/triton_custom_backend_ensemble/labels.txt create mode 100644 src/apps/sample_apps/TritonOnnxYolo/Makefile create mode 100644 src/apps/sample_apps/TritonOnnxYolo/README create mode 100644 src/apps/sample_apps/TritonOnnxYolo/config_triton_inferserver_primary_yolov3_onnx_custom.txt create mode 100644 src/apps/sample_apps/TritonOnnxYolo/deepstream_app_triton_config_yolov3.txt create mode 100644 src/apps/sample_apps/TritonOnnxYolo/nvdsinferserver_custom_impl_yolo/Makefile create mode 100644 src/apps/sample_apps/TritonOnnxYolo/nvdsinferserver_custom_impl_yolo/nvdsinferserver_custom_process_yolo.cpp create mode 100644 src/apps/sample_apps/TritonOnnxYolo/triton_model_repo/yolov3-10_onnx/config.pbtxt diff --git a/src/apps/sample_apps/README.md b/src/apps/sample_apps/README.md index 1309dc82..e5e0d1ce 100644 --- a/src/apps/sample_apps/README.md +++ b/src/apps/sample_apps/README.md @@ -106,3 +106,22 @@ Refer to the `README` in each app's directory for app-specific prerequisites, ru | App | Binary | Description | |---|---|---| | `deepstream-opencv-test` | `deepstream-opencv-test` | Access frame data in CPU memory for OpenCV processing. | + +### Triton Inference Server + +These samples do not produce a standalone binary. Each builds an `nvinferserver` +custom-process library (`.so`) and is driven by the deb-installed `deepstream-app` +with the included config file. See each sample's `README` for the prerequisite +Triton container setup and model download steps. + +| Sample | Library | Description | +|---|---|---| +| `TritonBackendEnsemble` | `libnvdstriton_custom_impl_ensemble.so` | Triton ensemble model used as SGIE plus a custom Triton C++ backend that reads DeepStream stream IDs. | +| `TritonOnnxYolo` | `libnvdstriton_custom_impl_yolo.so` | DS-Triton custom-lib for ONNX YoloV3 with dynamic-sized output tensors and multi-input postprocessing. | + +Run flow: + +```bash +cd src/apps/sample_apps/ +deepstream-app -c .txt +``` diff --git a/src/apps/sample_apps/TritonBackendEnsemble/Makefile b/src/apps/sample_apps/TritonBackendEnsemble/Makefile new file mode 100644 index 00000000..a38de0e6 --- /dev/null +++ b/src/apps/sample_apps/TritonBackendEnsemble/Makefile @@ -0,0 +1,22 @@ +################################################################################ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. +# All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +################################################################################ + +# Delegates to the nvdsinferserver custom-process library so the mono-repo +# build.sh sample_apps loop picks this sample up. The Triton C++ custom +# backend under nvds_triton_cpp_custom_backend/ is built separately via cmake +# (see README) and is not wired into build.sh. + +SUBDIRS := nvdsinferserver_custom_impl_ensemble + +.PHONY: all clean $(SUBDIRS) + +all: $(SUBDIRS) + +$(SUBDIRS): + $(MAKE) -C $@ + +clean: + $(MAKE) -C nvdsinferserver_custom_impl_ensemble clean diff --git a/src/apps/sample_apps/TritonBackendEnsemble/README b/src/apps/sample_apps/TritonBackendEnsemble/README new file mode 100644 index 00000000..2e308d12 --- /dev/null +++ b/src/apps/sample_apps/TritonBackendEnsemble/README @@ -0,0 +1,169 @@ +################################################################################ +# SPDX-FileCopyrightText: Copyright (c) 2023-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +# +################################################################################ + +This sample demonstrates +- Use of a Triton ensemble model for inference in a DeepStream pipeline. +- Implementation of a custom Triton C++ backend for accessing DeepStream + metadata like stream IDs in the Triton server. + +The included deepstream-app configuration file sets up a pipeline as follows: + source -> pgie -> tracker -> sgie -> tiler -> sink. +The PGIE, Primary_Detector, is a 4 class detector model and it detects the +classes "Vehicle , RoadSign, TwoWheeler, Person". The SGIE is a Triton +ensemble model that has Secondary_VehicleMake, Secondary_VehicleTypes, +and a custom Triton C++ backend as it's component +models. The vehicle objects detected by the PGIE and tracked by the tracker +are used as input to the ensemble model along with corresponding stream IDs. +The objects are inferred by the SGIE component classifier models. The stream +IDs are given as input to the C++ backend. In this example, the C++ backend +is used to keep track of number of inference requests seen for a particular +stream ID. The output of the C++ backend is the current count of inference +requests for corresponding stream IDs. +The stream IDs are sent as an extra input to the ensemble model. A custom +processor instance (nvdsinferserver::IInferCustomProcessor) is used to +set the stream ID input tensor and to parse the output tensors from the +model. + +The Triton custom C++ backend implementation is derived from the "Recommended +Triton Backend" example. Please refer this link for more details: +https://github.com/triton-inference-server/backend/blob/main/examples/README.md#recommended-triton-backend + +=============================================================================== +1. Sample Contents: +=============================================================================== +Sample contents for DeepStream Triton: +- deepstream_app_config_triton_backend_ensemble.txt: DeepStream reference app + configuration file for using the Triton ensemble model as the secondary detector. +- config_infer_primary.txt: nvinferserver configuration file for primary inference. +- config_infer_secondary_ensemble.txt: nvinferserver configuration file for + secondary inference using Triton ensemble model. +- nvds_triton_cpp_custom_backend: Implementation of the Triton C++ custom backend + along with the required cmake build files. Derived from the Triton recommended + backend example. +- nvdsinferserver_custom_impl_ensemble/nvdsinferserver_custom_process_ensemble.cpp: + nvdsinferserver::IInferCustomProcessor instance to do extra + input processing, ouptut tensor parsing and attaching classification metadata + into nvds metadata. +- triton_model_repo/triton_custom_backend_ensemble/config.pbtxt: + Configuration file for the example Triton Ensemble model. This ensemble works + as a secondary GIE. The component models of this ensemble are + Secondary_VehicleMake, Secondary_VehicleType and deepstream_triton_custom_cpp_backend. +- triton_model_repo/deepstream_triton_custom_cpp_backend/config.pbtxt: + Triton model configuration file for the Triton custom C++ backend that processes + DeepStream metadata, stream IDs in this example. +- triton_model_repo/Primary_Detector, Secondary_VehicleMake, Secondary_VehicleTypes: + Softlinks to the corresponding Triton models. These softlinks are NOT shipped + in the mono-repo source tree; they are created when you run + prepare_ds_triton_model_repo.sh (see Prerequisites section). The script + populates triton_model_repo/ with links pointing at the deb-installed models + under /opt/nvidia/deepstream/deepstream/samples/triton_model_repo/. + +=============================================================================== +2. Prerequisites: +=============================================================================== +Pre-requisites: +- For X86 platform, please follow instructions from + https://catalog.ngc.nvidia.com/orgs/nvidia/containers/deepstream + to download deepstream-triton image. + $ docker pull nvcr.io/nvidia/deepstream:[version]-triton + [version] means deepstream version numbers e.g. 9.0.0 + $ xhost + + Set correct $DISPLAY variables on host, then run deepstream-triton docker container. + $ docker run --gpus '"'device=0'"' -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix \ + -e DISPLAY=$DISPLAY --net=host nvcr.io/nvidia/deepstream:[version]-triton + Note: Set correct GPU id in `device=$id`. + +- For Jetson platform, please install Triton dependencies. + $ cd /opt/nvidia/deepstream/deepstream/samples + $ sudo ./triton_backend_setup.sh + $ rm -rf ~/.cache/gstreamer-1.0/ + +- Build the Triton model repository + $ cd /opt/nvidia/deepstream/deepstream/samples + $ ./prepare_ds_triton_model_repo.sh + +- Install Rapid JSON library: + $ apt install rapidjson-dev + +- Install cmake 3.31.8 or later: + $ mkdir -p $HOME/.local + + For x86: + $ wget -q -O /tmp/cmake-linux.sh https://github.com/Kitware/CMake/releases/download/v3.31.8/cmake-3.31.8-linux-x86_64.sh + For Jetson (aarch64): + $ wget -q -O /tmp/cmake-linux.sh https://github.com/Kitware/CMake/releases/download/v3.31.8/cmake-3.31.8-linux-aarch64.sh + + $ sh /tmp/cmake-linux.sh -- --skip-license --prefix=$HOME/.local + $ rm /tmp/cmake-linux.sh + + - Generate makefiles for the Triton custom C++ backend: + $ cd nvds_triton_cpp_custom_backend + $ mkdir build + $ cd build + + For x86: + $ ~/.local/bin/cmake -DCMAKE_INSTALL_PREFIX:PATH=`pwd`/install -DTRITON_ENABLE_GPU=ON \ + -DTRITON_BACKEND_REPO_TAG=r26.01 -DTRITON_CORE_REPO_TAG=r26.01 \ + -DTRITON_COMMON_REPO_TAG=r26.01 .. + For Jetson: + $ ~/.local/bin/cmake -DCMAKE_INSTALL_PREFIX:PATH=`pwd`/install -DTRITON_ENABLE_GPU=ON \ + -DTRITON_BACKEND_REPO_TAG=r25.08 -DTRITON_CORE_REPO_TAG=r25.08 \ + -DTRITON_COMMON_REPO_TAG=r25.08 .. + + $ mkdir -p /opt/tritonserver/backends/recommended/ + +NOTE: Run with "sudo -E" or root if there is file permission issues. + +=============================================================================== +3. To Compile: +=============================================================================== + - Compile the custom library: + Set CUDA_VER in the MakeFile as per platform. + For x86, CUDA_VER=13.1 + For jetson & SBSA, CUDA_VER=13.0 + + $ make -C nvdsinferserver_custom_impl_ensemble + + - Build the Triton custom C++ backend: + $ cd nvds_triton_cpp_custom_backend + $ cd build + $ make install + $ cp install/backends/recommended/libtriton_recommended.so ../triton_model_repo/deepstream_triton_custom_cpp_backend/1/ + +NOTE: To compile the sources, run make with "sudo -E" or root permission. + +=============================================================================== +4. Usage: +=============================================================================== +Run the sample with nvinferserver in Triton C-API mode: + Run cmdline: + $ deepstream-app -c deepstream_app_config_triton_backend_ensemble.txt + + +Run the with nvinferserver using Triton gRPC remote inference: + Follow instructions in `samples/configs/deepstream-app-triton-grpc/README` to + start tritonserver by + $ tritonserver \ + --model-repository=$(pwd)/triton_model_repo + + Update configuration files config_infer_primary.txt and config_infer_secondary_ensemble.txt to + disable 'model_repo{}' block and enable 'grpc{}' with correct url. + + Then run gRPC test: + $ deepstream-app -c deepstream_app_config_triton_backend_ensemble.txt + + +To print the inference count output returned by the model, rebuild the +custom processor with debug prints enabled: + $ make -C nvdsinferserver_custom_impl_ensemble/ clean + $ make -C nvdsinferserver_custom_impl_ensemble/ ENABLE_DEBUG=1 diff --git a/src/apps/sample_apps/TritonBackendEnsemble/config_infer_primary.txt b/src/apps/sample_apps/TritonBackendEnsemble/config_infer_primary.txt new file mode 100644 index 00000000..a4c70a1f --- /dev/null +++ b/src/apps/sample_apps/TritonBackendEnsemble/config_infer_primary.txt @@ -0,0 +1,84 @@ +#################################################################################################### +# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +#################################################################################################### + +infer_config { + unique_id: 1 + gpu_ids: [0] + max_batch_size: 30 + backend { + inputs: [ { + name: "input_1:0" + }] + outputs: [ + {name: "output_cov/Sigmoid:0"}, + {name: "output_bbox/BiasAdd:0"} + ] + triton { + model_name: "Primary_Detector" + version: -1 + + # model_repo block is enabled for using nvinferserver in + # Triton C-API mode. + model_repo { + root: "triton_model_repo" + strict_model_config: true + } + + # To run Triton client remote inference, enable 'grpc' block, + # and disalbe 'model_repo' block. + # Setup Tritonserver, goto current directory, run + # tritonserver --model-repository=triton_model_repo + #grpc { + # url: "localhost:8001" + #} + } + } + + preprocess { + network_format: MEDIA_FORMAT_NONE + tensor_order: TENSOR_ORDER_LINEAR + tensor_name: "input_1:0" + maintain_aspect_ratio: 0 + frame_scaling_hw: FRAME_SCALING_HW_DEFAULT + frame_scaling_filter: 1 + normalize { + scale_factor: 0.00392156862745098 + channel_offsets: [0, 0, 0] + } + } + + postprocess { + labelfile_path: "../../samples/models/Primary_Detector/labels.txt" + detection { + num_detected_classes: 4 + per_class_params { + key: 0 + value { pre_threshold: 0.4 } + } + nms { + confidence_threshold:0.2 + topk:20 + iou_threshold:0.5 + } + } + } + + extra { + copy_input_to_host_buffers: false + output_buffer_pool_size: 2 + } +} +input_control { + process_mode: PROCESS_MODE_FULL_FRAME + operate_on_gie_id: -1 + interval: 0 +} diff --git a/src/apps/sample_apps/TritonBackendEnsemble/config_infer_secondary_ensemble.txt b/src/apps/sample_apps/TritonBackendEnsemble/config_infer_secondary_ensemble.txt new file mode 100644 index 00000000..9a3e7b04 --- /dev/null +++ b/src/apps/sample_apps/TritonBackendEnsemble/config_infer_secondary_ensemble.txt @@ -0,0 +1,76 @@ +#################################################################################################### +# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +#################################################################################################### + +infer_config { + unique_id: 5 + gpu_ids: [0] + max_batch_size: 16 + backend { + output_mem_type: MEMORY_TYPE_CPU + triton { + model_name: "triton_custom_backend_ensemble" + version: -1 + + # model_repo block is enabled for using nvinferserver in + # Triton C-API mode. + model_repo { + root: "triton_model_repo" + strict_model_config: true + } + + # To run Triton client remote inference, enable 'grpc' block, + # and disalbe 'model_repo' block. + # Setup Tritonserver, goto current directory, run + # tritonserver --model-repository=triton_model_repo + #grpc { + # url: "localhost:8001" + #} + } + } + + preprocess { + network_format: IMAGE_FORMAT_BGR + tensor_order: TENSOR_ORDER_LINEAR + maintain_aspect_ratio: 0 + frame_scaling_hw: FRAME_SCALING_HW_DEFAULT + frame_scaling_filter: 1 + normalize { + scale_factor: 1 + } + } + + postprocess { + other { } + } + + extra { + custom_process_funcion: "CreateInferServerCustomProcess" + } + + custom_lib { + path: "nvdsinferserver_custom_impl_ensemble/libnvdstriton_custom_impl_ensemble.so" + } + +} +input_control { + process_mode: PROCESS_MODE_CLIP_OBJECTS + operate_on_gie_id: 1 + operate_on_class_ids: [0] + secondary_reinfer_interval: 90 + async_mode: true + object_control { + bbox_filter { + min_width: 128 + min_height: 128 + } + } +} diff --git a/src/apps/sample_apps/TritonBackendEnsemble/deepstream_app_config_triton_backend_ensemble.txt b/src/apps/sample_apps/TritonBackendEnsemble/deepstream_app_config_triton_backend_ensemble.txt new file mode 100644 index 00000000..72831a78 --- /dev/null +++ b/src/apps/sample_apps/TritonBackendEnsemble/deepstream_app_config_triton_backend_ensemble.txt @@ -0,0 +1,173 @@ +#################################################################################################### +# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +#################################################################################################### + +[application] +enable-perf-measurement=1 +perf-measurement-interval-sec=5 +#gie-kitti-output-dir=streamscl_new + +[tiled-display] +enable=1 +rows=2 +columns=2 +width=1280 +height=720 +gpu-id=0 +#(0): nvbuf-mem-default - Default memory allocated, specific to particular platform +#(1): nvbuf-mem-cuda-pinned - Allocate Pinned/Host cuda memory, applicable for Tesla +#(2): nvbuf-mem-cuda-device - Allocate Device cuda memory, applicable for Tesla +#(3): nvbuf-mem-cuda-unified - Allocate Unified cuda memory, applicable for Tesla +#(4): nvbuf-mem-surface-array - Allocate Surface Array memory, applicable for Jetson +nvbuf-memory-type=0 + +[source0] +enable=1 +#Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP +type=3 +uri=file://../../samples/streams/sample_1080p_h264.mp4 +num-sources=4 +#drop-frame-interval=2 +gpu-id=0 +# (0): memtype_device - Memory type Device +# (1): memtype_pinned - Memory type Host Pinned +# (2): memtype_unified - Memory type Unified +cudadec-memtype=0 + +[sink0] +enable=1 +#Type - 1=FakeSink 2=EglSink 3=File +type=2 +sync=1 +source-id=0 +gpu-id=0 +nvbuf-memory-type=0 + +[sink1] +enable=0 +#Type - 1=FakeSink 2=EglSink 3=File 4=RTSPStreaming +type=3 +#1=mp4 2=mkv +container=1 +#1=h264 2=h265 +codec=1 +#encoder type 0=Hardware 1=Software +enc-type=0 +sync=0 +#iframeinterval=10 +bitrate=2000000 +#H264 Profile - 0=Baseline 2=Main 4=High +#H265 Profile - 0=Main 1=Main10 +profile=0 +output-file=out.mp4 +source-id=0 + +[sink2] +enable=0 +#Type - 1=FakeSink 2=EglSink 3=File 4=RTSPStreaming +type=4 +#1=h264 2=h265 +codec=1 +#encoder type 0=Hardware 1=Software +enc-type=0 +sync=0 +#iframeinterval=10 +bitrate=400000 +#H264 Profile - 0=Baseline 2=Main 4=High +#H265 Profile - 0=Main 1=Main10 +profile=0 +# set below properties in case of RTSPStreaming +rtsp-port=8554 +udp-port=5400 + +[osd] +enable=1 +gpu-id=0 +border-width=1 +text-size=15 +text-color=1;1;1;1; +text-bg-color=0.3;0.3;0.3;1 +font=Serif +show-clock=0 +clock-x-offset=800 +clock-y-offset=820 +clock-text-size=12 +clock-color=1;0;0;0 +nvbuf-memory-type=0 + +[streammux] +gpu-id=0 +##Boolean property to inform muxer that sources are live +live-source=0 +batch-size=4 +##time out in usec, to wait after the first buffer is available +##to push the batch even if the complete batch is not formed +batched-push-timeout=40000 +## Set muxer output width and height +width=1920 +height=1080 +##Enable to maintain aspect ratio wrt source, and allow black borders, works +##along with width, height properties +enable-padding=0 +nvbuf-memory-type=0 +## If set to TRUE, system timestamp will be attached as ntp timestamp +## If set to FALSE, ntp timestamp from rtspsrc, if available, will be attached +# attach-sys-ts-as-ntp=1 + +# config-file property is mandatory for any gie section. +# Other properties are optional and if set will override the properties set in +# the infer config file. +[primary-gie] +enable=1 +gpu-id=0 +#(0): nvinfer - Default inference plugin based on Tensorrt +#(1): nvinferserver - inference plugin based on Tensorrt-Inference-Server +plugin-type=1 +batch-size=4 +#Required by the app for OSD, not a plugin property +bbox-border-color0=1;0;0;1 +bbox-border-color1=0;1;1;1 +bbox-border-color2=0;0;1;1 +bbox-border-color3=0;1;0;1 +interval=0 +gie-unique-id=1 +nvbuf-memory-type=0 +config-file=config_infer_primary.txt + +[tracker] +enable=1 +# For NvDCF and NvDeepSORT tracker, tracker-width and tracker-height must be a multiple of 32, respectively +tracker-width=960 +tracker-height=544 +ll-lib-file=/opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so +# ll-config-file required to set different tracker types +# ll-config-file=../deepstream-app/config_tracker_IOU.yml +# ll-config-file=../deepstream-app/config_tracker_NvSORT.yml +ll-config-file=../deepstream-app/config_tracker_NvDCF_perf.yml +# ll-config-file=../deepstream-app/config_tracker_NvDCF_accuracy.yml +# ll-config-file=../deepstream-app/config_tracker_NvDeepSORT.yml +gpu-id=0 +display-tracking-id=1 + +[secondary-gie0] +enable=1 +#(0): nvinfer; (1): nvinferserver +plugin-type=1 +# nvinferserserver's gpu-id can only set from its own config-file +#gpu-id=0 +batch-size=16 +gie-unique-id=4 +operate-on-gie-id=1 +operate-on-class-ids=0; +config-file=config_infer_secondary_ensemble.txt + +[tests] +file-loop=0 diff --git a/src/apps/sample_apps/TritonBackendEnsemble/nvds_triton_cpp_custom_backend/CMakeLists.txt b/src/apps/sample_apps/TritonBackendEnsemble/nvds_triton_cpp_custom_backend/CMakeLists.txt new file mode 100644 index 00000000..a8b914b8 --- /dev/null +++ b/src/apps/sample_apps/TritonBackendEnsemble/nvds_triton_cpp_custom_backend/CMakeLists.txt @@ -0,0 +1,208 @@ +################################################################################ +# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: MIT +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the "Software"), +# to deal in the Software without restriction, including without limitation +# the rights to use, copy, modify, merge, publish, distribute, sublicense, +# and/or sell copies of the Software, and to permit persons to whom the +# Software is furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL +# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +# DEALINGS IN THE SOFTWARE. +################################################################################ +# +# Source: https://github.com/triton-inference-server/backend/tree/main/examples/backends/recommended +# +################################################################################ + +# Copyright 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions +# are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of NVIDIA CORPORATION nor the names of its +# contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY +# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +cmake_minimum_required(VERSION 3.17) + +project(tutorialrecommendedbackend LANGUAGES C CXX) + +# +# Options +# +# Must include options required for this project as well as any +# projects included in this one by FetchContent. +# +# GPU support is disabled by default because recommended backend +# doesn't use GPUs. +# +option(TRITON_ENABLE_GPU "Enable GPU support in backend" OFF) +option(TRITON_ENABLE_STATS "Include statistics collections in backend" ON) + +set(TRITON_COMMON_REPO_TAG "main" CACHE STRING "Tag for triton-inference-server/common repo") +set(TRITON_CORE_REPO_TAG "main" CACHE STRING "Tag for triton-inference-server/core repo") +set(TRITON_BACKEND_REPO_TAG "main" CACHE STRING "Tag for triton-inference-server/backend repo") + +if(NOT CMAKE_BUILD_TYPE) + set(CMAKE_BUILD_TYPE Release) +endif() + +# +# Dependencies +# +# FetchContent requires us to include the transitive closure of all +# repos that we depend on so that we can override the tags. +# +include(FetchContent) + +FetchContent_Declare( + repo-common + GIT_REPOSITORY https://github.com/triton-inference-server/common.git + GIT_TAG ${TRITON_COMMON_REPO_TAG} + GIT_SHALLOW ON +) +FetchContent_Declare( + repo-core + GIT_REPOSITORY https://github.com/triton-inference-server/core.git + GIT_TAG ${TRITON_CORE_REPO_TAG} + GIT_SHALLOW ON +) +FetchContent_Declare( + repo-backend + GIT_REPOSITORY https://github.com/triton-inference-server/backend.git + GIT_TAG ${TRITON_BACKEND_REPO_TAG} + GIT_SHALLOW ON +) +FetchContent_MakeAvailable(repo-common repo-core repo-backend) + +# +# The backend must be built into a shared library. Use an ldscript to +# hide all symbols except for the TRITONBACKEND API. +# +configure_file(src/libtriton_recommended.ldscript libtriton_recommended.ldscript COPYONLY) + +add_library( + triton-recommended-backend SHARED + src/recommended.cc +) + +add_library( + TutorialRecommendedBackend::triton-recommended-backend ALIAS triton-recommended-backend +) + +target_include_directories( + triton-recommended-backend + PRIVATE + ${CMAKE_CURRENT_SOURCE_DIR}/src +) + +target_compile_features(triton-recommended-backend PRIVATE cxx_std_11) +target_compile_options( + triton-recommended-backend PRIVATE + $<$,$,$>: + -Wall -Wextra -Wno-unused-parameter -Wno-type-limits -Werror> + $<$:/Wall /D_WIN32_WINNT=0x0A00 /EHsc> +) + +target_link_libraries( + triton-recommended-backend + PRIVATE + triton-core-serverapi # from repo-core + triton-core-backendapi # from repo-core + triton-core-serverstub # from repo-core + triton-backend-utils # from repo-backend +) + +if(WIN32) + set_target_properties( + triton-recommended-backend PROPERTIES + POSITION_INDEPENDENT_CODE ON + OUTPUT_NAME triton_recommended + ) +else() + set_target_properties( + triton-recommended-backend PROPERTIES + POSITION_INDEPENDENT_CODE ON + OUTPUT_NAME triton_recommended + LINK_DEPENDS ${CMAKE_CURRENT_BINARY_DIR}/libtriton_recommended.ldscript + LINK_FLAGS "-Wl,--version-script libtriton_recommended.ldscript" + ) +endif() + +# +# Install +# +include(GNUInstallDirs) +set(INSTALL_CONFIGDIR ${CMAKE_INSTALL_LIBDIR}/cmake/TutorialRecommendedBackend) + +install( + TARGETS + triton-recommended-backend + EXPORT + triton-recommended-backend-targets + LIBRARY DESTINATION ${CMAKE_INSTALL_PREFIX}/backends/recommended + RUNTIME DESTINATION ${CMAKE_INSTALL_PREFIX}/backends/recommended +) + +install( + EXPORT + triton-recommended-backend-targets + FILE + TutorialRecommendedBackendTargets.cmake + NAMESPACE + TutorialRecommendedBackend:: + DESTINATION + ${INSTALL_CONFIGDIR} +) + +include(CMakePackageConfigHelpers) +configure_package_config_file( + ${CMAKE_CURRENT_LIST_DIR}/cmake/TutorialRecommendedBackendConfig.cmake.in + ${CMAKE_CURRENT_BINARY_DIR}/TutorialRecommendedBackendConfig.cmake + INSTALL_DESTINATION ${INSTALL_CONFIGDIR} +) + +install( + FILES + ${CMAKE_CURRENT_BINARY_DIR}/TutorialRecommendedBackendConfig.cmake + DESTINATION ${INSTALL_CONFIGDIR} +) + +# +# Export from build tree +# +export( + EXPORT triton-recommended-backend-targets + FILE ${CMAKE_CURRENT_BINARY_DIR}/TutorialRecommendedBackendTargets.cmake + NAMESPACE TutorialRecommendedBackend:: +) + +export(PACKAGE TutorialRecommendedBackend) diff --git a/src/apps/sample_apps/TritonBackendEnsemble/nvds_triton_cpp_custom_backend/cmake/TutorialRecommendedBackendConfig.cmake.in b/src/apps/sample_apps/TritonBackendEnsemble/nvds_triton_cpp_custom_backend/cmake/TutorialRecommendedBackendConfig.cmake.in new file mode 100644 index 00000000..5dd73e7e --- /dev/null +++ b/src/apps/sample_apps/TritonBackendEnsemble/nvds_triton_cpp_custom_backend/cmake/TutorialRecommendedBackendConfig.cmake.in @@ -0,0 +1,67 @@ +################################################################################ +# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: MIT +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the "Software"), +# to deal in the Software without restriction, including without limitation +# the rights to use, copy, modify, merge, publish, distribute, sublicense, +# and/or sell copies of the Software, and to permit persons to whom the +# Software is furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL +# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +# DEALINGS IN THE SOFTWARE. +################################################################################ +# +# Source: +# https://github.com/triton-inference-server/backend/tree/main/examples/backends/recommended +# +################################################################################ + +# Copyright 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions +# are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of NVIDIA CORPORATION nor the names of its +# contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY +# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +include(CMakeFindDependencyMacro) + +get_filename_component( + TUTORIALRECOMMENDEDBACKEND_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH +) + +list(APPEND CMAKE_MODULE_PATH ${TUTORIALRECOMMENDEDBACKEND_CMAKE_DIR}) + +if(NOT TARGET TutorialRecommendedBackend::triton-recommended-backend) + include("${TUTORIALRECOMMENDEDBACKEND_CMAKE_DIR}/TutorialRecommendedBackendTargets.cmake") +endif() + +set(TUTORIALRECOMMENDEDBACKEND_LIBRARIES TutorialRecommendedBackend::triton-recommended-backend) diff --git a/src/apps/sample_apps/TritonBackendEnsemble/nvds_triton_cpp_custom_backend/src/libtriton_recommended.ldscript b/src/apps/sample_apps/TritonBackendEnsemble/nvds_triton_cpp_custom_backend/src/libtriton_recommended.ldscript new file mode 100644 index 00000000..f393c9bb --- /dev/null +++ b/src/apps/sample_apps/TritonBackendEnsemble/nvds_triton_cpp_custom_backend/src/libtriton_recommended.ldscript @@ -0,0 +1,57 @@ +################################################################################ +# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: MIT +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the "Software"), +# to deal in the Software without restriction, including without limitation +# the rights to use, copy, modify, merge, publish, distribute, sublicense, +# and/or sell copies of the Software, and to permit persons to whom the +# Software is furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL +# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +# DEALINGS IN THE SOFTWARE. +################################################################################ +# +# Source: https://github.com/triton-inference-server/backend/tree/main/examples/backends/recommended +# +################################################################################ + +# Copyright 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions +# are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of NVIDIA CORPORATION nor the names of its +# contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY +# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +{ + global: + TRITONBACKEND_*; + local: *; +}; diff --git a/src/apps/sample_apps/TritonBackendEnsemble/nvds_triton_cpp_custom_backend/src/recommended.cc b/src/apps/sample_apps/TritonBackendEnsemble/nvds_triton_cpp_custom_backend/src/recommended.cc new file mode 100644 index 00000000..dd04f5f3 --- /dev/null +++ b/src/apps/sample_apps/TritonBackendEnsemble/nvds_triton_cpp_custom_backend/src/recommended.cc @@ -0,0 +1,813 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a + * copy of this software and associated documentation files (the "Software"), + * to deal in the Software without restriction, including without limitation + * the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the + * Software is furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in + * all copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL + * THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING + * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + * DEALINGS IN THE SOFTWARE. + */ +/* + * Source: + * https://github.com/triton-inference-server/backend/tree/main/examples/backends/recommended + * + */ + +// Copyright 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions +// are met: +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above copyright +// notice, this list of conditions and the following disclaimer in the +// documentation and/or other materials provided with the distribution. +// * Neither the name of NVIDIA CORPORATION nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY +// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +// OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#include "triton/backend/backend_common.h" +#include "triton/backend/backend_input_collector.h" +#include "triton/backend/backend_model.h" +#include "triton/backend/backend_model_instance.h" +#include "triton/backend/backend_output_responder.h" +#include "triton/core/tritonbackend.h" + +namespace triton { namespace backend { namespace recommended { + +// +// Backend that demonstrates the TRITONBACKEND API. This backend works +// for any model that has 1 input with any datatype and any shape and +// 1 output with the same shape and datatype as the input. The backend +// supports both batching and non-batching models. +// +// For each batch of requests, the backend returns the input tensor +// value in the output tensor. +// + +///////////// + +extern "C" { + +// Triton calls TRITONBACKEND_Initialize when a backend is loaded into +// Triton to allow the backend to create and initialize any state that +// is intended to be shared across all models and model instances that +// use the backend. The backend should also verify version +// compatibility with Triton in this function. +// +TRITONSERVER_Error* +TRITONBACKEND_Initialize(TRITONBACKEND_Backend* backend) +{ + const char* cname; + RETURN_IF_ERROR(TRITONBACKEND_BackendName(backend, &cname)); + std::string name(cname); + + LOG_MESSAGE( + TRITONSERVER_LOG_INFO, + (std::string("TRITONBACKEND_Initialize: ") + name).c_str()); + + // Check the backend API version that Triton supports vs. what this + // backend was compiled against. Make sure that the Triton major + // version is the same and the minor version is >= what this backend + // uses. + uint32_t api_version_major, api_version_minor; + RETURN_IF_ERROR( + TRITONBACKEND_ApiVersion(&api_version_major, &api_version_minor)); + + LOG_MESSAGE( + TRITONSERVER_LOG_INFO, + (std::string("Triton TRITONBACKEND API version: ") + + std::to_string(api_version_major) + "." + + std::to_string(api_version_minor)) + .c_str()); + LOG_MESSAGE( + TRITONSERVER_LOG_INFO, + (std::string("'") + name + "' TRITONBACKEND API version: " + + std::to_string(TRITONBACKEND_API_VERSION_MAJOR) + "." + + std::to_string(TRITONBACKEND_API_VERSION_MINOR)) + .c_str()); + + if ((api_version_major != TRITONBACKEND_API_VERSION_MAJOR) || + (api_version_minor < TRITONBACKEND_API_VERSION_MINOR)) { + return TRITONSERVER_ErrorNew( + TRITONSERVER_ERROR_UNSUPPORTED, + "triton backend API version does not support this backend"); + } + + // The backend configuration may contain information needed by the + // backend, such as tritonserver command-line arguments. This + // backend doesn't use any such configuration but for this example + // print whatever is available. + TRITONSERVER_Message* backend_config_message; + RETURN_IF_ERROR( + TRITONBACKEND_BackendConfig(backend, &backend_config_message)); + + const char* buffer; + size_t byte_size; + RETURN_IF_ERROR(TRITONSERVER_MessageSerializeToJson( + backend_config_message, &buffer, &byte_size)); + LOG_MESSAGE( + TRITONSERVER_LOG_INFO, + (std::string("backend configuration:\n") + buffer).c_str()); + + // This backend does not require any "global" state but as an + // example create a string to demonstrate. + std::string* state = new std::string("backend state"); + RETURN_IF_ERROR( + TRITONBACKEND_BackendSetState(backend, reinterpret_cast(state))); + + return nullptr; // success +} + +// Triton calls TRITONBACKEND_Finalize when a backend is no longer +// needed. +// +TRITONSERVER_Error* +TRITONBACKEND_Finalize(TRITONBACKEND_Backend* backend) +{ + // Delete the "global" state associated with the backend. + void* vstate; + RETURN_IF_ERROR(TRITONBACKEND_BackendState(backend, &vstate)); + std::string* state = reinterpret_cast(vstate); + + LOG_MESSAGE( + TRITONSERVER_LOG_INFO, + (std::string("TRITONBACKEND_Finalize: state is '") + *state + "'") + .c_str()); + + delete state; + + return nullptr; // success +} + +} // extern "C" + +///////////// + +// +// ModelState +// +// State associated with a model that is using this backend. An object +// of this class is created and associated with each +// TRITONBACKEND_Model. ModelState is derived from BackendModel class +// provided in the backend utilities that provides many common +// functions. +// +class ModelState : public BackendModel { + public: + static TRITONSERVER_Error* Create( + TRITONBACKEND_Model* triton_model, ModelState** state); + virtual ~ModelState() = default; + + // Name of the input and output tensor + const std::string& InputTensorName() const { return input_name_; } + const std::string& OutputTensorName() const { return output_name_; } + + // Datatype of the input and output tensor + TRITONSERVER_DataType TensorDataType() const { return datatype_; } + + // Shape of the input and output tensor as given in the model + // configuration file. This shape will not include the batch + // dimension (if the model has one). + const std::vector& TensorNonBatchShape() const { return nb_shape_; } + + // Shape of the input and output tensor, including the batch + // dimension (if the model has one). This method cannot be called + // until the model is completely loaded and initialized, including + // all instances of the model. In practice, this means that backend + // should only call it in TRITONBACKEND_ModelInstanceExecute. + TRITONSERVER_Error* TensorShape(std::vector& shape); + + // Validate that this model is supported by this backend. + TRITONSERVER_Error* ValidateModelConfig(); + + private: + ModelState(TRITONBACKEND_Model* triton_model); + + std::string input_name_; + std::string output_name_; + + TRITONSERVER_DataType datatype_; + + bool shape_initialized_; + std::vector nb_shape_; + std::vector shape_; +}; + +ModelState::ModelState(TRITONBACKEND_Model* triton_model) + : BackendModel(triton_model), shape_initialized_(false) +{ + // Validate that the model's configuration matches what is supported + // by this backend. + THROW_IF_BACKEND_MODEL_ERROR(ValidateModelConfig()); +} + +TRITONSERVER_Error* +ModelState::Create(TRITONBACKEND_Model* triton_model, ModelState** state) +{ + try { + *state = new ModelState(triton_model); + } + catch (const BackendModelException& ex) { + RETURN_ERROR_IF_TRUE( + ex.err_ == nullptr, TRITONSERVER_ERROR_INTERNAL, + std::string("unexpected nullptr in BackendModelException")); + RETURN_IF_ERROR(ex.err_); + } + + return nullptr; // success +} + +TRITONSERVER_Error* +ModelState::TensorShape(std::vector& shape) +{ + // This backend supports models that batch along the first dimension + // and those that don't batch. For non-batch models the output shape + // will be the shape from the model configuration. For batch models + // the output shape will be the shape from the model configuration + // prepended with [ -1 ] to represent the batch dimension. The + // backend "responder" utility used below will set the appropriate + // batch dimension value for each response. The shape needs to be + // initialized lazily because the SupportsFirstDimBatching function + // cannot be used until the model is completely loaded. + if (!shape_initialized_) { + bool supports_first_dim_batching; + RETURN_IF_ERROR(SupportsFirstDimBatching(&supports_first_dim_batching)); + if (supports_first_dim_batching) { + shape_.push_back(-1); + } + + shape_.insert(shape_.end(), nb_shape_.begin(), nb_shape_.end()); + shape_initialized_ = true; + } + + shape = shape_; + + return nullptr; // success +} + +TRITONSERVER_Error* +ModelState::ValidateModelConfig() +{ + // If verbose logging is enabled, dump the model's configuration as + // JSON into the console output. + if (TRITONSERVER_LogIsEnabled(TRITONSERVER_LOG_VERBOSE)) { + common::TritonJson::WriteBuffer buffer; + RETURN_IF_ERROR(ModelConfig().PrettyWrite(&buffer)); + LOG_MESSAGE( + TRITONSERVER_LOG_VERBOSE, + (std::string("model configuration:\n") + buffer.Contents()).c_str()); + } + + // ModelConfig is the model configuration as a TritonJson + // object. Use the TritonJson utilities to parse the JSON and + // determine if the configuration is supported by this backend. + common::TritonJson::Value inputs, outputs; + RETURN_IF_ERROR(ModelConfig().MemberAsArray("input", &inputs)); + RETURN_IF_ERROR(ModelConfig().MemberAsArray("output", &outputs)); + + // The model must have exactly 1 input and 1 output. + RETURN_ERROR_IF_FALSE( + inputs.ArraySize() == 1, TRITONSERVER_ERROR_INVALID_ARG, + std::string("model configuration must have 1 input")); + RETURN_ERROR_IF_FALSE( + outputs.ArraySize() == 1, TRITONSERVER_ERROR_INVALID_ARG, + std::string("model configuration must have 1 output")); + + common::TritonJson::Value input, output; + RETURN_IF_ERROR(inputs.IndexAsObject(0, &input)); + RETURN_IF_ERROR(outputs.IndexAsObject(0, &output)); + + // Record the input and output name in the model state. + const char* input_name; + size_t input_name_len; + RETURN_IF_ERROR(input.MemberAsString("name", &input_name, &input_name_len)); + input_name_ = std::string(input_name); + + const char* output_name; + size_t output_name_len; + RETURN_IF_ERROR( + output.MemberAsString("name", &output_name, &output_name_len)); + output_name_ = std::string(output_name); + + // Input and output must have same datatype + std::string input_dtype, output_dtype; + RETURN_IF_ERROR(input.MemberAsString("data_type", &input_dtype)); + RETURN_IF_ERROR(output.MemberAsString("data_type", &output_dtype)); + RETURN_ERROR_IF_FALSE( + input_dtype == output_dtype, TRITONSERVER_ERROR_INVALID_ARG, + std::string("expected input and output datatype to match, got ") + + input_dtype + " and " + output_dtype); + datatype_ = ModelConfigDataTypeToTritonServerDataType(input_dtype); + + // Input and output must have same shape. Reshape is not supported + // on either input or output so flag an error is the model + // configuration uses it. + triton::common::TritonJson::Value reshape; + RETURN_ERROR_IF_TRUE( + input.Find("reshape", &reshape), TRITONSERVER_ERROR_UNSUPPORTED, + std::string("reshape not supported for input tensor")); + RETURN_ERROR_IF_TRUE( + output.Find("reshape", &reshape), TRITONSERVER_ERROR_UNSUPPORTED, + std::string("reshape not supported for output tensor")); + + std::vector input_shape, output_shape; + RETURN_IF_ERROR(backend::ParseShape(input, "dims", &input_shape)); + RETURN_IF_ERROR(backend::ParseShape(output, "dims", &output_shape)); + + RETURN_ERROR_IF_FALSE( + input_shape == output_shape, TRITONSERVER_ERROR_INVALID_ARG, + std::string("expected input and output shape to match, got ") + + backend::ShapeToString(input_shape) + " and " + + backend::ShapeToString(output_shape)); + + nb_shape_ = input_shape; + + return nullptr; // success +} + +extern "C" { + +// Triton calls TRITONBACKEND_ModelInitialize when a model is loaded +// to allow the backend to create any state associated with the model, +// and to also examine the model configuration to determine if the +// configuration is suitable for the backend. Any errors reported by +// this function will prevent the model from loading. +// +TRITONSERVER_Error* +TRITONBACKEND_ModelInitialize(TRITONBACKEND_Model* model) +{ + // Create a ModelState object and associate it with the + // TRITONBACKEND_Model. If anything goes wrong with initialization + // of the model state then an error is returned and Triton will fail + // to load the model. + ModelState* model_state; + RETURN_IF_ERROR(ModelState::Create(model, &model_state)); + RETURN_IF_ERROR( + TRITONBACKEND_ModelSetState(model, reinterpret_cast(model_state))); + + return nullptr; // success +} + +// Triton calls TRITONBACKEND_ModelFinalize when a model is no longer +// needed. The backend should cleanup any state associated with the +// model. This function will not be called until all model instances +// of the model have been finalized. +// +TRITONSERVER_Error* +TRITONBACKEND_ModelFinalize(TRITONBACKEND_Model* model) +{ + void* vstate; + RETURN_IF_ERROR(TRITONBACKEND_ModelState(model, &vstate)); + ModelState* model_state = reinterpret_cast(vstate); + delete model_state; + + return nullptr; // success +} + +} // extern "C" + +///////////// + +// +// ModelInstanceState +// +// State associated with a model instance. An object of this class is +// created and associated with each +// TRITONBACKEND_ModelInstance. ModelInstanceState is derived from +// BackendModelInstance class provided in the backend utilities that +// provides many common functions. +// +class ModelInstanceState : public BackendModelInstance { + public: + static TRITONSERVER_Error* Create( + ModelState* model_state, + TRITONBACKEND_ModelInstance* triton_model_instance, + ModelInstanceState** state); + virtual ~ModelInstanceState() = default; + + // Get the state of the model that corresponds to this instance. + ModelState* StateForModel() const { return model_state_; } + + // Increment the inference counter for corresponding stream ID. + void log_request(unsigned int stream_id) { + if (inference_counts.count(stream_id)) { + inference_counts[stream_id] = inference_counts[stream_id] + 1; + } else { + inference_counts.emplace(stream_id, 1); + } + } + + // Get the inference counter for corresponding stream ID. + unsigned int get_inference_count(unsigned int stream_id) { + auto stream_info = inference_counts.find(stream_id); + if (stream_info == inference_counts.end()) { + return 0; + } else { + return stream_info->second; + } + } + + private: + ModelInstanceState( + ModelState* model_state, + TRITONBACKEND_ModelInstance* triton_model_instance) + : BackendModelInstance(model_state, triton_model_instance), + model_state_(model_state) + { + } + + // Map of stream IDs, inference count + std::unordered_map inference_counts; + + ModelState* model_state_; +}; + +TRITONSERVER_Error* +ModelInstanceState::Create( + ModelState* model_state, TRITONBACKEND_ModelInstance* triton_model_instance, + ModelInstanceState** state) +{ + try { + *state = new ModelInstanceState(model_state, triton_model_instance); + } + catch (const BackendModelInstanceException& ex) { + RETURN_ERROR_IF_TRUE( + ex.err_ == nullptr, TRITONSERVER_ERROR_INTERNAL, + std::string("unexpected nullptr in BackendModelInstanceException")); + RETURN_IF_ERROR(ex.err_); + } + + return nullptr; // success +} + +extern "C" { + +// Triton calls TRITONBACKEND_ModelInstanceInitialize when a model +// instance is created to allow the backend to initialize any state +// associated with the instance. +// +TRITONSERVER_Error* +TRITONBACKEND_ModelInstanceInitialize(TRITONBACKEND_ModelInstance* instance) +{ + // Get the model state associated with this instance's model. + TRITONBACKEND_Model* model; + RETURN_IF_ERROR(TRITONBACKEND_ModelInstanceModel(instance, &model)); + + void* vmodelstate; + RETURN_IF_ERROR(TRITONBACKEND_ModelState(model, &vmodelstate)); + ModelState* model_state = reinterpret_cast(vmodelstate); + + // Create a ModelInstanceState object and associate it with the + // TRITONBACKEND_ModelInstance. + ModelInstanceState* instance_state; + RETURN_IF_ERROR( + ModelInstanceState::Create(model_state, instance, &instance_state)); + RETURN_IF_ERROR(TRITONBACKEND_ModelInstanceSetState( + instance, reinterpret_cast(instance_state))); + + return nullptr; // success +} + +// Triton calls TRITONBACKEND_ModelInstanceFinalize when a model +// instance is no longer needed. The backend should cleanup any state +// associated with the model instance. +// +TRITONSERVER_Error* +TRITONBACKEND_ModelInstanceFinalize(TRITONBACKEND_ModelInstance* instance) +{ + void* vstate; + RETURN_IF_ERROR(TRITONBACKEND_ModelInstanceState(instance, &vstate)); + ModelInstanceState* instance_state = + reinterpret_cast(vstate); + delete instance_state; + + return nullptr; // success +} + +} // extern "C" + +///////////// + +extern "C" { + +// When Triton calls TRITONBACKEND_ModelInstanceExecute it is required +// that a backend create a response for each request in the batch. A +// response may be the output tensors required for that request or may +// be an error that is returned in the response. +// +TRITONSERVER_Error* +TRITONBACKEND_ModelInstanceExecute( + TRITONBACKEND_ModelInstance* instance, TRITONBACKEND_Request** requests, + const uint32_t request_count) +{ + // Collect various timestamps during the execution of this batch or + // requests. These values are reported below before returning from + // the function. + + uint64_t exec_start_ns = 0; + SET_TIMESTAMP(exec_start_ns); + + // Triton will not call this function simultaneously for the same + // 'instance'. But since this backend could be used by multiple + // instances from multiple models the implementation needs to handle + // multiple calls to this function at the same time (with different + // 'instance' objects). Best practice for a high-performance + // implementation is to avoid introducing mutex/lock and instead use + // only function-local and model-instance-specific state. + ModelInstanceState* instance_state; + RETURN_IF_ERROR(TRITONBACKEND_ModelInstanceState( + instance, reinterpret_cast(&instance_state))); + ModelState* model_state = instance_state->StateForModel(); + + // 'responses' is initialized as a parallel array to 'requests', + // with one TRITONBACKEND_Response object for each + // TRITONBACKEND_Request object. If something goes wrong while + // creating these response objects, the backend simply returns an + // error from TRITONBACKEND_ModelInstanceExecute, indicating to + // Triton that this backend did not create or send any responses and + // so it is up to Triton to create and send an appropriate error + // response for each request. RETURN_IF_ERROR is one of several + // useful macros for error handling that can be found in + // backend_common.h. + + std::vector responses; + responses.reserve(request_count); + for (uint32_t r = 0; r < request_count; ++r) { + TRITONBACKEND_Request* request = requests[r]; + TRITONBACKEND_Response* response; + RETURN_IF_ERROR(TRITONBACKEND_ResponseNew(&response, request)); + responses.push_back(response); + } + + // At this point, the backend takes ownership of 'requests', which + // means that it is responsible for sending a response for every + // request. From here, even if something goes wrong in processing, + // the backend must return 'nullptr' from this function to indicate + // success. Any errors and failures must be communicated via the + // response objects. + // + // To simplify error handling, the backend utilities manage + // 'responses' in a specific way and it is recommended that backends + // follow this same pattern. When an error is detected in the + // processing of a request, an appropriate error response is sent + // and the corresponding TRITONBACKEND_Response object within + // 'responses' is set to nullptr to indicate that the + // request/response has already been handled and no futher processing + // should be performed for that request. Even if all responses fail, + // the backend still allows execution to flow to the end of the + // function so that statistics are correctly reported by the calls + // to TRITONBACKEND_ModelInstanceReportStatistics and + // TRITONBACKEND_ModelInstanceReportBatchStatistics. + // RESPOND_AND_SET_NULL_IF_ERROR, and + // RESPOND_ALL_AND_SET_NULL_IF_ERROR are macros from + // backend_common.h that assist in this management of response + // objects. + + // The backend could iterate over the 'requests' and process each + // one separately. But for performance reasons it is usually + // preferred to create batched input tensors that are processed + // simultaneously. This is especially true for devices like GPUs + // that are capable of exploiting the large amount parallelism + // exposed by larger data sets. + // + // The backend utilities provide a "collector" to facilitate this + // batching process. The 'collector's ProcessTensor function will + // combine a tensor's value from each request in the batch into a + // single contiguous buffer. The buffer can be provided by the + // backend or 'collector' can create and manage it. In this backend, + // there is not a specific buffer into which the batch should be + // created, so use ProcessTensor arguments that cause collector to + // manage it. ProcessTensor does NOT support TRITONSERVER_TYPE_BYTES + // data type. + + BackendInputCollector collector( + requests, request_count, &responses, model_state->TritonMemoryManager(), + true /* pinned_disabled*/, nullptr /* stream*/); + + // To instruct ProcessTensor to "gather" the entire batch of input + // tensors into a single contiguous buffer in CPU memory, set the + // "allowed input types" to be the CPU ones (see tritonserver.h in + // the triton-inference-server/core repo for allowed memory types). + std::vector> allowed_input_types = + {{TRITONSERVER_MEMORY_CPU, 0}}; + + const char* input_buffer; + size_t input_buffer_byte_size; + TRITONSERVER_MemoryType input_buffer_memory_type; + int64_t input_buffer_memory_type_id; + + RESPOND_ALL_AND_SET_NULL_IF_ERROR( + responses, request_count, + collector.ProcessTensor( + model_state->InputTensorName().c_str(), nullptr /* existing_buffer */, + 0 /* existing_buffer_byte_size */, allowed_input_types, &input_buffer, + &input_buffer_byte_size, &input_buffer_memory_type, + &input_buffer_memory_type_id)); + + + // Finalize the collector. If 'true' is returned, 'input_buffer' + // will not be valid until the backend synchronizes the CUDA + // stream or event that was used when creating the collector. For + // this backend, GPU is not supported and so no CUDA sync should + // be needed; so if 'true' is returned simply log an error. + const bool need_cuda_input_sync = collector.Finalize(); + if (need_cuda_input_sync) { + LOG_MESSAGE( + TRITONSERVER_LOG_ERROR, + "'recommended' backend: unexpected CUDA sync required by collector"); + } + + // 'input_buffer' contains the batched input tensor. The backend can + // implement whatever logic is necessary to produce the output + // tensor. This backend simply logs the input tensor value and then + // returns the input tensor value in the output tensor so no actual + // computation is needed. + + uint64_t compute_start_ns = 0; + SET_TIMESTAMP(compute_start_ns); + + LOG_MESSAGE( + TRITONSERVER_LOG_INFO, + (std::string("model ") + model_state->Name() + ": requests in batch " + + std::to_string(request_count)) + .c_str()); + std::string tstr; + IGNORE_ERROR(BufferAsTypedString( + tstr, input_buffer, input_buffer_byte_size, + model_state->TensorDataType())); + LOG_MESSAGE( + TRITONSERVER_LOG_INFO, + (std::string("batched " + model_state->InputTensorName() + " value: ") + + tstr) + .c_str()); + + unsigned int batch_size = input_buffer_byte_size/ sizeof(unsigned int); + unsigned int* output_buffer = new unsigned int[batch_size]; + for (unsigned int batch_idx = 0; batch_idx < batch_size; batch_idx++) { + unsigned int stream_id = ((unsigned int*)input_buffer)[batch_idx]; + instance_state->log_request(stream_id); + } + + for (unsigned int batch_idx = 0; batch_idx < batch_size; batch_idx++) { + unsigned int stream_id = ((unsigned int*)input_buffer)[batch_idx]; + output_buffer[batch_idx] = instance_state->get_inference_count(stream_id); + } + + TRITONSERVER_MemoryType output_buffer_memory_type = input_buffer_memory_type; + int64_t output_buffer_memory_type_id = input_buffer_memory_type_id; + + uint64_t compute_end_ns = 0; + SET_TIMESTAMP(compute_end_ns); + + bool supports_first_dim_batching; + RESPOND_ALL_AND_SET_NULL_IF_ERROR( + responses, request_count, + model_state->SupportsFirstDimBatching(&supports_first_dim_batching)); + + std::vector tensor_shape; + RESPOND_ALL_AND_SET_NULL_IF_ERROR( + responses, request_count, model_state->TensorShape(tensor_shape)); + + // Because the output tensor values are concatenated into a single + // contiguous 'output_buffer', the backend must "scatter" them out + // to the individual response output tensors. The backend utilities + // provide a "responder" to facilitate this scattering process. + // BackendOutputResponder does NOT support TRITONSERVER_TYPE_BYTES + // data type. + + // The 'responders's ProcessTensor function will copy the portion of + // 'output_buffer' corresonding to each request's output into the + // response for that request. + + BackendOutputResponder responder( + requests, request_count, &responses, model_state->TritonMemoryManager(), + supports_first_dim_batching, false /* pinned_enabled */, + nullptr /* stream*/); + + responder.ProcessTensor( + model_state->OutputTensorName().c_str(), model_state->TensorDataType(), + tensor_shape, (char *)output_buffer, output_buffer_memory_type, + output_buffer_memory_type_id); + + // Finalize the responder. If 'true' is returned, the output + // tensors' data will not be valid until the backend synchronizes + // the CUDA stream or event that was used when creating the + // responder. For this backend, GPU is not supported and so no CUDA + // sync should be needed; so if 'true' is returned simply log an + // error. + const bool need_cuda_output_sync = responder.Finalize(); + if (need_cuda_output_sync) { + LOG_MESSAGE( + TRITONSERVER_LOG_ERROR, + "'recommended' backend: unexpected CUDA sync required by responder"); + } + + // Send all the responses that haven't already been sent because of + // an earlier error. + for (auto& response : responses) { + if (response != nullptr) { + LOG_IF_ERROR( + TRITONBACKEND_ResponseSend( + response, TRITONSERVER_RESPONSE_COMPLETE_FINAL, nullptr), + "failed to send response"); + } + } + delete[] output_buffer; + + uint64_t exec_end_ns = 0; + SET_TIMESTAMP(exec_end_ns); + +#ifdef TRITON_ENABLE_STATS + // For batch statistics need to know the total batch size of the + // requests. This is not necessarily just the number of requests, + // because if the model supports batching then any request can be a + // batched request itself. + size_t total_batch_size = 0; + if (!supports_first_dim_batching) { + total_batch_size = request_count; + } else { + for (uint32_t r = 0; r < request_count; ++r) { + auto& request = requests[r]; + TRITONBACKEND_Input* input = nullptr; + LOG_IF_ERROR( + TRITONBACKEND_RequestInputByIndex(request, 0 /* index */, &input), + "failed getting request input"); + if (input != nullptr) { + const int64_t* shape = nullptr; + LOG_IF_ERROR( + TRITONBACKEND_InputProperties( + input, nullptr, nullptr, &shape, nullptr, nullptr, nullptr), + "failed getting input properties"); + if (shape != nullptr) { + total_batch_size += shape[0]; + } + } + } + } +#else + (void)exec_start_ns; + (void)exec_end_ns; + (void)compute_start_ns; + (void)compute_end_ns; +#endif // TRITON_ENABLE_STATS + + // Report statistics for each request, and then release the request. + for (uint32_t r = 0; r < request_count; ++r) { + auto& request = requests[r]; + +#ifdef TRITON_ENABLE_STATS + LOG_IF_ERROR( + TRITONBACKEND_ModelInstanceReportStatistics( + instance_state->TritonModelInstance(), request, + (responses[r] != nullptr) /* success */, exec_start_ns, + compute_start_ns, compute_end_ns, exec_end_ns), + "failed reporting request statistics"); +#endif // TRITON_ENABLE_STATS + + LOG_IF_ERROR( + TRITONBACKEND_RequestRelease(request, TRITONSERVER_REQUEST_RELEASE_ALL), + "failed releasing request"); + } + +#ifdef TRITON_ENABLE_STATS + // Report batch statistics. + LOG_IF_ERROR( + TRITONBACKEND_ModelInstanceReportBatchStatistics( + instance_state->TritonModelInstance(), total_batch_size, + exec_start_ns, compute_start_ns, compute_end_ns, exec_end_ns), + "failed reporting batch request statistics"); +#endif // TRITON_ENABLE_STATS + + return nullptr; // success +} + +} // extern "C" + +}}} // namespace triton::backend::recommended diff --git a/src/apps/sample_apps/TritonBackendEnsemble/nvdsinferserver_custom_impl_ensemble/Makefile b/src/apps/sample_apps/TritonBackendEnsemble/nvdsinferserver_custom_impl_ensemble/Makefile new file mode 100644 index 00000000..34739353 --- /dev/null +++ b/src/apps/sample_apps/TritonBackendEnsemble/nvdsinferserver_custom_impl_ensemble/Makefile @@ -0,0 +1,49 @@ +#################################################################################################### +# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +#################################################################################################### + +CUDA_VER?= +ifeq ($(CUDA_VER),) + $(error "CUDA_VER is not set") +endif + +CC:= g++ + +CFLAGS := -Wall -std=c++14 -shared -fPIC -Wno-error=deprecated-declarations +CFLAGS += -I../../../../../includes \ + -I /usr/local/cuda-$(CUDA_VER)/include \ + -I../../../../../includes/nvdsinferserver \ + -I/opt/nvidia/deepstream/deepstream/sources/includes \ + -I/opt/nvidia/deepstream/deepstream/sources/includes/nvdsinferserver + +ifeq ($(ENABLE_DEBUG), 1) + CFLAGS += -DENABLE_DEBUG=1 +endif + +LIBS:= \ + -L/opt/nvidia/deepstream/deepstream/lib/ -lnvds_meta +LFLAGS:= -Wl,--start-group $(LIBS) -Wl,--end-group + +SRCFILES:= nvdsinferserver_custom_process_ensemble.cpp + +TARGET_LIB:= libnvdstriton_custom_impl_ensemble.so + +PKGS:= glib-2.0 gstreamer-1.0 +CFLAGS+=$(shell pkg-config --cflags $(PKGS)) +LIBS+=$(shell pkg-config --libs $(PKGS)) + +all: $(TARGET_LIB) + +$(TARGET_LIB) : $(SRCFILES) + $(CC) -o $@ $^ $(CFLAGS) $(LFLAGS) + +clean: + rm -rf $(TARGET_LIB) diff --git a/src/apps/sample_apps/TritonBackendEnsemble/nvdsinferserver_custom_impl_ensemble/nvdsinferserver_custom_process_ensemble.cpp b/src/apps/sample_apps/TritonBackendEnsemble/nvdsinferserver_custom_impl_ensemble/nvdsinferserver_custom_process_ensemble.cpp new file mode 100644 index 00000000..73451c05 --- /dev/null +++ b/src/apps/sample_apps/TritonBackendEnsemble/nvdsinferserver_custom_impl_ensemble/nvdsinferserver_custom_process_ensemble.cpp @@ -0,0 +1,566 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2023-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: LicenseRef-NvidiaProprietary + * + * NVIDIA CORPORATION, its affiliates and licensors retain all intellectual + * property and proprietary rights in and to this material, related + * documentation and any modifications thereto. Any use, reproduction, + * disclosure or distribution of this material and related documentation + * without an express license agreement from NVIDIA CORPORATION or + * its affiliates is strictly prohibited. + */ + +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "infer_custom_process.h" +#include "infer_post_datatypes.h" +#include "nvbufsurface.h" +#include "nvdsmeta.h" +#include "gstnvdsmeta.h" + +typedef struct _GstBuffer GstBuffer; + +/** This file implements the custom extra input preprocess and custom + * postprocess for the Triton ensemble model example in DeepStream. + */ + +namespace dsis = nvdsinferserver; + +#if ENABLE_DEBUG +#define LOG_DEBUG(fmt, ...) fprintf(stdout, "%s:%d " fmt "\n", __FILE__, __LINE__, ##__VA_ARGS__) +#else +#define LOG_DEBUG(fmt, ...) +#endif + +#define LOG_ERROR(fmt, ...) fprintf(stderr, "%s:%d " fmt "\n", __FILE__, __LINE__, ##__VA_ARGS__) + +#ifndef INFER_ASSERT +#define INFER_ASSERT(expr) \ + do { \ + if (!(expr)) { \ + fprintf(stderr, "%s:%d ASSERT(%s) \n", __FILE__, __LINE__, #expr); \ + std::abort(); \ + } \ + } while (0) +#endif + +#define CSTR(str) (str).empty() ? "" : (str).c_str() + +static inline const char* safeStr(const char* str) +{ + return !str ? "" : str; +} + +static inline const char* safeStr(const std::string& str) +{ + return str.c_str(); +} + +/** Labels for the VehicleMake classifier model. */ +static const std::vector kVehicleMakeLabels = { + "acura", "audi", "bmw", "chevrolet", + "chrysler", "dodge", "ford", "gmc", + "honda", "hyundai", "infiniti", "jeep", + "kia", "lexus", "mazda", "mercedes", + "nissan", "subaru", "toyota", "volkswagen" +}; + +/** Labels for the VehicleTypes classifier model. */ +static const std::vector kVehicleTypeLabels = { + "coupe", "largevehicle", "sedan", "suv", "truck", "van" +}; + +constexpr float kClassifierThreshold = 0.51f; + +/** Define a function for custom processor for DeepStream Triton plugin(nvinferserver) + * do custom extra input preprocess and custom postprocess on triton based models. + * The sysmbol is loaded through + * infer_config { + * custom_lib { path: "path/to/custom_impl_process.so" } + * extra { + * custom_process_funcion: "CreateInferServerCustomProcess" + * }} + */ +extern "C" dsis::IInferCustomProcessor* CreateInferServerCustomProcess( + const char* config, uint32_t configLen); + +namespace { +using namespace dsis; + +std::string +memType2Str(InferMemType t) +{ + switch (t) { + case InferMemType::kGpuCuda: + return "kGpuCuda"; + case InferMemType::kCpu: + return "kCpu"; + case InferMemType::kCpuCuda: + return "kCpuPinned"; + default: + return "Unknown"; + } +} + +std::string +dataType2Str(dsis::InferDataType t) +{ + switch (t) { + case InferDataType::kFp32: + return "kFp32"; + case InferDataType::kFp16: + return "kFp16"; + case InferDataType::kInt8: + return "kInt8"; + case InferDataType::kInt32: + return "kInt32"; + case InferDataType::kInt16: + return "kInt16"; + case InferDataType::kUint8: + return "kUint8"; + case InferDataType::kUint16: + return "kUint16"; + case InferDataType::kUint32: + return "kUint32"; + case InferDataType::kFp64: + return "kFp64"; + case InferDataType::kInt64: + return "kInt64"; + case InferDataType::kUint64: + return "kUint64"; + case InferDataType::kString: + return "kString"; + case InferDataType::kBool: + return "kBool"; + default: + return "Unknown"; + } +} + +// return buffer description string +std::string +strOfBufDesc(const dsis::InferBufferDescription& desc) +{ + std::stringstream ss; + ss << "*" << desc.name << "*, shape: "; + for (uint32_t i = 0; i < desc.dims.numDims; ++i) { + if (i != 0) { + ss << "x"; + } else { + ss << "["; + } + ss << desc.dims.d[i]; + if (i == desc.dims.numDims - 1) { + ss << "]"; + } + } + ss << ", dataType:" << dataType2Str(desc.dataType); + ss << ", memType:" << memType2Str(desc.memType); + return ss.str(); +} + +} // namespace + +/** Example of a Custom process instance for deepstream-triton(gst-nvinferserver) plugin + * It is derived from nvdsinferserver::IInferCustomProcessor + * If should be loaded through + * config_triton_inferserver_primary_fasterRCNN.txt: + * infer_config { + * custom_lib { path: "path/to/custom_impl_process.so" } + * extra { + * custom_process_funcion: "CreateInferServerCustomProcess" + * } + * } + */ +class NvInferServerCustomProcess : public dsis::IInferCustomProcessor { +private: + std::map> _streamFeedback; + std::mutex _streamMutex; + +public: + ~NvInferServerCustomProcess() override = default; + /** override function + * Specifies supported extraInputs memtype in extraInputProcess() + */ + void supportInputMemType(dsis::InferMemType& type) override { type = dsis::InferMemType::kCpu; } + + /** Override function + * check whether custom loop process needed. + * If return True, extraInputProcess() and inferenceDone() runs in order per stream_ids + * This is usually for LSTM loop purpose. FasterRCNN does not need it. + * The code for requireInferLoop() conditions just sample when user has + * a LSTM-like Loop model and requires loop custom processing. + * */ + bool requireInferLoop() const override { return false; } + + /** + * Override function + * Do custom processing on extra inputs. + * @primaryInput is already preprocessed. DO NOT update it again. + * @extraInputs, do custom processing and fill all data according the tensor shape + * @options, it has most of the common Deepstream metadata along with primary data. + * e.g. NvDsBatchMeta, NvDsObjectMeta, NvDsFrameMeta, stream ids... + * see infer_ioptions.h to see all the potential key name and structures + * in the key-value table. + */ + NvDsInferStatus extraInputProcess( + const std::vector& + primaryInputs, // primary tensor(image) has been processed + std::vector& extraInputs, const dsis::IOptions* options) override + { + INFER_ASSERT(primaryInputs.size() > 0); + INFER_ASSERT(extraInputs.size() == 1); + // primary input tensor: INPUT [batch, channel, height, width] + dsis::InferBufferDescription input0Desc = primaryInputs[0]->getBufDesc(); + // extra input tensor: STREAM_ID [batch, 1] + dsis::InferBufferDescription extra1Desc = extraInputs[0]->getBufDesc(); + INFER_ASSERT(extra1Desc.dataType == dsis::InferDataType::kInt32); + INFER_ASSERT(extra1Desc.elementSize == sizeof(int32_t)); // bytes per element + + INFER_ASSERT(!strOfBufDesc(input0Desc).empty()); + LOG_DEBUG("extraInputProcess: primary input %s", strOfBufDesc(input0Desc).c_str()); + LOG_DEBUG("extraInputProcess: extra input %s", strOfBufDesc(extra1Desc).c_str()); + + // batch size must be get from primary input tensor. + // extra inputs 'image_shape' does not have a batch size in this specific model + int batchSize = input0Desc.dims.d[0]; + INFER_ASSERT(extra1Desc.dims.numDims == 2 && extra1Desc.dims.d[0] == batchSize); + INFER_ASSERT(batchSize >= 1); + if (!options) { + LOG_ERROR("custom process does not receive IOptions"); + return NVDSINFER_CUSTOM_LIB_FAILED; + } + + NvDsBatchMeta* batchMeta = nullptr; + std::vector frameMetaList; + NvBufSurface* bufSurf = nullptr; + std::vector surfParamsList; + std::vector streamIds; + int64_t unique_id = 0; + INFER_ASSERT(options->getValueArray(OPTION_NVDS_SREAM_IDS, streamIds) == NVDSINFER_SUCCESS); + INFER_ASSERT(streamIds.size() == (uint32_t)batchSize); + + // get NvBufSurface + if (options->hasValue(OPTION_NVDS_BUF_SURFACE)) { + INFER_ASSERT(options->getObj(OPTION_NVDS_BUF_SURFACE, bufSurf) == NVDSINFER_SUCCESS); + } + INFER_ASSERT(bufSurf); + + // get NvDsBatchMeta + if (options->hasValue(OPTION_NVDS_BATCH_META)) { + INFER_ASSERT(options->getObj(OPTION_NVDS_BATCH_META, batchMeta) == NVDSINFER_SUCCESS); + } + INFER_ASSERT(batchMeta); + + // get all frame meta list into vector + if (options->hasValue(OPTION_NVDS_FRAME_META_LIST)) { + INFER_ASSERT( + options->getValueArray(OPTION_NVDS_FRAME_META_LIST, frameMetaList) == + NVDSINFER_SUCCESS); + } + + // get unique_id + if (options->hasValue(OPTION_NVDS_UNIQUE_ID)) { + INFER_ASSERT(options->getInt(OPTION_NVDS_UNIQUE_ID, unique_id) == NVDSINFER_SUCCESS); + } + + // get all surface params list into vector + if (options->hasValue(OPTION_NVDS_BUF_SURFACE_PARAMS_LIST)) { + INFER_ASSERT( + options->getValueArray(OPTION_NVDS_BUF_SURFACE_PARAMS_LIST, surfParamsList) == + NVDSINFER_SUCCESS); + } + + // fill extra input tensor "STREAM_ID[-1,1]" + int32_t* streamIdTensor = (int32_t*)extraInputs[0]->getBufPtr(0); + + for (int iBatch = 0; iBatch < batchSize; ++iBatch) { + streamIdTensor[iBatch] = streamIds[iBatch]; + } + return NVDSINFER_SUCCESS; + } + + /** Override function + * Custom processing for inferenced output tensors. + * output memory types is controlled by gst-nvinferserver config file + * infer_config { + * backend { output_mem_type: MEMORY_TYPE_CPU } + * } + * User can even attach parsed metadata into GstBuffer from this function + */ + NvDsInferStatus inferenceDone( + const dsis::IBatchArray* outputs, const dsis::IOptions* inOptions) override + { + std::vector streamIds; + INFER_ASSERT( + inOptions->getValueArray(OPTION_NVDS_SREAM_IDS, streamIds) == NVDSINFER_SUCCESS); + INFER_ASSERT(!streamIds.empty()); + uint32_t batchSize = streamIds.size(); + + // add output tensors into map + std::unordered_map tensors; + + for (uint32_t iTensor = 0; iTensor < outputs->getSize(); ++iTensor) { + const dsis::IBatchBuffer* outTensor = outputs->getBuffer(iTensor); + INFER_ASSERT(outTensor); + auto desc = outTensor->getBufDesc(); + LOG_DEBUG("out tensor: %s, desc: %s \n", CSTR(desc.name), strOfBufDesc(desc).c_str()); + tensors.emplace(desc.name, outTensor); + } + + auto outputBuf = tensors["INFERENCE_COUNT"]; + INFER_ASSERT(outputBuf); + auto outDesc = outputBuf->getBufDesc(); + INFER_ASSERT(outDesc.dims.numDims == 2 && outDesc.dims.d[1] == 1); + INFER_ASSERT(outDesc.dims.d[0] == (int32_t)batchSize); + + /* Read the inference count output and print using debug logs. */ + int32_t* outInferenceCounts = (int32_t*)outputBuf->getBufPtr(0); + for (int idx = 0; idx < outDesc.dims.d[0]; ++idx) { + LOG_DEBUG("Inference count = %d for batch index %d\n", outInferenceCounts[idx], idx); + (void)outInferenceCounts; // avoid warning + } + + /* Parse the classifier outputs. */ + std::vector classTensorOutput; + std::vector> labels; + + classTensorOutput.push_back(tensors["VEHICLE_MAKE"]); + classTensorOutput.push_back(tensors["VEHICLE_TYPE"]); + + labels.push_back(kVehicleMakeLabels); + labels.push_back(kVehicleTypeLabels); + + /* Get the number of attributes supported by the classifier. */ + unsigned int numAttributes = classTensorOutput.size(); + /* Iterate through all the output coverage layers of the classifier. */ + for (unsigned int l = 0; l < numAttributes; l++) { + const dsis::IBatchBuffer* outputBuf = classTensorOutput[l]; + INFER_ASSERT(outputBuf); + auto outDesc = outputBuf->getBufDesc(); + // Support both 2D [batch, classes] and 4D [batch, classes, 1, 1] tensor formats + LOG_DEBUG("Classifier output %u: name=%s, numDims=%d, dims=[%d, %d, %d, %d]\n", + l, CSTR(outDesc.name), outDesc.dims.numDims, + outDesc.dims.d[0], outDesc.dims.d[1], + outDesc.dims.numDims > 2 ? outDesc.dims.d[2] : 0, + outDesc.dims.numDims > 3 ? outDesc.dims.d[3] : 0); + + INFER_ASSERT(outDesc.dims.numDims == 2 || outDesc.dims.numDims == 4); + + int numClasses = outDesc.dims.d[1]; + INFER_ASSERT(numClasses == (int)labels[l].size()); + LOG_DEBUG("Verified: numClasses=%d matches labels[%u].size()=%zu\n", + numClasses, l, labels[l].size()); + INFER_ASSERT(outDesc.dims.d[0] == (int32_t)batchSize); + } + + for (unsigned int idx = 0; idx < batchSize; ++idx) { + std::string attrString; + std::vector attributes; + + for (unsigned int l = 0; l < numAttributes; l++) { + const dsis::IBatchBuffer* outputBuf = classTensorOutput[l]; + auto outDesc = outputBuf->getBufDesc(); + + // Calculate numClasses and pointer offset based on tensor dimensions + unsigned int numClasses = outDesc.dims.d[1]; + int stridePerBatch = numClasses; + + // For 4D tensors [batch, classes, h, w], stride is classes * h * w + if (outDesc.dims.numDims == 4) { + stridePerBatch = outDesc.dims.d[1] * outDesc.dims.d[2] * outDesc.dims.d[3]; + } + + float* outPtr = (float*)outputBuf->getBufPtr(0) + idx * stridePerBatch; + float maxProbability = 0; + bool attrFound = false; + NvDsInferAttribute attr; + + /* Iterate through all the probabilities that the object belongs to + * each class. Find the maximum probability and the corresponding class + * which meets the minimum threshold. */ + for (unsigned int c = 0; c < numClasses; c++) { + float probability = outPtr[c]; + if (probability > kClassifierThreshold && + probability > maxProbability) { + maxProbability = probability; + attrFound = true; + attr.attributeIndex = l; + attr.attributeValue = c; + attr.attributeConfidence = probability; + } + } + if (attrFound) { + if (labels.size() > attr.attributeIndex && + attr.attributeValue < labels[attr.attributeIndex].size()) + attr.attributeLabel = + strdup(labels[attr.attributeIndex][attr.attributeValue].c_str()); + else + attr.attributeLabel = nullptr; + attributes.push_back(attr); + if (attr.attributeLabel) + attrString.append(attr.attributeLabel).append(" "); + } + } + + InferClassificationOutput output; + + for (auto& attr : attributes) { + InferAttribute safeAttr; + static_cast(safeAttr) = attr; + safeAttr.safeAttributeLabel = safeStr(attr.attributeLabel); + safeAttr.attributeLabel = (char *) safeAttr.safeAttributeLabel.c_str(); + output.attributes.emplace_back(safeAttr); + free(attr.attributeLabel); + } + + INFER_ASSERT(attachClassificationMeta(inOptions, output, idx) + == NVDSINFER_SUCCESS); + + } + + return NVDSINFER_SUCCESS; + } + + /** override function + * Receiving errors if anything wrong inside lowlevel lib + */ + void notifyError(NvDsInferStatus s) override + { + std::unique_lock locker(_streamMutex); + _streamFeedback.clear(); + } + +private: + /** function for loop processing only. not requried for fasterRCNN + */ + NvDsInferStatus feedbackStreamInput( + const dsis::IBatchArray* outputs, const dsis::IOptions* inOptions); + + /** + * attach bounding boxes into NvDsBatchMeta and NvDsFrameMeta + */ + NvDsInferStatus attachClassificationMeta( + const dsis::IOptions* inOptions, InferClassificationOutput& objInfo, + uint32_t batchIdx); +}; + +/** Implementation to create a custom processor for DeepStream Triton + * plugin (nvinferserver) to do custom extra input preprocess and custom + * postprocess on triton based models. + */ +extern "C" { +dsis::IInferCustomProcessor* +CreateInferServerCustomProcess(const char* config, uint32_t configLen) +{ + return new NvInferServerCustomProcess(); +} +} + +/** + * Attach classification results into NvDsObjectMeta + */ +NvDsInferStatus +NvInferServerCustomProcess::attachClassificationMeta( + const dsis::IOptions* inOptions, InferClassificationOutput& objInfo, + uint32_t batchIdx) +{ + INFER_ASSERT(inOptions); + GstBuffer* gstBuf = nullptr; + NvDsBatchMeta* batchMeta = nullptr; + std::vector objectMetaList; + NvBufSurface* bufSurf = nullptr; + std::vector surfParamsList; + int64_t unique_id = 0; + + // get GstBuffer + if (inOptions->hasValue(OPTION_NVDS_GST_BUFFER)) { + INFER_ASSERT(inOptions->getObj(OPTION_NVDS_GST_BUFFER, gstBuf) == NVDSINFER_SUCCESS); + } + INFER_ASSERT(gstBuf); + + // get NvBufSurface + if (inOptions->hasValue(OPTION_NVDS_BUF_SURFACE)) { + INFER_ASSERT(inOptions->getObj(OPTION_NVDS_BUF_SURFACE, bufSurf) == NVDSINFER_SUCCESS); + } + INFER_ASSERT(bufSurf); + + // get NvDsBatchMeta + if (inOptions->hasValue(OPTION_NVDS_BATCH_META)) { + INFER_ASSERT(inOptions->getObj(OPTION_NVDS_BATCH_META, batchMeta) == NVDSINFER_SUCCESS); + } + INFER_ASSERT(batchMeta); + + // get all object meta list into vector for secondary classifier + if (inOptions->hasValue(OPTION_NVDS_OBJ_META_LIST)) { + INFER_ASSERT( + inOptions->getValueArray(OPTION_NVDS_OBJ_META_LIST, objectMetaList) == + NVDSINFER_SUCCESS); + } + INFER_ASSERT(batchIdx < objectMetaList.size()); // batchsize + + // get unique_id + if (inOptions->hasValue(OPTION_NVDS_UNIQUE_ID)) { + INFER_ASSERT(inOptions->getInt(OPTION_NVDS_UNIQUE_ID, unique_id) == NVDSINFER_SUCCESS); + } + + // get all surface params list into vector + if (inOptions->hasValue(OPTION_NVDS_BUF_SURFACE_PARAMS_LIST)) { + INFER_ASSERT( + inOptions->getValueArray(OPTION_NVDS_BUF_SURFACE_PARAMS_LIST, surfParamsList) == + NVDSINFER_SUCCESS); + } + INFER_ASSERT(batchIdx < surfParamsList.size()); // batchsize + + auto objMeta = objectMetaList[batchIdx]; + std::string string_label = objInfo.label; + + /* Fill the attribute info structure for the object. */ + guint numAttrs = objInfo.attributes.size(); + + nvds_acquire_meta_lock(batchMeta); + + NvDsClassifierMeta* classifier_meta = + nvds_acquire_classifier_meta_from_pool(batchMeta); + + classifier_meta->unique_component_id = unique_id; + + for (unsigned int i = 0; i < numAttrs; i++) { + NvDsLabelInfo* label_info = + nvds_acquire_label_info_meta_from_pool(batchMeta); + const InferAttribute& attr = objInfo.attributes[i]; + label_info->label_id = attr.attributeIndex; + label_info->result_class_id = attr.attributeValue; + label_info->result_prob = attr.attributeConfidence; + if (!attr.safeAttributeLabel.empty()) { + g_strlcpy( + label_info->result_label, attr.safeAttributeLabel.c_str(), + MAX_LABEL_SIZE); + if (objInfo.label.length() == 0) + string_label.append(attr.safeAttributeLabel).append(" "); + } + + nvds_add_label_info_meta_to_classifier(classifier_meta, label_info); + } + + if (string_label.length() > 0 && objMeta) { + gchar* temp = objMeta->text_params.display_text; + objMeta->text_params.display_text = + g_strconcat(temp, " ", string_label.c_str(), nullptr); + g_free(temp); + } + + nvds_add_classifier_meta_to_object(objMeta, classifier_meta); + nvds_release_meta_lock(batchMeta); + + return NVDSINFER_SUCCESS; +} diff --git a/src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/deepstream_triton_custom_cpp_backend/1/placeholder.txt b/src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/deepstream_triton_custom_cpp_backend/1/placeholder.txt new file mode 100644 index 00000000..fd2c27e6 --- /dev/null +++ b/src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/deepstream_triton_custom_cpp_backend/1/placeholder.txt @@ -0,0 +1 @@ +Place holder for DeepStream Triton custom C++ backend model version 1. diff --git a/src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/deepstream_triton_custom_cpp_backend/config.pbtxt b/src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/deepstream_triton_custom_cpp_backend/config.pbtxt new file mode 100644 index 00000000..7155f2a3 --- /dev/null +++ b/src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/deepstream_triton_custom_cpp_backend/config.pbtxt @@ -0,0 +1,47 @@ +################################################################################ +# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: MIT +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the "Software"), +# to deal in the Software without restriction, including without limitation +# the rights to use, copy, modify, merge, publish, distribute, sublicense, +# and/or sell copies of the Software, and to permit persons to whom the +# Software is furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL +# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +# DEALINGS IN THE SOFTWARE. +################################################################################ + + +backend: "recommended" +max_batch_size: 16 +dynamic_batching { +} +input [ + { + name: "INPUT" + data_type: TYPE_INT32 + dims: [1] + } +] +output [ + { + name: "OUTPUT" + data_type: TYPE_INT32 + dims: [1] + } +] +instance_group [ + { + kind: KIND_CPU + } +] diff --git a/src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/triton_custom_backend_ensemble/1/placeholder.txt b/src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/triton_custom_backend_ensemble/1/placeholder.txt new file mode 100644 index 00000000..f6ab2ee5 --- /dev/null +++ b/src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/triton_custom_backend_ensemble/1/placeholder.txt @@ -0,0 +1 @@ +Place holder for Triton Ensemble model version 1. diff --git a/src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/triton_custom_backend_ensemble/config.pbtxt b/src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/triton_custom_backend_ensemble/config.pbtxt new file mode 100644 index 00000000..48959ee8 --- /dev/null +++ b/src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/triton_custom_backend_ensemble/config.pbtxt @@ -0,0 +1,97 @@ +################################################################################ +# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: MIT +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the "Software"), +# to deal in the Software without restriction, including without limitation +# the rights to use, copy, modify, merge, publish, distribute, sublicense, +# and/or sell copies of the Software, and to permit persons to whom the +# Software is furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL +# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +# DEALINGS IN THE SOFTWARE. +################################################################################ + +name: "triton_custom_backend_ensemble" +platform: "ensemble" +max_batch_size: 16 +input [ + { + name: "OBJECT" + data_type: TYPE_FP32 + format: FORMAT_NCHW + dims: [3, 224, 224] + }, + { + name: "STREAM_ID" + data_type: TYPE_INT32 + dims: [1] + } +] +output [ + { + name: "VEHICLE_MAKE" + data_type: TYPE_FP32 + dims: [20] + }, + { + name: "VEHICLE_TYPE" + data_type: TYPE_FP32 + dims: [6] + }, + { + name: "INFERENCE_COUNT" + data_type: TYPE_INT32 + dims: [1] + } + +] +ensemble_scheduling { + step [ + { + model_name: "deepstream_triton_custom_cpp_backend" + model_version: 1 + input_map { + key: "INPUT" + value: "STREAM_ID" + } + output_map { + key: "OUTPUT" + value: "INFERENCE_COUNT" + } + }, + { + model_name: "Secondary_VehicleMake" + model_version: 1 + input_map { + key: "input_1:0" + value: "OBJECT" + } + output_map { + key: "predictions/Softmax:0" + value: "VEHICLE_MAKE" + } + }, + { + model_name: "Secondary_VehicleTypes" + model_version: 1 + input_map { + key: "input_1:0" + value: "OBJECT" + } + output_map { + key: "predictions/Softmax:0" + value: "VEHICLE_TYPE" + } + } + ] +} diff --git a/src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/triton_custom_backend_ensemble/labels.txt b/src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/triton_custom_backend_ensemble/labels.txt new file mode 100644 index 00000000..8308ab75 --- /dev/null +++ b/src/apps/sample_apps/TritonBackendEnsemble/triton_model_repo/triton_custom_backend_ensemble/labels.txt @@ -0,0 +1,3 @@ +black;blue;brown;gold;green;grey;maroon;orange;red;silver;white;yellow +acura;audi;bmw;chevrolet;chrysler;dodge;ford;gmc;honda;hyundai;infiniti;jeep;kia;lexus;mazda;mercedes;nissan;subaru;toyota;volkswagen +coupe;largevehicle;sedan;suv;truck;van diff --git a/src/apps/sample_apps/TritonOnnxYolo/Makefile b/src/apps/sample_apps/TritonOnnxYolo/Makefile new file mode 100644 index 00000000..6f4b2916 --- /dev/null +++ b/src/apps/sample_apps/TritonOnnxYolo/Makefile @@ -0,0 +1,20 @@ +################################################################################ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. +# All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +################################################################################ + +# Delegates to the nvdsinferserver custom-process library so the mono-repo +# build.sh sample_apps loop picks this sample up. + +SUBDIRS := nvdsinferserver_custom_impl_yolo + +.PHONY: all clean $(SUBDIRS) + +all: $(SUBDIRS) + +$(SUBDIRS): + $(MAKE) -C $@ + +clean: + $(MAKE) -C nvdsinferserver_custom_impl_yolo clean diff --git a/src/apps/sample_apps/TritonOnnxYolo/README b/src/apps/sample_apps/TritonOnnxYolo/README new file mode 100644 index 00000000..4665ec59 --- /dev/null +++ b/src/apps/sample_apps/TritonOnnxYolo/README @@ -0,0 +1,105 @@ +################################################################################ +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +# +################################################################################ + +This sample shows +- using DS-Triton to run models with dynamic-sized output tensors. +- implmenting DS-Triton(nvdsinferserver) custom-lib to run ONNX YoloV3 models with + multi-input tensors and how to postprocess mixed-batch tensor data and attach them + into nvds metadata. + +=============================================================================== +1. Sample Contents: +=============================================================================== +Sample contents for DeepStream Triton: +- deepstream_app_triton_config_yolov3.txt - DeepStream triton reference app + configuration for ONNX YoloV3 model as the primary detector. +- config_triton_inferserver_primary_yolov3_onnx_custom.txt, an custom processing config + file for Gst-nvinferserver plugin. It configured with `output_mem_type: MEMORY_TYPE_CPU` + and `custom_process_funcion: "CreateInferServerCustomProcess"` for custom-lib to parsing + ONNX YoloV3 output tensors manually. +- nvdsinferserver_custom_impl_yolo/nvdsinferserver_custom_process_yolo.cpp - Example + how to create an customized nvdsinferserver::IInferCustomProcessor instance to do extra + input processing, ouptut tensor parsing and detection boundingbox attaching into nvds + metadata. +- triton_model_repo/yolov3-10_onnx/config.pbtxt - Triton config file for FasterRCNN model +- triton_model_repo/yolov3-10_onnx/1/yolov3-10.onnx - Users need to follow pre-requisites + to download the model manually. + +=============================================================================== +2. Prerequisites: +=============================================================================== +Pre-requisites: +- For X86 platform, please follow instructions from + https://catalog.ngc.nvidia.com/orgs/nvidia/containers/deepstream + to download deepstream-triton image. + $ docker pull nvcr.io/nvidia/deepstream:[version]-triton + [version] means deepstream version numbers e.g. 9.0.0 + $ xhost + + Set correct $DISPLAY variables on host, then run deepstream-triton docker container. + $ docker run --gpus '"'device=0'"' -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix \ + -e DISPLAY=$DISPLAY --net=host nvcr.io/nvidia/deepstream:[version]-triton + Note: Set correct GPU id in `device=$id`. + +- For Jetson platform, please install Triton dependencies. + $ cd /opt/nvidia/deepstream/deepstream/samples + $ sudo ./triton_backend_setup.sh + $ rm -rf ~/.cache/gstreamer-1.0/ + +- Back to current directory and download ONNX yolo-v3 model file. + $ mkdir -p triton_model_repo/yolov3-10_onnx/1/ + $ wget --no-check-certificate \ + https://github.com/onnx/models/raw/main/validated/vision/object_detection_segmentation/yolov3/model/yolov3-10.onnx \ + -O triton_model_repo/yolov3-10_onnx/1/yolov3-10.onnx +NOTE: Run with "sudo -E" or root if there is file permission issues. + +=============================================================================== +3. To Compile: +=============================================================================== +Compile the custom library: + + $ Set CUDA_VER in the MakeFile as per platform. + For x86, CUDA_VER=13.1 + For jetson & SBSA, CUDA_VER=13.0 + + $ make -C nvdsinferserver_custom_impl_yolo + +NOTE: To compile the sources, run make with "sudo -E" or root permission. + +=============================================================================== +4. Usage: +=============================================================================== +Run the ONNX sample with DeepStream Triton(Gst-nvinferserver) Native CAPI: + Run cmdline: + $ deepstream-app -c deepstream_app_triton_config_yolov3.txt + + NOTE: Regarding dynamic-sized output tensors, users could update + `max_buffer_bytes` to preserve a large memory size for each output tensors. e.g. + outputs { + name: "yolonms_layer_1/concat_2:0" + max_buffer_bytes: 2048 + } + Even this value is not configured or the value is smaller than actual required + size, DS-Triton could still auto-grow the memory size during runtime which might + affect performance a bit. + +Run the ONNX sample with DeepStream Triton gRPC remote inference: + Follow instructions in `samples/configs/deepstream-app-triton-grpc/README` to + start tritonserver by + $ tritonserver \ + --model-repository=$(pwd)/triton_model_repo + + Update config_triton_inferserver_primary_yolov3_onnx_custom.txt, + disable 'model_repo{}' block and enable 'grpc{}' with correct url. + + Then run gRPC test: + $ deepstream-app -c deepstream_app_triton_config_yolov3.txt diff --git a/src/apps/sample_apps/TritonOnnxYolo/config_triton_inferserver_primary_yolov3_onnx_custom.txt b/src/apps/sample_apps/TritonOnnxYolo/config_triton_inferserver_primary_yolov3_onnx_custom.txt new file mode 100644 index 00000000..75c0ad94 --- /dev/null +++ b/src/apps/sample_apps/TritonOnnxYolo/config_triton_inferserver_primary_yolov3_onnx_custom.txt @@ -0,0 +1,89 @@ +################################################################################ +# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +################################################################################ + +infer_config { + unique_id: 1 + gpu_ids: [0] + max_batch_size: 4 + backend { + triton { + model_name: "yolov3-10_onnx" + version: -1 + + # model_repo block is enabled for Triton Server CAPI inference. + model_repo { + root: "./triton_model_repo" + log_level: 2 + strict_model_config: true + # Triton runtime would reserve 64MB pinned memory + pinned_memory_pool_byte_size: 67108864 + # Triton runtim would reserve 64MB CUDA device memory on GPU 0 + cuda_device_memory { device: 0, memory_pool_byte_size: 67108864 } + } + + # To run Triton client remote inference, users need enable 'grpc' block, + # and disalbe 'model_repo' block. + # Setup Tritonserver, goto current directory, run + # tritonserver --model-repository=triton_model_repo + #grpc { + # url: "localhost:8001" + #} + } + output_mem_type: MEMORY_TYPE_CPU + outputs [ + { + name: "yolonms_layer_1/ExpandDims_1:0" + }, + { + name: "yolonms_layer_1/ExpandDims_3:0" + }, + { + name: "yolonms_layer_1/concat_2:0" + max_buffer_bytes: 2048 + } + ] + } + + preprocess { + network_format: IMAGE_FORMAT_RGB + tensor_order: TENSOR_ORDER_LINEAR + maintain_aspect_ratio: 1 + symmetric_padding: 1 + frame_scaling_hw: FRAME_SCALING_HW_DEFAULT + frame_scaling_filter: 1 + normalize { + scale_factor: 0.00392157 + channel_offsets: [0, 0, 0] + } + } + + postprocess { + other { } + } + + extra { + custom_process_funcion: "CreateInferServerCustomProcess" + } + + custom_lib { + path: "nvdsinferserver_custom_impl_yolo/libnvdstriton_custom_impl_yolo.so" + } +} + +input_control { + process_mode: PROCESS_MODE_FULL_FRAME + interval: 0 +} + +output_control { + output_tensor_meta: false +} diff --git a/src/apps/sample_apps/TritonOnnxYolo/deepstream_app_triton_config_yolov3.txt b/src/apps/sample_apps/TritonOnnxYolo/deepstream_app_triton_config_yolov3.txt new file mode 100644 index 00000000..e10f2a59 --- /dev/null +++ b/src/apps/sample_apps/TritonOnnxYolo/deepstream_app_triton_config_yolov3.txt @@ -0,0 +1,79 @@ +################################################################################ +# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +################################################################################ + +[application] +enable-perf-measurement=1 +perf-measurement-interval-sec=1 +gie-kitti-output-dir=streamscl + +[tiled-display] +enable=0 +rows=1 +columns=1 +width=1280 +height=720 +gpu-id=0 +nvbuf-memory-type=0 + +[source0] +enable=1 +#Type - 1=CameraV4L2 2=URI 3=MultiURI +type=3 +num-sources=1 +uri=file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.mp4 +gpu-id=0 +cudadec-memtype=0 + +[streammux] +gpu-id=0 +batch-size=1 +batched-push-timeout=-1 +## Set muxer output width and height +width=1920 +height=1080 +nvbuf-memory-type=0 + +[sink0] +enable=1 +#Type - 1=FakeSink 2=EglSink 3=File +type=2 +sync=0 +source-id=0 +gpu-id=0 + +[osd] +enable=1 +gpu-id=0 +border-width=3 +text-size=15 +text-color=1;1;1;1; +text-bg-color=0.3;0.3;0.3;1 +font=Serif +show-clock=0 +clock-x-offset=800 +clock-y-offset=820 +clock-text-size=12 +clock-color=1;0;0;0 +nvbuf-memory-type=0 + +[primary-gie] +enable=1 +gpu-id=0 +batch-size=1 +gie-unique-id=1 +nvbuf-memory-type=0 + +# plugin-type +#(0): nvinfer - Default inference plugin based on Tensorrt +#(1): nvinferserver - inference plugin based on Tensorrt-Inference-Server +plugin-type=1 +config-file=config_triton_inferserver_primary_yolov3_onnx_custom.txt diff --git a/src/apps/sample_apps/TritonOnnxYolo/nvdsinferserver_custom_impl_yolo/Makefile b/src/apps/sample_apps/TritonOnnxYolo/nvdsinferserver_custom_impl_yolo/Makefile new file mode 100644 index 00000000..257a4551 --- /dev/null +++ b/src/apps/sample_apps/TritonOnnxYolo/nvdsinferserver_custom_impl_yolo/Makefile @@ -0,0 +1,45 @@ +################################################################################ +# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +################################################################################ + +CUDA_VER?= +ifeq ($(CUDA_VER),) + $(error "CUDA_VER is not set") +endif + +CC:= g++ + +CFLAGS:= -Wall -std=c++14 -shared -fPIC -Wno-error=deprecated-declarations +CFLAGS+= -I../../../../../includes \ + -I /usr/local/cuda-$(CUDA_VER)/include \ + -I../../../../../includes/nvdsinferserver \ + -I/opt/nvidia/deepstream/deepstream/sources/includes \ + -I/opt/nvidia/deepstream/deepstream/sources/includes/nvdsinferserver + +LIBS:= \ + -L/opt/nvidia/deepstream/deepstream/lib/ -lnvds_meta +LFLAGS:= -Wl,--start-group $(LIBS) -Wl,--end-group + +SRCFILES:= nvdsinferserver_custom_process_yolo.cpp + +TARGET_LIB:= libnvdstriton_custom_impl_yolo.so + +PKGS:= glib-2.0 +CFLAGS+=$(shell pkg-config --cflags $(PKGS)) +LIBS+=$(shell pkg-config --libs $(PKGS)) + +all: $(TARGET_LIB) + +$(TARGET_LIB) : $(SRCFILES) + $(CC) -o $@ $^ $(CFLAGS) $(LFLAGS) + +clean: + rm -rf $(TARGET_LIB) diff --git a/src/apps/sample_apps/TritonOnnxYolo/nvdsinferserver_custom_impl_yolo/nvdsinferserver_custom_process_yolo.cpp b/src/apps/sample_apps/TritonOnnxYolo/nvdsinferserver_custom_impl_yolo/nvdsinferserver_custom_process_yolo.cpp new file mode 100644 index 00000000..874508a6 --- /dev/null +++ b/src/apps/sample_apps/TritonOnnxYolo/nvdsinferserver_custom_impl_yolo/nvdsinferserver_custom_process_yolo.cpp @@ -0,0 +1,506 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: LicenseRef-NvidiaProprietary + * + * NVIDIA CORPORATION, its affiliates and licensors retain all intellectual + * property and proprietary rights in and to this material, related + * documentation and any modifications thereto. Any use, reproduction, + * disclosure or distribution of this material and related documentation + * without an express license agreement from NVIDIA CORPORATION or + * its affiliates is strictly prohibited. + */ + +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "infer_custom_process.h" +#include "nvbufsurface.h" +#include "nvdsmeta.h" + +typedef struct _GstBuffer GstBuffer; + +/** This is a example how DeepStream Triton plugin(gst-nvinferserver) do + * custom extra input preprocess and custom postprocess on triton based models. + */ + +// enable debug log +#define ENABLE_DEBUG 0 + +namespace dsis = nvdsinferserver; + +#if ENABLE_DEBUG +#define LOG_DEBUG(fmt, ...) fprintf(stdout, "%s:%d" fmt "\n", __FILE__, __LINE__, ##__VA_ARGS__) +#else +#define LOG_DEBUG(fmt, ...) +#endif + +#define LOG_ERROR(fmt, ...) fprintf(stderr, "%s:%d" fmt "\n", __FILE__, __LINE__, ##__VA_ARGS__) + +#ifndef INFER_ASSERT +#define INFER_ASSERT(expr) \ + do { \ + if (!(expr)) { \ + fprintf(stderr, "%s:%d ASSERT(%s) \n", __FILE__, __LINE__, #expr); \ + std::abort(); \ + } \ + } while (0) +#endif + +#define CSTR(str) (str).empty() ? "" : (str).c_str() + +// constant values definition +static const std::vector kClassLabels = { + "person", "bicycle", "car", "motorbike", + "aeroplane", "bus", "train", "truck", + "boat", "traffic light", "fire hydrant", "stop sign", + "parking meter", "bench", "bird", "cat", + "dog", "horse", "sheep", "cow", + "elephant", "bear", "zebra", "giraffe", + "backpack", "umbrella", "handbag", "tie", + "suitcase", "frisbee", "skis", "snowboard", + "sports ball", "kite", "baseball bat", "baseball glove", + "skateboard", "surfboard", "tennis racket", "bottle", + "wine glass", "cup", "fork", "knife", + "spoon", "bowl", "banana", "apple", + "sandwich", "orange", "broccoli", "carrot", + "hot dog", "pizza", "donut", "cake", + "chair", "sofa", "pottedplant", "bed", + "diningtable", "toilet", "tvmonitor", "laptop", + "mouse", "remote", "keyboard", "cell phone", + "microwave", "oven", "toaster", "sink", + "refrigerator", "book", "clock", "vase", + "scissors", "teddy bear", "hair drier", "toothbrush", +}; + +/** Define a function for custom processor for DeepStream Triton plugin(nvinferserver) + * do custom extra input preprocess and custom postprocess on triton based models. + * The sysmbol is loaded through + * infer_config { + * custom_lib { path: "path/to/custom_impl_process.so" } + * extra { + * custom_process_funcion: "CreateInferServerCustomProcess" + * }} + */ +extern "C" dsis::IInferCustomProcessor* CreateInferServerCustomProcess( + const char* config, uint32_t configLen); + +namespace { +using namespace dsis; + +std::string +memType2Str(InferMemType t) +{ + switch (t) { + case InferMemType::kGpuCuda: + return "kGpuCuda"; + case InferMemType::kCpu: + return "kCpu"; + case InferMemType::kCpuCuda: + return "kCpuPinned"; + default: + return "Unknown"; + } +} + +std::string +dataType2Str(dsis::InferDataType t) +{ + switch (t) { + case InferDataType::kFp32: + return "kFp32"; + case InferDataType::kFp16: + return "kFp16"; + case InferDataType::kInt8: + return "kInt8"; + case InferDataType::kInt32: + return "kInt32"; + case InferDataType::kInt16: + return "kInt16"; + case InferDataType::kUint8: + return "kUint8"; + case InferDataType::kUint16: + return "kUint16"; + case InferDataType::kUint32: + return "kUint32"; + case InferDataType::kFp64: + return "kFp64"; + case InferDataType::kInt64: + return "kInt64"; + case InferDataType::kUint64: + return "kUint64"; + case InferDataType::kString: + return "kString"; + case InferDataType::kBool: + return "kBool"; + default: + return "Unknown"; + } +} + +// return buffer description string +std::string +strOfBufDesc(const dsis::InferBufferDescription& desc) +{ + std::stringstream ss; + ss << "*" << desc.name << "*, shape: "; + for (uint32_t i = 0; i < desc.dims.numDims; ++i) { + if (i != 0) { + ss << "x"; + } else { + ss << "["; + } + ss << desc.dims.d[i]; + if (i == desc.dims.numDims - 1) { + ss << "]"; + } + } + ss << ", dataType:" << dataType2Str(desc.dataType); + ss << ", memType:" << memType2Str(desc.memType); + return ss.str(); +} + +} // namespace + +/** Example of a Custom process instance for deepstream-triton(gst-nvinferserver) plugin + * It is derived from nvdsinferserver::IInferCustomProcessor + * If should be loaded through + * config_triton_inferserver_primary_fasterRCNN.txt: + * infer_config { + * custom_lib { path: "path/to/custom_impl_process.so" } + * extra { + * custom_process_funcion: "CreateInferServerCustomProcess" + * } + * } + */ +class NvInferServerCustomProcess : public dsis::IInferCustomProcessor { +private: + std::map> _streamFeedback; + std::mutex _streamMutex; + +public: + ~NvInferServerCustomProcess() override = default; + /** override function + * Specifies supported extraInputs memtype in extraInputProcess() + */ + void supportInputMemType(dsis::InferMemType& type) override { type = dsis::InferMemType::kCpu; } + + /** override function + * check whether custom loop process needed. + * If return True, extraInputProcess() and inferenceDone() runs in order per stream_ids + * This is usually for LSTM loop purpose. FasterRCNN does not need it. + * The code for requireInferLoop() conditions just sample when user has + * a LSTM-like Loop model and requires loop custom processing. + * */ + bool requireInferLoop() const override { return false; } + + /** + * override function + * Do custom processing on extra inputs. + * @primaryInput is already preprocessed. DO NOT update it again. + * @extraInputs, do custom processing and fill all data according the tensor shape + * @options, it has most of the common Deepstream metadata along with primary data. + * e.g. NvDsBatchMeta, NvDsObjectMeta, NvDsFrameMeta, stream ids... + * see infer_ioptions.h to see all the potential key name and structures + * in the key-value table. + */ + NvDsInferStatus extraInputProcess( + const std::vector& + primaryInputs, // primary tensor(image) has been processed + std::vector& extraInputs, const dsis::IOptions* options) override + { + INFER_ASSERT(primaryInputs.size() > 0); + INFER_ASSERT(extraInputs.size() == 1); + // primary input tensor: input_1 [batch, channel, height, width] + dsis::InferBufferDescription input0Desc = primaryInputs[0]->getBufDesc(); + // extra input tensor: image_shape [batch, 2] + dsis::InferBufferDescription extra1Desc = extraInputs[0]->getBufDesc(); + INFER_ASSERT(extra1Desc.dataType == dsis::InferDataType::kFp32); + INFER_ASSERT(extra1Desc.elementSize == sizeof(float)); // bytes per element + + INFER_ASSERT(!strOfBufDesc(input0Desc).empty()); + LOG_DEBUG("extraInputProcess: primary input %s", strOfBufDesc(input0Desc).c_str()); + LOG_DEBUG("extraInputProcess: extra input %s", strOfBufDesc(extra1Desc).c_str()); + + // batch size must be get from primary input tensor. + // extra inputs 'image_shape' does not have a batch size in this specific model + int batchSize = input0Desc.dims.d[0]; + INFER_ASSERT(extra1Desc.dims.numDims == 2 && extra1Desc.dims.d[0] == batchSize); + INFER_ASSERT(batchSize >= 1); + if (!options) { + LOG_ERROR("custom process does not receive IOptions"); + return NVDSINFER_CUSTOM_LIB_FAILED; + } + + NvDsBatchMeta* batchMeta = nullptr; + std::vector frameMetaList; + NvBufSurface* bufSurf = nullptr; + std::vector surfParamsList; + std::vector streamIds; + int64_t unique_id = 0; + INFER_ASSERT(options->getValueArray(OPTION_NVDS_SREAM_IDS, streamIds) == NVDSINFER_SUCCESS); + INFER_ASSERT(streamIds.size() == (uint32_t)batchSize); + + // get NvBufSurface + if (options->hasValue(OPTION_NVDS_BUF_SURFACE)) { + INFER_ASSERT(options->getObj(OPTION_NVDS_BUF_SURFACE, bufSurf) == NVDSINFER_SUCCESS); + } + INFER_ASSERT(bufSurf); + + // get NvDsBatchMeta + if (options->hasValue(OPTION_NVDS_BATCH_META)) { + INFER_ASSERT(options->getObj(OPTION_NVDS_BATCH_META, batchMeta) == NVDSINFER_SUCCESS); + } + INFER_ASSERT(batchMeta); + + // get all frame meta list into vector + if (options->hasValue(OPTION_NVDS_FRAME_META_LIST)) { + INFER_ASSERT( + options->getValueArray(OPTION_NVDS_FRAME_META_LIST, frameMetaList) == + NVDSINFER_SUCCESS); + } + + // get unique_id + if (options->hasValue(OPTION_NVDS_UNIQUE_ID)) { + INFER_ASSERT(options->getInt(OPTION_NVDS_UNIQUE_ID, unique_id) == NVDSINFER_SUCCESS); + } + + // get all surface params list into vector + if (options->hasValue(OPTION_NVDS_BUF_SURFACE_PARAMS_LIST)) { + INFER_ASSERT( + options->getValueArray(OPTION_NVDS_BUF_SURFACE_PARAMS_LIST, surfParamsList) == + NVDSINFER_SUCCESS); + } + + // fill extra input tensor "image_shape[-1,2]" + float* image_shape = (float*)extraInputs[0]->getBufPtr(0); + + for (int iBatch = 0; iBatch < batchSize; ++iBatch) { + image_shape[iBatch * 2 + 1] = (float)surfParamsList[iBatch]->width; + image_shape[iBatch * 2] = (float)surfParamsList[iBatch]->height; + } + return NVDSINFER_SUCCESS; + } + + /** override function + * Custom processing for inferenced output tensors. + * output memory types is controlled by gst-nvinferserver config file + * config_triton_inferserver_primary_fasterRCNN.txt: + * infer_config { + * backend { output_mem_type: MEMORY_TYPE_CPU } + * } + * User can even attach parsed metadata into GstBuffer from this function + */ + NvDsInferStatus inferenceDone( + const dsis::IBatchArray* outputs, const dsis::IOptions* inOptions) override + { + std::vector streamIds; + INFER_ASSERT( + inOptions->getValueArray(OPTION_NVDS_SREAM_IDS, streamIds) == NVDSINFER_SUCCESS); + INFER_ASSERT(!streamIds.empty()); + uint32_t batchSize = streamIds.size(); + std::vector> parsedBboxes(batchSize); + + // add 3 output tensors into map + std::unordered_map tensors; + for (uint32_t iTensor = 0; iTensor < outputs->getSize(); ++iTensor) { + const dsis::IBatchBuffer* outTensor = outputs->getBuffer(iTensor); + INFER_ASSERT(outTensor); + auto desc = outTensor->getBufDesc(); + LOG_DEBUG("out tensor: %s, desc: %s \n", CSTR(desc.name), strOfBufDesc(desc).c_str()); + tensors.emplace(desc.name, outTensor); + } + + // indices dim format is [total, 3], each row represents [batch_index, class_index, + // box_index] + auto indices = tensors["yolonms_layer_1/concat_2:0"]; + // boxes dim format is [batch, n_candidates, 4] + auto boxes = tensors["yolonms_layer_1/ExpandDims_1:0"]; + // scores dim format is [1, n_classes, n_candidates] + auto scores = tensors["yolonms_layer_1/ExpandDims_3:0"]; + INFER_ASSERT(indices && boxes && scores); + auto idxDesc = indices->getBufDesc(); + auto boxDesc = boxes->getBufDesc(); + auto scoreDesc = scores->getBufDesc(); + INFER_ASSERT(idxDesc.dims.numDims == 2 && idxDesc.dims.d[1] == 3); + INFER_ASSERT(boxDesc.dims.numDims == 3 && boxDesc.dims.d[2] == 4); + INFER_ASSERT( + scoreDesc.dims.numDims == 3 && scoreDesc.dims.d[1] == (int)kClassLabels.size()); + int32_t* indicesPtr = (int32_t*)indices->getBufPtr(0); + float* boxesPtr = (float*)boxes->getBufPtr(0); + float* scoresPtr = (float*)scores->getBufPtr(0); + for (int idx = 0; idx < idxDesc.dims.d[0]; ++idx) { + NvDsInferObjectDetectionInfo obj{}; + uint32_t batchId = (uint32_t)indicesPtr[idx * 3]; + obj.classId = (uint32_t)indicesPtr[idx * 3 + 1]; + INFER_ASSERT(obj.classId < kClassLabels.size()); + uint32_t boxIdx = indicesPtr[idx * 3 + 2]; + float* bbox = &boxesPtr[4 * boxIdx]; + obj.top = bbox[0]; + obj.left = bbox[1]; + float bottom = bbox[2]; + float right = bbox[3]; + obj.height = bottom - obj.top; + obj.width = right - obj.left; + float score = scoresPtr + [batchId * scoreDesc.dims.d[1] * scoreDesc.dims.d[2] + + obj.classId * scoreDesc.dims.d[2] + boxIdx]; + obj.detectionConfidence = score; + LOG_DEBUG( + "cid: %u, n: %u, obj [%.2f, %.2f, %.2f, %.2f], score:%.2f\n", obj.classId, batchId, + obj.top, obj.left, bottom, right, score); + parsedBboxes[batchId].emplace_back(obj); + } + + for (uint32_t iBatch = 0; iBatch < batchSize; ++iBatch) { + INFER_ASSERT(attachObjMeta(inOptions, parsedBboxes[iBatch], 0) == NVDSINFER_SUCCESS); + } + + return NVDSINFER_SUCCESS; + } + + /** override function + * Receiving errors if anything wrong inside lowlevel lib + */ + void notifyError(NvDsInferStatus s) override + { + std::unique_lock locker(_streamMutex); + _streamFeedback.clear(); + } + +private: + /** function for loop processing only. not requried for fasterRCNN + */ + NvDsInferStatus feedbackStreamInput( + const dsis::IBatchArray* outputs, const dsis::IOptions* inOptions); + + /** + * attach bounding boxes into NvDsBatchMeta and NvDsFrameMeta + */ + NvDsInferStatus attachObjMeta( + const dsis::IOptions* inOptions, const std::vector& objs, + uint32_t batchIdx); +}; + +/** Implementation to Create a custom processor for DeepStream Triton + * plugin(nvinferserver) to do custom extra input preprocess and custom + * postprocess on triton based models. + */ +extern "C" { +dsis::IInferCustomProcessor* +CreateInferServerCustomProcess(const char* config, uint32_t configLen) +{ + return new NvInferServerCustomProcess(); +} +} + +/** + * attach bounding boxes into NvDsBatchMeta and NvDsFrameMeta + */ +NvDsInferStatus +NvInferServerCustomProcess::attachObjMeta( + const dsis::IOptions* inOptions, const std::vector& detectObjs, + uint32_t batchIdx) +{ + INFER_ASSERT(inOptions); + GstBuffer* gstBuf = nullptr; + NvDsBatchMeta* batchMeta = nullptr; + std::vector frameMetaList; + NvBufSurface* bufSurf = nullptr; + std::vector surfParamsList; + int64_t unique_id = 0; + + // get GstBuffer + if (inOptions->hasValue(OPTION_NVDS_GST_BUFFER)) { + INFER_ASSERT(inOptions->getObj(OPTION_NVDS_GST_BUFFER, gstBuf) == NVDSINFER_SUCCESS); + } + INFER_ASSERT(gstBuf); + + // get NvBufSurface + if (inOptions->hasValue(OPTION_NVDS_BUF_SURFACE)) { + INFER_ASSERT(inOptions->getObj(OPTION_NVDS_BUF_SURFACE, bufSurf) == NVDSINFER_SUCCESS); + } + INFER_ASSERT(bufSurf); + + // get NvDsBatchMeta + if (inOptions->hasValue(OPTION_NVDS_BATCH_META)) { + INFER_ASSERT(inOptions->getObj(OPTION_NVDS_BATCH_META, batchMeta) == NVDSINFER_SUCCESS); + } + INFER_ASSERT(batchMeta); + + // get all frame meta list into vector + if (inOptions->hasValue(OPTION_NVDS_FRAME_META_LIST)) { + INFER_ASSERT( + inOptions->getValueArray(OPTION_NVDS_FRAME_META_LIST, frameMetaList) == + NVDSINFER_SUCCESS); + } + INFER_ASSERT(batchIdx < frameMetaList.size()); // batchsize + + // get unique_id + if (inOptions->hasValue(OPTION_NVDS_UNIQUE_ID)) { + INFER_ASSERT(inOptions->getInt(OPTION_NVDS_UNIQUE_ID, unique_id) == NVDSINFER_SUCCESS); + } + + // get all surface params list into vector + if (inOptions->hasValue(OPTION_NVDS_BUF_SURFACE_PARAMS_LIST)) { + INFER_ASSERT( + inOptions->getValueArray(OPTION_NVDS_BUF_SURFACE_PARAMS_LIST, surfParamsList) == + NVDSINFER_SUCCESS); + } + INFER_ASSERT(batchIdx < surfParamsList.size()); // batchsize + + // attach object's boundingbox + for (const auto& obj : detectObjs) { + NvDsObjectMeta* objMeta = nvds_acquire_obj_meta_from_pool(batchMeta); + objMeta->unique_component_id = unique_id; + objMeta->confidence = obj.detectionConfidence; + + /* This is an untracked object. Set tracking_id to -1. */ + objMeta->object_id = UNTRACKED_OBJECT_ID; + objMeta->class_id = obj.classId; + + NvOSD_RectParams& rect_params = objMeta->rect_params; + NvOSD_TextParams& text_params = objMeta->text_params; + + rect_params.left = obj.left; + rect_params.top = obj.top; + rect_params.width = obj.width; + rect_params.height = obj.height; + + /* Border of width 3. */ + rect_params.border_width = 3; + rect_params.has_bg_color = 0; + rect_params.border_color = (NvOSD_ColorParams){1, 0, 0, 1}; + + /* display_text requires heap allocated memory. */ + if (obj.classId < kClassLabels.size()) { + text_params.display_text = g_strdup(kClassLabels[obj.classId].c_str()); + strncpy(objMeta->obj_label, kClassLabels[obj.classId].c_str(), MAX_LABEL_SIZE - 1); + objMeta->obj_label[MAX_LABEL_SIZE - 1] = 0; + } + /* Display text above the left top corner of the object. */ + text_params.x_offset = rect_params.left; + text_params.y_offset = rect_params.top - 10; + /* Set black background for the text. */ + text_params.set_bg_clr = 1; + text_params.text_bg_clr = (NvOSD_ColorParams){0, 0, 0, 1}; + /* Font face, size and color. */ + text_params.font_params.font_name = (gchar*)"Serif"; + text_params.font_params.font_size = 11; + text_params.font_params.font_color = (NvOSD_ColorParams){1, 1, 1, 1}; + + nvds_acquire_meta_lock(batchMeta); + nvds_add_obj_meta_to_frame(frameMetaList[batchIdx], objMeta, NULL); + frameMetaList[batchIdx]->bInferDone = TRUE; + nvds_release_meta_lock(batchMeta); + } + + return NVDSINFER_SUCCESS; +} diff --git a/src/apps/sample_apps/TritonOnnxYolo/triton_model_repo/yolov3-10_onnx/config.pbtxt b/src/apps/sample_apps/TritonOnnxYolo/triton_model_repo/yolov3-10_onnx/config.pbtxt new file mode 100644 index 00000000..2cee3d39 --- /dev/null +++ b/src/apps/sample_apps/TritonOnnxYolo/triton_model_repo/yolov3-10_onnx/config.pbtxt @@ -0,0 +1,56 @@ +################################################################################ +# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +################################################################################ + +name: "yolov3-10_onnx" +platform: "onnxruntime_onnx" +max_batch_size: 0 +default_model_filename: "yolov3-10.onnx" + +input [ + { + name: "input_1" + data_type: TYPE_FP32 + dims: [-1, 3, 320, 320] + }, + { + name: "image_shape" + data_type: TYPE_FP32 + dims: [-1, 2] + } +] +output [ + { + name: "yolonms_layer_1/ExpandDims_1:0" + data_type: TYPE_FP32 + dims: [1, -1, 4] + }, + + { + name: "yolonms_layer_1/ExpandDims_3:0" + data_type: TYPE_FP32 + dims: [1, 80, -1] + }, + { + name: "yolonms_layer_1/concat_2:0" + data_type: TYPE_INT32 + dims: [-1, -1] + } +] + +instance_group [ + { + kind: KIND_GPU + count: 1 + gpus: 0 + } +] + diff --git a/src/utils/README.md b/src/utils/README.md index dc1528e9..d306200c 100644 --- a/src/utils/README.md +++ b/src/utils/README.md @@ -68,3 +68,15 @@ For build and installation instructions, see the repo root [BUILD.md](../../BUIL | Library | Output | Description | |---|---|---| | `ds3d` | `libnvds_3d_*.so` | DS3D utility library for 3D sensor pipelines — data loaders (LiDAR, depth camera), data filters, data bridges, and inference custom libs for 3D detection and V2X fusion. See [README](ds3d/README.md). | + +--- + +## Tooling & Model Setup + +These entries are not built libraries — they ship setup scripts and model-prep +documentation consumed alongside the DeepStream runtime. + +| Entry | Description | +|---|---| +| `nvds_logger` | `setup_nvds_logger.sh` configures `rsyslog` to capture DeepStream `DSLOG` messages to `/var/log/nvds/`. See [README](nvds_logger/README). | +| `nvmultiobjecttracker/model/tracker_ReID` | Model preparation guide (TensorRT) for deep-learning models used by the multi-object tracker: SAM2 segmentation, TAO ReidentificationNet, TAO BodyPose3DNet. See [README](nvmultiobjecttracker/model/tracker_ReID/README). | From d3b4b10f91098cae6b04c6318ef7c27de840e664 Mon Sep 17 00:00:00 2001 From: ankitat Date: Thu, 14 May 2026 14:33:27 +0530 Subject: [PATCH 4/7] docs(README): replace OSS template skeleton with DeepStream content Fills the OSS template skeleton with content from the GitLab DeepStream mono-repo README. Sections without a clear mono-repo counterpart (Performance, Releases & Roadmap, Contribution Guidelines, Governance & Maintainers, Security, Support, Community, References) are left as the template's placeholder text intentionally. Sections filled in: - Project title + value proposition - Overview: repository structure tree, components list, deprecated components subsection (folds in MR !87's deprecation notice) - Getting Started: prerequisites, clone+build command, link to BUILD.md - Requirements: supported platforms, platform-specific components, components with external prerequisites - Usage: Running Sample Apps section + Running with Triton Inference Server (Docker) section (folds in MR !87's Triton Docker docs; custom model-repo snippet stays absent per MR !88) - License: OSRB tracking note Repository-structure tree adapted to the actual GitHub-side repo (no artifacts/ or ISSUE_TEMPLATE/; only sam2-onnx-tensorrt and yolo_deepstream under tools/; service-maker/python/ subdir omitted -- Python lives under sources/apps/python/). Committed-By: ankitat's Claude Agent Codex Review: Approved: --- README.md | 415 ++++++++++++++++++++++++++++++++++++++++-------------- 1 file changed, 309 insertions(+), 106 deletions(-) diff --git a/README.md b/README.md index 56b69ecf..fb531664 100644 --- a/README.md +++ b/README.md @@ -1,119 +1,320 @@ -# __NVIDIA_OSS__ Standard Repo Template - -This README file is from the NVIDIA_OSS standard repo template of [PLC-OSS-Template](https://github.com/NVIDIA-GitHub-Management/PLC-OSS-Template?tab=readme-ov-file). It provides a list of files in the PLC-OSS-Template and guidelines on how to use (clone and customize) them. - -**Upon completing the customization for the project repo, the repo admin should replace this README template with the project specific README file.** - -- Files (org-wide templates in the NVIDIA .github org repo; per-repo overrides allowed) in [PLC-OSS-Template](https://github.com/NVIDIA-GitHub-Management/PLC-OSS-Template?tab=readme-ov-file) - - - Root - - README.md skeleton (CTA + Quickstart + Support/Security/Governance links) - - LICENSE (Apache 2.0 by default) - - For other licenses, see the [Confluence page](https://confluence.nvidia.com/pages/viewpage.action?pageId=788418816) for other licenses - - CLA.md file (delete if not using MIT or BSD licenses) - - CODE_OF_CONDUCT.md - - SECURITY.md (vuln reporting path) - - CONTRIBUTING.md (base; repo can add specifics) - - SUPPORT.md (Support levels/channels) - - GOVERNANCE.md (baseline; repo may extend) - - CITATION.md (for projects that need citation) - - - .github/ - - ISSUE_TEMPLATE/ () - - bug.yml, feature.yml, task.yml, config.yml - - PULL_REQUEST_TEMPLATE.md () - - workflows/ - - Note: workflow-templates/ for starter workflows should live in the org-level .github repo, not per-repo - - - Repo-specific (not org-template, maintained by the team) - - CODEOWNERS (place at .github/CODEOWNERS or repo root) - - CHANGELOG.md (or RELEASE.md) - - ROADMAP.md - - MAINTAINERS.md - - NOTICE or THIRD_PARTY_NOTICES / THIRD_PARTY_LICENSES (dependency specific) - - Build/package files (CMake, pyproject, Dockerfile, etc.) - - - Recommended structure and hygiene - - docs/ - - examples/ - - tests/ - - scripts/ - - Container/dev env: Dockerfile, docker/, .devcontainer/ (optional) - - Build/package (language-specific): - - Python: pyproject.toml, setup.cfg/setup.py, requirements.txt, environment.yml - - C++: CMakeLists.txt, cmake/, vcpkg.json - - Repo hygiene: .gitignore, .gitattributes, .editorconfig, .pre-commit-config.yaml, .clang-format - - -## Usage of [PLC-OSS-Template](https://github.com/NVIDIA-GitHub-Management/PLC-OSS-Template?tab=readme-ov-file) for NEW NVIDIA OSS repos - -1. Clone the [PLC-OSS-Template](https://github.com/NVIDIA-GitHub-Management/PLC-OSS-Template?tab=readme-ov-file) -2. Find/replace all in the clone of `___PROJECT___` and `__PROJECT_NAME__` with the name of the specific project. -3. Inspect all files to make sure all replacements work and update text as needed - - -**What you can reuse immediately** -- CODE_OF_CONDUCT.md -- SECURITY.md -- CONTRIBUTING.md (base) -- .github/ISSUE_TEMPLATE/.yml (bug/feature/task + config.yml) -- .github/PULL_REQUEST_TEMPLATE.md -- Reusable workflows - -**What you must customize per repo** -- README.md: copy the skeleton and fill in product-specific details (Quickstart, Requirements, Usage, Support level, links) -- LICENSE: check file is correct, update year, consult Confluence for alternatives https://confluence.nvidia.com/pages/viewpage.action?pageId=788418816, add CLA.md only if your license/process requires it -- CODEOWNERS: replace with your GitHub team handle(s). Place at .github/CODEOWNERS (or repo root) -- MAINTAINERS.md: list maintainers names/roles, escalation path -- CHANGELOG.md (or RELEASE.md): track releases/changes -- SUPPORT.md: Update for your project -- ROADMAP.md (optional): upcoming milestones -- NOTICE / THIRD_PARTY_NOTICES (if you ship third‑party content) -- Build/package files (CMake/pyproject/Dockerfile/etc.), tests/, docs/, examples/, scripts/ as appropriate -- Workflows: Edit if you need custom behavior - - -4. Change git origin to point to new repo and push -5. Remove the line break below and everything above it - -## Usage for existing NVIDIA OSS repos - -1. Follow the steps above, but add the files to your existing repo and merge - - ------------------------------------------ -# [Project Title] -One-sentence value proposition for users. Who is it for, and why it matters. + + +# NVIDIA DeepStream SDK 9.0 — Source Repository + +[NVIDIA DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) is a streaming analytics toolkit for AI-based video and image understanding. It provides a GStreamer-based framework for building multi-stream, multi-model inference pipelines that run on NVIDIA GPUs (dGPU and Jetson). # Overview -What the project does? Why the project is useful? -Provide a brief overview, highlighting key features or problem-solving capabilities. + +This repository contains the complete source code for DeepStream 9.0: GStreamer plugins, utility libraries, sample applications, reference applications, TAO-integrated apps, and the Service Maker C++/Python SDK. + +## Repository Structure + +> **Note:** Currently applicable only for DeepStream 9.0. + +``` +DeepStream/ +├── .agents/ # placeholder — reserved for AI agent definitions +├── .claude/ # placeholder — Claude Code configuration +├── .github/ # placeholder — GitHub workflows / metadata +├── BUILD.md # build instructions +├── build.sh # top-level build driver +├── SECURITY.md # vulnerability disclosure +├── example_prompts/ # example prompts for AI coding agents +├── skills/ # placeholder — AI agent skills +├── scripts/ +│ └── install_opensource_deps.sh # builds open-source deps (OpenTelemetry, civetweb, …) +├── includes/ # shared public headers +│ ├── ds3d/ +│ ├── nvdsinferserver/ +│ ├── opentelemetry/ +│ └── prometheus/ +├── tools/ # standalone tools (see Components section) +│ ├── sam2-onnx-tensorrt/ # SAM2 ONNX-to-TensorRT conversion +│ └── yolo_deepstream/ # YOLO + TensorRT integration +└── src/ + ├── apps/ + │ ├── common/ # shared app helpers (config parsers, etc.) + │ ├── sample_apps/ # GStreamer-based sample applications + │ ├── reference_apps/ # advanced reference applications + │ └── tao_apps/ # TAO-model integration apps + ├── gst-plugins/ # GStreamer plugin sources (per-plugin subdirs) + ├── gst-utils/ # GStreamer utility library sources + │ ├── gstnvcustomhelper/ + │ ├── gstnvdscustomhelper/ + │ └── gst-nvdssr/ + ├── service-maker/ # Service Maker C++ / Python SDK and apps + │ ├── cmake/ # CMake configuration + │ ├── includes/ # Service Maker public headers + │ └── sources/ + │ ├── apps/ + │ │ ├── cpp/ # C++ sample apps + │ │ └── python/ # Python sample apps (flow + pipeline API) + │ └── modules/ # reusable signal handlers, probes, sinks + └── utils/ # utility library sources (per-library subdirs) +``` + +## Components + +### Sample Applications + +Located in `src/apps/sample_apps/`. These apps demonstrate core DeepStream pipeline patterns using GStreamer APIs directly. + +See [`src/apps/sample_apps/README.md`](src/apps/sample_apps/README.md) for the full list with descriptions and run commands. Each app has its own `README` at `src/apps/sample_apps//README`. + +### Reference Applications + +Located in `src/apps/reference_apps/`. Advanced apps demonstrating specialized use cases. + +See [`src/apps/reference_apps/README.md`](src/apps/reference_apps/README.md) for details. Each app has its own `README` at `src/apps/reference_apps//README`. + +### TAO Apps + +Located in `src/apps/tao_apps/`. Sample applications for running [NVIDIA TAO Toolkit](https://developer.nvidia.com/tao-toolkit) models with DeepStream. + +See [`src/apps/tao_apps/README.md`](src/apps/tao_apps/README.md) for supported models, build, and run instructions. + +Supported models include: PeopleNet Transformer, CitySemSegFormer, Mask2Former, Re-Identification, BodyPose3DNet, PoseClassification, OCDNet, OCRNet, LPDNet, LPRNet, Retail Detector, Retail Object Recognition. + +### Service Maker + +Located in `src/service-maker/`. A C++ and Python SDK that abstracts GStreamer/GLib APIs for building DeepStream applications declaratively. + +See [`src/service-maker/README.md`](src/service-maker/README.md) for build instructions, API overview, and app list. See also the [Service Maker introduction](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_service_maker_intro.html). + +- **C++ apps** — `src/service-maker/sources/apps/cpp/` +- **Python apps** — `src/service-maker/sources/apps/python/` (flow API and pipeline API) +- **Modules** — reusable signal handlers, probes, video sinks/sources, and more + +### GStreamer Plugins + +Located in `src/gst-plugins/`. Source for all DeepStream GStreamer plugins. + +See [`src/gst-plugins/README.md`](src/gst-plugins/README.md) for the full list. Each plugin has its own `README` at `src/gst-plugins//README` with properties and usage. See also the [DeepStream Plugins introduction](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_Intro.html). + +### GStreamer Utilities + +Located in `src/gst-utils/`. Each utility has its own `README` at `src/gst-utils//README`: + +- [`gstnvcustomhelper`](src/gst-utils/gstnvcustomhelper/README) +- [`gstnvdscustomhelper`](src/gst-utils/gstnvdscustomhelper/README) +- [`gst-nvdssr`](src/gst-utils/gst-nvdssr/README) + +### Utility Libraries + +Located in `src/utils/`. + +See [`src/utils/README.md`](src/utils/README.md) for the full list. Each library has its own `README` at `src/utils//README`. + +### Tools + +Located in `tools/`. Each tool has its own `README` at `tools//README.md`: + +- [`sam2-onnx-tensorrt`](tools/sam2-onnx-tensorrt/README.md) +- [`yolo_deepstream`](tools/yolo_deepstream/README.md) + +## Deprecated Components + +The following components are **legacy and no longer maintained**. They are still shipped with the DeepStream 9.0 public release but have been excluded from this repository. Deprecation was announced as part of the DS 9.0 public release. + +### Sample Applications + +- `deepstream-audio` +- `deepstream-avsync` +- `deepstream-mrcnn-test` +- `deepstream-segmentation-test` +- `deepstream-ucx-test` +- `deepstream_asr_app` +- `deepstream_asr_tts_app` + +### GStreamer Plugins + +- `gst-nvdsA2Vtemplate` +- `gst-nvdsaudiotemplate` +- `gst-nvdsspeech` +- `gst-nvdstexttospeech` + +For component details, source, and configuration, refer to the [DeepStream 9.0 public release documentation](https://docs.nvidia.com/metropolis/deepstream/dev-guide). # Getting Started -Guide users on how they can get started with the project. This should include basic installation step, quick-start examples -```bash -# Option A: Package manager (pip/conda/npm/etc.) - -# Option B: Container -docker run +Before using this repository, ensure the following are already set up on your system: + +1. **NVIDIA compute stack** — driver, CUDA, cuDNN, and TensorRT at the versions listed in the [Requirements](#requirements) section. Refer to the [DeepStream SDK Installation Guide](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Installation.html) for installation steps. -# Verify (hello world) - +2. **DeepStream 9.0** — installed via the DS 9.0 public released Debian package and its `install.sh` script. + +> **SBSA / DGX Spark:** No DS deb package exists for this platform. The NVIDIA SBSA Docker container serves as the environment — it includes the compute stack and DeepStream. See [BUILD.md](BUILD.md) for container setup steps. + +Once the above are in place, clone this repository and follow [BUILD.md](BUILD.md) to build and run: + +```bash +sudo apt-get install git-lfs +git clone https://github.com/NVIDIA/DeepStream && cd DeepStream +git lfs install && git lfs pull +sudo bash build.sh ``` + +See **[BUILD.md](BUILD.md)** for complete instructions covering: + +- System package dependencies (x86, aarch64, SBSA / DGX Spark) +- Git LFS setup for prebuilt artifact tarballs +- `build.sh` usage and environment variables (`CUDA_VER`, `NVDS_VERSION`) +- Build output locations under `/opt/nvidia/deepstream/deepstream-9.0/` +- Service Maker local CMake build + # Requirements -Include a list of pre-requisites. -- OS/Arch: -- Runtime/Compiler: -- GPU/Drivers (if applicable): CUDA , driver , etc. + +## Supported Platforms + +| Platform | Architecture | Notes | +|---|---|---| +| x86 dGPU | x86_64 | Ubuntu 24.04, CUDA 13.1, TensorRT 10.14.x, driver 590+ | +| Jetson | aarch64 | JetPack 7.1 GA (CUDA 13.0, TensorRT 10.13.x) | +| SBSA / DGX Spark | aarch64 | No DS tar/deb package; build and run inside the NVIDIA SBSA Docker container | + +## Platform-Specific Components + +| Component | Platform | Notes | +|---|---|---| +| `deepstream-ipc-test` | Jetson (aarch64) only | Relies on NVDEC engine sharing not available on x86 or SBSA | +| `deepstream-multigpu-nvlink-test` | x86 dGPU only | Requires NVLink; not supported on Jetson | + +## Components with External Prerequisites + +The following components build successfully only when the listed external SDK is installed on the system. See each component's README for installation links and setup details. + +| Component | External Prerequisite | Details | +|---|---|---| +| `gst-dsexample-cuda` | OpenCV 4.x built with CUDA support | [src/gst-plugins/gst-dsexample-cuda/README](src/gst-plugins/gst-dsexample-cuda/README) | +| `gst-nvdsudp` | Rivermax SDK (`rivermax_api.h`) | [src/gst-plugins/gst-nvdsudp/README](src/gst-plugins/gst-nvdsudp/README) | +| `libnvdsgst_inferserver` | Triton Inference Server client | [src/gst-plugins/gst-nvinferserver/README](src/gst-plugins/gst-nvinferserver/README) | # Usage + +## Running Sample Apps + +After building with `build.sh`, binaries are installed to `/opt/nvidia/deepstream/deepstream-9.0/bin/`. + +All apps must be run from their source directory so relative config paths resolve correctly. Refer to the `README` in each app's directory for app-specific run instructions and config file options. + +### Run `deepstream-app` (the reference application) + +Navigate to the `configs/deepstream-app` directory on the development kit: + +```bash +cd /opt/nvidia/deepstream/deepstream-9.0/samples/configs/deepstream-app +``` + +Run the reference application: + +```bash +deepstream-app -c +``` + +For example: + +```bash +deepstream-app -c source30_1080p_dec_infer-resnet_tiled_display.txt +``` + +`` is the path to one of the reference application's configuration files in `configs/deepstream-app/`. See *Package Contents in `configs/deepstream-app/`* for the list of available files. See also the [DeepStream Quickstart — Run deepstream-app](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Quickstart.html#run-deepstream-app-the-reference-application). + +After first install, clear the GStreamer plugin cache: + ```bash -# Minimal runnable snippet (≤20 lines) - +rm -rf ~/.cache/gstreamer-1.0/ +``` + +## Running with Triton Inference Server (Docker) + +NVIDIA publishes a DeepStream Docker image bundled with Triton Inference Server. This is the recommended path when you need Triton-backed inference (`gst-nvinferserver` / `libnvdsgst_inferserver`), and is the only supported runtime path on SBSA / DGX Spark, where no native DeepStream deb package is available. + +### Prerequisites + +- **Docker** — install `docker-ce` per [Docker's official install guide](https://docs.docker.com/engine/install). +- **NVIDIA Container Toolkit** — required for GPU access; see the [install guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). +- **NVIDIA Driver** — 590+ for dGPU. +- **NGC login** — pulling the image requires NGC authentication. + +```bash +# Get an API key from https://ngc.nvidia.com, then: +docker login nvcr.io +# Username: $oauthtoken +# Password: +``` + +### Pull the Triton Image + +| Platform | Image | +|---|---| +| x86 dGPU / Jetson | `nvcr.io/nvidia/deepstream:9.0-triton-multiarch` | +| SBSA / DGX Spark (ARM dGPU) | `nvcr.io/nvidia/deepstream:9.0-triton-arm-sbsa` | + +```bash +docker pull nvcr.io/nvidia/deepstream:9.0-triton-multiarch +``` + +The image bundles the DeepStream runtime, Triton Inference Server, and a development environment (compilers + headers) so the repository sources can also be built from inside the container. + +### Launch the Container + +With display (for `nveglglessink` / `nv3dsink`): + +```bash +export DISPLAY=:0 +xhost + + +docker run -it --rm \ + --gpus all \ + --network=host \ + -e DISPLAY=$DISPLAY \ + -v /tmp/.X11-unix/:/tmp/.X11-unix \ + nvcr.io/nvidia/deepstream:9.0-triton-multiarch +``` + +Headless (use `fakesink` / `filesink` in your pipeline): + +```bash +docker run -it --rm --gpus all \ + nvcr.io/nvidia/deepstream:9.0-triton-multiarch +``` + +### Triton Model Repository + +The container ships sample Triton model repositories at: + +``` +/opt/nvidia/deepstream/deepstream/samples/triton_model_repo +/opt/nvidia/deepstream/deepstream/samples/triton_tao_model_repo +``` + +The `*_inferserver_config.txt` files shipped under `samples/configs/deepstream-app-triton/` and the `tritonclient` reference apps point at these paths via `infer_config.backend.triton.model_repo.root`. + +### Run a Triton-Backed Sample + +Inside the container: + +```bash +cd /opt/nvidia/deepstream/deepstream-9.0/samples/configs/deepstream-app-triton +deepstream-app -c source30_1080p_dec_infer-resnet_tiled_display.txt +``` + +For gRPC-backed Triton, use `samples/configs/deepstream-app-triton-grpc/` instead. After the first launch, clear the GStreamer plugin cache once: + +```bash +rm -rf ~/.cache/gstreamer-1.0/ ``` -- More examples/tutorials: -- API reference: # Performance (Optional) Summary of benchmarks; link to detailed results and hardware used. @@ -150,5 +351,7 @@ Provide the channel for community communications. Provide a list of related references # License -This project is licensed under the [NAME HERE] License - see the LICENSE.md file for details -- License: + +Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + +LICENSE details yet to be finalized — tracked in OSRB Bug 6155166. From 76a583bfe2dd13938950a00164704bad2ac5938d Mon Sep 17 00:00:00 2001 From: ankitat Date: Thu, 14 May 2026 17:25:16 +0530 Subject: [PATCH 5/7] Revert "docs(README): replace OSS template skeleton with DeepStream content" This reverts commit d3b4b10f91098cae6b04c6318ef7c27de840e664. --- README.md | 415 ++++++++++++++---------------------------------------- 1 file changed, 106 insertions(+), 309 deletions(-) diff --git a/README.md b/README.md index fb531664..56b69ecf 100644 --- a/README.md +++ b/README.md @@ -1,320 +1,119 @@ - - -# NVIDIA DeepStream SDK 9.0 — Source Repository - -[NVIDIA DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) is a streaming analytics toolkit for AI-based video and image understanding. It provides a GStreamer-based framework for building multi-stream, multi-model inference pipelines that run on NVIDIA GPUs (dGPU and Jetson). +# __NVIDIA_OSS__ Standard Repo Template + +This README file is from the NVIDIA_OSS standard repo template of [PLC-OSS-Template](https://github.com/NVIDIA-GitHub-Management/PLC-OSS-Template?tab=readme-ov-file). It provides a list of files in the PLC-OSS-Template and guidelines on how to use (clone and customize) them. + +**Upon completing the customization for the project repo, the repo admin should replace this README template with the project specific README file.** + +- Files (org-wide templates in the NVIDIA .github org repo; per-repo overrides allowed) in [PLC-OSS-Template](https://github.com/NVIDIA-GitHub-Management/PLC-OSS-Template?tab=readme-ov-file) + + - Root + - README.md skeleton (CTA + Quickstart + Support/Security/Governance links) + - LICENSE (Apache 2.0 by default) + - For other licenses, see the [Confluence page](https://confluence.nvidia.com/pages/viewpage.action?pageId=788418816) for other licenses + - CLA.md file (delete if not using MIT or BSD licenses) + - CODE_OF_CONDUCT.md + - SECURITY.md (vuln reporting path) + - CONTRIBUTING.md (base; repo can add specifics) + - SUPPORT.md (Support levels/channels) + - GOVERNANCE.md (baseline; repo may extend) + - CITATION.md (for projects that need citation) + + - .github/ + - ISSUE_TEMPLATE/ () + - bug.yml, feature.yml, task.yml, config.yml + - PULL_REQUEST_TEMPLATE.md () + - workflows/ + - Note: workflow-templates/ for starter workflows should live in the org-level .github repo, not per-repo + + - Repo-specific (not org-template, maintained by the team) + - CODEOWNERS (place at .github/CODEOWNERS or repo root) + - CHANGELOG.md (or RELEASE.md) + - ROADMAP.md + - MAINTAINERS.md + - NOTICE or THIRD_PARTY_NOTICES / THIRD_PARTY_LICENSES (dependency specific) + - Build/package files (CMake, pyproject, Dockerfile, etc.) + + - Recommended structure and hygiene + - docs/ + - examples/ + - tests/ + - scripts/ + - Container/dev env: Dockerfile, docker/, .devcontainer/ (optional) + - Build/package (language-specific): + - Python: pyproject.toml, setup.cfg/setup.py, requirements.txt, environment.yml + - C++: CMakeLists.txt, cmake/, vcpkg.json + - Repo hygiene: .gitignore, .gitattributes, .editorconfig, .pre-commit-config.yaml, .clang-format + + +## Usage of [PLC-OSS-Template](https://github.com/NVIDIA-GitHub-Management/PLC-OSS-Template?tab=readme-ov-file) for NEW NVIDIA OSS repos + +1. Clone the [PLC-OSS-Template](https://github.com/NVIDIA-GitHub-Management/PLC-OSS-Template?tab=readme-ov-file) +2. Find/replace all in the clone of `___PROJECT___` and `__PROJECT_NAME__` with the name of the specific project. +3. Inspect all files to make sure all replacements work and update text as needed + + +**What you can reuse immediately** +- CODE_OF_CONDUCT.md +- SECURITY.md +- CONTRIBUTING.md (base) +- .github/ISSUE_TEMPLATE/.yml (bug/feature/task + config.yml) +- .github/PULL_REQUEST_TEMPLATE.md +- Reusable workflows + +**What you must customize per repo** +- README.md: copy the skeleton and fill in product-specific details (Quickstart, Requirements, Usage, Support level, links) +- LICENSE: check file is correct, update year, consult Confluence for alternatives https://confluence.nvidia.com/pages/viewpage.action?pageId=788418816, add CLA.md only if your license/process requires it +- CODEOWNERS: replace with your GitHub team handle(s). Place at .github/CODEOWNERS (or repo root) +- MAINTAINERS.md: list maintainers names/roles, escalation path +- CHANGELOG.md (or RELEASE.md): track releases/changes +- SUPPORT.md: Update for your project +- ROADMAP.md (optional): upcoming milestones +- NOTICE / THIRD_PARTY_NOTICES (if you ship third‑party content) +- Build/package files (CMake/pyproject/Dockerfile/etc.), tests/, docs/, examples/, scripts/ as appropriate +- Workflows: Edit if you need custom behavior + + +4. Change git origin to point to new repo and push +5. Remove the line break below and everything above it + +## Usage for existing NVIDIA OSS repos + +1. Follow the steps above, but add the files to your existing repo and merge + + +----------------------------------------- +# [Project Title] +One-sentence value proposition for users. Who is it for, and why it matters. # Overview - -This repository contains the complete source code for DeepStream 9.0: GStreamer plugins, utility libraries, sample applications, reference applications, TAO-integrated apps, and the Service Maker C++/Python SDK. - -## Repository Structure - -> **Note:** Currently applicable only for DeepStream 9.0. - -``` -DeepStream/ -├── .agents/ # placeholder — reserved for AI agent definitions -├── .claude/ # placeholder — Claude Code configuration -├── .github/ # placeholder — GitHub workflows / metadata -├── BUILD.md # build instructions -├── build.sh # top-level build driver -├── SECURITY.md # vulnerability disclosure -├── example_prompts/ # example prompts for AI coding agents -├── skills/ # placeholder — AI agent skills -├── scripts/ -│ └── install_opensource_deps.sh # builds open-source deps (OpenTelemetry, civetweb, …) -├── includes/ # shared public headers -│ ├── ds3d/ -│ ├── nvdsinferserver/ -│ ├── opentelemetry/ -│ └── prometheus/ -├── tools/ # standalone tools (see Components section) -│ ├── sam2-onnx-tensorrt/ # SAM2 ONNX-to-TensorRT conversion -│ └── yolo_deepstream/ # YOLO + TensorRT integration -└── src/ - ├── apps/ - │ ├── common/ # shared app helpers (config parsers, etc.) - │ ├── sample_apps/ # GStreamer-based sample applications - │ ├── reference_apps/ # advanced reference applications - │ └── tao_apps/ # TAO-model integration apps - ├── gst-plugins/ # GStreamer plugin sources (per-plugin subdirs) - ├── gst-utils/ # GStreamer utility library sources - │ ├── gstnvcustomhelper/ - │ ├── gstnvdscustomhelper/ - │ └── gst-nvdssr/ - ├── service-maker/ # Service Maker C++ / Python SDK and apps - │ ├── cmake/ # CMake configuration - │ ├── includes/ # Service Maker public headers - │ └── sources/ - │ ├── apps/ - │ │ ├── cpp/ # C++ sample apps - │ │ └── python/ # Python sample apps (flow + pipeline API) - │ └── modules/ # reusable signal handlers, probes, sinks - └── utils/ # utility library sources (per-library subdirs) -``` - -## Components - -### Sample Applications - -Located in `src/apps/sample_apps/`. These apps demonstrate core DeepStream pipeline patterns using GStreamer APIs directly. - -See [`src/apps/sample_apps/README.md`](src/apps/sample_apps/README.md) for the full list with descriptions and run commands. Each app has its own `README` at `src/apps/sample_apps//README`. - -### Reference Applications - -Located in `src/apps/reference_apps/`. Advanced apps demonstrating specialized use cases. - -See [`src/apps/reference_apps/README.md`](src/apps/reference_apps/README.md) for details. Each app has its own `README` at `src/apps/reference_apps//README`. - -### TAO Apps - -Located in `src/apps/tao_apps/`. Sample applications for running [NVIDIA TAO Toolkit](https://developer.nvidia.com/tao-toolkit) models with DeepStream. - -See [`src/apps/tao_apps/README.md`](src/apps/tao_apps/README.md) for supported models, build, and run instructions. - -Supported models include: PeopleNet Transformer, CitySemSegFormer, Mask2Former, Re-Identification, BodyPose3DNet, PoseClassification, OCDNet, OCRNet, LPDNet, LPRNet, Retail Detector, Retail Object Recognition. - -### Service Maker - -Located in `src/service-maker/`. A C++ and Python SDK that abstracts GStreamer/GLib APIs for building DeepStream applications declaratively. - -See [`src/service-maker/README.md`](src/service-maker/README.md) for build instructions, API overview, and app list. See also the [Service Maker introduction](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_service_maker_intro.html). - -- **C++ apps** — `src/service-maker/sources/apps/cpp/` -- **Python apps** — `src/service-maker/sources/apps/python/` (flow API and pipeline API) -- **Modules** — reusable signal handlers, probes, video sinks/sources, and more - -### GStreamer Plugins - -Located in `src/gst-plugins/`. Source for all DeepStream GStreamer plugins. - -See [`src/gst-plugins/README.md`](src/gst-plugins/README.md) for the full list. Each plugin has its own `README` at `src/gst-plugins//README` with properties and usage. See also the [DeepStream Plugins introduction](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_Intro.html). - -### GStreamer Utilities - -Located in `src/gst-utils/`. Each utility has its own `README` at `src/gst-utils//README`: - -- [`gstnvcustomhelper`](src/gst-utils/gstnvcustomhelper/README) -- [`gstnvdscustomhelper`](src/gst-utils/gstnvdscustomhelper/README) -- [`gst-nvdssr`](src/gst-utils/gst-nvdssr/README) - -### Utility Libraries - -Located in `src/utils/`. - -See [`src/utils/README.md`](src/utils/README.md) for the full list. Each library has its own `README` at `src/utils//README`. - -### Tools - -Located in `tools/`. Each tool has its own `README` at `tools//README.md`: - -- [`sam2-onnx-tensorrt`](tools/sam2-onnx-tensorrt/README.md) -- [`yolo_deepstream`](tools/yolo_deepstream/README.md) - -## Deprecated Components - -The following components are **legacy and no longer maintained**. They are still shipped with the DeepStream 9.0 public release but have been excluded from this repository. Deprecation was announced as part of the DS 9.0 public release. - -### Sample Applications - -- `deepstream-audio` -- `deepstream-avsync` -- `deepstream-mrcnn-test` -- `deepstream-segmentation-test` -- `deepstream-ucx-test` -- `deepstream_asr_app` -- `deepstream_asr_tts_app` - -### GStreamer Plugins - -- `gst-nvdsA2Vtemplate` -- `gst-nvdsaudiotemplate` -- `gst-nvdsspeech` -- `gst-nvdstexttospeech` - -For component details, source, and configuration, refer to the [DeepStream 9.0 public release documentation](https://docs.nvidia.com/metropolis/deepstream/dev-guide). +What the project does? Why the project is useful? +Provide a brief overview, highlighting key features or problem-solving capabilities. # Getting Started - -Before using this repository, ensure the following are already set up on your system: - -1. **NVIDIA compute stack** — driver, CUDA, cuDNN, and TensorRT at the versions listed in the [Requirements](#requirements) section. Refer to the [DeepStream SDK Installation Guide](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Installation.html) for installation steps. - -2. **DeepStream 9.0** — installed via the DS 9.0 public released Debian package and its `install.sh` script. - -> **SBSA / DGX Spark:** No DS deb package exists for this platform. The NVIDIA SBSA Docker container serves as the environment — it includes the compute stack and DeepStream. See [BUILD.md](BUILD.md) for container setup steps. - -Once the above are in place, clone this repository and follow [BUILD.md](BUILD.md) to build and run: - +Guide users on how they can get started with the project. This should include basic installation step, quick-start examples ```bash -sudo apt-get install git-lfs -git clone https://github.com/NVIDIA/DeepStream && cd DeepStream -git lfs install && git lfs pull -sudo bash build.sh -``` - -See **[BUILD.md](BUILD.md)** for complete instructions covering: +# Option A: Package manager (pip/conda/npm/etc.) + -- System package dependencies (x86, aarch64, SBSA / DGX Spark) -- Git LFS setup for prebuilt artifact tarballs -- `build.sh` usage and environment variables (`CUDA_VER`, `NVDS_VERSION`) -- Build output locations under `/opt/nvidia/deepstream/deepstream-9.0/` -- Service Maker local CMake build +# Option B: Container +docker run +# Verify (hello world) + +``` # Requirements - -## Supported Platforms - -| Platform | Architecture | Notes | -|---|---|---| -| x86 dGPU | x86_64 | Ubuntu 24.04, CUDA 13.1, TensorRT 10.14.x, driver 590+ | -| Jetson | aarch64 | JetPack 7.1 GA (CUDA 13.0, TensorRT 10.13.x) | -| SBSA / DGX Spark | aarch64 | No DS tar/deb package; build and run inside the NVIDIA SBSA Docker container | - -## Platform-Specific Components - -| Component | Platform | Notes | -|---|---|---| -| `deepstream-ipc-test` | Jetson (aarch64) only | Relies on NVDEC engine sharing not available on x86 or SBSA | -| `deepstream-multigpu-nvlink-test` | x86 dGPU only | Requires NVLink; not supported on Jetson | - -## Components with External Prerequisites - -The following components build successfully only when the listed external SDK is installed on the system. See each component's README for installation links and setup details. - -| Component | External Prerequisite | Details | -|---|---|---| -| `gst-dsexample-cuda` | OpenCV 4.x built with CUDA support | [src/gst-plugins/gst-dsexample-cuda/README](src/gst-plugins/gst-dsexample-cuda/README) | -| `gst-nvdsudp` | Rivermax SDK (`rivermax_api.h`) | [src/gst-plugins/gst-nvdsudp/README](src/gst-plugins/gst-nvdsudp/README) | -| `libnvdsgst_inferserver` | Triton Inference Server client | [src/gst-plugins/gst-nvinferserver/README](src/gst-plugins/gst-nvinferserver/README) | +Include a list of pre-requisites. +- OS/Arch: +- Runtime/Compiler: +- GPU/Drivers (if applicable): CUDA , driver , etc. # Usage - -## Running Sample Apps - -After building with `build.sh`, binaries are installed to `/opt/nvidia/deepstream/deepstream-9.0/bin/`. - -All apps must be run from their source directory so relative config paths resolve correctly. Refer to the `README` in each app's directory for app-specific run instructions and config file options. - -### Run `deepstream-app` (the reference application) - -Navigate to the `configs/deepstream-app` directory on the development kit: - -```bash -cd /opt/nvidia/deepstream/deepstream-9.0/samples/configs/deepstream-app -``` - -Run the reference application: - -```bash -deepstream-app -c -``` - -For example: - -```bash -deepstream-app -c source30_1080p_dec_infer-resnet_tiled_display.txt -``` - -`` is the path to one of the reference application's configuration files in `configs/deepstream-app/`. See *Package Contents in `configs/deepstream-app/`* for the list of available files. See also the [DeepStream Quickstart — Run deepstream-app](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Quickstart.html#run-deepstream-app-the-reference-application). - -After first install, clear the GStreamer plugin cache: - ```bash -rm -rf ~/.cache/gstreamer-1.0/ -``` - -## Running with Triton Inference Server (Docker) - -NVIDIA publishes a DeepStream Docker image bundled with Triton Inference Server. This is the recommended path when you need Triton-backed inference (`gst-nvinferserver` / `libnvdsgst_inferserver`), and is the only supported runtime path on SBSA / DGX Spark, where no native DeepStream deb package is available. - -### Prerequisites - -- **Docker** — install `docker-ce` per [Docker's official install guide](https://docs.docker.com/engine/install). -- **NVIDIA Container Toolkit** — required for GPU access; see the [install guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). -- **NVIDIA Driver** — 590+ for dGPU. -- **NGC login** — pulling the image requires NGC authentication. - -```bash -# Get an API key from https://ngc.nvidia.com, then: -docker login nvcr.io -# Username: $oauthtoken -# Password: -``` - -### Pull the Triton Image - -| Platform | Image | -|---|---| -| x86 dGPU / Jetson | `nvcr.io/nvidia/deepstream:9.0-triton-multiarch` | -| SBSA / DGX Spark (ARM dGPU) | `nvcr.io/nvidia/deepstream:9.0-triton-arm-sbsa` | - -```bash -docker pull nvcr.io/nvidia/deepstream:9.0-triton-multiarch -``` - -The image bundles the DeepStream runtime, Triton Inference Server, and a development environment (compilers + headers) so the repository sources can also be built from inside the container. - -### Launch the Container - -With display (for `nveglglessink` / `nv3dsink`): - -```bash -export DISPLAY=:0 -xhost + - -docker run -it --rm \ - --gpus all \ - --network=host \ - -e DISPLAY=$DISPLAY \ - -v /tmp/.X11-unix/:/tmp/.X11-unix \ - nvcr.io/nvidia/deepstream:9.0-triton-multiarch -``` - -Headless (use `fakesink` / `filesink` in your pipeline): - -```bash -docker run -it --rm --gpus all \ - nvcr.io/nvidia/deepstream:9.0-triton-multiarch -``` - -### Triton Model Repository - -The container ships sample Triton model repositories at: - -``` -/opt/nvidia/deepstream/deepstream/samples/triton_model_repo -/opt/nvidia/deepstream/deepstream/samples/triton_tao_model_repo -``` - -The `*_inferserver_config.txt` files shipped under `samples/configs/deepstream-app-triton/` and the `tritonclient` reference apps point at these paths via `infer_config.backend.triton.model_repo.root`. - -### Run a Triton-Backed Sample - -Inside the container: - -```bash -cd /opt/nvidia/deepstream/deepstream-9.0/samples/configs/deepstream-app-triton -deepstream-app -c source30_1080p_dec_infer-resnet_tiled_display.txt -``` - -For gRPC-backed Triton, use `samples/configs/deepstream-app-triton-grpc/` instead. After the first launch, clear the GStreamer plugin cache once: - -```bash -rm -rf ~/.cache/gstreamer-1.0/ +# Minimal runnable snippet (≤20 lines) + ``` +- More examples/tutorials: +- API reference: # Performance (Optional) Summary of benchmarks; link to detailed results and hardware used. @@ -351,7 +150,5 @@ Provide the channel for community communications. Provide a list of related references # License - -Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. - -LICENSE details yet to be finalized — tracked in OSRB Bug 6155166. +This project is licensed under the [NAME HERE] License - see the LICENSE.md file for details +- License: From a6aec32ae3d7915929b214b0246306511da4db93 Mon Sep 17 00:00:00 2001 From: ankitat Date: Thu, 14 May 2026 17:29:01 +0530 Subject: [PATCH 6/7] migrate: copy tools/inference_builder/ from mono-repo Brings the Inference Builder tool over as-is from the GitLab DeepStream mono-repo. The tool generates inference pipelines and integrates them into a microservice or standalone application from an inference config + OpenAPI spec. Includes the tool's own .gitmodules registering dependencies/triton-inference-server/common as a submodule -- left uninitialized to match mono-repo state (the path is not on disk in the mono-repo either, and the top-level .gitmodules in this repo is not modified). Source: GitLab DeepStream mono-repo main, tools/inference_builder/ (VERSION 0.2.0). 304 files, ~8 MB. 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tools/inference_builder/templates/common/custom.__init__.jinja.py create mode 100644 tools/inference_builder/templates/generic/model.jinja.py create mode 100644 tools/inference_builder/templates/responder/add_file.jinja.py create mode 100644 tools/inference_builder/templates/responder/add_live_stream.jinja.py create mode 100644 tools/inference_builder/templates/responder/del_file.jinja.py create mode 100644 tools/inference_builder/templates/responder/del_live_stream.jinja.py create mode 100644 tools/inference_builder/templates/responder/healthy_ready.jinja.py create mode 100644 tools/inference_builder/templates/responder/infer.jinja.py create mode 100644 tools/inference_builder/templates/responder/list_files.jinja.py create mode 100644 tools/inference_builder/templates/responder/list_live_streams.jinja.py create mode 100644 tools/inference_builder/templates/serverless/inference.jinja.py create mode 100644 tools/inference_builder/templates/triton/model.jinja.py create mode 100644 tools/inference_builder/tools/trt_build.py diff --git a/tools/inference_builder/.Dockerfile.builder b/tools/inference_builder/.Dockerfile.builder new file mode 100644 index 00000000..a0e357cf --- /dev/null +++ b/tools/inference_builder/.Dockerfile.builder @@ -0,0 +1,42 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Dockerfile.builder +FROM python:3.10-slim + +# Install system dependencies +RUN apt-get update && apt-get install -y \ + protobuf-compiler \ + git \ + default-jre \ + && rm -rf /var/lib/apt/lists/* + +# Configure pip to use NVIDIA's PyPI +ENV PIP_INDEX_URL=https://urm.nvidia.com/artifactory/api/pypi/nv-shared-pypi/simple + +# Install NIM tools and other Python dependencies +RUN pip install --no-cache-dir \ + openapi-generator-cli + +# Install project dependencies +COPY requirements.txt /tmp/ +RUN pip install --no-cache-dir -r /tmp/requirements.txt + +# Set JAVA_HOME +ENV JAVA_HOME=/usr/lib/jvm/java-11-openjdk-amd64 +ENV PATH="$JAVA_HOME/bin:$PATH" + +# Set working directory +WORKDIR /workspace diff --git a/tools/inference_builder/.gitattributes b/tools/inference_builder/.gitattributes new file mode 100644 index 00000000..58cee12d --- /dev/null +++ b/tools/inference_builder/.gitattributes @@ -0,0 +1,7 @@ +builder/boilerplates/nim/** merge=ours +builder/boilerplates/triton/** merge=ours +templates/api_server/nim/** merge=ours +templates/api_server/triton/** merge=ours +builder/allowed_servers.txt merge=ours +.gitlab-ci.yml merge=ours +builder/samples/tao/ib_client.py merge=ours diff --git a/tools/inference_builder/.gitignore b/tools/inference_builder/.gitignore new file mode 100644 index 00000000..ee1eb869 --- /dev/null +++ b/tools/inference_builder/.gitignore @@ -0,0 +1,12 @@ +**/__pycache__ +*.swp +*.vscode +.vscode/** +.vscode +dist +.venv +output +generated_client +*tmp* +*.tgz +.tmp diff --git a/tools/inference_builder/.gitkeep b/tools/inference_builder/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/tools/inference_builder/.gitmodules b/tools/inference_builder/.gitmodules new file mode 100644 index 00000000..5cb67da8 --- /dev/null +++ b/tools/inference_builder/.gitmodules @@ -0,0 +1,3 @@ +[submodule "dependencies/triton-inference-server/common"] + path = dependencies/triton-inference-server/common + url = https://github.com/triton-inference-server/common diff --git a/tools/inference_builder/CONTRIBUTING.md b/tools/inference_builder/CONTRIBUTING.md new file mode 100644 index 00000000..f8df0d30 --- /dev/null +++ b/tools/inference_builder/CONTRIBUTING.md @@ -0,0 +1,44 @@ +## Inference Builder OSS Contribution Rules + +#### Signing Your Work + +* We require that all contributors "sign-off" on their commits. This certifies that the contribution is your original work, or you have rights to submit it under the same license, or a compatible license. + + * Any contribution which contains commits that are not Signed-Off will not be accepted. + +* To sign off on a commit you simply use the `--signoff` (or `-s`) option when committing your changes: + ```bash + $ git commit -s -m "Add cool feature." + ``` + This will append the following to your commit message: + ``` + Signed-off-by: Your Name + ``` + +* Full text of the DCO: + + ``` + Developer Certificate of Origin + Version 1.1 + + Copyright (C) 2004, 2006 The Linux Foundation and its contributors. + 1 Letterman Drive + Suite D4700 + San Francisco, CA, 94129 + + Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. + ``` + + ``` + Developer's Certificate of Origin 1.1 + + By making a contribution to this project, I certify that: + + (a) The contribution was created in whole or in part by me and I have the right to submit it under the open source license indicated in the file; or + + (b) The contribution is based upon previous work that, to the best of my knowledge, is covered under an appropriate open source license and I have the right under that license to submit that work with modifications, whether created in whole or in part by me, under the same open source license (unless I am permitted to submit under a different license), as indicated in the file; or + + (c) The contribution was provided directly to me by some other person who certified (a), (b) or (c) and I have not modified it. + + (d) I understand and agree that this project and the contribution are public and that a record of the contribution (including all personal information I submit with it, including my sign-off) is maintained indefinitely and may be redistributed consistent with this project or the open source license(s) involved. + ``` diff --git a/tools/inference_builder/LICENSE b/tools/inference_builder/LICENSE new file mode 100644 index 00000000..56ee3c8c --- /dev/null +++ b/tools/inference_builder/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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It takes an inference configuration file and an OpenAPI specification (when integrated with an HTTP server) as inputs, and may also require custom code snippets in certain cases. + +The output of the tool is a Python package that can be used to build a microservice container image with a customized Dockerfile. + +![Overview](overview.png) + +The Inference Builder consists of three major components: + +- Code templates: These are reusable modules for various inference backends and frameworks, as well as for API servers. They are optimized and tested, making them suitable for any model with specified inputs, outputs, and configuration parameters. +- Common inference flow: It serves as the core logic that standardizes the end-to-end inference process—including data loading and pre-processing, model inference, post-processing, and integration with the API server. It supports pluggable inference backends and frameworks, enabling flexibility and performance optimization. +- Command line tool: It generates a source code package by combining predefined code templates with the Common Inference Flow. It also automatically produces corresponding test cases and evaluation scripts to support validation and performance assessment. + +Visit our [documentation](doc) for more details: + +- [Inference Builder Usage](doc/usage.md) +- [Inference Builder Architecture](doc/architecture.md) + +## Getting started + +First, be sure your system meets the requirement. + +| Operating System | Python | CPU | GPU[^1] | +|:-------------------|:-------|:---------------|:-----------------------------| +|Ubuntu 24.04 |3.12 | x86, aarch64 |Nvidia ADA, Hopper, Blackwell | + +[^1]: If you only generate the inference pipeline without running it, no GPU is required. + +Next, follow these steps to get started: + +### Install prerequisites + +```bash +sudo apt update +sudo apt install protobuf-compiler +sudo apt install python3.12-venv python3.12-dev +``` + +**Note for TEGRA users:** If you're using a TEGRA device, you'll also need to install the Docker buildx plugin: + +```bash +sudo apt install docker-buildx +``` + +### Clone the repository + +```bash +git clone https://github.com/NVIDIA-AI-IOT/inference_builder +``` + +### Set up the virtual environment + +```bash +cd inference_builder +git submodule update --init --recursive +python3 -m venv .venv +source .venv/bin/activate +pip3 install -r requirements.txt +``` + +## Play with the examples + +Docker environment must be properly set up with below packages for building and running the examples: + +- **Docker**: [Installation Guide](https://docs.docker.com/desktop/setup/install/linux/ubuntu/) +- **Docker Compose**: [Installation Guide](https://docs.docker.com/desktop/setup/install/linux/ubuntu/) +- **NVIDIA Container Toolkit**: [Installation Guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) + + +Ensure nvidia runtime added to `/etc/docker/daemon.json` to run GPU-enabled containers + +```bash +{ + "runtimes": { + "nvidia": { + "args": [], + "path": "nvidia-container-runtime" + } + } +} +``` + +The `docker` group must exist in your system, please check if it has been created using `getent group docker`. Your current user must belong to the `docker` group, If not, run the command below, then log out and back in for the group change to take effect. + +```bash +sudo usermod -aG docker $USER +``` + +Now you can try [our examples](builder/samples/README.md) to learn more. These examples span all supported backends and demonstrate their distinct inference flows. + +![Inference FLow of Different Backend](doc/assets/inference_flow.png) + +## Benefit of using Inference Builder + +Compared to manually crafting inference source code, Inference Builder offers developers the following advantages: +- Separation of concerns: Introduces a new programming paradigm that decouples inference data flow and server logic from the model implementation, allowing developers to focus solely on model behavior. +- Backend flexibility: Standardizes data flow across different inference backends, enabling developers to switch to the optimal backend for their specific requirement without rewriting the entire pipeline. +- Hardware acceleration: Automatically enables GPU-accelerated processing to boost performance. +- Streaming support: Provides built-in support for streaming protocols such as RTSP with minimal configuration. +- Standardized testing: Automates and standardizes test case generation to simplify validation and evaluation workflows. + +## MCP Integration + +Inference Builder includes MCP (Model Context Protocol) integration, enabling AI-assisted pipeline generation directly within Cursor or Claude Code. With MCP, you can use natural language to generate inference pipelines, build Docker images, and explore sample configurations. + +For detailed tool reference and advanced usage, see [mcp/README-MCP.md](mcp/README-MCP.md). + +### Prerequisites + +Before setting up MCP, ensure you have completed the [Getting Started](#getting-started) steps above. + +Ensure that you are in the repository's root directory. + +### Set Up for Cursor + +1. **Generate the MCP configuration** + + For a global configuration (available across all projects): + ```bash + python3 mcp/setup_mcp.py ~/.cursor/mcp.json + ``` + + For a project-specific configuration: + ```bash + python3 mcp/setup_mcp.py /path/to/your_project/.cursor/mcp.json + ``` + +2. **Open your project folder and verify the MCP server is successfully loaded** + + In Cursor, navigate to **File > Preferences > Cursor Settings > MCP**. You should see: + + ![MCP Server](mcp.png) + + A green status icon next to `deepstream-inference-builder` indicates the server is connected. + +3. **Start using the MCP tools:** + + Start a new Cursor agent and invoke the tools by mentioning "deepstream inference builder" in your prompt: + - "Show me what sample configurations are available from the inference builder?" + - "Generate a DeepStream object detection pipeline using the inference builder with PeopleNet transformer model from NGC." + +### Set Up for Claude Code + +1. **Generate the MCP configuration:** + + For a global configuration: + ```bash + python3 mcp/setup_mcp.py ~/.claude/.mcp.json + ``` + + For a project-specific configuration: + ```bash + python3 mcp/setup_mcp.py /path/to/your_project/.mcp.json + ``` + +2. **Start Claude Code from your project folder and Verify the MCP server is connected:** + + Run `/mcp` in the Claude Code console. You should see: + ``` + ❯ deepstream-inference-builder · ✔ connected + ``` + +3. **Start using the MCP tools:** + + Simply ask Claude Code to perform tasks by mentioning "deepstream inference builder" in your prompt: + - "Show me what sample configurations are available from the inference builder?" + - "Generate a DeepStream object detection pipeline using the inference builder with PeopleNet transformer model from NGC." + +### Available MCP Tools + +| Tool | Description | +|------|-------------| +| `generate_inference_pipeline` | Generate inference pipelines from YAML configuration files | +| `build_docker_image` | Build Docker images from generated pipelines | +| `docker_run_image` | Run Docker images for testing and troubleshooting | +| `prepare_model_repository` | Download models from NGC or HuggingFace and prepare repositories | +| `generate_nvinfer_config` | Generate DeepStream nvinfer runtime configuration files | + +### Available MCP Resources + +| Resource | Description | +|----------|-------------| +| `docs://README.md` | Project documentation | +| `docs://mcp/README-MCP.md` | MCP integration documentation | +| `schema://config.schema.json` | Configuration schema reference | +| `samples://config/*` | Sample pipeline configurations | +| `samples://dockerfile/*` | Sample Dockerfiles | +| `samples://processor/*` | Sample preprocessors and postprocessors | + +### Sample Prompts + +You can now try out the [example prompts](mcp/examples.md) using either Cursor or Claude Code. + +## Contributing + +Contributions are welcome! Please feel free to submit a PR. + + +## Project status and roadmap + +The project is under active development and the following features are expected to be supported in the near future: + +- Support for more backends and frameworks such as SGLANG. +- Support for more model types such as speech models. diff --git a/tools/inference_builder/VERSION b/tools/inference_builder/VERSION new file mode 100644 index 00000000..0ea3a944 --- /dev/null +++ b/tools/inference_builder/VERSION @@ -0,0 +1 @@ +0.2.0 diff --git a/tools/inference_builder/builder/__init__.py b/tools/inference_builder/builder/__init__.py new file mode 100644 index 00000000..3159bfe6 --- /dev/null +++ b/tools/inference_builder/builder/__init__.py @@ -0,0 +1,14 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tools/inference_builder/builder/allowed_servers.txt b/tools/inference_builder/builder/allowed_servers.txt new file mode 100644 index 00000000..08e5d42f --- /dev/null +++ b/tools/inference_builder/builder/allowed_servers.txt @@ -0,0 +1,3 @@ +fastapi +serverless + diff --git a/tools/inference_builder/builder/boilerplates/fastapi/cookiecutter.json b/tools/inference_builder/builder/boilerplates/fastapi/cookiecutter.json new file mode 100644 index 00000000..ebeca75a --- /dev/null +++ b/tools/inference_builder/builder/boilerplates/fastapi/cookiecutter.json @@ -0,0 +1,3 @@ +{ + "service_name": "my-service" +} diff --git a/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/__main__.py b/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/__main__.py new file mode 100644 index 00000000..b5442ae4 --- /dev/null +++ b/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/__main__.py @@ -0,0 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import server.inference + +if __name__ == "__main__": + server.inference.main() \ No newline at end of file diff --git a/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/config/__init__.py b/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/config/__init__.py new file mode 100644 index 00000000..f9c99939 --- /dev/null +++ b/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/config/__init__.py @@ -0,0 +1,14 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. \ No newline at end of file diff --git a/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/custom/__init__.py b/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/custom/__init__.py new file mode 100644 index 00000000..f9c99939 --- /dev/null +++ b/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/custom/__init__.py @@ -0,0 +1,14 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. \ No newline at end of file diff --git a/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/lib/__init__.py b/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/lib/__init__.py new file mode 100644 index 00000000..f9c99939 --- /dev/null +++ b/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/lib/__init__.py @@ -0,0 +1,14 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. \ No newline at end of file diff --git a/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/server/__init__.py b/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/server/__init__.py new file mode 100644 index 00000000..f9c99939 --- /dev/null +++ b/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/server/__init__.py @@ -0,0 +1,14 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. \ No newline at end of file diff --git a/tools/inference_builder/builder/boilerplates/serverless/cookiecutter.json b/tools/inference_builder/builder/boilerplates/serverless/cookiecutter.json new file mode 100644 index 00000000..ebeca75a --- /dev/null +++ b/tools/inference_builder/builder/boilerplates/serverless/cookiecutter.json @@ -0,0 +1,3 @@ +{ + "service_name": "my-service" +} diff --git a/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/__main__.py b/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/__main__.py new file mode 100644 index 00000000..613fefcd --- /dev/null +++ b/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/__main__.py @@ -0,0 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import app.inference + +if __name__ == "__main__": + app.inference.main() \ No newline at end of file diff --git a/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/app/__init__.py b/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/app/__init__.py new file mode 100644 index 00000000..3159bfe6 --- /dev/null +++ b/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/app/__init__.py @@ -0,0 +1,14 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/config/__init__.py b/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/config/__init__.py new file mode 100644 index 00000000..3159bfe6 --- /dev/null +++ b/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/config/__init__.py @@ -0,0 +1,14 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/custom/__init__.py b/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/custom/__init__.py new file mode 100644 index 00000000..3159bfe6 --- /dev/null +++ b/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/custom/__init__.py @@ -0,0 +1,14 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/lib/__init__.py b/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/lib/__init__.py new file mode 100644 index 00000000..3159bfe6 --- /dev/null +++ b/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/lib/__init__.py @@ -0,0 +1,14 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tools/inference_builder/builder/main.py b/tools/inference_builder/builder/main.py new file mode 100644 index 00000000..e8085c30 --- /dev/null +++ b/tools/inference_builder/builder/main.py @@ -0,0 +1,677 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import base64 +import tempfile +import shutil +from omegaconf import OmegaConf +import cookiecutter.main +import cookiecutter +import logging +from typing import Dict, List +from pathlib import Path +from utils import get_resource_path, copy_files, create_tar_gz +from triton.utils import generate_pbtxt +from omegaconf.errors import ConfigKeyError +from jinja2 import Environment, FileSystemLoader +import ast +import os +import sys +import subprocess +import validate +import re +import json +import sys + +# Import schema validation functions +try: + from jsonschema import Draft7Validator +except ImportError: + Draft7Validator = None + logging.warning("jsonschema not installed. Schema validation will be skipped. Install with: pip install jsonschema") + +""" +Inference Builder Main Module + +Security Features: +- Input validation for file paths and directory paths to prevent directory traversal attacks +- Server type validation against allowed values to prevent injection +- Safe subprocess calls using argument lists instead of shell string interpolation +- Comprehensive argument validation in main function to prevent command injection +- Logging of all executed commands for audit trails +""" + +LICENSE_HEADER = """ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" + + +def load_allowed_servers(file_path: str = None) -> List[str]: + """Load allowed server types from a text file. + + Args: + file_path: Path to the text file containing allowed server types. + If None, uses the default 'allowed_servers.txt' + in the builder directory. + + Returns: + List of allowed server type strings. + + Raises: + FileNotFoundError: If the configuration file is not found. + ValueError: If the file is empty or contains invalid entries. + """ + if file_path is None: + # Use default file in the same directory as this script + script_dir = Path(__file__).parent + file_path = script_dir / "allowed_servers.txt" + else: + file_path = Path(file_path) + + if not file_path.exists(): + raise FileNotFoundError( + f"Allowed servers configuration file not found: {file_path}" + ) + + allowed_servers = [] + with open(file_path, 'r', encoding='utf-8') as f: + for line in f: + # Strip whitespace and ignore empty lines and comments + line = line.strip() + if line and not line.startswith('#'): + allowed_servers.append(line) + + if not allowed_servers: + raise ValueError(f"No valid server types found in {file_path}") + + return allowed_servers + + +# Load allowed server types from configuration file +ALLOWED_SERVER = load_allowed_servers() + + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger("Main") +OmegaConf.register_new_resolver("multiline", lambda x: x, replace=False) + + +def validate_file_path(file_path: str) -> str: + """Validate and sanitize a file path to prevent directory traversal and command injection. + + Returns: + str: Validated and resolved absolute file path. + Raises: + ValueError: If path is invalid or potentially dangerous. + """ + if not file_path: + raise ValueError("File path cannot be empty") + + try: + abs_path = os.path.abspath(file_path) + resolved_path = os.path.realpath(abs_path) + except (OSError, ValueError) as e: + raise ValueError(f"Invalid file path: {e}") + + if ".." in os.path.normpath(file_path): + raise ValueError("Path traversal detected in file path") + + invalid_chars = ['<', '>', ':', '"', '|', '?', '*'] + if any(char in file_path for char in invalid_chars): + raise ValueError("Invalid characters in file path") + + system_dirs = ['/etc', '/sys', '/proc', '/dev', '/boot', '/usr/bin', '/usr/sbin'] + for sys_dir in system_dirs: + if resolved_path.startswith(sys_dir): + raise ValueError(f"Access to system directory {sys_dir} is not allowed") + + path = Path(resolved_path) + if not path.exists() or not path.is_file(): + raise ValueError(f"File not found or not a regular file: {resolved_path}") + + return resolved_path + + +def validate_directory_path(dir_path: str) -> str: + """Validate and sanitize a directory path to prevent directory traversal attacks. + + Returns: + str: Validated and resolved absolute directory path. + Raises: + ValueError: If path is invalid or potentially dangerous. + """ + if not dir_path: + raise ValueError("Directory path cannot be empty") + + try: + abs_path = os.path.abspath(dir_path) + resolved_path = os.path.realpath(abs_path) + except (OSError, ValueError) as e: + raise ValueError(f"Invalid directory path: {e}") + + if ".." in os.path.normpath(dir_path): + raise ValueError("Path traversal detected in directory path") + + invalid_chars = ['<', '>', ':', '"', '|', '?', '*'] + if any(char in dir_path for char in invalid_chars): + raise ValueError("Invalid characters in directory path") + + system_dirs = ['/etc', '/sys', '/proc', '/dev', '/boot', '/usr/bin', '/usr/sbin'] + for sys_dir in system_dirs: + if resolved_path.startswith(sys_dir): + raise ValueError(f"Access to system directory {sys_dir} is not allowed") + + return resolved_path + + +def validate_server_type(server_type: str) -> bool: + """Validate server type against allowed values.""" + return server_type in ALLOWED_SERVER + + +def validate_config_against_schema(config_dict: Dict, schema_dir: Path = None) -> bool: + """Validate configuration against JSON schema. + + Args: + config_dict: Configuration dictionary to validate + schema_dir: Directory containing schema files (default: ../schemas from this file) + + Returns: + True if valid, False otherwise + """ + if Draft7Validator is None: + logger.warning("Skipping schema validation (jsonschema not installed)") + return True + + # Determine schema directory + if schema_dir is None: + schema_dir = Path(__file__).parent.parent / "schemas" + + if not schema_dir.exists(): + logger.warning(f"Schema directory not found: {schema_dir}. Skipping schema validation.") + return True + + # Load main schema + schema_path = schema_dir / "config.schema.json" + try: + with open(schema_path, 'r') as f: + schema = json.load(f) + except FileNotFoundError: + logger.warning(f"Schema file not found: {schema_path}. Skipping schema validation.") + return True + except json.JSONDecodeError as e: + logger.error(f"Invalid schema file: {e}") + return False + + # Validate configuration + try: + validator = Draft7Validator(schema) + errors = list(validator.iter_errors(config_dict)) + + if not errors: + logger.info("✓ Configuration schema validation passed") + return True + + logger.error(f"✗ Configuration validation failed with {len(errors)} error(s):") + for i, error in enumerate(errors, 1): + path = '.'.join(str(p) for p in error.path) if error.path else 'root' + logger.error(f" Error {i} at '{path}': {error.message}") + + if error.context: + for ctx_error in error.context: + logger.error(f" - {ctx_error.message}") + + return False + + except Exception as e: + logger.error(f"Error during schema validation: {e}") + return False + + +def get_version(): + version_file = Path(__file__).parent.parent / "VERSION" + with open(version_file, 'r') as f: + return f.read().strip() + + +def build_args(parser): + parser.add_argument( + "-v", + "--version", + action="version", + version=f"%(prog)s {get_version()}" + ) + parser.add_argument( + "--server-type", + type=str, + nargs='?', + default='fastapi', + choices=ALLOWED_SERVER, + help="Choose the server type" + ) + parser.add_argument( + "-o", + "--output-dir", + type=str, + nargs='?', + default='.', + help="Output directory" + ) + parser.add_argument( + "-a", + "--api-spec", + type=argparse.FileType('r'), + nargs='?', + help="File for OpenAPI specification" + ) + parser.add_argument( + "-c", + "--custom-module", + type=argparse.FileType('r'), + nargs='*', + help="Custom python modules" + ) + parser.add_argument( + "-x", + "--exclude-lib", + action='store_true', + help="Don't include common lib to the generated code." + ) + parser.add_argument( + "-t", + "--tar-output", + action='store_true', + help="Zip the output to a single file" + ) + parser.add_argument("config", type=str, help="Path the the configuration") + parser.add_argument( + "--validation-dir", + type=str, + help="valid validation directory path to build validator" + ) + parser.add_argument( + "--no-docker", + action="store_true", + help="Use local OpenAPI Generator instead of Docker for OpenAPI client generation") + parser.add_argument( + "--test-cases-abs-path", + action="store_true", + help="Use absolute paths in generated test_cases.yaml" + ) + +def build_tree(server_type, config, temp_dir): + cookiecutter.main.cookiecutter( + get_resource_path(f"builder/boilerplates/{server_type}"), + no_input=True, + extra_context={"service_name": config.name}, + output_dir=temp_dir) + return Path(temp_dir) / Path(config.name) + +def build_custom_modules(custom_modules: List, tree): + cls_list = [] + tpl_dir = get_resource_path("templates") + jinja_env = Environment(loader=FileSystemLoader(tpl_dir)) + tree = tree / "custom" + for m in custom_modules: + filename = os.path.basename(m.name) + module_id = os.path.splitext(filename)[0] + with m as f: + valid = False + source = f.read() + py = ast.parse(source) + for node in ast.walk(py): + if isinstance(node, ast.ClassDef): + name = None + has_call = False + for cls_node in node.body: + if isinstance(cls_node, ast.Assign): + for target in cls_node.targets: + if isinstance(target, ast.Name) and target.id == "name": + name = ast.literal_eval(cls_node.value) + if isinstance(cls_node, ast.FunctionDef): + if cls_node.name == "__call__": + has_call = True + if name and has_call: + if next((i for i in cls_list if i["name"] == name), None): + logger.warning(f"Custom class {name} defined more than once") + continue + cls_list.append({ + "name": name, + "module": module_id, + "class_name": node.name + }) + valid = True + if valid: + # write the python module + target_path = tree / f"{module_id}.py" + with open(target_path, "w") as t: + t.write(source) + custom_tpl = jinja_env.get_template('common/custom.__init__.jinja.py') + output = custom_tpl.render(classes=cls_list) + with open(tree/"__init__.py", 'w') as f: + f.write(output) + +def build_inference(server_type, config, output_dir: Path): + tpl_dir = get_resource_path("templates") + jinja_env = Environment(loader=FileSystemLoader(tpl_dir)) + triton_model_repo_dir = output_dir/'model_repo' + triton_tpl = jinja_env.get_template("triton/model.jinja.py") + generic_tpl = jinja_env.get_template("generic/model.jinja.py") + + # collect the backend: build model.py and pbtxt if the backend is triton + t_backends = [] + for model in config.models: + backend_spec = model.backend.split('/') + if backend_spec[0] == "triton": + os.makedirs(triton_model_repo_dir/f"{model.name}/1", exist_ok=True) + if len(backend_spec) < 2: + raise Exception("Triton backend needs a triton backend type") + if backend_spec[1] == "python": + if len(backend_spec) < 3: + raise Exception("Triton python backend needs an implementation type") + # generating triton model for triton backend + target_dir = triton_model_repo_dir/f"{model.name}/1" + # Triton python backend needs a model.py + backend_tpl = jinja_env.get_template(f"backend/{backend_spec[2]}.jinja.py") + backend = backend_tpl.render(server_type=server_type) + output = triton_tpl.render(backends=[backend], top_level=False, license=LICENSE_HEADER) + with open (target_dir/"model.py", 'w') as o: + o.write(output) + # triton python backend to communicate with triton fastapi server + if "triton" not in t_backends: + t_backends.append("triton") + # write the pbtxt + pbtxt_str = generate_pbtxt(OmegaConf.to_container(model), backend_spec[1] ) + pbtxt_path = triton_model_repo_dir/model.name/"config.pbtxt" + with open(pbtxt_path, 'w') as f: + f.write(pbtxt_str) + else: + bare_backend = backend_spec[0] + if bare_backend not in t_backends: + t_backends.append(bare_backend) + + # create backends and model.py + backends = [] + if server_type == "serverless": + target_dir = output_dir / "app" + elif server_type == "triton": + target_dir = triton_model_repo_dir/f"{config.name}"/"1/" + else: + target_dir = output_dir / "server" + for backend in t_backends: + backend_tpl = jinja_env.get_template(f"backend/{backend}.jinja.py") + backends.append(backend_tpl.render(server_type=server_type)) + if server_type == "triton": + # render top level triton backend + output = triton_tpl.render(backends=backends, top_level=True) + else: + # render generic model.y + output = generic_tpl.render(backends=backends, license=LICENSE_HEADER) + with open (target_dir/"model.py", 'w') as o: + o.write(output) + +def build_serverless(name: str, output_dir: Path): + output_dir = output_dir / "app" + tpl_dir = get_resource_path("templates") + jinja_env = Environment(loader=FileSystemLoader(tpl_dir)) + app_tpl = jinja_env.get_template("serverless/inference.jinja.py") + output = app_tpl.render(service_name=name, license=LICENSE_HEADER) + with open(output_dir/"inference.py", 'w') as f: + f.write(output) + +def build_server(server_type, model_name, api_spec, config: Dict, output_dir): + output_dir = output_dir / "server" + # generate pydantic data models and inference base class from swagger spec + tpl_dir = get_resource_path("templates") + jinja_env = Environment(loader=FileSystemLoader(tpl_dir)) + api_tpl_dir = get_resource_path(f"templates/api_server/{server_type}/route") + + # Use safer subprocess call with argument list instead of shell string interpolation + # Find fastapi-codegen in the same directory as the current Python executable + python_dir = Path(sys.executable).parent + fastapi_codegen = python_dir / "fastapi-codegen" + + command = [ + str(fastapi_codegen), + "--input", api_spec.name, + "--output", str(output_dir), + "--output-model-type", "pydantic_v2.BaseModel", + "--template-dir", str(api_tpl_dir), + "-m", "data_model.py", + "--disable-timestamp" + ] + + logger.info(f"Executing command: {' '.join(command)}") + result = subprocess.run(command, capture_output=True, text=True) + if result.returncode != 0: + raise Exception(f"Failed to generate fastapi data models: {result.stderr}") + + responders = [] + for name, r in config["responders"].items(): + responder = { + "name": name, + "operation": r["operation"], + } + tpl = jinja_env.get_template(f"responder/{name}.jinja.py") + responder["implementation"] = tpl.render(**responder) + responders.append(responder) + svr_tpl = jinja_env.get_template(f"api_server/{server_type}/responder.jinja.py") + # render the responder.py + if server_type == "triton": + req_cls = [k for k in config["responders"]["infer"]["requests"].keys()] + res_cls = [k for k in config["responders"]["infer"]["responses"].keys()] + triton_config = { + "request_class": req_cls[0], + "response_class": res_cls[0], + "streaming_response_class": res_cls[1] if len(res_cls) > 1 else res_cls[0] + } + output = svr_tpl.render( + service_name=model_name, + license=LICENSE_HEADER, + responders=responders, + triton=triton_config + ) + elif server_type == "fastapi": + output = svr_tpl.render( + service_name=model_name, + license=LICENSE_HEADER, + responders=responders + ) + else: + raise ValueError(f"Unsupported server type: {server_type}") + with open(output_dir/"responder.py", 'w') as f: + f.write(output) + + +def generate_configuration(config, tree): + def encode_templates(templates): + encoded_templates = dict() + for key, value in templates.items(): + # these are json templates and need be encoded before being + # embeded as yaml strings + if isinstance(value, str): + encoded_templates[key] = base64.b64encode(value.encode()) + else: + encoded_templates[key] = value + return encoded_templates + # base64 encode the templates if found in the config + config_map = OmegaConf.to_container(config) + if "input" not in config_map: + config_map["input"] = [] + for m in config_map["models"]: + for i in m["input"]: + config_map["input"].append(i) + if "output" not in config_map: + config_map["output"] = [] + for m in config_map["models"]: + for o in m["output"]: + config_map["output"].append(o) + if "server" in config_map: + try: + for responder in config_map["server"]["responders"].values(): + input_templates = responder.get("requests", None) + output_templates = responder.get("responses", None) + if input_templates: + responder["requests"] = encode_templates(input_templates) + if output_templates: + responder["responses"] = encode_templates(output_templates) + except ConfigKeyError: + raise ValueError("Server config error: responders not found") + # write the config to a python file + tpl_dir = get_resource_path("templates") + jinja_env = Environment(loader=FileSystemLoader(tpl_dir)) + config_tpl = jinja_env.get_template('common/config.jinja.py') + config = OmegaConf.create(config_map) + output = config_tpl.render(config=OmegaConf.to_yaml(config), license=LICENSE_HEADER) + with open(tree/"config/__init__.py", 'w') as f: + f.write(output) + + +def main(args): + # Defense-in-depth: validate and sanitize all path arguments + try: + args.config = validate_file_path(args.config) + except ValueError as e: + raise ValueError(f"Invalid config file path: {e}") + + if not validate_server_type(args.server_type): + raise ValueError(f"Invalid server type: {args.server_type}") + + try: + args.output_dir = validate_directory_path(args.output_dir) + except ValueError as e: + raise ValueError(f"Invalid output directory: {e}") + + if args.validation_dir: + try: + args.validation_dir = validate_directory_path(args.validation_dir) + except ValueError as e: + raise ValueError(f"Invalid validation directory: {e}") + + if args.custom_module: + for module in args.custom_module: + try: + validate_file_path(module.name) + except ValueError as e: + raise ValueError(f"Invalid custom module file path: {e}") + + # Load configuration + logger.info(f"Loading configuration from: {args.config}") + config = OmegaConf.load(args.config) + + # Validate configuration against JSON schema + logger.info("Validating configuration against JSON schema...") + config_dict = OmegaConf.to_container(config, resolve=True) + + if not validate_config_against_schema(config_dict): + logger.error("Configuration validation failed. Fix the errors above and try again.") + sys.exit(1) + + with tempfile.TemporaryDirectory() as temp_dir: + tree = build_tree(args.server_type, config, temp_dir) + if args.server_type == "serverless": + build_serverless(config.name, tree) + else: + build_server(args.server_type, config.name, args.api_spec, OmegaConf.to_container(config.server), tree) + build_inference(args.server_type, config, tree) + generate_configuration(config, tree) + if not args.exclude_lib : + copy_files(get_resource_path("lib"), tree/"lib") + if args.custom_module: + build_custom_modules(args.custom_module, tree) + if args.tar_output: + target = Path(args.output_dir).resolve() / f"{config.name}.tgz" + create_tar_gz(target, tree) + else: + try: + target = Path(args.output_dir).resolve() / config.name + shutil.copytree(tree, target, dirs_exist_ok=True) + except FileExistsError: + logging.error(f"{target} already exists in the output directory") + # Build validator if validation dir provided + if args.validation_dir: + try: + validation_dir = Path(args.validation_dir).resolve() + api_spec_path = Path(args.api_spec.name).resolve() + if validate.build_validation(api_spec_path, validation_dir, not args.no_docker, args.test_cases_abs_path): + logger.info("✓ Successfully built validation") + else: + logger.error("✗ Failed to build validation") + except Exception as e: + logger.error(f"validation build failed: {e}") + # Continue with other builds + # Continue to finish the build without validation + print("Build completed successfully") + + + +if __name__ == "__main__": + parser = argparse.ArgumentParser("Inference Builder") + build_args(parser) + + try: + args = parser.parse_args() + except Exception as e: + print(f"Error: Argument parsing failed: {str(e)}") + sys.exit(1) + + # Comprehensive security validation + validation_errors = [] + + # Validate and sanitize config file path + try: + args.config = validate_file_path(args.config) + except ValueError as e: + validation_errors.append(f"Invalid config file path: {e}") + + # Validate server type (already constrained by argparse choices) + if not validate_server_type(args.server_type): + validation_errors.append(f"Invalid server type: {args.server_type}") + + # Validate and sanitize output directory + try: + args.output_dir = validate_directory_path(args.output_dir) + except ValueError as e: + validation_errors.append(f"Invalid output directory: {e}") + + # Validate and sanitize validation directory + if args.validation_dir: + try: + args.validation_dir = validate_directory_path(args.validation_dir) + except ValueError as e: + validation_errors.append(f"Invalid validation directory: {e}") + + # Exit if any validation errors + if validation_errors: + print("Security validation failed:") + for error in validation_errors: + print(f" - {error}") + sys.exit(1) + + main(args) \ No newline at end of file diff --git a/tools/inference_builder/builder/openapitools.json b/tools/inference_builder/builder/openapitools.json new file mode 100644 index 00000000..b3ae3aa4 --- /dev/null +++ b/tools/inference_builder/builder/openapitools.json @@ -0,0 +1,8 @@ +{ + "$schema": "./node_modules/@openapitools/openapi-generator-cli/config.schema.json", + "spaces": 2, + "generator-cli": { + "version": "7.14.0" + } +} + diff --git a/tools/inference_builder/builder/samples/README.md b/tools/inference_builder/builder/samples/README.md new file mode 100644 index 00000000..c90fb155 --- /dev/null +++ b/tools/inference_builder/builder/samples/README.md @@ -0,0 +1,49 @@ +## Introduction + +The examples in this folder demonstrates how to use Inference Builder to create inference pipelines with various backend, such as Deepstream, TensorRT, Triton, TensorRT-LLM, etc. + +With the provided Dockerfile, you can package the generated pipeline into a container image and run it as a standalone app or a microservice. Build steps vary by backend. Check the corresponding README.md in each example folder for exact instructions. For examples that run as microservices, we've provided an all-in-one [docker-compose.yml](./docker-compose.yml) to manage them together. You can customize the container behavior by changing the configurations there accordingly. + +## Models + +Some of the models for examples are from the NVIDIA GPU Cloud (NGC) repository, and certain models from NGC require active subscription. Please download and install the NGC CLI from the [NGC page](https://org.ngc.nvidia.com/setup/installers/cli) and follow the [NGC CLI Guide](https://docs.ngc.nvidia.com/cli/index.html) to set up the tool. + +## List of Examples + +### DeepStream Backend Examples + +| Name | Description | Models | Backend | Output | +|------|-------------|---------|---------|---------| +| [ds_app](./ds_app/) | examples of building standalone deepstream application | TAO Computer Vision models | Deepstream | command line interface application | +| [tao](./tao/) | examples of building inference microservices using deepstream pipeline and fastapi | TAO Computer Vision models | Deepstream | microservice | + +### Triton Backend Examples + +| Name | Description | Models | Backend | Output | +|------|-------------|---------|---------|---------| +| [changenet](./changenet/) | example of building inference microservices with triton server | Visual ChangeNet | Triton/TensorRT | microservice | + +### TensorRT Backend Examples + +| Name | Description | Models | Backend | Output | +|------|-------------|---------|---------|---------| +| [nvclip](./nvclip/) | example of building inference microservices with TensorRT backend | NVCLIP | TensorRT | microservice | + +### TensorRT-LLM Backend Examples + +| Name | Description | Models | Backend | Output | +|------|-------------|---------|---------|---------| +| [qwen](./qwen/) | example of building inference microservices with TensorRT-LLM backend for vlm models | Qwen 2.5 VL models | TensorRT-LLM, Pytorch | microservice | + +### Multiple Model Examples + +| Name | Description | Models | Backend | Output | +|------|-------------|---------|---------|---------| +| [cradio](./cradio/) | two-stage pipeline: PeopleNet Transformer (detection) then C-RADIOv3-H (per-detection embeddings) | PeopleNet Transformer, C-RADIOv3-H | DeepStream/nvinfer, TensorRT (polygraphy) | command line interface application | + +### VLLM Backend Examples + +| Name | Description | Models | Backend | Output | +|------|-------------|---------|---------|---------| +| [vllm](./vllm/) | example of building inference microservices with vLLM backend and DeepStream MediaExtractor | Cosmos-Reason2-2B, Qwen3-VL-2B-Instruct | vLLM, DeepStream | microservice | + diff --git a/tools/inference_builder/builder/samples/changenet/Dockerfile b/tools/inference_builder/builder/samples/changenet/Dockerfile new file mode 100644 index 00000000..0ca172a0 --- /dev/null +++ b/tools/inference_builder/builder/samples/changenet/Dockerfile @@ -0,0 +1,85 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# A sample Dockerfile for a Triton inference backend with DeepStream- +# accelerated ImageInputDataFlow and CVCUDA for pre-processing. + +# Pull the base image from Deepstream Triton multiarch image. +FROM "nvcr.io/nvidia/deepstream:9.0-triton-multiarch" AS builder_base + +# Install the Python dependencies required by THIS changenet sample +# backend. These versions are pinned for reproducibility and may be +# adjusted for other pipelines or environments. +RUN --mount=type=cache,target=/root/.cache/pip \ + pip install \ + requests==2.32.3 \ + python-multipart==0.0.9 \ + aiofiles==23.2.1 \ + aiohttp==3.11.0 \ + annotated-types==0.7.0 \ + python-magic==0.4.27 \ + torch==2.7.0 --extra-index-url https://download.pytorch.org/whl/cu128 \ + torchvision==0.22.0 \ + Pillow==11.1.0 \ + open_clip_torch==2.30.0 \ + numpy==1.26.4 \ + omegaconf==2.3.0 \ + transformers==4.48.0 \ + fastapi==0.115.12 \ + uvicorn==0.29.0 \ + tritonclient[all]==2.63.0 + +# Install CVCUDA for pre-processing. +# NOTE: These .deb files are built for x86_64 and specific CUDA/Python +# versions. For different architectures or CUDA/Python versions you may +# need to update these URLs to a compatible CVCUDA release. +RUN wget https://github.com/CVCUDA/CV-CUDA/releases/download/v0.16.0/cvcuda-lib-0.16.0-cuda13-x86_64-linux.deb \ + https://github.com/CVCUDA/CV-CUDA/releases/download/v0.16.0/cvcuda-dev-0.16.0-cuda13-x86_64-linux.deb \ + https://github.com/CVCUDA/CV-CUDA/releases/download/v0.16.0/cvcuda-python3.12-0.16.0-cuda13-x86_64-linux.deb \ + && dpkg -i *.deb + +ENV TRANSFORMERS_CACHE=/tmp + +WORKDIR /workspace + +# Copy the inference flow package generated by Inference Builder. +# This tarball (here: changenet.tgz) is typically the result of running +# the builder with tar ball output enabled, and its name should match the +# generated pipeline artifact you want to deploy. +ADD changenet.tgz /workspace + +RUN groupadd --gid 1000 --non-unique nvs && \ + useradd --create-home --shell /usr/sbin/nologin --uid 1000 --non-unique --gid 1000 nvs && \ + chown 1000.1000 /workspace + +USER nvs:1000 + +# Create the entrypoint script that runs the generated +# /workspace/__main__.py (the Inference Builder pipeline entrypoint) +# and then starts Triton with /workspace/model_repo as the model repository. +RUN touch /workspace/start_server.sh && \ + chmod a+rx /workspace/start_server.sh && \ + cat > /workspace/start_server.sh <<-EOF + #!/usr/bin/env bash + set -eu + + # SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + # SPDX-License-Identifier: LicenseRef-NvidiaProprietary + python3 /workspace/__main__.py & tritonserver --model-repository /workspace/model_repo +# while true; do sleep 5; done +EOF + +ENTRYPOINT ["/workspace/start_server.sh"] +CMD [] \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/changenet/README.md b/tools/inference_builder/builder/samples/changenet/README.md new file mode 100644 index 00000000..4bdb3f23 --- /dev/null +++ b/tools/inference_builder/builder/samples/changenet/README.md @@ -0,0 +1,96 @@ +# Introduction + +This sample demonstrates how to build an inference pipeline for models that use triton backend. It also shows how to incorporate CVCUDA in customized processors to accelerate media processing. + +We provide a sample Dockerfile for the example, which you can use to build a Docker image and test the microservice on any x86 system with an NVIDIA Ampere, Hopper, and Blackwell GPU. + +# Prerequisites + +**Note:** Make sure you are in the root directory (`path/to/inference_builder`) to execute the commands in this README. All relative paths and commands assume you are running from the inference_builder root directory. Also ensure that your virtual environment is activated before running any commands. + +The model used in the sample can be found on NGC: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/visual_changenet_segmentation_landsatscd + +Before downloading the model file, you need to set up your model repository: + +```bash +mkdir -p ~/.cache/model-repo && chmod 777 ~/.cache/model-repo +export MODEL_REPO=~/.cache/model-repo +``` + +## Download the model file using NGC CLI: + +```bash +ngc registry model download-version "nvidia/tao/visual_changenet_segmentation_landsatscd:deployable_v1.2" +# Move the folder to the model-repo directory, and the sample uses ~/.cache/model-repo by default +mv visual_changenet_segmentation_landsatscd_vdeployable_v1.2 $MODEL_REPO/visual_changenet +# Change the access right for docker command +chmod 777 $MODEL_REPO/visual_changenet +chmod 666 $MODEL_REPO/visual_changenet/* # correct the permissions in case they're wrong +``` + +Run `ls $MODEL_REPO/visual_changenet -l`, be sure you have `changenet_768.onnx` in your model folder and the file permissions are correct. + +**Note:** If NGC commands fail, make sure you have access to the models you are trying to download. Some models require an active subscription. Ensure NGC is set up properly, or alternatively try using the NGC web UI to directly download the model from the links provided above within the prerequisites section. + +## Create the optimized TensorRT Engine: + +```bash +docker login nvcr.io +docker run -it --rm --gpus all \ +-v ~/.cache/model-repo/visual_changenet/:/changenet \ +nvcr.io/nvidia/tensorrt:25.12-py3 \ +trtexec --onnx=/changenet/changenet_768.onnx --saveEngine=/changenet/model.plan --fp16 +``` + +Run `ls $MODEL_REPO/visual_changenet -l`, be sure you have `model.plan` generated in your model folder and the file permissions are correct. The process takes approximately 10 minutes and you only need to run it once unless the model file changes. + +# Build the inference flow: + +Assume you've followed the [top level instructions](../../../README.md#getting-started) to set up the environment, and you're already in the inference_builder folder. + +The following command activates the virtual environment and generates the inference source package in `builder/samples/changenet` + +```bash +source .venv/bin/activate +python builder/main.py \ + --server-type fastapi \ + -a builder/samples/changenet/openapi.yaml \ + -c builder/samples/changenet/processors.py \ + -o builder/samples/changenet -t \ + builder/samples/changenet/trt_changenet.yaml +``` + +# Build and run the docker image: + +The process takes about 10 minutes, though the actual time may vary depending on your hardware and network bandwidth. + +```bash +cd builder/samples +docker compose up --build ms-changenet +``` + +# Test the microservice with a client + +After the server has started successfully, open a new terminal in the inference_builder folder and launch the client to compare two sample images. A display environment is required for the client to visualize the difference between the two input pictures. + +To avoid errors related to display, you need to set the DISPLAY environment variable based on your system: + +```bash +export DISPLAY=:0 # or :1 depending on your system +``` + +Then, allow X server connections from any host: +```bash +xhost + +``` + +If the configuration is successful, you will see this message in the log: `access control disabled, clients can connect from any host`. + +Then start the client: + +```bash +source .venv/bin/activate && cd builder/samples/changenet +python ib_client.py --host 127.0.0.1 --port 8803 --file test_0.jpg golden_0.jpg +``` + +If the inference request is successfully processed, you'll get a HTTP 200 response and a new window shows the difference between two images. \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/changenet/golden_0.jpg b/tools/inference_builder/builder/samples/changenet/golden_0.jpg new 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All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import requests, base64 +import time +import numpy as np +import os +import io +from PIL import Image + +API_KEY_REQUIRED_IF_EXECUTING_OUTSIDE_NGC="shfklsjlfjsljgl" + + +def main(host , port, files, out_format): + if not files: + print("Need the file path for inference") + return + + # invoke_url = "http://localhost:8001/inference" + invoke_url = "http://" + host + ":" + port + "/v1/infer" + + file_exts = [] + b64_images = [] + for file in files: + file_exts.append(os.path.splitext(file)[1][1:]) + with open(file, "rb") as f: + b64_images.append(base64.b64encode(f.read()).decode()) + +# assert len(image_b64) < 180_000, \ +# "To upload larger images, use the assets API (see docs)" + + headers = { + "Authorization": "Bearer $API_KEY_REQUIRED_IF_EXECUTING_OUTSIDE_NGC", + "Accept": "application/json", + # "NVCF-ASSET-DIR": "some-temp-dir", + # "NVCF-FUNCTION-ASSET-IDS": "udjflsjo-jfoisjof-lsdfjofdj" + } + + payload = { + "input": [f"data:image/{e};base64,{i}" for e, i in zip(file_exts, b64_images)], + "model": "nvidia/changenet" + } + start_time = time.time() + response = requests.post(invoke_url, headers=headers, json=payload) + infer_time = time.time() - start_time + print(response) + print(infer_time) + + if response.status_code == 200: + output = response.json() + print(f"Usage: num_images= {output['usage']['num_images']}") + mask = base64.b64decode(output["data"]) + image_buffer = io.BytesIO(mask) + image = Image.open(image_buffer) + image.show() + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + + parser.add_argument("--host", type=str, + help= "Server IP Address", default="0.0.0.0") + parser.add_argument("--port", type=str, + help="Server port", default="8000") + parser.add_argument("--file", type=str, help="File to send for inference", nargs='*', default=None) + parser.add_argument("--format", type=str, help="Output embedding format, integer or base64", + default="integer") + + args = parser.parse_args() + main(args.host, args.port, args.file, args.format) diff --git a/tools/inference_builder/builder/samples/changenet/openapi.yaml b/tools/inference_builder/builder/samples/changenet/openapi.yaml new file mode 100644 index 00000000..8f35e32c --- /dev/null +++ b/tools/inference_builder/builder/samples/changenet/openapi.yaml @@ -0,0 +1,248 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +openapi: 3.1.0 +info: + title: NVIDIA INFERENCE SERVICE API for nvidia/changenet + description: NVIDIA INFERENCE SERVICE API + version: 1.0.0 + termsOfService: https://nvidia.com/legal/terms-of-use + contact: + name: NVIDIA Support + url: https://help.nvidia.com/ + license: + name: NVIDIA AI Foundation Models Community License + url: https://docs.nvidia.com/ai-foundation-models-community-license.pdf +tags: + - name: "NVIDIA CHANGENET API" + description: Get a segmentation representation for the difference between two pictures. + - name: "Health" + description: Health check endpoints +paths: + "/v1/health/ready": + get: + tags: + - Health + summary: Health Ready + description: Provide your implementation of readiness to know when container ready to accept traffic + operationId: health_ready_v1_health_ready_get + responses: + '200': + description: Successful Response + content: + application/json: + schema: {} + "/v1/infer": + post: + tags: + - "NVIDIA ChangeNet API" + summary: Change detection on images. + description: "Invokes inference for the input images." + operationId: inference + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/DetectionRequest' + required: true + responses: + "200": + description: Invocation is fulfilled. + content: + application/json: + schema: + $ref: '#/components/schemas/DetectionResponse' + "202": + description: Result is pending. Client should poll using the requestId. + content: + application/json: + example: {} + schema: {} + headers: + NVCF-REQID: + description: requestId required for pooling. + schema: + type: string + format: uuid + maxLength: 36 + NVCF-STATUS: + description: Invocation status. + schema: + type: string + format: ^[a-zA-Z-]{1,64}$ + maxLength: 64 + "402": + description: Payment Required. + content: + application/json: + schema: + properties: + detail: + type: string + maxLength: 256 + format: ^[a-zA-Z-]{1,64}$ + description: Contains specific information related to the error and why it occurred. + example: You have reached your limit of credits. + type: object + title: PaymentRequiredError + "422": + description: Validation failed, provided entity could not be processed. + content: + application/json: + schema: + $ref: '#/components/schemas/Errors' + example: + type: urn:nvcf-worker-service:problem-details:unprocessable-entity + title: Unprocessable Entity + status: 422 + detail: string + instance: /v2/nvcf/pexec/functions/9446dad0-ff4f-46d1-89f4-726e113cfca0 + requestId: 3fa85f64-5717-4562-b3fc-2c963f66afa6 + "500": + description: The invocation ended with an error. + content: + application/json: + schema: + $ref: '#/components/schemas/Errors' + example: + type: urn:nvcf-worker-service:problem-details:internal-server-error + title: Internal Server Error + status: 500 + detail: string + instance: /v2/nvcf/pexec/functions/9446dad0-ff4f-46d1-89f4-726e113cfca0 + requestId: 3fa85f64-5717-4562-b3fc-2c963f66afa6 +security: + - Token: [] +components: + securitySchemes: + Token: + type: http + scheme: bearer + schemas: + DetectionRequest: + additionalProperties: false + properties: + input: + type: array + items: + type: string + pattern: ^.*$ + maxLength: 4294967296 + maxItems: 2 + minItems: 2 + title: Input + description: 'The two of images that you want to detect change + for. Images should be in form of `data:image/{format};base64,{base64encodedimage}`. + For NVCF deployment, if the size of an image is more than 200KB, it needs to be uploaded to + a presigned S3 bucket using NVCF Asset APIs. Once uploaded you can refer + to it using the following format: ``. Accepted formats are `jpg`, `png` and `jpeg`.' + model: + type: string + enum: [nvidia/changenet] + title: Model + description: ID of the mask model. + examples: + - nvidia/changenet + type: object + required: + - input + - model + title: DetectionRequest + description: Input images for the inference request. + DetectionResponse: + type: object + properties: + data: + type: string + description: base64 encoded image for change representation + pattern: ^.*$ + maxLength: 4294967296 + model: + type: string + description: Model used to generate masks. + example: nvidia/changenet + title: Model + const: nvidia/changenet + usage: + $ref: '#/components/schemas/Usage' + description: Usage statistics for the masks request. + examples: + - num_images: 1 + additionalProperties: false + required: + - data + - usage + - model + title: DetectionResponse + description: Response schema for the inference request. + Usage: + properties: + num_images: + type: integer + format: int32 + title: Num Images + minimum: 0 + maximum: 64 + description: Number of images processed. + additionalProperties: false + type: object + required: + - num_images + title: Usage + description: Usage information for the inference request. + Errors: + properties: + type: + type: string + format: "^.{1, 128}$" + maxLength: 128 + description: Error type + title: + type: string + format: "^.{1, 128}$" + maxLength: 128 + description: Error title + status: + type: integer + format: int32 + minimum: 100 + maximum: 999 + description: Error status code + detail: + type: string + format: "^.{1, 1024}$" + maxLength: 1024 + description: Detailed information about the error + instance: + type: string + format: "^.{1, 256}$" + maxLength: 256 + description: Function instance used to invoke the request + requestId: + type: string + format: uuid + maxLength: 36 + description: UUID of the request + type: object + required: + - type + - title + - status + - detail + - instance + - requestId + title: InvokeError diff --git a/tools/inference_builder/builder/samples/changenet/payloads/payloads.jsonl b/tools/inference_builder/builder/samples/changenet/payloads/payloads.jsonl new file mode 100644 index 00000000..76efd9cd --- /dev/null +++ b/tools/inference_builder/builder/samples/changenet/payloads/payloads.jsonl @@ -0,0 +1 @@ +{"input": ["data:image/jpeg;base64,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", 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"], "model": "nvidia/changenet"} diff --git a/tools/inference_builder/builder/samples/changenet/processors.py b/tools/inference_builder/builder/samples/changenet/processors.py new file mode 100644 index 00000000..e0b1104c --- /dev/null +++ b/tools/inference_builder/builder/samples/changenet/processors.py @@ -0,0 +1,107 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import numpy as np +import cvcuda +import torch +from PIL import Image +from io import BytesIO +import base64 + +class PreprocessorCvcuda: + """This module converts the input RGB frame into the format expected by the model.""" + name = "changenet-normalizer" + def __init__(self, config): + self.device_id = config["device_id"] + self.network_size = config['network_size'] + self.mean_tensor = torch.Tensor([0.5, 0.5, 0.5]) + self.mean_tensor = self.mean_tensor.reshape(1, 1, 1, 3).cuda(self.device_id) + self.mean_tensor = cvcuda.as_tensor(self.mean_tensor, "NHWC") + self.stddev_tensor = torch.Tensor([0.5, 0.5, 0.5]) + self.stddev_tensor = self.stddev_tensor.reshape(1, 1, 1, 3).cuda(self.device_id) + self.stddev_tensor = cvcuda.as_tensor(self.stddev_tensor, "NHWC") + + def __call__(self, *args): + images = [arg for arg in args] + frame_nhwc = torch.stack(images) + resized = cvcuda.resize( + cvcuda.as_tensor(frame_nhwc, "NHWC"), + ( + frame_nhwc.shape[0], + self.network_size[1], + self.network_size[0], + frame_nhwc.shape[3], + ), + cvcuda.Interp.LINEAR, + ) + + # Convert to floating point range 0-1. + normalized = cvcuda.convertto(resized, np.float32, scale=1 / 255) + + # Normalize with mean and std-dev. + normalized = cvcuda.normalize( + normalized, + base=self.mean_tensor, + scale=self.stddev_tensor, + flags=cvcuda.NormalizeFlags.SCALE_IS_STDDEV, + ) + + # Convert it to NCHW layout and return it. + normalized = cvcuda.reformat(normalized, "NCHW") + result = torch.as_tensor(normalized.cuda(), device=f"cuda:{self.device_id}") + result = list(torch.unbind(result, dim=0)) + + return tuple(result) + +class PostprocessorCvcuda: + name = "changenet-masking" + def __init__(self, config): + self.color_map = { + "0": torch.ByteTensor([255, 255, 255]), + "1": torch.ByteTensor([255, 165, 0]), + "2": torch.ByteTensor([230, 30, 100]), + "3": torch.ByteTensor([70, 140, 0]), + "4": torch.ByteTensor([218, 112, 214]), + "5": torch.ByteTensor([0, 170, 240]), + "6": torch.ByteTensor([127, 235, 170]), + "7": torch.ByteTensor([230, 80, 0]), + "8": torch.ByteTensor([205, 220, 57]), + "9": torch.ByteTensor([218, 165, 32]), + } + self.network_size = config['network_size'] + self.n_class = config['n_class'] + + def get_color_coded_img(self, vis, num_class=10): + color_coded = torch.ones( + (self.network_size[1], self.network_size[0], 3), + dtype=torch.uint8, + ) + for idx in range(num_class): + filtered_indices = vis == idx + color_coded[filtered_indices] = self.color_map[str(idx)] + return color_coded + + def __call__(self, *args): + if not (isinstance(args[0], torch.Tensor) or isinstance(args[0], np.ndarray)): + raise Exception(f"Unsupported type {type(args[0])}") + output = args[0] if isinstance(args[0], torch.Tensor) else torch.from_numpy(args[0]) + pred = torch.argmax(output, dim=0, keepdim=True) + pred = pred.squeeze(0) + image_tensor = self.get_color_coded_img(pred, self.n_class) + buffer = BytesIO() + image = Image.fromarray(image_tensor.numpy()) + image.save(buffer, format="JPEG") + return np.array([base64.b64encode(buffer.getvalue())]) diff --git a/tools/inference_builder/builder/samples/changenet/test_0.jpg b/tools/inference_builder/builder/samples/changenet/test_0.jpg new file mode 100644 index 0000000000000000000000000000000000000000..f99d78af18e3d81bdc3842cd3cd3ebd1fcb89506 GIT binary patch literal 32942 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b/tools/inference_builder/builder/samples/changenet/test_config.json new file mode 100644 index 00000000..5748c00d --- /dev/null +++ b/tools/inference_builder/builder/samples/changenet/test_config.json @@ -0,0 +1,58 @@ +[ + { + "name": "changenet_inference", + "description": "Test ChangeNet inference for change detection on images", + "build_args": { + "TEST_APP_NAME": "changenet", + "CACHE_BUSTER": "changenet_test", + "APP_YAML_PATH": "trt_changenet.yaml", + "OUTPUT_DIR": ".", + "PROCESSORS_PATH": "processors.py", + "SERVER_TYPE": "fastapi", + "OPENAPI_SPEC": "openapi.yaml" + }, + "test_config": { + "default_enable": true, + "timeout": 600, + "env": { + "NVIDIA_DRIVER_CAPABILITIES": "compute,utility,video,graphics", + "MODEL_REPO": "/workspace/model_repo", + "HTTP_PORT": "8003" + }, + "volumes": { + "/tmp/model-repo/visual_changenet": "/workspace/model_repo/visual_changenet/1" + }, + "test_requests": [ + { + "payload_path": "payloads/payloads.jsonl", + "method": "POST", + "headers": { + "Content-Type": "application/json" + } + } + ], + "endpoint": "/v1/infer", + "error_detection": { + "enabled": true, + "patterns": [ + "ERROR", + "Exception:", + "Traceback", + "Fatal error", + "CRITICAL" + ] + }, + "prerequisite_script": null, + "models": { + "visual_changenet": { + "source": "NGC", + "target": "/tmp/model-repo", + "path": "nvidia/tao/visual_changenet_segmentation_landsatscd", + "version": "deployable_v1.2", + "post_script": "docker run --rm --gpus all -v $(pwd):/changenet nvcr.io/nvidia/tensorrt:25.12-py3 trtexec --onnx=/changenet/changenet_768.onnx --saveEngine=/changenet/model.plan --fp16" + } + } + } + } +] + diff --git a/tools/inference_builder/builder/samples/changenet/trt_changenet.yaml b/tools/inference_builder/builder/samples/changenet/trt_changenet.yaml new file mode 100644 index 00000000..c1efecd1 --- /dev/null +++ b/tools/inference_builder/builder/samples/changenet/trt_changenet.yaml @@ -0,0 +1,109 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +name: "changenet" +model_repo: /workspace/model_repo/ + +input: + - name: "reference_image" + data_type: TYPE_CUSTOM_IMAGE_BASE64 + dims: [ -1 ] + optional: false + + - name: "test_image" + data_type: TYPE_CUSTOM_IMAGE_BASE64 + dims: [ -1 ] + optional: false + +output: + - name: "output" + data_type: TYPE_STRING + dims: [ -1 ] + +server: + responders: + infer: + operation: inference + requests: + DetectionRequest: > + { + "reference_image": [{{ request.input[0]|tojson }}], + "test_image": [{{ request.input[1]|tojson }}] + } + responses: + DetectionResponse: > + { + "data": {{ response.output[0]|tojson }}, + "usage": { "num_images": 1}, + "model": "nvidia/changenet" + } + healthy_ready: + operation: health_ready_v1_health_ready_get + +models: +- name: visual_changenet + backend: "triton/tensorrt" + max_batch_size: 1 + parameters: + FORCE_CPU_ONLY_INPUT_TENSORS: "no" + input: + - name: input0 + data_type: TYPE_FP32 + dims: [3, 768, 768] + - name: input1 + data_type: TYPE_FP32 + dims: [3, 768, 768] + output: + - name: output_final + data_type: TYPE_FP32 + dims: [10,768, 768] + - name: output0 + data_type: TYPE_FP32 + dims: [10, 48, 48] + - name: output1 + data_type: TYPE_FP32 + dims: [10, 48, 48] + - name: output2 + data_type: TYPE_FP32 + dims: [10, 96, 96] + - name: output3 + data_type: TYPE_FP32 + dims: [10, 192, 192] + preprocessors: + - kind: "custom" + name: "changenet-normalizer" + input: ["reference_image", "test_image"] + output: ["input0", "input1"] + config: + network_size: [768, 768] + postprocessors: + - kind: "custom" + name: "changenet-masking" + input: ["output_final"] + output: ["output_final"] + config: + network_size: [768, 768] + n_class: 10 + +# route map +# A route is defined as +# Source and destination should be defined as +# Model name is optional, so <:["TENSOR_NAME1", "TENSOR_NAME2", ...]> is valid +# Tensor list is optional too and is valid +routes: { + ':["reference_image", "test_image"]': 'visual_changenet', + 'visual_changenet:["output_final"]': ':["output"]', +} diff --git a/tools/inference_builder/builder/samples/cradio/Dockerfile b/tools/inference_builder/builder/samples/cradio/Dockerfile new file mode 100644 index 00000000..de47c315 --- /dev/null +++ b/tools/inference_builder/builder/samples/cradio/Dockerfile @@ -0,0 +1,102 @@ +# Dockerfile for ds-cradio: PeopleNet Transformer + C-RADIOv3-H pipeline +# Uses DeepStream/nvinfer for detection and polygraphy (TensorRT) for embeddings + +FROM "nvcr.io/nvidia/deepstream:9.0-triton-multiarch" AS inference_base + +# Install Python dependencies required by this pipeline. +RUN --mount=type=cache,target=/root/.cache/pip \ + pip install \ + torch==2.7.0 \ + numpy==1.26.4 \ + omegaconf==2.3.0 \ + transformers==4.54.1 \ + opencv-python + +# Build TensorRT OSS to provide customized plugins (e.g. for PeopleNet +# Transformer ONNX model) and the trtexec tool. +ARG TRT_VERSION_MAJOR=10 +ARG TRT_VERSION_MINOR=14 +ARG TRT_VERSION_PATCH=1 +ARG TRT_VERSION_BUILD=48 + +ARG TRT_VERSION_MAJOR_MINOR=$TRT_VERSION_MAJOR.$TRT_VERSION_MINOR +ARG TRT_VERSION_MAJOR_MINOR_PATCH=$TRT_VERSION_MAJOR.$TRT_VERSION_MINOR.$TRT_VERSION_PATCH +ARG TRT_VERSION_FULL=$TRT_VERSION_MAJOR_MINOR_PATCH.$TRT_VERSION_BUILD + +ARG CUDA_VERSION_MAJOR=13 +ARG CUDA_VERSION_MINOR=1 +ARG CUDA_VERSION_PATCH=80 +ARG CUDA_VERSION_BUILD=36836380_0 +ARG CUDA_VERSION_MAJOR_MINOR=$CUDA_VERSION_MAJOR.$CUDA_VERSION_MINOR +ARG CUDA_VERSION_FULL=$CUDA_VERSION_MAJOR_MINOR.$CUDA_VERSION_PATCH.$CUDA_VERSION_BUILD +ARG CUDNN_VERSION=9.17.0.29 + +ENV TRT_VERSION=$TRT_VERSION_FULL+cuda$CUDA_VERSION_FULL + +WORKDIR /tmp +ENV TRT_TAG="release/$TRT_VERSION_MAJOR_MINOR" +RUN mkdir trt_oss_src && \ + cd trt_oss_src && \ + echo "$PWD Building TRT OSS..." && \ + git clone -b $TRT_TAG https://github.com/nvidia/TensorRT TensorRT && \ + cd TensorRT && \ + git submodule update --init --recursive && \ + mkdir -p build && cd build && \ + cmake .. \ + -DGPU_ARCHS="80;86;90;100;120" \ + -DTRT_LIB_DIR=/usr/lib/x86_64-linux-gnu \ + -DTRT_OUT_DIR=/tmp/out \ + -DCUDA_VERSION=$CUDA_VERSION_MAJOR_MINOR \ + -DCUDNN_VERSION=$CUDNN_VERSION \ + && for i in {1..50}; do make -j 12 && break; done +RUN cp /tmp/out/libnvinfer_plugin.so.$TRT_VERSION_MAJOR_MINOR_PATCH /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.$TRT_VERSION_MAJOR_MINOR_PATCH && \ + cp /tmp/out/libnvinfer_plugin_static.a /usr/lib/x86_64-linux-gnu/libnvinfer_plugin_static.a && \ + cp /tmp/out/libnvonnxparser.so.$TRT_VERSION_MAJOR_MINOR_PATCH /usr/lib/x86_64-linux-gnu/libnvonnxparser.so.$TRT_VERSION_MAJOR_MINOR_PATCH && \ + cp /tmp/out/trtexec /usr/local/bin/ && \ + cd ../../../ && \ + rm -rf trt_oss_src + +# Build DeepStream TAO app post-processors (provides NvDsInferParseCustomDDETRTAO +# for PeopleNet Transformer). +ENV DS_TAO_APPS_REPO=https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git +ENV DS_TAO_APPS_TAG="master" +ARG CACHE_BUSTER=unique_value_to_bypass_docker_cache +RUN mkdir ds_tao_apps_src && \ + cd ds_tao_apps_src && \ + echo "$PWD Pulling DS TAO apps repo..." && \ + git clone -b $DS_TAO_APPS_TAG $DS_TAO_APPS_REPO ds_tao_apps && \ + cd ds_tao_apps/post_processor && \ + echo "$PWD Building DS TAO apps post processing..." && \ + CUDA_MODULE_LOADING=LAZY CUDA_VER=$CUDA_VERSION_MAJOR.$CUDA_VERSION_MINOR make && \ + [ -f libnvds_infercustomparser_tao.so ] && echo "$PWD post processing build successful" || echo "$PWD post processing build failed" && \ + cp libnvds_infercustomparser_tao.so /opt/nvidia/deepstream/deepstream/lib && \ + cd ../../../ && \ + rm -rf ds_tao_apps_src + +# Install Python dependencies for the polygraphy backend +RUN --mount=type=cache,target=/root/.cache/pip \ + pip install --extra-index-url https://pypi.nvidia.com \ + Pillow==11.1.0 \ + timm \ + torchvision \ + einops \ + onnx \ + onnxscript \ + tensorrt==10.14.1.48.post1 \ + polygraphy==0.49.24 + +WORKDIR /workspace + +# Copy the generated inference pipeline +ADD ds-cradio.tgz /workspace + +# Copy the ONNX export script (for model preparation inside container) +COPY export_onnx.py /workspace/export_onnx.py + +# Set environment variables required for DeepStream runtime +ENV GST_PLUGIN_PATH=/opt/nvidia/deepstream/deepstream/lib/gst-plugins +ENV LD_LIBRARY_PATH=/opt/nvidia/deepstream/deepstream/lib:$LD_LIBRARY_PATH +ENV NVSTREAMMUX_ADAPTIVE_BATCHING=yes + +ENTRYPOINT ["python3", "/workspace/__main__.py"] +CMD [] diff --git a/tools/inference_builder/builder/samples/cradio/README.md b/tools/inference_builder/builder/samples/cradio/README.md new file mode 100644 index 00000000..11b097f2 --- /dev/null +++ b/tools/inference_builder/builder/samples/cradio/README.md @@ -0,0 +1,255 @@ +# PeopleNet Transformer + C-RADIOv3-H Pipeline + +A two-stage serverless inference pipeline that detects people/objects in images using PeopleNet Transformer (DeepStream/nvinfer), then generates per-detection embeddings using C-RADIOv3-H (TensorRT/polygraphy). The pipeline runs as a CLI application (serverless, no HTTP server). + +We provide a sample Dockerfile for the example, which you can use to build a Docker image and run inference on any x86 system with an NVIDIA Ampere, Hopper, or Blackwell GPU. + +## Pipeline Structure + +``` +Input (image path + MIME) + | + v + +--------------+ + | PeopleNet | DeepStream/nvinfer backend + | Transformer | - Decodes image (DS_IMAGE) + | | - Runs detection (DS_METADATA) + | | - C++ parser: NvDsInferParseCustomDDETRTAO + +--------------+ + | | + | +---> detections (direct to output) + | {shape, bboxes, probs, labels, ...} + v + BboxCropPostprocessor + - Crops each bbox from decoded frame + - Resizes to 256x256 (bicubic) + - CLIP mean/std normalization + - Returns N [3, 256, 256] crops (batched by model operator) + | + v + +--------------+ + | C-RADIOv3-H | polygraphy/TensorRT backend + | | - Batch up to 16 crops + +--------------+ + | + v + Output: + - detections: {shape, bboxes, probs, labels, seg_maps, objects, timestamp} + - summary: [N, 3840] per-detection embeddings + - spatial_features: [N, 256, 1280] per-detection spatial tokens +``` + +### Data Flow + +``` +route: input[media_url, mime] --> peoplenet +route: peoplenet[pixel_values] --> cradio_v3_h +route: peoplenet[detections] --> output +route: cradio_v3_h[summary, spatial_features] --> output +``` + +### Custom Processors + +- **BboxCropPostprocessor** (postprocessor on peoplenet): Crops detected bounding boxes from the decoded frame, resizes/normalizes for C-RADIO input, and returns a list of `[3, 256, 256]` tensors. The model operator batches them into `[N, 3, 256, 256]` for the TRT engine. + +# Prerequisites + +**Note:** Make sure you are in the root directory (`path/to/inference_builder`) to execute the commands in this README. All relative paths and commands assume you are running from the inference_builder root directory. Also ensure that your virtual environment is activated before running any commands. + +Refer to the [top-level README](../../../README.md#getting-started) for base setup (virtual environment, protobuf, etc.). + +Before downloading the model files, set up your model repository: + +```bash +mkdir -p ~/.cache/model-repo && chmod 777 ~/.cache/model-repo +export MODEL_REPO=~/.cache/model-repo +``` + +# Download Models + +## PeopleNet Transformer (NGC) + +The PeopleNet Transformer model is available from NGC: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplenet_transformer + +Download using the NGC CLI and move the files into the expected model directory: + +```bash +ngc registry model download-version \ + nvidia/tao/peoplenet_transformer:deployable_v1.1 \ + --dest /tmp +mv /tmp/peoplenet_transformer_vdeployable_v1.1 $MODEL_REPO/peoplenet +``` + +Run `ls $MODEL_REPO/peoplenet/` and verify you have `resnet50_peoplenet_transformer_op17.onnx`. + +Copy the nvinfer config and labels file to the model directory: + +```bash +cp builder/samples/ds_app/detection/peoplenet_transformer/nvdsinfer_config.yaml \ + builder/samples/ds_app/detection/peoplenet_transformer/labels.txt \ + $MODEL_REPO/peoplenet/ +``` + +**Note:** The ONNX model uses custom TensorRT plugins. The Dockerfile builds TRT OSS from source to provide these plugins. The TensorRT engine is built automatically by DeepStream on first run. + +**Note:** If NGC commands fail, make sure you have access to the models you are trying to download. Some models require an active subscription. Ensure NGC is set up properly, or alternatively try using the NGC web UI to directly download the model. + +## C-RADIOv3-H (HuggingFace) + +Make sure you have the HuggingFace CLI installed and are logged in: + +```bash +pip install huggingface_hub +hf auth login +``` + +Or set the `HF_TOKEN` environment variable: + +```bash +export HF_TOKEN= +``` + +Then download the model: + +```bash +git lfs install +git clone https://huggingface.co/nvidia/C-RADIOv3-H $MODEL_REPO/cradio_v3_h +``` + +**Note:** ONNX export and TRT engine build for this model happen after the Docker image is built, since they require the GPU and container environment. See [Export C-RADIOv3-H ONNX & Build TRT Engine](#export-c-radiov3-h-onnx--build-trt-engine) below. + +# Build the Inference Pipeline + +```bash +source .venv/bin/activate +python builder/main.py builder/samples/cradio/ds_cradio.yaml \ + -c builder/samples/cradio/processors.py \ + -o builder/samples/cradio \ + --server-type serverless -t +``` + +The `-t` flag creates `ds-cradio.tgz` in the output directory (from the `name` in `ds_cradio.yaml`). The Dockerfile expects this tarball. + +# Build the Docker Image + +```bash +docker build -f builder/samples/cradio/Dockerfile -t cradio:latest builder/samples/cradio +``` + +# Export C-RADIOv3-H ONNX & Build TRT Engine + +## Export to ONNX + +Run the export script inside the container (requires GPU): + +```bash +docker run --rm --gpus all \ + -v $MODEL_REPO:/models \ + -v $MODEL_REPO/cradio_v3_h:/root/.cache/huggingface/modules/transformers_modules/cradio_v3_h \ + --entrypoint python3 \ + cradio:latest /workspace/export_onnx.py \ + --output-dir /models/cradio_v3_h \ + --resolution 256 \ + --model-path /models/cradio_v3_h +``` + +## Build TensorRT Engine + +Build the TensorRT engine with polygraphy: + +```bash +docker run --rm --gpus all \ + -v $MODEL_REPO:/models \ + --entrypoint polygraphy \ + cradio:latest convert /models/cradio_v3_h/model.onnx \ + --fp16 \ + --trt-min-shapes pixel_values:[1,3,256,256] \ + --trt-opt-shapes pixel_values:[8,3,256,256] \ + --trt-max-shapes pixel_values:[16,3,256,256] \ + -o /models/cradio_v3_h/model.plan +``` + +Run `ls $MODEL_REPO/cradio_v3_h/model.plan` to verify the engine was created. + +## Automated Script + +Alternatively, `prepare_engine.sh` automates both steps (skips if outputs already exist): + +```bash +bash builder/samples/cradio/prepare_engine.sh cradio:latest $MODEL_REPO +``` + +This is the same script used by the CI test via `prerequisite_script` in `test_config.json`. + +# Run Inference + +Run inference on a single image (output to stdout): + +```bash +docker run --rm --gpus all \ + -v $MODEL_REPO:/models \ + -e MODEL_REPO=/models \ + cradio:latest \ + --media-url --mime image/jpeg -s stdout +``` + +To save the output to a file: + +```bash +docker run --rm --gpus all \ + -v $MODEL_REPO:/models \ + -e MODEL_REPO=/models \ + cradio:latest \ + --media-url --mime image/jpeg -s /models/output.json +``` + +**Important:** Replace `` with an actual image path accessible inside the container (e.g., mount a volume or use a path already in the image such as `/opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.jpg`). + +## Expected Output Format + +```json +{ + "detections": { + "shape": [544, 960], + "bboxes": [[x1, y1, x2, y2], ...], + "probs": [0.977, 0.953, ...], + "labels": [["Person"], ["Person"], ...], + "seg_maps": [], + "objects": [], + "timestamp": 1234567890 + }, + "summary": [[...3840 floats...], ...], + "spatial_features": [[[...1280 floats...] x 256], ...] +} +``` + +- `detections`: PeopleNet bounding boxes, labels, and probabilities +- `summary`: `[N, 3840]` per-detection embeddings (N = number of detections, max 16) +- `spatial_features`: `[N, 256, 1280]` per-detection spatial tokens +- When no objects are detected, N=1 with a dummy zero-crop embedding + +# Model Repository Layout + +``` +$MODEL_REPO/ + peoplenet/ + resnet50_peoplenet_transformer_op17.onnx + nvdsinfer_config.yaml + labels.txt + cradio_v3_h/ + model.plan # TensorRT engine (built from ONNX) + model.onnx # Exported ONNX model + ... # HuggingFace model files (used for ONNX export) +``` + +# Project Files + +| File | Description | +|------|-------------| +| `ds_cradio.yaml` | Pipeline configuration (models, routes, I/O types) | +| `processors.py` | Custom postprocessor (BboxCropPostprocessor) | +| `Dockerfile` | Docker build (DeepStream base + TRT OSS + TAO parser + pipeline) | +| `export_onnx.py` | C-RADIOv3-H ONNX export script | +| `prepare_engine.sh` | Automated ONNX export + TRT engine build (used by CI) | +| `nvdsinfer_config.yaml` | PeopleNet nvinfer runtime config (copy from `ds_app/detection/peoplenet_transformer/` to `$MODEL_REPO/peoplenet/`) | +| `test_config.json` | Test configuration for CI | diff --git a/tools/inference_builder/builder/samples/cradio/ds_cradio.yaml b/tools/inference_builder/builder/samples/cradio/ds_cradio.yaml new file mode 100644 index 00000000..4a235135 --- /dev/null +++ b/tools/inference_builder/builder/samples/cradio/ds_cradio.yaml @@ -0,0 +1,85 @@ +name: ds-cradio +model_repo: /models + +# Top-level input: image URL/path + MIME type. +input: + - name: "media_url" + data_type: TYPE_STRING + dims: [ -1 ] + - name: "mime" + data_type: TYPE_STRING + dims: [ -1 ] + +# Top-level output: detections + per-detection embeddings +output: + - name: "detections" + data_type: TYPE_CUSTOM_OBJECT + dims: [ -1 ] + - name: "summary" + data_type: TYPE_FP32 + dims: [-1, -1] + - name: "spatial_features" + data_type: TYPE_FP32 + dims: [-1, -1, -1] + +models: + # Stage 1: PeopleNet Transformer (object detection via DeepStream) + - name: peoplenet + backend: "deepstream/nvinfer" + max_batch_size: 1 + input: + - name: "image_bytes" + data_type: TYPE_CUSTOM_DS_IMAGE + dims: [ -1 ] + - name: "media_url" + data_type: TYPE_CUSTOM_DS_MEDIA_URL + dims: [ -1 ] + - name: "mime" + data_type: TYPE_CUSTOM_DS_MIME + dims: [1] + output: + - name: "detections" + data_type: TYPE_CUSTOM_DS_METADATA + dims: [ -1 ] + - name: "decoded_frames" + data_type: TYPE_CUSTOM_DS_IMAGE + dims: [-1, 3] + parameters: + infer_config_path: + - nvdsinfer_config.yaml + inference_timeout: 10 + postprocessors: + # After DS inference: crop each detected bbox from decoded_frames, + # resize+normalize for C-RADIO, and return a stacked [N,3,256,256] + # tensor. Because peoplenet is on the implicit path the tensor + # flows directly to cradio via kwargs (no stack_tensors_in_dict). + - kind: "custom" + name: "bbox-crop-postprocessor" + input: ["detections", "decoded_frames"] + output: ["pixel_values", "detections"] + config: + max_crops: 16 # must match cradio_v3_h max_batch_size below + + # Stage 2: C-RADIOv3-H (per-crop embedding via TensorRT) + - name: cradio_v3_h + backend: "polygraphy" + max_batch_size: 16 + input: + - name: "pixel_values" + data_type: TYPE_FP32 + dims: [3, 256, 256] + output: + - name: "summary" + data_type: TYPE_FP32 + dims: [-1, -1] + - name: "spatial_features" + data_type: TYPE_FP32 + dims: [-1, -1, -1] + parameters: + tensorrt_engine: "model.plan" + +routes: + ':[\"media_url\", \"mime\"]': 'peoplenet' + 'peoplenet:[\"pixel_values\"]': 'cradio_v3_h' + 'peoplenet:[\"detections\"]': ':' + 'cradio_v3_h:[\"summary\", \"spatial_features\"]': ':' diff --git a/tools/inference_builder/builder/samples/cradio/export_onnx.py b/tools/inference_builder/builder/samples/cradio/export_onnx.py new file mode 100644 index 00000000..57dd58a1 --- /dev/null +++ b/tools/inference_builder/builder/samples/cradio/export_onnx.py @@ -0,0 +1,128 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +""" +Export C-RADIOv3-H model from HuggingFace to ONNX format. + +Usage: + python export_onnx.py --output-dir /models/cradio_v3_h --resolution 256 + +After export, build a TensorRT engine: + polygraphy convert /models/cradio_v3_h/model.onnx \ + --fp16 \ + -o /models/cradio_v3_h/model.plan +""" + +import argparse +import os +import sys +import torch + + +def validate_directory_path(dir_path): + """ + Validate directory path to prevent OS access violations. + Args: + dir_path: Directory path to validate + Returns: + str: Validated and sanitized directory path + Raises: + ValueError: If path is invalid or potentially dangerous + """ + if not dir_path: + raise ValueError("Directory path cannot be empty") + + try: + abs_path = os.path.abspath(dir_path) + resolved_path = os.path.realpath(abs_path) + except (OSError, ValueError) as e: + raise ValueError(f"Invalid directory path: {e}") + + if ".." in os.path.normpath(dir_path): + raise ValueError("Path traversal detected in directory path") + + invalid_chars = ['<', '>', ':', '"', '|', '?', '*'] + if any(char in dir_path for char in invalid_chars): + raise ValueError("Invalid characters in directory path") + + system_dirs = ['/etc', '/sys', '/proc', '/dev', '/boot', '/usr/bin', '/usr/sbin'] + for sys_dir in system_dirs: + if resolved_path.startswith(sys_dir): + raise ValueError(f"Access to system directory {sys_dir} is not allowed") + + return resolved_path + + +def export_onnx(output_dir: str, resolution: int = 256, model_path: str = None): + from transformers import AutoModel + + output_dir = validate_directory_path(output_dir) + if model_path is not None: + model_path = validate_directory_path(model_path) + if not isinstance(resolution, int) or resolution <= 0: + raise ValueError(f"Resolution must be a positive integer, got: {resolution}") + + os.makedirs(output_dir, exist_ok=True) + onnx_path = os.path.join(output_dir, "model.onnx") + + source = model_path or "nvidia/C-RADIOv3-H" + print(f"Loading C-RADIOv3-H model from {source}...") + # trust_remote_code=True is required by the C-RADIOv3-H architecture; + # model_path is sanitized by validate_directory_path() above. + model = AutoModel.from_pretrained( + source, trust_remote_code=True # nosec - required by model, path is validated + ) + model.train(False).cuda() + + dummy_input = torch.randn(1, 3, resolution, resolution, device="cuda") + + print(f"Exporting to ONNX: {onnx_path}") + torch.onnx.export( + model, + dummy_input, + onnx_path, + input_names=["pixel_values"], + output_names=["summary", "spatial_features"], + dynamic_axes={ + "pixel_values": {0: "batch_size"}, + "summary": {0: "batch_size"}, + "spatial_features": {0: "batch_size"}, + }, + opset_version=17, + ) + print(f"ONNX model saved to {onnx_path}") + print( + f"\nNext step - build TensorRT engine:\n" + f" polygraphy convert {onnx_path} --fp16 -o {os.path.join(output_dir, 'model.plan')}" + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Export C-RADIOv3-H to ONNX") + parser.add_argument( + "--output-dir", + default="/models/cradio_v3_h", + help="Directory for output ONNX and TensorRT engine files", + ) + parser.add_argument( + "--resolution", + type=int, + default=256, + help="Input image resolution (default: 256)", + ) + parser.add_argument( + "--model-path", + default=None, + help="Local path to the model directory (skips HF download)", + ) + try: + args = parser.parse_args() + except Exception as e: + print(f"Error: Argument parsing failed: {str(e)}") + sys.exit(1) + + try: + export_onnx(args.output_dir, args.resolution, args.model_path) + except ValueError as e: + print(f"Security validation failed: {e}") + sys.exit(1) diff --git a/tools/inference_builder/builder/samples/cradio/prepare_engine.sh b/tools/inference_builder/builder/samples/cradio/prepare_engine.sh new file mode 100644 index 00000000..e774b933 --- /dev/null +++ b/tools/inference_builder/builder/samples/cradio/prepare_engine.sh @@ -0,0 +1,61 @@ +#!/bin/bash +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Prepare C-RADIOv3-H TensorRT engine: export ONNX and build engine. +# Usage: prepare_engine.sh +# +# Requires: model files at /cradio_v3_h (from HuggingFace) +# Produces: /cradio_v3_h/model.plan (TensorRT FP16 engine) + +set -e + +IMAGE="$1" +MODEL_REPO="${2:-/tmp/model-repo}" +MODEL_DIR="$MODEL_REPO/cradio_v3_h" +RESOLUTION=256 + +if [ -z "$IMAGE" ]; then + echo "Usage: $0 [model_repo]" + exit 1 +fi + +# Skip if engine already exists +if [ -f "$MODEL_DIR/model.plan" ]; then + echo "TensorRT engine already exists at $MODEL_DIR/model.plan, skipping" + exit 0 +fi + +# Check that model files exist +if [ ! -d "$MODEL_DIR" ]; then + echo "Error: Model directory not found: $MODEL_DIR" + exit 1 +fi + +echo "=== Step 1: Export ONNX ===" +if [ ! -f "$MODEL_DIR/model.onnx" ]; then + docker run --rm --gpus all \ + -v "$MODEL_REPO:/models" \ + -v "$MODEL_DIR:/root/.cache/huggingface/modules/transformers_modules/cradio_v3_h" \ + --entrypoint python3 \ + "$IMAGE" /workspace/export_onnx.py \ + --output-dir /models/cradio_v3_h \ + --resolution "$RESOLUTION" \ + --model-path /models/cradio_v3_h + echo "ONNX export complete: $MODEL_DIR/model.onnx" +else + echo "ONNX model already exists at $MODEL_DIR/model.onnx, skipping export" +fi + +echo "=== Step 2: Build TensorRT Engine ===" +docker run --rm --gpus all \ + -v "$MODEL_REPO:/models" \ + --entrypoint polygraphy \ + "$IMAGE" convert /models/cradio_v3_h/model.onnx \ + --fp16 \ + --trt-min-shapes "pixel_values:[1,3,${RESOLUTION},${RESOLUTION}]" \ + --trt-opt-shapes "pixel_values:[8,3,${RESOLUTION},${RESOLUTION}]" \ + --trt-max-shapes "pixel_values:[16,3,${RESOLUTION},${RESOLUTION}]" \ + -o /models/cradio_v3_h/model.plan + +echo "TensorRT engine built: $MODEL_DIR/model.plan" diff --git a/tools/inference_builder/builder/samples/cradio/processors.py b/tools/inference_builder/builder/samples/cradio/processors.py new file mode 100644 index 00000000..533aff9f --- /dev/null +++ b/tools/inference_builder/builder/samples/cradio/processors.py @@ -0,0 +1,97 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +import torch +import torch.nn.functional as F + + +class BboxCropPostprocessor: + """Postprocessor on PeopleNet: crop detected bboxes for C-RADIO. + + Runs after DeepStream inference on peoplenet. Receives the + DS_METADATA ``detections`` dict and the DS_IMAGE + ``decoded_frames`` (RGB uint8 HWC tensor extracted from the + DeepStream pipeline). + + For each detected bounding box the processor: + 1. Crops the bbox region from ``decoded_frames``. + 2. Resizes to 256x256 (bicubic). + 3. Normalises with CLIP mean/std. + + Returns: + * ``pixel_values`` — list of N ``[3, 256, 256]`` float32 tensors + * ``detections`` — the original detection dict (passthrough) + + The list of crops is passed to the cradio ModelOperator, which + batches them via ``stack_tensors_in_dict`` so the TRT engine + receives ``[N, 3, 256, 256]``. + + If no bboxes are detected a single dummy crop is produced so the + downstream model always receives a valid batch. + """ + name = "bbox-crop-postprocessor" + + def __init__(self, config): + # must not exceed cradio max_batch_size + self._max_crops = int(config.get("max_crops", 16)) + self._mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).view(3, 1, 1) + self._std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).view(3, 1, 1) + + def __call__(self, detections, decoded_frames): + # decoded_frames is an RGB uint8 HWC tensor from DS_IMAGE output + if decoded_frames is None: + raise ValueError("decoded_frames is None") + if not isinstance(decoded_frames, torch.Tensor): + try: + decoded_frames = torch.as_tensor(decoded_frames) + except (TypeError, ValueError) as e: + raise ValueError(f"decoded_frames could not be converted to tensor: {e}") from e + if decoded_frames.ndim != 3: + raise ValueError( + f"decoded_frames must be 3D (HWC or CHW), got ndim={decoded_frames.ndim}, shape={decoded_frames.shape}" + ) + if decoded_frames.ndim == 3 and decoded_frames.shape[0] == 3: + decoded_frames = decoded_frames.permute(1, 2, 0) # CHW → HWC + # Expect HWC: (height, width, 3) + if decoded_frames.shape[2] != 3: + raise ValueError( + f"decoded_frames must have 3 channels (RGB), got shape={decoded_frames.shape}" + ) + frame_h, frame_w = decoded_frames.shape[0], decoded_frames.shape[1] + # -- Crop each bbox --------------------------------------------------- + bboxes = detections.get("bboxes", []) + meta_shape = detections.get("shape", [frame_h, frame_w]) + scale_y = frame_h / meta_shape[0] if meta_shape[0] > 0 else 1.0 + scale_x = frame_w / meta_shape[1] if meta_shape[1] > 0 else 1.0 + + crops = [] + for bbox in bboxes[: self._max_crops]: + left, top, right, bottom = bbox + left = int(left * scale_x) + top = int(top * scale_y) + right = int(right * scale_x) + bottom = int(bottom * scale_y) + + left = max(0, min(left, frame_w - 1)) + top = max(0, min(top, frame_h - 1)) + right = max(left + 1, min(right, frame_w)) + bottom = max(top + 1, min(bottom, frame_h)) + + crop = decoded_frames[top:bottom, left:right, :] # HWC + crop = crop.permute(2, 0, 1).float() / 255.0 # CHW [0,1] + crop = F.interpolate( + crop.unsqueeze(0), size=(256, 256), + mode="bicubic", align_corners=False, + ).squeeze(0) # [3, 256, 256] + + mean = self._mean.to(crop.device) + std = self._std.to(crop.device) + crop = (crop - mean) / std + crops.append(crop) + + if len(crops) == 0: + crops = [torch.zeros(3, 256, 256)] + + # Return list of [3, 256, 256] crops — the model operator will + # batch them via stack_tensors_in_dict into [N, 3, 256, 256]. + return (crops, detections) diff --git a/tools/inference_builder/builder/samples/cradio/test_config.json b/tools/inference_builder/builder/samples/cradio/test_config.json new file mode 100644 index 00000000..4296b97c --- /dev/null +++ b/tools/inference_builder/builder/samples/cradio/test_config.json @@ -0,0 +1,47 @@ +[ + { + "name": "cradio_peoplenet_inference", + "description": "Test PeopleNet Transformer + C-RADIOv3-H sequential multi-model pipeline", + "build_args": { + "TEST_APP_NAME": "ds-cradio", + "CACHE_BUSTER": "cradio_peoplenet_test", + "APP_YAML_PATH": "ds_cradio.yaml", + "OUTPUT_DIR": ".", + "PROCESSORS_PATH": "processors.py", + "SERVER_TYPE": "serverless" + }, + "test_config": { + "default_enable": false, + "timeout": 1000, + "env": { + "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes", + "NVIDIA_DRIVER_CAPABILITIES": "compute,utility,video,graphics", + "MODEL_REPO": "/models" + }, + "volumes": { + "/tmp/model-repo": "/models" + }, + "cmd": [ + "--media-url", + "/opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.jpg", + "--mime", + "image/jpeg" + ], + "prerequisite_script": "bash prepare_engine.sh {image_name} /tmp/model-repo", + "models": { + "peoplenet": { + "source": "NGC", + "target": "/tmp/model-repo", + "path": "nvidia/tao/peoplenet_transformer", + "version": "deployable_v1.1", + "configs": "../ds_app/detection/peoplenet_transformer" + }, + "cradio_v3_h": { + "source": "HF", + "target": "/tmp/model-repo", + "path": "nvidia/C-RADIOv3-H" + } + } + } + } +] diff --git a/tools/inference_builder/builder/samples/docker-compose.yml b/tools/inference_builder/builder/samples/docker-compose.yml new file mode 100644 index 00000000..01271415 --- /dev/null +++ b/tools/inference_builder/builder/samples/docker-compose.yml @@ -0,0 +1,155 @@ +################################################################################ +# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +################################################################################ + +services: + ms-nvclip: + build: + context: nvclip + target: builder_base + dockerfile: Dockerfile + image: ms-nvclip + volumes: + - '~/.cache/model-repo/:/workspace/model-repo' + ipc: host + # init: true + ports: + - "8800-8803:8000-8003" + deploy: + resources: + reservations: + devices: + - driver: nvidia + #count: 1 + device_ids: ['0'] + capabilities: [gpu] + environment: + NGC_API_KEY: "" +# DEBUG: 1 + ms-changenet: + build: + context: changenet + target: builder_base + dockerfile: Dockerfile + image: ms-changenet + volumes: + - '~/.cache/model-repo/visual_changenet:/workspace/model_repo/visual_changenet/1' + ipc: host + # init: true + ports: + - "8800-8803:8000-8003" + deploy: + resources: + reservations: + devices: + - driver: nvidia + #count: 1 + device_ids: ['0'] + capabilities: [gpu] + environment: + HTTP_PORT: 8003 + DEBUG: 0 + tao-cv: + user: nvs:1000 + build: + context: tao + target: cv_tao_base + dockerfile: Dockerfile + image: tao-cv + # entrypoint: ["/bin/bash"] + # # Keep container running + # command: ["-c", "sleep infinity"] + volumes: + - '~/.cache/model-repo/:/workspace/.cache/model-repo' + ipc: host + # init: true + ports: + - "8800-8803:8000-8003" + deploy: + resources: + reservations: + devices: + - driver: nvidia + #count: 1 + device_ids: ['0'] + capabilities: [gpu] + environment: + DEBUG: 0 + TAO_MODEL_NAME: rtdetr + dry-run: + user: nvs:1000 + build: + context: dummy + target: inference_base + dockerfile: Dockerfile + image: dry-run + volumes: + - '~/.cache:/workspace/.cache' + ipc: host + # init: true + ports: + - "8800-8803:8000-8003" + deploy: + resources: + reservations: + devices: + - driver: nvidia + #count: 1 + device_ids: ['0'] + capabilities: [gpu] + environment: + DEBUG: 1 + ms-qwen: + build: + context: qwen + target: inference_base + dockerfile: Dockerfile + image: ms-qwen + volumes: + - '~/.cache/model-repo:/workspace/model-repo' + ipc: host + # init: true + ports: + - "8800-8803:8000-8003" + deploy: + resources: + reservations: + devices: + - driver: nvidia + #count: 1 + device_ids: ['0'] + capabilities: [gpu] + environment: + DEBUG: 0 + ms-vllm: + build: + context: vllm + target: inference_base + dockerfile: Dockerfile + image: ms-vllm + volumes: + - '~/.cache/model-repo:/workspace/model-repo' + ipc: host + # init: true + ports: + - "8800-8803:8000-8003" + deploy: + resources: + reservations: + devices: + - driver: nvidia + device_ids: ['0'] + #count: 1 + capabilities: [gpu] + environment: + DEBUG: 0 + # number of codec instances based on the hardware specification + N_CODEC_INSTANCES: 7 diff --git a/tools/inference_builder/builder/samples/ds_app/Dockerfile b/tools/inference_builder/builder/samples/ds_app/Dockerfile new file mode 100644 index 00000000..43363aba --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/Dockerfile @@ -0,0 +1,130 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +# Sample Dockerfile for a DeepStream TAO ds_app-style pipeline using +# a Triton-enabled DeepStream base image. +# +# Target platform: x86_64 (amd64) +# Intended for standard datacenter/workstation GPUs (e.g. A100, H100, B200, +# RTX series) running Linux on x86_64. Uses pre-built PyTorch wheels from +# PyTorch's CUDA index and links against x86_64 system libraries. +FROM "nvcr.io/nvidia/deepstream:9.0-triton-multiarch" AS inference_base + +# Install Python dependencies required by THIS ds_app sample. These +# versions are pinned for reproducibility and can be adjusted for +# other pipelines or environments. +RUN --mount=type=cache,target=/root/.cache/pip \ + pip install \ + --extra-index-url https://download.pytorch.org/whl/cu130 \ + torch==2.9.0+cu130 \ + numpy==1.26.4 \ + omegaconf==2.3.0 \ + transformers==4.54.1 \ + opencv-python + +# Build TensorRT OSS to provide customized plugins (e.g. for ONNX) and +# the trtexec tool. The version and GPU architectures are controlled +# by the ARGs below; update them if you target different CUDA/TRT +# versions or GPU architectures. +ARG TRT_VERSION_MAJOR=10 +ARG TRT_VERSION_MINOR=14 +ARG TRT_VERSION_PATCH=1 +ARG TRT_VERSION_BUILD=48 + +ARG TRT_VERSION_MAJOR_MINOR=$TRT_VERSION_MAJOR.$TRT_VERSION_MINOR +ARG TRT_VERSION_MAJOR_MINOR_PATCH=$TRT_VERSION_MAJOR.$TRT_VERSION_MINOR.$TRT_VERSION_PATCH +ARG TRT_VERSION_FULL=$TRT_VERSION_MAJOR_MINOR_PATCH.$TRT_VERSION_BUILD + +ARG CUDA_VERSION_MAJOR=13 +ARG CUDA_VERSION_MINOR=1 +ARG CUDA_VERSION_PATCH=80 +ARG CUDA_VERSION_BUILD=36836380_0 +ARG CUDA_VERSION_MAJOR_MINOR=$CUDA_VERSION_MAJOR.$CUDA_VERSION_MINOR +ARG CUDA_VERSION_FULL=$CUDA_VERSION_MAJOR_MINOR.$CUDA_VERSION_PATCH.$CUDA_VERSION_BUILD +ARG CUDNN_VERSION=9.17.0.29 + +ENV TRT_VERSION=$TRT_VERSION_FULL+cuda$CUDA_VERSION_FULL + +WORKDIR /tmp +ENV TRT_TAG="release/$TRT_VERSION_MAJOR_MINOR" +RUN mkdir trt_oss_src && \ + cd trt_oss_src && \ + echo "$PWD Building TRT OSS..." && \ + git clone -b $TRT_TAG https://github.com/nvidia/TensorRT TensorRT && \ + cd TensorRT && \ + git submodule update --init --recursive && \ + mkdir -p build && cd build && \ + cmake .. \ + -DGPU_ARCHS="80;86;90;100;120" \ + -DTRT_LIB_DIR=/usr/lib/x86_64-linux-gnu \ + -DTRT_OUT_DIR=/tmp/out \ + -DCUDA_VERSION=$CUDA_VERSION_MAJOR_MINOR \ + -DCUDNN_VERSION=$CUDNN_VERSION \ + && for i in {1..50}; do make -j 12 && break; done +RUN cp /tmp/out/libnvinfer_plugin.so.$TRT_VERSION_MAJOR_MINOR_PATCH /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.$TRT_VERSION_MAJOR_MINOR_PATCH && \ + cp /tmp/out/libnvinfer_plugin_static.a /usr/lib/x86_64-linux-gnu/libnvinfer_plugin_static.a && \ + cp /tmp/out/libnvonnxparser.so.$TRT_VERSION_MAJOR_MINOR_PATCH /usr/lib/x86_64-linux-gnu/libnvonnxparser.so.$TRT_VERSION_MAJOR_MINOR_PATCH && \ + cp /tmp/out/trtexec /usr/local/bin/ && \ + cd ../../../ && \ + rm -rf trt_oss_src + + +# Build DeepStream TAO app post-processors. This must come AFTER the +# DeepStream SDK is installed in the base image. The resulting +# libnvds_infercustomparser_tao.so is copied into the DeepStream lib +# directory for use by ds_app pipelines. Adjust the repo/tag or CUDA +# version if you use different DS/TAO releases. +ENV DS_TAO_APPS_REPO=https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git +ENV DS_TAO_APPS_TAG="master" +ENV DS_SRC_PATH=/opt/nvidia/deepstream/deepstream +ARG CACHE_BUSTER=unique_value_to_bypass_docker_cache +RUN mkdir ds_tao_apps_src && \ + cd ds_tao_apps_src && \ + echo "$PWD Pulling DS TAO apps repo..." && \ + git clone -b $DS_TAO_APPS_TAG $DS_TAO_APPS_REPO ds_tao_apps && \ + cd ds_tao_apps/post_processor && \ + echo "$PWD Building DS TAO apps post processing..." && \ + CUDA_MODULE_LOADING=LAZY CUDA_VER=$CUDA_VERSION_MAJOR.$CUDA_VERSION_MINOR make DS_SRC_PATH=$DS_SRC_PATH && \ + [ -f libnvds_infercustomparser_tao.so ] && echo "$PWD post processing build successful" || echo "$PWD post processing build failed" && \ + cp libnvds_infercustomparser_tao.so /opt/nvidia/deepstream/deepstream/lib && \ + cd ../../../ && \ + rm -rf ds_tao_apps_src + +WORKDIR /workspace + +# Copy the inference flow package generated by Inference Builder. +# This tarball (here: deepstream-app.tgz) is typically produced when +# running the builder with tarball output enabled, and its name should +# match the generated pipeline artifact you want to deploy. +ADD deepstream-app.tgz /workspace + +# Copy test data for CI/regression testing of this sample pipeline. +# This is optional and specific to the ds_app sample setup. +COPY test_data/* /workspace/test_data/ + +## Set Environment variables required for DeepStream runtime. +# Make sure DeepStream GStreamer plugins and libraries are discoverable. +ENV GST_PLUGIN_PATH=/opt/nvidia/deepstream/deepstream/lib/gst-plugins +ENV LD_LIBRARY_PATH=/opt/nvidia/deepstream/deepstream/lib:$LD_LIBRARY_PATH +# Enable adaptive batching in nvstreammux for better GPU utilization. +ENV NVSTREAMMUX_ADAPTIVE_BATCHING=yes +# Optional: enable detailed latency measurement if needed. +#ENV NVDS_ENABLE_LATENCY_MEASUREMENT=1 +#ENV NVDS_ENABLE_COMPONENT_LATENCY_MEASUREMENT=1 + +ENTRYPOINT ["python3", "/workspace/__main__.py"] +#ENTRYPOINT ["/bin/bash"] +CMD [] diff --git a/tools/inference_builder/builder/samples/ds_app/Dockerfile.dgxspark b/tools/inference_builder/builder/samples/ds_app/Dockerfile.dgxspark new file mode 100644 index 00000000..267c98d4 --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/Dockerfile.dgxspark @@ -0,0 +1,149 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Sample Dockerfile for a DeepStream TAO ds_app-style pipeline on DGX +# with Spark, using a Triton-enabled DeepStream base image and a PyTorch +# source image for copying Python packages. +# +# Target platform: aarch64 / NVIDIA server-grade ARM64 (GB300, DGX Spark) +# Intended for NVIDIA ARM64 server systems such as GB300 NVL and DGX Spark +# that run the arm-sbsa DeepStream variant. Like the Tegra variant, PyTorch +# packages are copied from the NGC pytorch image. Unlike Tegra, this targets +# high-performance datacenter ARM64 hardware (SBSA) rather than embedded +# Jetson devices. +FROM "nvcr.io/nvidia/pytorch:25.08-py3" AS pytorch_src + +FROM "nvcr.io/nvidia/deepstream:9.0-triton-sbsa-dgx-spark" AS inference_base + +RUN --mount=type=cache,target=/root/.cache/pip \ + --mount=type=cache,target=/var/cache/apt,sharing=locked \ + --mount=type=cache,target=/var/lib/apt,sharing=locked \ + apt-get update && apt-get install -y --no-install-recommends libyaml-cpp-dev pkg-config cmake + +RUN --mount=type=bind,from=pytorch_src,source=/usr/local/lib/python3.12/dist-packages,target=/tmp/pytorch_src \ + cp -r /tmp/pytorch_src/{torch,functorch,torchgen,torch-*,torchvision*,flash_attn*,tensorrt*} \ + /usr/local/lib/python3.12/dist-packages/ && \ + cp -r /tmp/pytorch_src/triton /usr/local/lib/python3.12/dist-packages/ && \ + sed -i '/self.capability = target.arch/a\ if self.capability == 121: self.capability = 120' /usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py +COPY --from=pytorch_src /usr/local/lib/libnvpl* /usr/local/lib/ + +RUN --mount=type=cache,target=/root/.cache/pip \ + pip install \ + jinja2==3.1.6 \ + numpy==1.26.4 \ + omegaconf==2.3.0 \ + networkx==3.5 \ + sympy==1.14.0 \ + pillow==11.3 \ + transformers==4.54.1 \ + opencv-python + +# Build TensorRT OSS to provide customized plugins (e.g. for ONNX) and +# the trtexec tool. The version and GPU architectures are controlled +# by the ARGs below; update them if you target different CUDA/TRT +# versions or GPU architectures. +ARG TRT_VERSION_MAJOR=10 +ARG TRT_VERSION_MINOR=13 +ARG TRT_VERSION_PATCH=0 +ARG TRT_VERSION_BUILD=0 + +ARG TRT_VERSION_MAJOR_MINOR=$TRT_VERSION_MAJOR.$TRT_VERSION_MINOR +ARG TRT_VERSION_MAJOR_MINOR_PATCH=$TRT_VERSION_MAJOR.$TRT_VERSION_MINOR.$TRT_VERSION_PATCH +ARG TRT_VERSION_FULL=$TRT_VERSION_MAJOR_MINOR_PATCH.$TRT_VERSION_BUILD + +ARG CUDA_VERSION_MAJOR=13 +ARG CUDA_VERSION_MINOR=0 +ARG CUDA_VERSION_PATCH=0 +ARG CUDA_VERSION_BUILD=1 +ARG CUDA_VERSION_MAJOR_MINOR=$CUDA_VERSION_MAJOR.$CUDA_VERSION_MINOR +ARG CUDA_VERSION_FULL=$CUDA_VERSION_MAJOR_MINOR.$CUDA_VERSION_PATCH.$CUDA_VERSION_BUILD +ARG CUDNN_VERSION=9.12.0.46-1 + +ENV TRT_VERSION=$TRT_VERSION_FULL+cuda$CUDA_VERSION_FULL + +WORKDIR /tmp +ENV TRT_TAG="release/$TRT_VERSION_MAJOR_MINOR" +RUN mkdir trt_oss_src && \ + cd trt_oss_src && \ + echo "$PWD Building TRT OSS..." && \ + git clone -b $TRT_TAG https://github.com/nvidia/TensorRT TensorRT && \ + cd TensorRT && \ + git submodule update --init --recursive && \ + mkdir -p build && cd build && \ + cmake .. \ + -DGPU_ARCHS="110;120;121" \ + -DTRT_LIB_DIR=/usr/lib/aarch64-linux-gnu \ + -DTRT_OUT_DIR=/tmp/out \ + -DCUDA_VERSION=$CUDA_VERSION_MAJOR_MINOR \ + -DCUDNN_VERSION=$CUDNN_VERSION \ + -DCMAKE_C_COMPILER=/usr/bin/gcc \ + && for i in {1..50}; do make -j 12 && break; done +RUN echo "Listing /tmp/out:" && ls -l /tmp/out/libnvinfer_plugin.so.* +RUN cp /tmp/out/libnvinfer_plugin.so.$TRT_VERSION_MAJOR_MINOR_PATCH /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so.$TRT_VERSION_MAJOR_MINOR_PATCH && \ + cp /tmp/out/libnvinfer_plugin_static.a /usr/lib/aarch64-linux-gnu/libnvinfer_plugin_static.a && \ + cp /tmp/out/libnvonnxparser.so.$TRT_VERSION_MAJOR_MINOR_PATCH /usr/lib/aarch64-linux-gnu/libnvonnxparser.so.$TRT_VERSION_MAJOR_MINOR_PATCH && \ + cp /tmp/out/trtexec /usr/local/bin/ && \ + cd ../../../ && \ + rm -rf trt_oss_src + + +# Build DeepStream TAO app post-processors. This must come AFTER the +# DeepStream SDK is installed in the base image. The resulting +# libnvds_infercustomparser_tao.so is copied into the DeepStream lib +# directory for use by ds_app pipelines. Adjust the repo/tag or CUDA +# version if you use different DS/TAO releases. +ENV DS_TAO_APPS_REPO=https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git +ENV DS_TAO_APPS_TAG="master" +ENV DS_SRC_PATH=/opt/nvidia/deepstream/deepstream +ARG CACHE_BUSTER=unique_value_to_bypass_docker_cache +RUN mkdir ds_tao_apps_src && \ + cd ds_tao_apps_src && \ + echo "$PWD Pulling DS TAO apps repo..." && \ + git clone -b $DS_TAO_APPS_TAG $DS_TAO_APPS_REPO ds_tao_apps && \ + cd ds_tao_apps/post_processor && \ + echo "$PWD Building DS TAO apps post processing..." && \ + CUDA_MODULE_LOADING=LAZY CUDA_VER=$CUDA_VERSION_MAJOR_MINOR make DS_SRC_PATH=$DS_SRC_PATH && \ + [ -f libnvds_infercustomparser_tao.so ] && echo "$PWD post processing build successful" || echo "$PWD post processing build falied" && \ + cp libnvds_infercustomparser_tao.so /opt/nvidia/deepstream/deepstream/lib && \ + cd ../../../ && \ + rm -rf ds_tao_apps_src + +RUN rm -rf ~/.cache/gstreamer-1.0 + +WORKDIR /workspace + +# Copy the inference flow package generated by Inference Builder. +# This tarball (here: deepstream-app.tgz) is typically produced when +# running the builder with tarball output enabled, and its name should +# match the generated pipeline artifact you want to deploy. +ADD deepstream-app.tgz /workspace + +# Copy test data for CI/regression testing of this sample pipeline. +# This is optional and specific to the ds_app sample setup. +COPY test_data/* /workspace/test_data/ + +## Set Environment variables required for DeepStream runtime. +# Make sure DeepStream GStreamer plugins and libraries are discoverable. +ENV GST_PLUGIN_PATH=/opt/nvidia/deepstream/deepstream/lib/gst-plugins +ENV LD_LIBRARY_PATH=/opt/nvidia/deepstream/deepstream/lib:$LD_LIBRARY_PATH +# Enable adaptive batching in nvstreammux for better GPU utilization. +ENV NVSTREAMMUX_ADAPTIVE_BATCHING=yes +# Optional: enable detailed latency measurement if needed. +#ENV NVDS_ENABLE_LATENCY_MEASUREMENT=1 +#ENV NVDS_ENABLE_COMPONENT_LATENCY_MEASUREMENT=1 + +ENTRYPOINT ["python3", "/workspace/__main__.py"] +#ENTRYPOINT ["/bin/bash"] +CMD [] diff --git a/tools/inference_builder/builder/samples/ds_app/Dockerfile.tegra b/tools/inference_builder/builder/samples/ds_app/Dockerfile.tegra new file mode 100644 index 00000000..a1d593c1 --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/Dockerfile.tegra @@ -0,0 +1,147 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Sample Dockerfile for a DeepStream TAO ds_app-style pipeline on Tegra +# (Jetson), using a Triton-enabled DeepStream base image and a PyTorch +# source image for copying Python packages for aarch64. +# +# Target platform: aarch64 / NVIDIA Tegra (Jetson) +# Intended for Jetson devices (e.g. Orin, AGX Orin, Thor) running JetPack / +# Tegra Linux. PyTorch and related packages are copied from the NGC +# pytorch image rather than installed via pip, since Tegra-compatible +# wheels are not available on the standard PyTorch index. +FROM "nvcr.io/nvidia/pytorch:25.08-py3" AS pytorch_src + +# Pull the base image from Deepstream Triton multiarch image. +FROM "nvcr.io/nvidia/deepstream:9.0-triton-multiarch" AS inference_base + +RUN --mount=type=cache,target=/root/.cache/pip \ + --mount=type=cache,target=/var/cache/apt,sharing=locked \ + --mount=type=cache,target=/var/lib/apt,sharing=locked \ + apt-get update && apt-get install -y --no-install-recommends libyaml-cpp-dev pkg-config cmake + +RUN --mount=type=bind,from=pytorch_src,source=/usr/local/lib/python3.12/dist-packages,target=/tmp/pytorch_src \ + cp -r /tmp/pytorch_src/{torch,functorch,torchgen,torch-*,torchvision*,flash_attn*,tensorrt*} \ + /usr/local/lib/python3.12/dist-packages/ && \ + cp -r /tmp/pytorch_src/triton /usr/local/lib/python3.12/dist-packages/ && \ + sed -i '/self.capability = target.arch/a\ if self.capability == 121: self.capability = 120' /usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py +COPY --from=pytorch_src /usr/local/lib/libnvpl* /usr/local/lib/ + +RUN --mount=type=cache,target=/root/.cache/pip \ + pip install \ + jinja2==3.1.6 \ + numpy==1.26.4 \ + omegaconf==2.3.0 \ + networkx==3.5 \ + sympy==1.14.0 \ + pillow==11.3 \ + transformers==4.54.1 \ + opencv-python + +# Build TensorRT OSS to provide customized plugins (e.g. for ONNX) and +# the trtexec tool. The version and GPU architectures are controlled +# by the ARGs below; update them if you target different CUDA/TRT +# versions or GPU architectures. +ARG TRT_VERSION_MAJOR=10 +ARG TRT_VERSION_MINOR=13 +ARG TRT_VERSION_PATCH=0 +ARG TRT_VERSION_BUILD=0 + +ARG TRT_VERSION_MAJOR_MINOR=$TRT_VERSION_MAJOR.$TRT_VERSION_MINOR +ARG TRT_VERSION_MAJOR_MINOR_PATCH=$TRT_VERSION_MAJOR.$TRT_VERSION_MINOR.$TRT_VERSION_PATCH +ARG TRT_VERSION_FULL=$TRT_VERSION_MAJOR_MINOR_PATCH.$TRT_VERSION_BUILD + +ARG CUDA_VERSION_MAJOR=13 +ARG CUDA_VERSION_MINOR=0 +ARG CUDA_VERSION_PATCH=0 +ARG CUDA_VERSION_BUILD=1 +ARG CUDA_VERSION_MAJOR_MINOR=$CUDA_VERSION_MAJOR.$CUDA_VERSION_MINOR +ARG CUDA_VERSION_FULL=$CUDA_VERSION_MAJOR_MINOR.$CUDA_VERSION_PATCH.$CUDA_VERSION_BUILD +ARG CUDNN_VERSION=9.12.0.46-1 + +ENV TRT_VERSION=$TRT_VERSION_FULL+cuda$CUDA_VERSION_FULL + +WORKDIR /tmp +ENV TRT_TAG="release/$TRT_VERSION_MAJOR_MINOR" +RUN mkdir trt_oss_src && \ + cd trt_oss_src && \ + echo "$PWD Building TRT OSS..." && \ + git clone -b $TRT_TAG https://github.com/nvidia/TensorRT TensorRT && \ + cd TensorRT && \ + git submodule update --init --recursive && \ + mkdir -p build && cd build && \ + cmake .. \ + -DGPU_ARCHS="110;120;121" \ + -DTRT_LIB_DIR=/usr/lib/aarch64-linux-gnu \ + -DTRT_OUT_DIR=/tmp/out \ + -DCUDA_VERSION=$CUDA_VERSION_MAJOR_MINOR \ + -DCUDNN_VERSION=$CUDNN_VERSION \ + -DCMAKE_C_COMPILER=/usr/bin/gcc \ + && for i in {1..50}; do make -j 12 && break; done +RUN echo "Listing /tmp/out:" && ls -l /tmp/out/libnvinfer_plugin.so.* +RUN cp /tmp/out/libnvinfer_plugin.so.$TRT_VERSION_MAJOR_MINOR_PATCH /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so.$TRT_VERSION_MAJOR_MINOR_PATCH && \ + cp /tmp/out/libnvinfer_plugin_static.a /usr/lib/aarch64-linux-gnu/libnvinfer_plugin_static.a && \ + cp /tmp/out/libnvonnxparser.so.$TRT_VERSION_MAJOR_MINOR_PATCH /usr/lib/aarch64-linux-gnu/libnvonnxparser.so.$TRT_VERSION_MAJOR_MINOR_PATCH && \ + cp /tmp/out/trtexec /usr/local/bin/ && \ + cd ../../../ && \ + rm -rf trt_oss_src + + +# Build DeepStream TAO app post-processors. This must come AFTER the +# DeepStream SDK is installed in the base image. The resulting +# libnvds_infercustomparser_tao.so is copied into the DeepStream lib +# directory for use by ds_app pipelines. Adjust the repo/tag or CUDA +# version if you use different DS/TAO releases. +ENV DS_TAO_APPS_REPO=https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git +ENV DS_TAO_APPS_TAG="master" +ENV DS_SRC_PATH=/opt/nvidia/deepstream/deepstream +ARG CACHE_BUSTER=unique_value_to_bypass_docker_cache +RUN mkdir ds_tao_apps_src && \ + cd ds_tao_apps_src && \ + echo "$PWD Pulling DS TAO apps repo..." && \ + git clone -b $DS_TAO_APPS_TAG $DS_TAO_APPS_REPO ds_tao_apps && \ + cd ds_tao_apps/post_processor && \ + echo "$PWD Building DS TAO apps post processing..." && \ + CUDA_MODULE_LOADING=LAZY CUDA_VER=$CUDA_VERSION_MAJOR_MINOR make DS_SRC_PATH=$DS_SRC_PATH && \ + [ -f libnvds_infercustomparser_tao.so ] && echo "$PWD post processing build successful" || echo "$PWD post processing build falied" && \ + cp libnvds_infercustomparser_tao.so /opt/nvidia/deepstream/deepstream/lib && \ + cd ../../../ && \ + rm -rf ds_tao_apps_src + +WORKDIR /workspace + +# Copy the inference flow package generated by Inference Builder. +# This tarball (here: deepstream-app.tgz) is typically produced when +# running the builder with tarball output enabled, and its name should +# match the generated pipeline artifact you want to deploy. +ADD deepstream-app.tgz /workspace + +# Copy test data for CI/regression testing of this sample pipeline. +# This is optional and specific to the ds_app sample setup. +COPY test_data/* /workspace/test_data/ + +## Set Environment variables required for DeepStream runtime. +# Make sure DeepStream GStreamer plugins and libraries are discoverable. +ENV GST_PLUGIN_PATH=/opt/nvidia/deepstream/deepstream/lib/gst-plugins +ENV LD_LIBRARY_PATH=/opt/nvidia/deepstream/deepstream/lib:$LD_LIBRARY_PATH +# Enable adaptive batching in nvstreammux for better GPU utilization. +ENV NVSTREAMMUX_ADAPTIVE_BATCHING=yes +# Optional: enable detailed latency measurement if needed. +#ENV NVDS_ENABLE_LATENCY_MEASUREMENT=1 +#ENV NVDS_ENABLE_COMPONENT_LATENCY_MEASUREMENT=1 + +ENTRYPOINT ["python3", "/workspace/__main__.py"] +#ENTRYPOINT ["/bin/bash"] +CMD [] diff --git a/tools/inference_builder/builder/samples/ds_app/README.md b/tools/inference_builder/builder/samples/ds_app/README.md new file mode 100644 index 00000000..ff66dedc --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/README.md @@ -0,0 +1,251 @@ +# DeepStream Application Samples + +This directory contains sample configurations demonstrating how to build DeepStream applications using Inference Builder. + +While the samples support Ampere, Hopper, and Blackwell architectures, the model and the backend set the real hardware requirements. + +**Platform Support:** All ds_app samples are compatible with both x86-64 systems and NVIDIA Jetson Thor device. + +## Models used in the samples + +All the models can be downloaded from NGC and certain models need active subscription. + +If you don't have NGC CLI installed, please download and install it from [this page](https://org.ngc.nvidia.com/setup/installers/cli). + +### Image Classification +- **PCB Classification**: [PCB Classification Model](https://catalog.ngc.nvidia.com/orgs/nvaie/models/pcbclassification) + +### Visual Change Detection +- **Visual Changenet Classification**: [Visual Changenet Classification Model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/visual_changenet_classification) + +### Semantic Segmentation +- **CitySemSegFormer**: [CitySemSegFormer Model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer) + +### Instance Segmentation +- **Mask2Fomer**: [Masked-attention Mask Transformer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/mask2former) + +### Grounding Dino +- **Grounding DINO**: [Grounding DINO Model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/grounding_dino) +- **Mask Grounding DINO**: [Mask Grounding DINO Model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/mask_grounding_dino) + +### RT-DETR Detector +- **RT-DETR**: [TrafficCamNet Lite](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/trafficcamnet_transformer_lite) +- **RT-DETR**: [PeopleNet Transformer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplenet_transformer) + +## Getting Started + +Each sample includes its own README with specific instructions for building and running the DeepStream application. Please refer to the individual sample directories for detailed setup and usage instructions. + +The provided samples can serve as references for creating inference pipelines across a wide range of TAO computer vision models. Each model falls into one of the following categories, with corresponding instructions available: + +- classification +- detection +- segmentation +- GDINO (Open vocabulary detection and segmentation) + +Required model files including the onnx, nvinfer configuration, preprocess configuration can be found from NGC model repository or exported through TAO Finetune Microservice. + +## Configurable Parameters for the DeepStream Backend + +You can enable these features under the [`parameters section of the configuration file`](#complete-configuration-example). + +### Inference Configuration + +The core inference configuration that specifies the model and inference parameters. + +```yaml +parameters: + infer_config_path: + - nvdsinfer_config.yaml # Path to the nvinfer configuration file +``` + +### Preprocess Configuration + +Used to define preprocessing operations that run before inference. This is required when non-image tensors must be injected into the pipeline (e.g., for models that require additional metadata or custom tensor inputs). Refer to [nvdspreprocess plugin documentation](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvdspreprocess.html) for more details. + +```yaml +parameters: + preprocess_config_path: + - nvdspreprocess_config.yaml # Path to the nvdspreprocess configuration file +``` + +### Tracker Configuration + +Used for object tracking along with inference using nvtracker. This enables multi-object tracking across frames. + +```yaml +tracker_config: + ll_lib_file: /opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so # Path to the tracker library + ll_config_file: /opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/config_tracker_IOU.yml # Path to tracker configuration file + width: 1920 # Tracker width + height: 1088 # Tracker height + display_tracking_id: true # Display tracking id in object text +``` + +### Analytics Configuration + +Used for object activity analysis such as line-crossing, ROI enetering/exiting, crowding and direction detection. + +```yaml +analytics_config: + config_file_path: /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-nvdsanalytics-test/config_nvdsanalytics.txt # Path to the nvdsanalytics configuration file + gpu_id: 0 # GPU core id for analytics running +``` + + +### Message Broker Configuration + +Used to send inference data over the cloud using nvmsgbroker and nvmsgconverter. Supports various message brokers like Kafka, MQTT, etc. + +> **Note:** To use message broker functionality, you need to set up a broker service first and then update the parameters below accordingly. + +```yaml +msgbroker_config: + msgbroker_proto_lib_path: /opt/nvidia/deepstream/deepstream/lib/libnvds_kafka_proto.so # Path to the message broker protocol library + msgbroker_conn_str: localhost:9092 # Message broker connection string (host:port) + msgbroker_topic: ds_app # Topic name for publishing inference results + msgconv_config_path: /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-test5/configs/dstest5_msgconv_sample_config.txt # Path to message converter configuration + msgconv_payload_type: 2 # Payload type for message conversion (0=DeepStream JSON, 1=DeepStream Minimal JSON, 2=DeepStream Protobuf) + msgconv_msg2p_new_api: false # Use new API which supports publishing multiple payloads using NvDsFrameMeta + msgconv_frame_interval: 1 # Frame interval at which payload is generated (every Nth frame) + msgconv_msg2p_lib: /opt/nvidia/deepstream/deepstream/lib/libnvds_msgconv.so # Path to the message converter library +``` + +### Render Configuration + +To visually see the inference output, you have two options: + +#### 1. Display on Host Machine Terminal + +```yaml +render_config: + enable_display: true # Enable display output on host machine + enable_osd: true # Enable on-screen display with bounding boxes and metadata using nvdsosd +``` + +#### 2. Stream to RTSP Port + +```yaml +render_config: + enable_stream: true # Enable RTSP streaming output + enable_osd: true # Enable on-screen display with bounding boxes and metadata using nvdsosd + rtsp_mount_point: /ds_app # RTSP mount point for the stream + rtsp_port: 8554 # RTSP server port number +``` + +This will start RTSP streaming at `rtsp://localhost:8554/ds_app` + +### Performance Measurement Configuration + +Used to monitor the performance of the inference pipeline and individual components. + +```yaml +perf_config: + enable_fps_logs: true # Enable FPS logging for each source on the host machine terminal + enable_latency_logs: true # Enable element-specific latency logging on host machine terminal +``` + +>**Note:** For latency measurements to work properly, make sure to export these environment variables: + +```bash +export NVDS_ENABLE_LATENCY_MEASUREMENT=1 +export NVDS_ENABLE_COMPONENT_LATENCY_MEASUREMENT=1 +``` + +### KITTI Data Configuration + +When enabled, this configuration dumps inference and tracking data in KITTI format to the specified directories. + +```yaml +kitti_output_path: + infer: /workspace/models/infer-kitti-dump/ # Directory for inference KITTI data + tracker: /workspace/models/tracker-kitti-dump/ # Directory for tracking KITTI data +``` + +> **Note:** Make sure to create the directories mentioned above before running the application to ensure proper KITTI data dumping. + +### Resolution Configuration + +When specified in the configuration, the input stream will be resized to the specified dimensions before processing. + +```yaml +resize_video: [height, width] # Format: [height, width] +resize_video: [1080, 1920] # Example: Resize to 1080p (1920x1080) +``` + +### Timeout Configuration + +Inference timeout ensures the inference process completes within a specified time limit. The timeout value is specified in seconds. + +```yaml +parameters: + inference_timeout: 5 # Timeout in seconds for inference operations +``` + +Batch timeout specifies the time in microseconds to wait for batched buffers to be sent out after the first buffer is available. + +- **Default value:** `1000 * max_batch_size` microseconds +- **Set to -1:** Wait infinitely for batch formation + +> **Warning:** If your `max_batch_size` in the models section is greater than the number of streams you're running, do not set the timeout to -1. This will cause the pipeline to wait infinitely for batch formation and become unresponsive. In such scenarios, set an appropriate timeout value. + +```yaml +parameters: + batch_timeout: 33000 # Timeout in microseconds for batching +``` + +### [Complete Configuration Example](#complete-configuration-example) + +Here's a complete example showing all the features enabled: + +```yaml +parameters: + # Core inference configuration + infer_config_path: + - nvdsinfer_config.yaml # Path to nvinfer configuration file + + # Resolution configuration + resize_video: [1080, 1920] # Resize input stream to 1080p (1920x1080) + + # Object tracking configuration + tracker_config: + ll_lib_file: /opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so # Tracker library path + ll_config_file: /opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/config_tracker_IOU.yml # Tracker config path + + # Message broker configuration for cloud integration + # Note: To use message broker functionality, you need to set up a broker service first and then update the parameters below accordingly. + msgbroker_config: + msgbroker_proto_lib_path: /opt/nvidia/deepstream/deepstream/lib/libnvds_kafka_proto.so # Protocol library + msgbroker_conn_str: localhost:9092 # Broker connection string + msgbroker_topic: ds_app # Topic for publishing results + msgconv_config_path: /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-test5/configs/dstest5_msgconv_sample_config.txt # Message converter config + msgconv_payload_type: 2 # Payload type for message conversion (0=DeepStream JSON, 1=DeepStream Minimal JSON, 2=DeepStream Protobuf) + msgconv_msg2p_new_api: false # Use new API which supports publishing multiple payloads using NvDsFrameMeta + msgconv_frame_interval: 1 # Frame interval at which payload is generated (every Nth frame) + msgconv_msg2p_lib: /opt/nvidia/deepstream/deepstream/lib/libnvds_msgconv.so # Path to the message converter library + + # Rendering and display configuration + # Choose your output method: enable_display (local display), enable_stream (RTSP streaming), or both + render_config: + enable_display: true # Enable display output + enable_osd: true # Enable on-screen display with metadata + enable_stream: true # Enable RTSP streaming + rtsp_mount_point: /ds_app # RTSP mount point + rtsp_port: 8554 # RTSP server port + + # Performance monitoring configuration + perf_config: + enable_fps_logs: true # Enable FPS logging + enable_latency_logs: true # Enable latency logging + + # KITTI dump configuration + kitti_output_path: + infer: /workspace/models/infer-kitti-dump/ # Directory for inference KITTI data + tracker: /workspace/models/tracker-kitti-dump/ # Directory for tracking KITTI data + + # Inference timeout configuration + inference_timeout: 5 # Timeout in seconds for inference operations + + # Batch timeout configuration + batch_timeout: 33000 # Timeout in microseconds to wait for batching operation +``` diff --git a/tools/inference_builder/builder/samples/ds_app/classification/README.md b/tools/inference_builder/builder/samples/ds_app/classification/README.md new file mode 100644 index 00000000..83f75a8b --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/classification/README.md @@ -0,0 +1,201 @@ +## Introduction + +This sample demonstrates how to build a deepstream application with Inference Builder using classification models: +1. changenet-classify: visual_changenet_nvpcb_deployable_v1.0 from https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/visual_changenet_classification +2. pcbclassification: deployable_v1.1 from https://catalog.ngc.nvidia.com/orgs/nvaie/models/pcbclassification + +## Prerequisites + +**Note:** Make sure you are in the root directory (`path/to/inference_builder`) to execute the commands in this README. All relative paths and commands assume you are running from the inference_builder root directory. Also ensure that your virtual environment is activated before running any commands. + +Model files are loaded from '/workspace/models/{MODEL_NAME}' within the container, thus the volume must be correctly mapped from the host. +You need to export MODEL_REPO environment variable to the path where you want to store the model files. + +```bash +mkdir ~/.cache/model-repo/ +sudo chmod -R 777 ~/.cache/model-repo/ +export MODEL_REPO=~/.cache/model-repo +``` + +You need first download the model files from the NGC catalog and put them in the $MODEL_REPO/{model-name}/ directory, then copy the other required configurations to the same directory: + +**Note:** If NGC commands fail, make sure you have access to the models you are trying to download. Some models require an active subscription. Ensure NGC is set up properly, or alternatively try using the NGC web UI to directly download the model from the links provided [here](../README.md#models-used-in-the-samples) + +### For pcbclassification sample + +Please use `pcbclassification` as the directory: + +```bash +ngc registry model download-version "nvaie/pcbclassification:deployable_v1.1" +# Move the folder to the model-repo directory, and the sample uses ~/.cache/model-repo by default +mv pcbclassification_vdeployable_v1.1 $MODEL_REPO/pcbclassification +chmod 777 $MODEL_REPO/pcbclassification +cp -r builder/samples/ds_app/classification/pcbclassification/* $MODEL_REPO/pcbclassification/ +``` + +### For changenet-classify sample + +Please use `changenet-classify` as the directory: + +```bash +ngc registry model download-version "nvidia/tao/visual_changenet_classification:visual_changenet_nvpcb_deployable_v1.0" +mv visual_changenet_classification_vvisual_changenet_nvpcb_deployable_v1.0 $MODEL_REPO/changenet-classify/ +# Move the folder to the model-repo directory, and the sample uses ~/.cache/model-repo by default +chmod 777 $MODEL_REPO/changenet-classify +cp -r builder/samples/ds_app/classification/changenet-classify/* $MODEL_REPO/changenet-classify/ +``` + +## Generate the DeepStream Application Package and Build Container Image + +Assume you've followed the [top level instructions](../../../README.md#getting-started) to set up the environment and be sure you're in the inference_builder folder, then activate your virtual environment: + +```bash +source .venv/bin/activate +``` + +### For pcbclassification sample + +Please use ds_pcb.yaml as the configuration: + +#### For x86 Architecture + +```bash +python builder/main.py builder/samples/ds_app/classification/ds_pcb.yaml \ + -o builder/samples/ds_app \ + --server-type serverless \ + -t \ +&& docker build -t deepstream-app builder/samples/ds_app +``` + +#### For Tegra Architecture + +```bash +python builder/main.py builder/samples/ds_app/classification/ds_pcb.yaml \ + -o builder/samples/ds_app \ + --server-type serverless \ + -t \ +&& docker build -t deepstream-app \ + -f builder/samples/ds_app/Dockerfile.tegra \ + builder/samples/ds_app +``` + +#### For Dgx Spark + +```bash +python builder/main.py builder/samples/ds_app/classification/ds_pcb.yaml \ + -o builder/samples/ds_app \ + --server-type serverless \ + -t \ +&& docker build \ + -t deepstream-app \ + -f builder/samples/ds_app/Dockerfile.dgxspark \ + builder/samples/ds_app +``` + +### For changenet-classify sample + +Please use ds_changenet.yaml as the configuration: + +#### For x86 Architecture + +```bash +python builder/main.py builder/samples/ds_app/classification/ds_changenet.yaml \ + -o builder/samples/ds_app \ + --server-type serverless \ + -t \ +&& docker build -t deepstream-app builder/samples/ds_app +``` + +#### For Tegra Architecture + +```bash +python builder/main.py builder/samples/ds_app/classification/ds_changenet.yaml \ + -o builder/samples/ds_app \ + --server-type serverless \ + -t \ +&& docker build \ + -t deepstream-app \ + -f builder/samples/ds_app/Dockerfile.tegra \ + builder/samples/ds_app +``` + +#### For Dgx Spark + +```bash +python builder/main.py builder/samples/ds_app/classification/ds_changenet.yaml \ + -o builder/samples/ds_app \ + --server-type serverless \ + -t \ +&& docker build \ + -t deepstream-app \ + -f builder/samples/ds_app/Dockerfile.dgxspark \ + builder/samples/ds_app +``` + +## Run the deepstream app with image inputs: + +**Note:** The TensorRT engine is generated during the first time run and it takes several minutes. + +**Note:** You can optionally set the `$SAMPLE_INPUT` environment variable to point to your input media directory if you want to perform inference on media files stored on your host machine. + +**Note:** To save the inference results, append the `-s result.json` option to your `docker run` command. + +```bash +# Update this with your actual samples directory path +export SAMPLE_INPUT=/path/to/your/samples/directory +``` + +### For pcbclassification sample + +```bash +# media-url: the path or URL to the input media. +# mime: the media type (e.g., "video/mp4" or "image/jpeg"). +export SAMPLE_INPUT=$(realpath builder/samples/ds_app/classification/sample-inputs/) +docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ + -v $SAMPLE_INPUT:/sample_input \ + -v $MODEL_REPO:/workspace/models \ + -v /tmp/.X11-unix/:/tmp/.X11-unix \ + -e DISPLAY=$DISPLAY \ + -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ + deepstream-app \ + --media-url /sample_input/IMG_0002_C71.png \ + --mime image/png +``` + +### For changenet-classify sample + +**Note:** For changenet classification model, it requires two images as input. Sample test images (`pass_0.png` and `pass_1.png`) are embedded in the Docker image at `/workspace/test_data/` for testing purposes. + +**Using embedded test images (recommended for testing):** + +```bash +docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ + -v $MODEL_REPO:/workspace/models \ + -v /tmp/.X11-unix/:/tmp/.X11-unix \ + -e DISPLAY=$DISPLAY \ + -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ + deepstream-app \ + --media-url /workspace/test_data/pass_0.png /workspace/test_data/pass_1.png \ + --mime image/png image/png +``` + +**Using your own images from host:** + +```bash +# media-url: the path or URL to the input media. +# mime: the media type (e.g., "video/mp4" or "image/jpeg"). +export SAMPLE_INPUT=/path/to/your/images +docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ + -v $SAMPLE_INPUT:/sample_input \ + -v $MODEL_REPO:/workspace/models \ + -v /tmp/.X11-unix/:/tmp/.X11-unix \ + -e DISPLAY=$DISPLAY \ + -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ + deepstream-app \ + --media-url /sample_input/ /sample_input/ \ + --mime image/png image/png +``` + +For classification samples, the output is a list of labels defined in labels.txt. e.g, for pcbclassification model, the output label will be "missing" if there is a part missing from the input image. + +**Note:** You may see `pybind11::handle::dec_ref()` related errors after the inference is completed. This is a known issue and will be fixed in the next release. \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/labels.txt b/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/labels.txt new file mode 100644 index 00000000..8be0ce5e --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/labels.txt @@ -0,0 +1,2 @@ +notdefect +defect diff --git a/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdsinfer_config.yaml new file mode 100644 index 00000000..e7a51da0 --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdsinfer_config.yaml @@ -0,0 +1,29 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +property: + gie-unique-id: 1 + net-scale-factor: 0.00392156862745098 + onnx-file: changenet-classify.onnx + model-engine-file: changenet-classify.onnx_b2_gpu0_fp16.engine + network-mode: 2 + network-type: 1 + offsets: 127.5;127.5;127.5 + num-detected-classes: 2 + input-tensor-from-meta: 1 + infer-dims: 3;512;128 + classifier-threshold: 0 + labelfile-path: labels.txt + custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so diff --git a/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdspreprocess_config_0.yaml b/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdspreprocess_config_0.yaml new file mode 100644 index 00000000..1d3d860f --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdspreprocess_config_0.yaml @@ -0,0 +1,46 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +property: + tensor-name: input_1 + processing-width: 128 + processing-height: 512 + network-input-shape: 2;3;512;128 + network-color-format: 0 + network-input-order: 0 + tensor-data-type: 0 + maintain-aspect-ratio: 1 + symmetric-padding: 1 + scaling-pool-memory-type: 0 + scaling-pool-compute-hw: 0 + scaling-filter: 0 + scaling-buf-pool-size: 6 + tensor-buf-pool-size: 6 + target-unique-ids: 1 + process-on-frame: 1 + unique-id: 5 + custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/gst-plugins/libcustom2d_preprocess.so + custom-tensor-preparation-function: CustomTensorPreparation + enable: 1 + +user-configs: + pixel-normalization-factor: 0.007843137 + offsets: 127.5;127.5;127.5 + +group: + - src-ids: 0;2;4;6;8;10;12;14 + custom-input-transformation-function: CustomAsyncTransformation + process-on-roi: 0 + diff --git a/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdspreprocess_config_1.yaml b/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdspreprocess_config_1.yaml new file mode 100644 index 00000000..c700c9b6 --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdspreprocess_config_1.yaml @@ -0,0 +1,46 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +property: + tensor-name: input_2 + processing-width: 128 + processing-height: 512 + network-input-shape: 2;3;512;128 + network-color-format: 0 + network-input-order: 0 + tensor-data-type: 0 + maintain-aspect-ratio: 1 + symmetric-padding: 1 + scaling-pool-memory-type: 0 + scaling-pool-compute-hw: 0 + scaling-filter: 0 + scaling-buf-pool-size: 6 + tensor-buf-pool-size: 6 + target-unique-ids: 1 + process-on-frame: 1 + unique-id: 5 + custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/gst-plugins/libcustom2d_preprocess.so + custom-tensor-preparation-function: CustomTensorPreparation + enable: 1 + +user-configs: + pixel-normalization-factor: 0.007843137 + offsets: 127.5;127.5;127.5 + +group: + - src-ids: 1;3;5;7;9;11;13;15 + custom-input-transformation-function: CustomAsyncTransformation + process-on-roi: 0 + diff --git a/tools/inference_builder/builder/samples/ds_app/classification/ds_changenet.yaml b/tools/inference_builder/builder/samples/ds_app/classification/ds_changenet.yaml new file mode 100644 index 00000000..c43b2d04 --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/classification/ds_changenet.yaml @@ -0,0 +1,59 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +name: "deepstream-app" +model_repo: "/workspace/models" + +input: +- name: media_url + data_type: TYPE_CUSTOM_LIST + dims: [ -1 ] +- name: mime + data_type: TYPE_CUSTOM_DS_MIME + dims: [ -1 ] + +output: + - name: output + data_type: TYPE_CUSTOM_DS_METADATA + dims: [ -1 ] + +models: +- name: changenet-classify + backend: deepstream/nvinfer + max_batch_size: 2 + input: + - name: images + data_type: TYPE_CUSTOM_DS_IMAGE + dims: [-1, -1, 3] + optional: true + - name: media_url + data_type: TYPE_CUSTOM_DS_MEDIA_URL + dims: [ -1 ] + - name: mime + data_type: TYPE_CUSTOM_DS_MIME + dims: [ -1 ] + optional: false + output: + - name: output + data_type: TYPE_CUSTOM_DS_METADATA + dims: [ -1 ] + parameters: + infer_config_path: + - nvdsinfer_config.yaml + preprocess_config_path: + - nvdspreprocess_config_0.yaml + - nvdspreprocess_config_1.yaml + inference_timeout: "$INFERENCE_TIMEOUT|3" diff --git a/tools/inference_builder/builder/samples/ds_app/classification/ds_pcb.yaml b/tools/inference_builder/builder/samples/ds_app/classification/ds_pcb.yaml new file mode 100644 index 00000000..caf2e051 --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/classification/ds_pcb.yaml @@ -0,0 +1,55 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +name: "deepstream-app" +model_repo: "/workspace/models" + +input: +- name: media_url + data_type: TYPE_CUSTOM_LIST + dims: [ -1 ] +- name: mime + data_type: TYPE_CUSTOM_DS_MIME + dims: [ -1 ] + +output: + - name: output + data_type: TYPE_CUSTOM_DS_METADATA + dims: [ -1 ] + +models: +- name: pcbclassification + backend: deepstream/nvinfer + max_batch_size: 1 + input: + - name: images + data_type: TYPE_CUSTOM_DS_IMAGE + dims: [-1, -1, 3] + optional: true + - name: media_url + data_type: TYPE_CUSTOM_DS_MEDIA_URL + dims: [ -1 ] + - name: mime + data_type: TYPE_CUSTOM_DS_MIME + dims: [ -1 ] + output: + - name: output + data_type: TYPE_CUSTOM_DS_METADATA + dims: [ -1 ] + parameters: + infer_config_path: + - nvdsinfer_config.yaml + inference_timeout: 5 \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/ds_app/classification/pcbclassification/labels.txt b/tools/inference_builder/builder/samples/ds_app/classification/pcbclassification/labels.txt new file mode 100644 index 00000000..33e45d56 --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/classification/pcbclassification/labels.txt @@ -0,0 +1 @@ +missing diff --git a/tools/inference_builder/builder/samples/ds_app/classification/pcbclassification/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/classification/pcbclassification/nvdsinfer_config.yaml new file mode 100644 index 00000000..6ed69a4a --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/classification/pcbclassification/nvdsinfer_config.yaml @@ -0,0 +1,30 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +property: + gie-unique-id: 1 + infer-dims: 3;224;224 + maintain-aspect-ratio: 1 + # 0.226 + net-scale-factor: 0.0173520735728 + network-mode: 2 + network-type: 1 # classification + onnx-file: pcbclassification.onnx + model-engine-file: ./pcbclassification.onnx_b1_gpu0_fp16.engine + workspace-size: 1048576 + classifier-threshold: 0 + scaling-filter: 1 + scaling-compute-hw: 1 + 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The application supports various RTSP stream formats including H.264, H.265, and MJPEG. + +```bash +# media-url: the path or URL to the input media. +# mime: the media type (e.g., "video/mp4" or "image/jpeg"). + +# Note: Replace rtsp:// with your actual RTSP stream URL + +docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ + -v $MODEL_REPO:/workspace/models \ + -v /tmp/.X11-unix/:/tmp/.X11-unix \ + -e DISPLAY=$DISPLAY \ + -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ + deepstream-app \ + --media-url rtsp:// \ + --mime video/mp4 +``` + +### Run with source config + +```bash +# source-config: path to the source configuration file that defines input sources +docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ + -v $MODEL_REPO:/workspace/models \ + -v /tmp/.X11-unix/:/tmp/.X11-unix \ + -e DISPLAY=$DISPLAY \ + -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ + deepstream-app \ + --source-config /opt/nvidia/deepstream/deepstream/service-maker/sources/apps/python/pipeline_api/deepstream_test5_app/source_list_dynamic.yaml +``` + +**Note:** To use a custom source configuration file, you need to mount your file into the docker container and reference it from within the container's filesystem. This allows you to use your own source configuration instead of the default one. + +```bash +# source-config: path to the source configuration file that defines input sources + +# Note: /workspace/inputs/source_list_dynamic.yaml is just a placeholder for any config present in $SAMPLE_INPUT directory + +docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ + -v $MODEL_REPO:/workspace/models \ + -v $SAMPLE_INPUT:/workspace/inputs \ + -v /tmp/.X11-unix/:/tmp/.X11-unix \ + -e DISPLAY=$DISPLAY \ + -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ + deepstream-app \ + --source-config /workspace/inputs/source_list_dynamic.yaml +``` + +### Run with image input + +```bash +# media-url: the path or URL to the input media. +# mime: the media type (e.g., "video/mp4" or "image/jpeg"). + +# Note: /sample_input/test_1.jpg is just a placeholder for any image present in $SAMPLE_INPUT directory + +docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ + -v $SAMPLE_INPUT:/sample_input \ + -v $MODEL_REPO:/workspace/models \ + -v /tmp/.X11-unix/:/tmp/.X11-unix \ + -e DISPLAY=$DISPLAY \ + -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ + deepstream-app \ + --media-url /sample_input/test_1.jpg \ + --mime image/jpeg +``` + + diff --git a/tools/inference_builder/builder/samples/ds_app/detection/ds_detect.yaml b/tools/inference_builder/builder/samples/ds_app/detection/ds_detect.yaml new file mode 100644 index 00000000..2f19b82d --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/detection/ds_detect.yaml @@ -0,0 +1,95 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +name: "deepstream-app" +model_repo: "/workspace/models" + +input: +- name: media_url + data_type: TYPE_CUSTOM_LIST + dims: [ -1 ] + optional: true +- name: mime + data_type: TYPE_CUSTOM_DS_MIME + dims: [ -1 ] + optional: true +- name: source_config + data_type: TYPE_CUSTOM_DS_SOURCE_CONFIG + dims: [ 1 ] + optional: true + +output: + - name: output + data_type: TYPE_CUSTOM_DS_METADATA + dims: [ -1 ] + +models: +- name: rtdetr + backend: deepstream/nvinfer + max_batch_size: "$MAX_BATCH_SIZE|4" + input: + - name: images + data_type: TYPE_CUSTOM_DS_IMAGE + dims: [ -1 ] + optional: true + - name: media_url + data_type: TYPE_CUSTOM_DS_MEDIA_URL + dims: [ -1 ] + optional: true + - name: mime + data_type: TYPE_CUSTOM_DS_MIME + dims: [ -1 ] + optional: true + - name: source_config + data_type: TYPE_CUSTOM_DS_SOURCE_CONFIG + dims: [ 1 ] + optional: true + output: + - name: output + data_type: TYPE_CUSTOM_DS_METADATA + dims: [ -1 ] + parameters: + # timeout in microseconds for batching multiple inputs + batch_timeout: 33333 + infer_config_path: + - nvdsinfer_config.yaml + tracker_config: + ll_lib_file: /opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so + ll_config_file: /opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/config_tracker_NvDCF_perf.yml + width: 960 + height: 544 + display_tracking_id: true + # analytics_config: + # config_file_path: /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-nvdsanalytics-test/config_nvdsanalytics.txt + # gpu_id: 0 + # msgbroker_config: + # msgbroker_proto_lib_path: /opt/nvidia/deepstream/deepstream/lib/libnvds_kafka_proto.so + # msgbroker_conn_str: localhost;9092 + # msgbroker_topic: ds_app + # msgconv_config_path: /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-test5/configs/dstest5_msgconv_sample_config.txt + render_config: + enable_display: "$ENABLE_DISPLAY|true" + enable_osd: true + # enable_stream: true + # rtsp_mount_point: /ds_app + # rtsp_port: 8554 + perf_config: + enable_fps_logs: true + enable_latency_logs: true + # kitti_output_path: + # infer: /workspace/models/resnet/infer-kitti-dump/ + # tracker: /workspace/models/resnet/tracker-kitti-dump/ + inference_timeout: "$INFERENCE_TIMEOUT|5" diff --git a/tools/inference_builder/builder/samples/ds_app/detection/mask2former/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/detection/mask2former/nvdsinfer_config.yaml new file mode 100644 index 00000000..ec9a1dc3 --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/detection/mask2former/nvdsinfer_config.yaml @@ -0,0 +1,48 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +property: + gpu-id: 0 + net-scale-factor: 0.017507 + offsets: 123.675;116.280;103.53 + model-color-format: 0 + onnx-file: mask2former_swint.onnx + network-type: 3 ## 3 is for instance segmentation network + infer-dims: 3;800;800 + num-detected-classes: 2 + batch-size: 1 + ## 0=FP32, 1=INT8, 2=FP16 mode + network-mode: 2 + interval: 0 + gie-unique-id: 1 + #no cluster + ## 0=Group Rectangles, 1=DBSCAN, 2=NMS, 3= DBSCAN+NMS Hybrid, 4 = None(No clustering) + ## MRCNN supports only cluster-mode=4; Clustering is done by the model itself + cluster-mode: 4 + output-instance-mask: 1 + input-object-min-width: 32 + input-object-min-height: 32 + output-blob-names: pred_masks;pred_scores;pred_classes + parse-bbox-instance-mask-func-name: NvDsInferParseCustomMask2Former + custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so + scaling-compute-hw: 1 + layer-device-precision: 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+ +class-attrs-all: + pre-cluster-threshold: 0.6 + + + + diff --git a/tools/inference_builder/builder/samples/ds_app/detection/peoplenet_transformer/labels.txt b/tools/inference_builder/builder/samples/ds_app/detection/peoplenet_transformer/labels.txt new file mode 100644 index 00000000..4b02dbbc --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/detection/peoplenet_transformer/labels.txt @@ -0,0 +1,4 @@ +BG +Person +Face +Bag diff --git a/tools/inference_builder/builder/samples/ds_app/detection/peoplenet_transformer/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/detection/peoplenet_transformer/nvdsinfer_config.yaml new file mode 100644 index 00000000..3f7ffc8c --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/detection/peoplenet_transformer/nvdsinfer_config.yaml @@ -0,0 +1,38 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Reference configuration for PeopleNetTransformer v2 + +class-attrs-all: + pre-cluster-threshold: 0.3 + topk: 20 + +property: + gpu-id: 0 + offsets: "123.675;116.28;103.53" + net-scale-factor: 0.0173520735728 + labelfile-path: labels.txt + onnx-file: resnet50_peoplenet_transformer_op17.onnx + + # 0=FP32, 1=INT8, 2=FP16 mode + network-mode: 2 + network-type: 0 # detection + num-detected-classes: 4 # BG, Person, Face, Bag + gie-unique-id: 1 + output-blob-names: pred_boxes;pred_logits + infer-dims: 3;544;960 + cluster-mode: 4 + parse-bbox-func-name: NvDsInferParseCustomDDETRTAO + custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so diff --git a/tools/inference_builder/builder/samples/ds_app/detection/resnet/labels.txt b/tools/inference_builder/builder/samples/ds_app/detection/resnet/labels.txt new file mode 100644 index 00000000..0181d7d4 --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/detection/resnet/labels.txt @@ -0,0 +1,4 @@ +car +bicycle +person +road_sign \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/ds_app/detection/resnet/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/detection/resnet/nvdsinfer_config.yaml new file mode 100644 index 00000000..9f44632a --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/detection/resnet/nvdsinfer_config.yaml @@ -0,0 +1,37 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +property: + gpu-id: 0 + net-scale-factor: 0.00392156862745098 + onnx-file: resnet18_trafficcamnet_pruned.onnx + model-engine-file: resnet18_trafficcamnet_pruned.onnx_b4_gpu0_fp32.engine + labelfile-path: labels.txt + process-mode: 1 + model-color-format: 0 + network-mode: 0 + num-detected-classes: 4 + interval: 0 + gie-unique-id: 1 + cluster-mode: 2 + infer-dims: 3;544;960 +class-attrs-all: + topk: 20 + nms-iou-threshold: 0.5 + pre-cluster-threshold: 0.2 +class-attrs-0: + topk: 20 + nms-iou-threshold: 0.5 + pre-cluster-threshold: 0.4 \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/ds_app/detection/rtdetr/labels.txt b/tools/inference_builder/builder/samples/ds_app/detection/rtdetr/labels.txt new file mode 100644 index 00000000..92284f3d --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/detection/rtdetr/labels.txt @@ -0,0 +1,5 @@ +background +bicycle +car +person +road_sign diff --git a/tools/inference_builder/builder/samples/ds_app/detection/rtdetr/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/detection/rtdetr/nvdsinfer_config.yaml new file mode 100644 index 00000000..18da65ac --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/detection/rtdetr/nvdsinfer_config.yaml @@ -0,0 +1,36 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +class-attrs-all: + pre-cluster-threshold: 0.5 +property: + cluster-mode: 4 + gie-unique-id: 1 + custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so + data-format: channels_first + infer-dims: 3;544;960 + maintain-aspect-ratio: 1 + model-color-format: 0 + symmetric-padding: 1 + # std: 1 + net-scale-factor: 0.00392156863 + network-mode: 2 + network-type: 0 + num-detected-classes: 5 + onnx-file: resnet50_trafficamnet_rtdetr.onnx + model-engine-file: resnet50_trafficamnet_rtdetr.onnx_b4_gpu0_fp16.engine + parse-bbox-func-name: NvDsInferParseCustomDDETRTAO + workspace-size: 1048576 + labelfile-path: labels.txt diff --git a/tools/inference_builder/builder/samples/ds_app/gdino/README.md b/tools/inference_builder/builder/samples/ds_app/gdino/README.md new file mode 100644 index 00000000..25aa72b4 --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/gdino/README.md @@ -0,0 +1,184 @@ +## Introduction + +This sample demonstrates how to build a deepstream application with Inference Builder using grounding dino models: +1. gdino: grounding_dino_swin_tiny_commercial_deployable_v1.0 from https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/grounding_dino +2. mask_gdino: mask_grounding_dino_swin_tiny_commercial_deployable_v1.0 from https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/mask_grounding_dino + +**Note:** For both gdino and mask_gdino models, the steps are the same as described below. Users just need to replace the model name from "gdino" to "mask_gdino" in the commands and directory names. + +## Prerequisites + +**Note:** Make sure you are in the root directory (`path/to/inference_builder`) to execute the commands in this README. All relative paths and commands assume you are running from the inference_builder root directory. Also ensure that your virtual environment is activated before running any commands. + +Model files are loaded from '/workspace/models/{MODEL_NAME}' within the container, thus the volume must be correctly mapped from the host. +You need to export MODEL_REPO environment variable to the path where you want to store the model files. + +```bash +mkdir ~/.cache/model-repo/ +sudo chmod -R 777 ~/.cache/model-repo/ +export MODEL_REPO=~/.cache/model-repo +``` + +You need first download the model files from the NGC catalog and put them in the $MODEL_REPO/{model-name}/ directory, then copy the other required configurations to the same directory: + +**Note:** If NGC commands fail, make sure you have access to the models you are trying to download. Some models require an active subscription. Ensure NGC is set up properly, or alternatively try using the NGC web UI to directly download the model from the links provided [here](../README.md#models-used-in-the-samples) + +### for gdino sample + +Please use `gdino` as the directory: + +```bash +ngc registry model download-version "nvidia/tao/grounding_dino:grounding_dino_swin_tiny_commercial_deployable_v1.0" +# Move the folder to the model-repo directory, and the sample uses ~/.cache/model-repo by default +mv grounding_dino_vgrounding_dino_swin_tiny_commercial_deployable_v1.0 $MODEL_REPO/gdino +chmod 777 $MODEL_REPO/gdino +cp -r builder/samples/ds_app/gdino/gdino/* $MODEL_REPO/gdino/ +``` + +### for mask_gdino sample + +Please use `mask_gdino` as the directory: + +```bash +ngc registry model download-version "nvidia/tao/mask_grounding_dino:mask_grounding_dino_swin_tiny_commercial_deployable_v1.0" +# Move the folder to the model-repo directory, and the sample uses ~/.cache/model-repo by default +mv mask_grounding_dino_vmask_grounding_dino_swin_tiny_commercial_deployable_v1.0 $MODEL_REPO/mask_gdino +chmod 777 $MODEL_REPO/mask_gdino +cp -r builder/samples/ds_app/gdino/mask_gdino/* $MODEL_REPO/mask_gdino/ +``` + +## Generate the deepstream application package and build it into a container image: + +Assume you've followed the [top level instructions](../../../README.md#getting-started) to set up the environment and be sure you're in the inference_builder folder, then activate your virtual environment: + +```bash +source .venv/bin/activate +``` + +### For x86 Architecture + +```bash +python builder/main.py builder/samples/ds_app/gdino/ds_gdino.yaml \ + -o builder/samples/ds_app \ + --server-type serverless \ + -c builder/samples/tao/processors.py \ + -t \ +&& docker build -t deepstream-app builder/samples/ds_app +``` + +### For Tegra Architecture + +```bash +python builder/main.py builder/samples/ds_app/gdino/ds_gdino.yaml \ + -o builder/samples/ds_app \ + --server-type serverless \ + -c builder/samples/tao/processors.py \ + -t \ +&& docker build \ + -t deepstream-app \ + -f builder/samples/ds_app/Dockerfile.tegra \ + builder/samples/ds_app +``` + +#### For Dgx Spark + +```bash +python builder/main.py builder/samples/ds_app/gdino/ds_gdino.yaml \ + -o builder/samples/ds_app \ + --server-type serverless \ + -c builder/samples/tao/processors.py \ + -t \ +&& docker build \ + -t deepstream-app \ + -f builder/samples/ds_app/Dockerfile.dgxspark \ + builder/samples/ds_app +``` + +## Run the deepstream app with different inputs: + +**Note:** The TensorRT engine is generated during the first time run and it takes several minutes. + +**Note:** You can optionally set the `$SAMPLE_INPUT` environment variable to point to your input media directory if you want to perform inference on media files stored on your host machine. + +**Note:** To save the inference results, append the `-s result.json` option to your `docker run` command. + +```bash +# Update this with your actual samples directory path +export SAMPLE_INPUT=/path/to/your/samples/directory +``` + +**Note:** When you set `enable_display: true` under the `render_config` section of +[ds_gdino.yaml](ds_gdino.yaml), you need to have a display on your host and run both commands in this order to give the container access to it. For more information about render configuration options, see the [render configuration section](../README.md#render-configuration). + + +First, set the display environment variable: +```bash +export DISPLAY=:0 # or :1 depending on your system +``` + +Then, allow X server connections from any host: +```bash +xhost + +``` + +If the configuration is successful, you will see this message in the log: `access control disabled, clients can connect from any host`. + +### Run with video input + +```bash +# media-url: the path or URL to the input media. +# mime: the media type (e.g., "video/mp4" or "image/jpeg"). +# text: the text prompt for object detection (e.g., "car,person"). +docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ + -v $MODEL_REPO:/workspace/models \ + -v /tmp/.X11-unix/:/tmp/.X11-unix \ + -e DISPLAY=$DISPLAY \ + -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ + deepstream-app \ + --media-url /opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4 \ + --mime video/mp4 \ + --text "car,person" +``` + +### Run with RTSP input + +**Note:** Replace `rtsp://` (which is just a placeholder) with your actual RTSP stream URL. The application supports various RTSP stream formats including H.264, H.265, and MJPEG. + +```bash +# media-url: the path or URL to the input media. +# mime: the media type (e.g., "video/mp4" or "image/jpeg"). +# text: the text prompt for object detection (e.g., "car,person"). + +# Note: Replace rtsp:// with your actual RTSP stream URL + +docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ + -v $MODEL_REPO:/workspace/models \ + -v /tmp/.X11-unix/:/tmp/.X11-unix \ + -e DISPLAY=$DISPLAY \ + -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ + deepstream-app \ + --media-url rtsp:// \ + --mime video/mp4 \ + --text "car,person" +``` + +### Run with image input + +```bash +# media-url: the path or URL to the input media. +# mime: the media type (e.g., "video/mp4" or "image/jpeg"). +# text: the text prompt for object detection (e.g., "car,person"). + +# Note: /sample_input/test.jpg is just a placeholder for any image present in $SAMPLE_INPUT directory + +docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ + -v $SAMPLE_INPUT:/sample_input \ + -v $MODEL_REPO:/workspace/models \ + -v /tmp/.X11-unix/:/tmp/.X11-unix \ + -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ + -e DISPLAY=$DISPLAY \ + deepstream-app \ + --media-url /sample_input/test.jpg \ + --mime image/jpeg \ + --text "car,person" +``` \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/ds_app/gdino/ds_gdino.yaml b/tools/inference_builder/builder/samples/ds_app/gdino/ds_gdino.yaml new file mode 100644 index 00000000..7862abcf --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/gdino/ds_gdino.yaml @@ -0,0 +1,104 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: "deepstream-app" +model_repo: /workspace/models + +input: +- name: media_url + data_type: TYPE_CUSTOM_LIST + dims: [ -1 ] +- name: mime + data_type: TYPE_CUSTOM_DS_MIME + dims: [ -1 ] +- name: text + data_type: TYPE_STRING + dims: [ -1 ] + +output: + - name: output + data_type: TYPE_CUSTOM_DS_METADATA + dims: [ -1 ] + +models: +- name: gdino + backend: deepstream/nvinfer + max_batch_size: "$MAX_BATCH_SIZE|1" + input: + - name: images + data_type: TYPE_CUSTOM_DS_IMAGE + dims: [ -1 ] + optional: true + - name: media_url + data_type: TYPE_CUSTOM_DS_MEDIA_URL + dims: [ -1 ] + - name: mime + data_type: TYPE_CUSTOM_DS_MIME + dims: [ -1 ] + - name: input_ids + data_type: TYPE_INT64 + dims: [ 256 ] + - name: attention_mask + data_type: TYPE_BOOL + dims: [ 256 ] + - name: position_ids + data_type: TYPE_INT64 + dims: [ 256 ] + - name: token_type_ids + data_type: TYPE_INT64 + dims: [ 256 ] + - name: text_token_mask + data_type: TYPE_BOOL + dims: [ 256, 256 ] + output: + - name: pred_logits + data_type: TYPE_FP32 + dims: [ -1, -1 ] + - name: pred_boxes + data_type: TYPE_FP32 + dims: [ -1, 4 ] + - name: pred_masks + data_type: TYPE_FP32 + dims: [ -1, -1, -1, -1 ] + - name: decoded_frames + data_type: TYPE_CUSTOM_DS_IMAGE + dims: [ -1, -1, 3 ] + parameters: + # timeout in nanoseconds for batching multiple inputs + batch_timeout: -1 + infer_config_path: + - nvdsinfer_config.yaml + preprocess_config_path: + - nvdspreprocess_config.yaml + inference_timeout: "$INFERENCE_TIMEOUT|5" + preprocessors: + - kind: "custom" + name: "gdino-tokenizer" + input: ["text"] + output: ["input_ids", "attention_mask", "position_ids", "token_type_ids", "text_token_mask", "pos_map"] + postprocessors: + - kind: "custom" + name: "gdino-postprocessor" + input: ["pred_logits", "pred_boxes", "pred_masks", "pos_map"] + output: ["output"] + config: + infer_config_path: /workspace/models/gdino/nvdsinfer_config.yaml + - kind: "custom" + name: "bbox-overlay-visualizer" + input: ["output", "decoded_frames"] + output: ["output"] + config: + output_dir: /tmp/bbox_overlay + diff --git a/tools/inference_builder/builder/samples/ds_app/gdino/gdino/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/gdino/gdino/nvdsinfer_config.yaml new file mode 100644 index 00000000..0212835b --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/gdino/gdino/nvdsinfer_config.yaml @@ -0,0 +1,32 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +class-attrs-all: + topk: 20 + pre-cluster-threshold: 0.2 +property: + gie-unique-id: 1 + # std: 0.5 + net-scale-factor: 0.0078431372549 + onnx-file: grounding_dino_swin_tiny_commercial_deployable.onnx + model-engine-file: grounding_dino_swin_tiny_commercial_deployable.onnx_b1_gpu0_fp16.engine + network-mode: 2 + network-type: 100 # detection + offsets: 127.5;127.5;127.5 + output-tensor-meta: 1 + input-tensor-from-meta: 1 + infer-dims: 3;544;960 + custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so + diff --git a/tools/inference_builder/builder/samples/ds_app/gdino/gdino/nvdspreprocess_config.yaml b/tools/inference_builder/builder/samples/ds_app/gdino/gdino/nvdspreprocess_config.yaml new file mode 100644 index 00000000..d185608f --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/gdino/gdino/nvdspreprocess_config.yaml @@ -0,0 +1,44 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +property: + tensor-name: inputs + processing-width: 960 + processing-height: 544 + network-input-shape: 1;3;544;960 + network-color-format: 0 + network-input-order: 0 + tensor-data-type: 0 + maintain-aspect-ratio: 1 + symmetric-padding: 1 + scaling-pool-memory-type: 0 + scaling-pool-compute-hw: 0 + scaling-filter: 0 + scaling-buf-pool-size: 6 + tensor-buf-pool-size: 6 + target-unique-ids: 1 + process-on-frame: 1 + unique-id: 5 + custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/gst-plugins/libcustom2d_preprocess.so + custom-tensor-preparation-function: CustomTensorPreparation + enable: 1 +user-configs: + pixel-normalization-factor: 0.007843137 + offsets: 127.5;127.5;127.5 +group: + - src-ids: -1 + custom-input-transformation-function: CustomAsyncTransformation + process-on-roi: 0 + diff --git a/tools/inference_builder/builder/samples/ds_app/gdino/mask_gdino/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/gdino/mask_gdino/nvdsinfer_config.yaml new file mode 100644 index 00000000..4577bf84 --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/gdino/mask_gdino/nvdsinfer_config.yaml @@ -0,0 +1,32 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +class-attrs-all: + topk: 20 + pre-cluster-threshold: 0.2 +property: + gie-unique-id: 1 + # std: 0.5 + net-scale-factor: 0.0078431372549 + onnx-file: model_epoch_049.onnx + model-engine-file: model_epoch_049.onnx_b1_gpu0_fp16.engine + network-mode: 2 + network-type: 100 # detection + offsets: 127.5;127.5;127.5 + output-tensor-meta: 1 + input-tensor-from-meta: 1 + infer-dims: 3;544;960 + custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so + diff --git a/tools/inference_builder/builder/samples/ds_app/gdino/mask_gdino/nvdspreprocess_config.yaml b/tools/inference_builder/builder/samples/ds_app/gdino/mask_gdino/nvdspreprocess_config.yaml new file mode 100644 index 00000000..1ba6d07c --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/gdino/mask_gdino/nvdspreprocess_config.yaml @@ -0,0 +1,45 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +property: + tensor-name: inputs + processing-width: 960 + processing-height: 544 + network-input-shape: 1;3;544;960 + network-color-format: 0 + network-input-order: 0 + tensor-data-type: 0 + maintain-aspect-ratio: 1 + symmetric-padding: 1 + scaling-pool-memory-type: 0 + scaling-pool-compute-hw: 0 + scaling-filter: 0 + scaling-buf-pool-size: 6 + tensor-buf-pool-size: 6 + target-unique-ids: 1 + process-on-frame: 1 + unique-id: 5 + custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/gst-plugins/libcustom2d_preprocess.so + custom-tensor-preparation-function: CustomTensorPreparation + enable: 1 +user-configs: + pixel-normalization-factor: 0.007843137 + offsets: 127.5;127.5;127.5 +group: + - src-ids: -1 + custom-input-transformation-function: CustomAsyncTransformation + process-on-roi: 0 + diff --git a/tools/inference_builder/builder/samples/ds_app/segmentation/README.md b/tools/inference_builder/builder/samples/ds_app/segmentation/README.md new file mode 100644 index 00000000..96f4afd4 --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/segmentation/README.md @@ -0,0 +1,154 @@ +## Introduction + +This sample demonstrates how to build a deepstream application with Inference Builder using segmentation models: +1. citysemsegformer: deployable_onnx_v1.0 from https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer + +## Prerequisites + +**Note:** Make sure you are in the root directory (`path/to/inference_builder`) to execute the commands in this README. All relative paths and commands assume you are running from the inference_builder root directory. Also ensure that your virtual environment is activated before running any commands. + +Model files are loaded from '/workspace/models/{MODEL_NAME}' within the container, thus the volume must be correctly mapped from the host. +You need to export MODEL_REPO environment variable to the path where you want to store the model files. + +```bash +mkdir ~/.cache/model-repo/ +sudo chmod -R 777 ~/.cache/model-repo/ +export MODEL_REPO=~/.cache/model-repo +``` + +You need first download the model files from the NGC catalog and put them in the $MODEL_REPO/{model-name}/ directory, then copy the other required configurations to the same directory: + +**Note:** If NGC commands fail, make sure you have access to the models you are trying to download. Some models require an active subscription. Ensure NGC is set up properly, or alternatively try using the NGC web UI to directly download the model from the links provided [here](../README.md#models-used-in-the-samples) + +```bash +ngc registry model download-version "nvidia/tao/citysemsegformer:deployable_onnx_v1.0" +# Move the folder to the model-repo directory, and the sample uses ~/.cache/model-repo by default +mv citysemsegformer_vdeployable_onnx_v1.0 $MODEL_REPO/citysemsegformer +chmod 777 $MODEL_REPO/citysemsegformer +cp -r builder/samples/ds_app/segmentation/citysemsegformer/* $MODEL_REPO/citysemsegformer/ +``` + +## Generate the deepstream application package and build it into a container image: + +Assume you've followed the [top level instructions](../../../README.md#getting-started) to set up the environment and be sure you're in the inference_builder folder, then activate your virtual environment: + +```bash +source .venv/bin/activate +``` + +### For x86 Architecture + +```bash +python builder/main.py builder/samples/ds_app/segmentation/ds_segformer.yaml \ + -o builder/samples/ds_app \ + --server-type serverless \ + -t \ +&& docker build -t deepstream-app builder/samples/ds_app +``` + +### For Tegra Architecture + +```bash +python builder/main.py builder/samples/ds_app/segmentation/ds_segformer.yaml \ + -o builder/samples/ds_app \ + --server-type serverless \ + -t \ +&& docker build \ + -t deepstream-app \ + -f builder/samples/ds_app/Dockerfile.tegra \ + builder/samples/ds_app +``` + +#### For Dgx Spark + +```bash +python builder/main.py builder/samples/ds_app/segmentation/ds_segformer.yaml \ + -o builder/samples/ds_app \ + --server-type serverless \ + -t \ +&& docker build \ + -t deepstream-app \ + -f builder/samples/ds_app/Dockerfile.dgxspark \ + builder/samples/ds_app +``` + +## Run the deepstream app with different inputs: + +**Note:** The TensorRT engine is generated during the first time run and it takes several minutes. + +**Note:** You can optionally set the `$SAMPLE_INPUT` environment variable to point to your input media directory if you want to perform inference on media files stored on your host machine. + +***Note:** To save the inference results, append the `-s result.json` option to your `docker run` command. + +```bash +# Update this with your actual samples directory path +export SAMPLE_INPUT=/path/to/your/samples/directory +``` + +**Note:** When you set `enable_display: true` under the `render_config` section of [ds_segformer.yaml](ds_segformer.yaml), you need to have a display on your host and run both commands in this order to give the container access to it. For more information about render configuration options, see the [render configuration section](../README.md#render-configuration). + +First, set the display environment variable: +```bash +export DISPLAY=:0 # or :1 depending on your system +``` + +Then, allow X server connections from any host: +```bash +xhost + +``` + +If the configuration is successful, you will see this message in the log: `access control disabled, clients can connect from any host`. + +### Run with video input + +```bash +# media-url: the path or URL to the input media. +# mime: the media type (e.g., "video/mp4" or "image/jpeg"). +docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ + -v $MODEL_REPO:/workspace/models \ + -v /tmp/.X11-unix/:/tmp/.X11-unix \ + -e DISPLAY=$DISPLAY \ + -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ + deepstream-app \ + --media-url /opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4 \ + --mime video/mp4 +``` + +### Run with RTSP input + +**Note:** Replace `rtsp://` (which is just a placeholder) with your actual RTSP stream URL. The application supports various RTSP stream formats including H.264, H.265, and MJPEG. + +```bash +# media-url: the path or URL to the input media. +# mime: the media type (e.g., "video/mp4" or "image/jpeg"). + +# Note: Replace rtsp:// with your actual RTSP stream URL + +docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ + -v $MODEL_REPO:/workspace/models \ + -v /tmp/.X11-unix/:/tmp/.X11-unix \ + -e DISPLAY=$DISPLAY \ + -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ + deepstream-app \ + --media-url rtsp:// \ + --mime video/mp4 +``` + +### Run with image input + +```bash +# media-url: the path or URL to the input media. +# mime: the media type (e.g., "video/mp4" or "image/jpeg"). + +# Note /sample_input/test_1.jpg is just a placeholder for any image present in $SAMPLE_INPUT directory + +docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ + -v $SAMPLE_INPUT:/sample_input \ + -v $MODEL_REPO:/workspace/models \ + -v /tmp/.X11-unix/:/tmp/.X11-unix \ + -e DISPLAY=$DISPLAY \ + -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ + deepstream-app \ + --media-url /sample_input/test_1.jpg \ + --mime image/jpeg +``` \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/ds_app/segmentation/citysemsegformer/labels.txt b/tools/inference_builder/builder/samples/ds_app/segmentation/citysemsegformer/labels.txt new file mode 100644 index 00000000..ff3edb4a --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/segmentation/citysemsegformer/labels.txt @@ -0,0 +1,20 @@ +road +sidewalk +building +wall +fence +pole +traffic light +traffic sign +vegetation +terrain +sky +person +rider +car +truck +bus +train +motorcycle +bicycle + diff --git a/tools/inference_builder/builder/samples/ds_app/segmentation/citysemsegformer/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/segmentation/citysemsegformer/nvdsinfer_config.yaml new file mode 100644 index 00000000..03fc5065 --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/segmentation/citysemsegformer/nvdsinfer_config.yaml @@ -0,0 +1,35 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +property: + gie-unique-id: 6 + infer-dims: 3;1024;1820 + network-input-order: 0 + model-color-format: 1 + maintain-aspect-ratio: 1 + # std 0.226 + net-scale-factor: 0.01735207357279195 + network-mode: 2 + model-color-format: 1 + offsets: 123.675;116.28;103.53 + network-type: 2 # seg + num-detected-classes: 19 + onnx-file: citysemsegformer.onnx + model-engine-file: ./citysemsegformer.onnx_b1_gpu0_fp16.engine + workspace-size: 1048576 + parse-segmentation-func-name: NvDsInferParseCustomSegformerTAO + custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so + labelfile-path: labels.txt + diff --git a/tools/inference_builder/builder/samples/ds_app/segmentation/ds_segformer.yaml b/tools/inference_builder/builder/samples/ds_app/segmentation/ds_segformer.yaml new file mode 100644 index 00000000..e5187f52 --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/segmentation/ds_segformer.yaml @@ -0,0 +1,66 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: "deepstream-app" +model_repo: "/workspace/models" + +input: +- name: media_url + data_type: TYPE_CUSTOM_LIST + dims: [ -1 ] +- name: mime + data_type: TYPE_CUSTOM_DS_MIME + dims: [ -1 ] + +output: + - name: output + data_type: TYPE_CUSTOM_DS_METADATA + dims: [ -1 ] + +models: +- name: citysemsegformer + backend: deepstream/nvinfer + max_batch_size: "$MAX_BATCH_SIZE|2" + input: + - name: images + data_type: TYPE_CUSTOM_DS_IMAGE + dims: [ -1 ] + optional: true + - name: media_url + data_type: TYPE_CUSTOM_DS_MEDIA_URL + dims: [ -1 ] + - name: mime + data_type: TYPE_CUSTOM_DS_MIME + dims: [ -1 ] + output: + - name: output + data_type: TYPE_CUSTOM_DS_METADATA + dims: [ -1 ] + parameters: + # timeout in microseconds for batching multiple inputs + batch_timeout: 33333 + infer_config_path: + - nvdsinfer_config.yaml + render_config: + enable_display: "$ENABLE_DISPLAY|true" + # enable segmentation mask visualization + seg_mask_visualization: + width: 1820 + height: 1024 + # enable_osd: true + # enable_stream: true + # rtsp_mount_point: /ds_app + # rtsp_port: 8554 + inference_timeout: "$INFERENCE_TIMEOUT|5" \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/ds_app/test_config.json b/tools/inference_builder/builder/samples/ds_app/test_config.json new file mode 100644 index 00000000..33ff2d6b --- /dev/null +++ b/tools/inference_builder/builder/samples/ds_app/test_config.json @@ -0,0 +1,241 @@ +[ + { + "name": "gdino_video_inference", + "description": "Test gdino inference application with video input", + "build_args": { + "TEST_APP_NAME": "deepstream-app", + "CACHE_BUSTER": "gdino_video_test", + "APP_YAML_PATH": "./gdino/ds_gdino.yaml", + "OUTPUT_DIR": ".", + "PROCESSORS_PATH": "../tao/processors.py", + "SERVER_TYPE": "serverless" + }, + "test_config": { + "default_enable": true, + "timeout": 600, + "env": { + "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes", + "NVIDIA_DRIVER_CAPABILITIES": "compute,utility,video,graphics" + }, + "volumes": { + "/tmp/model-repo/gdino": "/workspace/models/gdino" + }, + "cmd": [ + "--media-url", + "/opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4", + "--mime", + "video/mp4", + "--text", + "car,person" + ], + "prerequisite_script": null, + "models": { + "gdino": { + "source": "NGC", + "target": "/tmp/model-repo", + "path": "nvidia/tao/grounding_dino", + "version": "grounding_dino_swin_tiny_commercial_deployable_v1.0", + "configs": "./gdino/gdino" + } + } + } + }, + { + "name": "gdino_image_inference", + "description": "Test gdino inference application with image input", + "build_args": { + "TEST_APP_NAME": "deepstream-app", + "CACHE_BUSTER": "gdino_image_test", + "APP_YAML_PATH": "./gdino/ds_gdino.yaml", + "OUTPUT_DIR": ".", + "PROCESSORS_PATH": "../tao/processors.py", + "SERVER_TYPE": "serverless" + }, + "test_config": { + "default_enable": true, + "timeout": 600, + "env": { + "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes", + "NVIDIA_DRIVER_CAPABILITIES": "compute,utility,video,graphics" + }, + "volumes": { + "/tmp/model-repo/gdino": "/workspace/models/gdino" + }, + "cmd": [ + "--media-url", + "/opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.jpg", + "--mime", + "image/jpeg", + "--text", + "car,person" + ], + "prerequisite_script": null, + "models": { + "gdino": { + "source": "NGC", + "target": "/tmp/model-repo", + "path": "nvidia/tao/grounding_dino", + "version": "grounding_dino_swin_tiny_commercial_deployable_v1.0", + "configs": "./gdino/gdino" + } + } + } + }, + { + "name": "changenet_classification_inference", + "description": "Test Visual ChangeNet classification application with image input", + "build_args": { + "TEST_APP_NAME": "deepstream-app", + "CACHE_BUSTER": "changenet_classify_test", + "APP_YAML_PATH": "./classification/ds_changenet.yaml", + "OUTPUT_DIR": ".", + "PROCESSORS_PATH": "../tao/processors.py", + "SERVER_TYPE": "serverless" + }, + "test_config": { + "default_enable": true, + "timeout": 300, + "env": { + "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes", + "NVIDIA_DRIVER_CAPABILITIES": "compute,utility,video,graphics" + }, + "volumes": { + "/tmp/model-repo/changenet-classify": "/workspace/models/changenet-classify" + }, + "cmd": [ + "--media-url", + "/workspace/test_data/pass_0.png", + "/workspace/test_data/pass_1.png", + "--mime", + "image/png", + "image/png" + ], + "prerequisite_script": null, + "models": { + "changenet-classify": { + "source": "NGC", + "target": "/tmp/model-repo", + "path": "nvidia/tao/visual_changenet_classification", + "version": "visual_changenet_nvpcb_deployable_v1.0", + "configs": "./classification/changenet-classify" + } + } + } + }, + { + "name": "rtdetr_detection_inference", + "description": "Test RT-DETR detection application with video input", + "build_args": { + "TEST_APP_NAME": "deepstream-app", + "CACHE_BUSTER": "rtdetr_detection_test", + "APP_YAML_PATH": "./detection/ds_detect.yaml", + "OUTPUT_DIR": ".", + "SERVER_TYPE": "serverless" + }, + "test_config": { + "default_enable": true, + "timeout": 600, + "env": { + "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes", + "NVIDIA_DRIVER_CAPABILITIES": "compute,utility,video,graphics", + "ENABLE_DISPLAY": "false" + }, + "volumes": { + "/tmp/model-repo/rtdetr": "/workspace/models/rtdetr" + }, + "cmd": [ + "--media-url", + "/opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4", + "--mime", + "video/mp4" + ], + "prerequisite_script": null, + "models": { + "rtdetr": { + "source": "NGC", + "target": "/tmp/model-repo", + "path": "nvidia/tao/trafficcamnet_transformer_lite", + "version": "deployable_v1.0", + "configs": "./detection/rtdetr" + } + } + } + }, + { + "name": "rtdetr_detection_inference_source_file", + "description": "Test RT-DETR detection application with video input", + "build_args": { + "TEST_APP_NAME": "deepstream-app", + "CACHE_BUSTER": "rtdetr_detection_test", + "APP_YAML_PATH": "./detection/ds_detect.yaml", + "OUTPUT_DIR": ".", + "SERVER_TYPE": "serverless" + }, + "test_config": { + "default_enable": true, + "timeout": 600, + "env": { + "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes", + "NVIDIA_DRIVER_CAPABILITIES": "compute,utility,video,graphics", + "ENABLE_DISPLAY": "false" + }, + "volumes": { + "/tmp/model-repo/rtdetr": "/workspace/models/rtdetr" + }, + "cmd": [ + "--source-config", + "/workspace/test_data/source_config.yaml" + ], + "prerequisite_script": null, + "models": { + "rtdetr": { + "source": "NGC", + "target": "/tmp/model-repo", + "path": "nvidia/tao/trafficcamnet_transformer_lite", + "version": "deployable_v1.0", + "configs": "./detection/rtdetr" + } + } + } + }, + { + "name": "citysemsegformer_segmentation_inference", + "description": "Test CitySemsegFormer semantic segmentation application with image input", + "build_args": { + "TEST_APP_NAME": "deepstream-app", + "CACHE_BUSTER": "citysemsegformer_segmentation_test", + "APP_YAML_PATH": "./segmentation/ds_segformer.yaml", + "OUTPUT_DIR": ".", + "SERVER_TYPE": "serverless" + }, + "test_config": { + "default_enable": true, + "timeout": 1000, + "env": { + "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes", + "NVIDIA_DRIVER_CAPABILITIES": "compute,utility,video,graphics", + "ENABLE_DISPLAY": "false" + }, + "volumes": { + "/tmp/model-repo/citysemsegformer": "/workspace/models/citysemsegformer" + }, + 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Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +#################################################################################################### + +source-list: +- uri: "file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4" + sensor-id: UniqueSensorId1 + sensor-name: UniqueSensorName1 +- uri: "file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h265.mp4" + sensor-id: UniqueSensorId2 + sensor-name: UniqueSensorName2 +source-config: + source-bin: "nvurisrcbin" + properties: + file-loop: false diff --git a/tools/inference_builder/builder/samples/dummy/Dockerfile b/tools/inference_builder/builder/samples/dummy/Dockerfile new file mode 100644 index 00000000..836a725e --- /dev/null +++ b/tools/inference_builder/builder/samples/dummy/Dockerfile @@ -0,0 +1,75 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + + +FROM "nvcr.io/nvidia/deepstream:9.0-triton-multiarch" AS inference_base + +RUN --mount=type=cache,target=/root/.cache/pip \ + --mount=type=cache,target=/var/cache/apt,sharing=locked \ + --mount=type=cache,target=/var/lib/apt,sharing=locked \ + apt-get update && apt-get install -y --no-install-recommends curl ca-certificates libyaml-cpp-dev pkg-config cmake + + #TODO: remove curl? + #&& apt-get purge -y --auto-remove curl + +RUN --mount=type=cache,target=/root/.cache/pip \ + pip install \ + python-multipart==0.0.9 \ + aiofiles==23.2.1 \ + aiohttp==3.11.0 \ + annotated-types==0.7.0 \ + python-magic==0.4.27 \ + torch==2.4.0 \ + numpy==1.26.4 \ + omegaconf==2.3.0 \ + fastapi==0.115.12 \ + uvicorn==0.29.0 + +# Patch JPEG Decoder +RUN if [ -e "/usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstnvcodec.so" ]; then \ + mv /usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstnvcodec.so /usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstnvcodec.so_bkp; \ + else \ + echo "File 'libgstnvcodec.so' does not exist"; \ + fi +RUN rm -rf /root/.cache/gstreamer-1.0/ +RUN mkdir /tmp/assets && chmod -R 777 /tmp/assets + +WORKDIR /workspace +ADD dummy.tgz /workspace + +RUN groupadd --gid 1000 --non-unique nvs && \ + useradd --create-home --shell /usr/sbin/nologin --uid 1000 --non-unique --gid 1000 nvs && \ + chown -R 1000.1000 /workspace && chmod o+w /workspace + +USER nvs:1000 + +## Set Environment variables +ENV LD_LIBRARY_PATH /opt/tritonserver/lib:$LD_LIBRARY_PATH +ENV NVSTREAMMUX_ADAPTIVE_BATCHING=yes + +RUN touch /workspace/start_server.sh && \ + chmod a+rx /workspace/start_server.sh && \ + cat > /workspace/start_server.sh <<-EOF + #!/usr/bin/env bash + set -eu + # SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + # SPDX-License-Identifier: LicenseRef-NvidiaProprietary + python3 /workspace/__main__.py +# while true; do sleep 5; done +EOF + +ENTRYPOINT ["/workspace/start_server.sh"] +CMD [] diff --git a/tools/inference_builder/builder/samples/dummy/README.md b/tools/inference_builder/builder/samples/dummy/README.md new file mode 100644 index 00000000..8bc11a15 --- /dev/null +++ b/tools/inference_builder/builder/samples/dummy/README.md @@ -0,0 +1,25 @@ +This sample demonstrates how to build a inference pipeline with a dummy backend. The experiment is mainly for testing the basic flow. + +The sample configuration supports multiple input types including image data, image assets and video assets in one endpoint + +**Note:** Make sure you are in the root directory (`path/to/inference_builder`) to execute the commands in this README. All relative paths and commands assume you are running from the inference_builder root directory. Also ensure that your virtual environment is activated before running any commands. + +Build the dummy inference flow: + +```bash +python builder/main.py builder/samples/dummy/dummy.yaml -a builder/samples/dummy/openapi.yaml -c builder/samples/dummy/processors.py -o builder/samples/dummy --server-type fastapi -t +``` +Run the dummy inference flow: + +```bash +cd builder/samples +docker compose up --build dry-run +``` +Test the dummy inference flow: + +Go to http://localhost:8800/docs to see the swagger ui. + + + + + diff --git a/tools/inference_builder/builder/samples/dummy/dummy.yaml b/tools/inference_builder/builder/samples/dummy/dummy.yaml new file mode 100644 index 00000000..54f6f1b1 --- /dev/null +++ b/tools/inference_builder/builder/samples/dummy/dummy.yaml @@ -0,0 +1,233 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# the name will be used to identify the microservice. +name: dummy +# the root path to the directory containing the model files. +model_repo: /workspace/.cache/model-repo/ + +# top level inference input specification +input: + # a text input typically for LLM prompts + - name: "text" + data_type: TYPE_STRING + dims: [ -1 ] + optional: true + # base64 encoded image inputs typically for computer vision tasks + - name: "images" + data_type: TYPE_CUSTOM_IMAGE_BASE64 + dims: [ -1 ] + optional: true + # a list of asset ids for image files + - name: "image_assets" + data_type: TYPE_CUSTOM_IMAGE_ASSETS + dims: [ -1 ] + optional: true + # a list of asset ids for video files + - name: "video_assets" + data_type: TYPE_CUSTOM_LONG_VIDEO_ASSETS + dims: [ -1 ] + optional: true + +# top level inference output specification +output: + - name: "output" + data_type: TYPE_FP32 + dims: [ -1, -1, -1 ] + +server: + responders: + infer: + operation: inference + requests: + InferenceRequest: > + { + {% set input_items = request.input %} + {% if input_items|length > 0 %} + {% if input_items[0] is string %} + "images": [ + {% for item in input_items %} + {{ item|tojson }}{% if not loop.last %}, {% endif %} + {% endfor %} + ] + {% if request.text is not none %} + , + "text": [ + {% for item in request.text %} + {{ item|tojson }}{% if not loop.last %}, {% endif %} + {% endfor %} + ] + {% endif %} + {% elif input_items[0] is mapping %} + {% if input_items[0].contentType.startswith("image/") %} + "image_assets": [ + {% for item in input_items %} + {{ item.id|tojson }}{% if not loop.last %}, {% endif %} + {% endfor %} + ] + {% if request.text is not none %} + , + "text": [ + {% for item in request.text %} + {{ item|tojson }}{% if not loop.last %}, {% endif %} + {% endfor %} + ] + {% endif %} + {% elif input_items[0].contentType.startswith("video/") %} + "video_assets": [ + {% for item in input_items %} + {{ item.id|tojson }}{% if not loop.last %}, {% endif %} + {% endfor %} + ] + {% if request.text is not none %} + , + "text": [ + {% for item in request.text %} + {{ item|tojson }}{% if not loop.last %}, {% endif %} + {% endfor %} + ] + {% endif %} + {% endif %} + {% endif %} + {% endif %} + } + responses: + InferenceResponse: > + { + "data": [ + {% for item in response.output %} { + "index": {{loop.index0}}, + "output": {{item | tojson}} + } {% if not loop.last %}, {% endif %} {% endfor %} ], + "usage": { "num_images": 1}, + "model": "nvidia/nvdinov2" + } + add_file: + operation: add_media_file + responses: + AddFileResponse: > + { + "data": { + "id": {{response.id|tojson}}, + "path": {{response.path|tojson}}, + "size": {{response.size|tojson}}, + "duration": {{response.duration|tojson}}, + "contentType": {{response.mime_type|tojson}} + } + } + add_live_stream: + operation: add_live_stream + requests: + AddLiveStreamRequest: > + { + "url": {{request.url|tojson}}, + "description": {{request.description|tojson}}, + "username": {{request.username|tojson}}, + "password": {{request.password|tojson}} + } + responses: + AddLiveStreamResponse: > + { + "data": { + "id": {{response.id|tojson}}, + "path": {{response.path|tojson}}, + "size": {{response.size|tojson}}, + "contentType": {{response.mime_type|tojson}}, + "description": {{response.description|tojson}} + } + } + del_file: + operation: delete_media_file + responses: + DeleteAssetResponse: > + { + "deleted": {{response.status|tojson}} + } + del_live_stream: + operation: delete_live_stream + responses: + DeleteAssetResponse: > + { + "deleted": {{response.status|tojson}} + } + list_files: + operation: list_media_files + responses: + ListFilesResponse: > + { + "data": [ + {% for item in response.assets %} { + "id": {{item.id|tojson}}, + "path": {{item.path|tojson}}, + "size": {{item.size|tojson}}, + "contentType": {{item.mime_type|tojson}} + } {% if not loop.last %}, {% endif %} {% endfor %} + ] + } + list_live_streams: + operation: list_live_streams + responses: + ListLiveStreamsResponse: > + { + "data": [ + {% for item in response.assets %} { + "id": {{item.id|tojson}}, + "path": {{item.path|tojson}}, + "size": {{item.size|tojson}}, + "contentType": {{item.mime_type|tojson}} + } {% if not loop.last %}, {% endif %} {% endfor %} + ] + } + healthy_ready: + operation: health_ready_v1_health_ready_get + +models: + - name: dummy + backend: "dummy" + max_batch_size: 1 + parameters: {} + input: + - name: images + data_type: TYPE_FP32 + dims: [3, 768, 768] + - name: input_ids + data_type: TYPE_INT32 + dims: [ -1 ] + - name: input_lengths + data_type: TYPE_INT32 + dims: [ 1 ] + output: + - name: output + data_type: TYPE_FP32 + dims: [10, 10] + preprocessors: + - kind: "custom" + name: "dummy-preprocessor" + input: ["images"] + output: ["images"] + config: + network_size: [3, 768, 768] + - kind: "custom" + name: "dummy-tokenizer" + input: ["text"] + output: ["input_ids", "input_lengths"] + +routes: { + ':["images", "text"]': 'dummy', + ':["image_assets", "text"]': 'dummy:["images", "text"]', + ':["video_assets", "text"]': 'dummy:["images", "text"]', + 'dummy:["output"]': ':' +} + diff --git a/tools/inference_builder/builder/samples/dummy/openapi.yaml b/tools/inference_builder/builder/samples/dummy/openapi.yaml new file mode 100644 index 00000000..342734d6 --- /dev/null +++ b/tools/inference_builder/builder/samples/dummy/openapi.yaml @@ -0,0 +1,499 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +openapi: 3.1.0 +info: + title: NVIDIA INFERENCE SERVICE + description: NVIDIA INFERENCE SERVICE + version: "1.0.0" + termsOfService: https://nvidia.com/legal/terms-of-use + contact: + name: NVIDIA Support + url: https://help.nvidia.com/ + license: + name: NVIDIA AI Foundation Models Community License + url: https://docs.nvidia.com/ai-foundation-models-community-license.pdf +paths: + /v1/health/ready: + get: + tags: + - "NVIDIA METROPOLIS INFERENCE API" + summary: Health Ready + description: Provide your implementation of readiness to know when container + ready to accept traffic + operationId: health_ready_v1_health_ready_get + responses: + '200': + description: Successful Response + content: + application/json: + schema: {} + /v1/files: + post: + tags: + - Files + summary: Upload a media file that can be used as inference input. + description: Upload media files to be used as inference input. Currently, only images upto 50 MB are supported. + operationId: add_media_file + requestBody: + required: true + content: + multipart/form-data: + schema: + $ref: '#/components/schemas/AddFileRequest' + responses: + "200": + description: Successful Response. + content: + application/json: + schema: + $ref: '#/components/schemas/AddFileResponse' + "500": + description: The invocation ended with an error. + content: + application/json: + schema: + $ref: '#/components/schemas/Errors' + get: + tags: + - Files + summary: Returns a list of the uploaded files. + description: Returns a list that includes ID, file name, size, purpose, file size, and media type for the uploaded files. + operationId: list_media_files + responses: + "200": + description: Successful Response. + content: + application/json: + schema: + $ref: '#/components/schemas/ListFilesResponse' + "500": + description: The invocation ended with an error. + content: + application/json: + schema: + $ref: '#/components/schemas/Errors' + /v1/files/{file_id}: + delete: + tags: + - Files + summary: Delete a uploaded file. + description: Deletes an uploaded file based on ID. + operationId: delete_media_file + parameters: + - name: file_id + in: path + required: true + schema: + type: string + maxLength: 36 + pattern: '[0-9a-fA-F]{8}-?[0-9a-fA-F]{4}-?4[0-9a-fA-F]{3}-?[89abAB][0-9a-fA-F]{3}-?[0-9a-fA-F]{12}' + description: File having 'file_id' to be deleted. + title: File Id + description: File having 'file_id' to be deleted. + responses: + "200": + description: Successful Response. + content: + application/json: + schema: + $ref: '#/components/schemas/DeleteAssetResponse' + "500": + description: The invocation ended with an error. + content: + application/json: + schema: + $ref: '#/components/schemas/Errors' + /v1/live-streams: + post: + tags: + - Live Streams + summary: Add a live stream + description: Add a live stream as an asset + operationId: add_live_stream + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/AddLiveStreamRequest' + responses: + "200": + description: Successful Response. + content: + application/json: + schema: + $ref: '#/components/schemas/AddLiveStreamResponse' + "500": + description: The invocation ended with an error. + content: + application/json: + schema: + $ref: '#/components/schemas/Errors' + get: + tags: + - Live Streams + summary: List live streams + description: List all live streams + operationId: list_live_streams + responses: + "200": + description: Successful Response. + content: + application/json: + schema: + $ref: '#/components/schemas/ListLiveStreamsResponse' + "500": + description: The invocation ended with an error. + content: + application/json: + schema: + $ref: '#/components/schemas/Errors' + /v1/live-streams/{live_stream_id}: + delete: + tags: + - Live Streams + summary: Delete a live stream + description: Delete a live stream based on ID + operationId: delete_live_stream + parameters: + - name: live_stream_id + in: path + required: true + schema: + type: string + maxLength: 36 + pattern: '[0-9a-fA-F]{8}-?[0-9a-fA-F]{4}-?4[0-9a-fA-F]{3}-?[89abAB][0-9a-fA-F]{3}-?[0-9a-fA-F]{12}' + description: Live stream having 'live_stream_id' to be deleted. + title: Live Stream Id + description: Live stream having 'live_stream_id' to be deleted. + responses: + "200": + description: Successful Response. + content: + application/json: + schema: + $ref: '#/components/schemas/DeleteAssetResponse' + "500": + description: The invocation ended with an error. + content: + application/json: + schema: + $ref: '#/components/schemas/Errors' + /v1/inference: + post: + tags: + - "NVIDIA INFERENCE API" + summary: Inference sample + description: "Invokes inference for the input images or videos. + The input image can be Base64 encoded string in the request or it can be + specified using the file identifier of an uploaded file. " + operationId: inference + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/InferenceRequest' + required: true + responses: + "200": + description: Invocation is fulfilled. + content: + application/json: + schema: + $ref: '#/components/schemas/InferenceResponse' + application/x-ndjson: + schema: + $ref: '#/components/schemas/InferenceResponse' + "500": + description: The invocation ended with an error. + content: + application/json: + schema: + $ref: '#/components/schemas/Errors' +security: + - Token: [] + +components: + securitySchemes: + Token: + type: http + scheme: bearer + schemas: + MediaAsset: + properties: + id: + type: string + maxLength: 36 + pattern: '[0-9a-fA-F]{8}-?[0-9a-fA-F]{4}-?4[0-9a-fA-F]{3}-?[89abAB][0-9a-fA-F]{3}-?[0-9a-fA-F]{12}' + title: Id + description: The file identifier (asset_id), which can be referenced in the API endpoints. + description: + type: string + title: Description + description: Description of the asset. + path: + type: string + maxLength: 256 + pattern: ^(?:[a-zA-Z][a-zA-Z0-9+.-]*:(?://[^/?#]*)?)?(?:[A-Za-z0-9_.\-~:/?#\[\]@!$&'()*+,;=%]*|(?:[A-Za-z]:)?[\\/]?[A-Za-z0-9_.\-~:/?#\[\]@!$&'()*+,;=%\\]*)$ + title: Filename + description: Local path of the file or URI with optional protocol. + examples: + - myfile.jpg + - /tmp/assets/name.jpg + - file:///tmp/myfile.jpg + - rtsp://camera.example.com/stream + - udp://192.168.1.100:5000 + - rtsp:camera.example.com/stream + - udp:192.168.1.100:5000 + - C:\Users\username\Documents\file.jpg + - \\server\share\file.jpg + - https://example.com/image.jpg + - ftp://ftp.example.com/file.mp4 + size: + type: integer + format: int64 + title: Size + description: Size of the file in bytes. + duration: + type: integer + format: int64 + title: Duration + description: Duration of the file in nanoseconds. + contentType: + type: string + title: Content Type + description: MIME type of the file. + examples: + - image/jpeg + additionalProperties: false + type: object + required: + - id + - path + title: MediaAsset + description: Represents a media asset. + AddFileResponse: + properties: + data: + $ref: '#/components/schemas/MediaAsset' + additionalProperties: false + type: object + required: + - data + title: AddFileResponse + description: Response schema for the add file request. + AddFileRequest: + properties: + file: + type: string + maxLength: 52428800 + format: binary + title: File + description: File object (not file name) to be uploaded. + type: object + required: + - file + title: AddFileRequest + description: Schema for uploading media files. + AddLiveStreamResponse: + properties: + data: + $ref: '#/components/schemas/MediaAsset' + additionalProperties: false + type: object + required: + - data + title: AddLiveStreamResponse + description: Response schema for the add live stream request. + additionalProperties: false + AddLiveStreamRequest: + properties: + url: + type: string + title: URL + description: URL of the live stream. + description: + type: string + title: Description + description: Description of the live stream. + username: + type: string + title: Username + description: Username of the live stream. + password: + type: string + title: Password + description: Password of the live stream. + type: object + required: + - url + title: AddLiveStreamRequest + description: Schema for adding live streams. + ListFilesResponse: + properties: + data: + items: + $ref: '#/components/schemas/MediaAsset' + type: array + maxItems: 100000000000000000 + title: Data + additionalProperties: false + type: object + required: + - data + title: ListFilesResponse + description: Response schema for the list files API. + ListLiveStreamsResponse: + properties: + data: + items: + $ref: '#/components/schemas/MediaAsset' + type: array + maxItems: 100000000000000000 + title: Data + additionalProperties: false + type: object + required: + - data + title: ListLiveStreamsResponse + description: Response schema for the list live streams API. + DeleteAssetResponse: + properties: + deleted: + type: boolean + title: Deleted + description: Indicates if the file was deleted + additionalProperties: false + type: object + required: + - deleted + title: DeleteAssetResponse + description: Response schema for the delete asset request. + InferenceRequest: + additionalProperties: false + properties: + input: + type: array + items: + oneOf: + - type: string + pattern: ^.*$ + maxLength: 4294967296 + - $ref: '#/components/schemas/MediaAsset' + maxItems: 64 + title: Input + description: 'The list of images on which you want to perform inference. Images should be in form + of `data:image/{format};base64,{base64encodedimage}`.Accepted formats are `jpg`, `png` and `jpeg`. Or they + can be uploaded media assets.' + text: + type: array + items: + type: string + maxLength: 256 + title: Text + description: 'A list of text to be used for the extra prompt on the inference.' + model: + type: string + enum: [nvidia/nvdino-v2, nvidia/c-radio-p3-v3, nvidia/convnext] + title: Model + description: ID of the CV model. + example: nvidia/nvdino-v2 + type: object + title: InferenceRequest + description: List of input images for the inference request. + InferenceResponse: + type: object + properties: + data: + items: + $ref: '#/components/schemas/InferenceResult' + type: array + title: Data + description: The list of inference data generated by the model. + maxItems: 10 + model: + type: string + description: Model used to generate masks. + example: nvidia/nvdino + title: Model + usage: + $ref: '#/components/schemas/Usage' + description: Usage statistics for the masks request. + examples: + - num_images: 1 + additionalProperties: false + title: InferenceResponse + description: Response schema for the inference request. + InferenceResult: + type: object + description: Represents the inference result + additionalProperties: true + properties: + index: + type: integer + format: int32 + title: Index + description: The index of the mask in the list of masks. + minimum: 0 + maximum: 127 + required: + - index + - timestamp + title: InferenceResult + Usage: + properties: + num_images: + type: integer + format: int32 + title: Num Images + minimum: 0 + maximum: 64 + description: Number of images processed. + additionalProperties: false + required: + - num_images + title: Usage + Errors: + type: object + properties: + type: + type: string + maxLength: 128 + description: Error type + title: + type: string + maxLength: 128 + description: Error title + status: + type: integer + format: int32 + minimum: 100 + maximum: 999 + description: Error status code + detail: + type: string + maxLength: 1024 + description: Detailed information about the error + instance: + type: string + maxLength: 256 + description: Function instance used to invoke the request + required: + - type + - title + - status + - detail + - instance + title: Errors diff --git a/tools/inference_builder/builder/samples/dummy/processors.py b/tools/inference_builder/builder/samples/dummy/processors.py new file mode 100644 index 00000000..859c5ce8 --- /dev/null +++ b/tools/inference_builder/builder/samples/dummy/processors.py @@ -0,0 +1,44 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import numpy as np +import torch + +class DummyPreprocessor: + name = "dummy-preprocessor" + def __init__(self, config): + self.network_size = config['network_size'] + + def __call__(self, *args): + if len(args) != 1: + raise ValueError("DummyPreprocessor expects exactly one argument") + input = args[0] + if len(input.shape) != 3: + raise ValueError("DummyPreprocessor expects a 3D tensor") + if isinstance(input, np.ndarray) and input.dtype != np.uint8: + raise ValueError("DummyPreprocessor expects a uint8 tensor") + if isinstance(input, torch.Tensor) and input.dtype != torch.uint8: + raise ValueError("DummyPreprocessor expects a uint8 tensor") + return np.random.randn(*self.network_size) + + +class DummyTokenizer: + name = "dummy-tokenizer" + def __init__(self, config): + pass + + def __call__(self, *args): + return np.random.randint(0, 100, size=10), np.array([10]) diff --git a/tools/inference_builder/builder/samples/dummy/test_client.py b/tools/inference_builder/builder/samples/dummy/test_client.py new file mode 100644 index 00000000..d7412ab7 --- /dev/null +++ b/tools/inference_builder/builder/samples/dummy/test_client.py @@ -0,0 +1,86 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os +import mimetypes +import requests +from requests_toolbelt.multipart.encoder import MultipartEncoder + + +def main(host, port, image_path, video_path, text): + invoke_url = "http://" + host + ":" + port + "/v1/inference" + upload_url = "http://" + host + ":" + port + "/v1/files" + image_data =[] + video_data = [] + if image_path: + for image in image_path: + multipart_data = MultipartEncoder( + fields={ + 'file': (os.path.basename(image), open(image, 'rb'), mimetypes.guess_type(image)[0]) + } + ) + response = requests.post(upload_url, headers={"Content-Type": multipart_data.content_type}, data=multipart_data) + image_data.append(response.json()["data"]) + print(f"image_data uploaded: {image_data}") + payload = { + "input": image_data, + "text": text, + "model": "nvidia/nvdino-v2" + } + headers = { + "Content-Type": "application/json", + "accept": "application/json" + } + response = requests.post(invoke_url, headers=headers, json=payload) + print(f"Inference result from image data: {response.json()}") + + if video_path: + for video in video_path: + multipart_data = MultipartEncoder( + fields={ + 'file': (os.path.basename(video), open(video, 'rb'), mimetypes.guess_type(video)[0]) + } + ) + response = requests.post(upload_url, headers={"Content-Type": multipart_data.content_type}, data=multipart_data) + video_data.append(response.json()["data"]) + print(f"video_data uploaded: {video_data}") + + payload = { + "input": video_data, + "text": text, + "model": "nvidia/nvdino-v2" + } + headers = { + "Content-Type": "application/json", + "accept": "application/x-ndjson" + } + stream_response = requests.post(invoke_url, headers=headers, json=payload, stream=True) + for line in stream_response.iter_lines(): + if line: + print(f"Inference result from video data: {line}") + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--host", type=str, + help= "Server IP Address", default="127.0.0.1") + parser.add_argument("--port", type=str, + help="Server port", default="8000") + parser.add_argument("--image-path", type=str, help="Image path to send for inference", nargs='*', default=None) + parser.add_argument("--video-path", type=str, help="Video path to send for inference", nargs='*', default=None) + parser.add_argument("--text", type=str, help="Extra text to send for inference", nargs='*', default=None) + args = parser.parse_args() + main(args.host, args.port, args.image_path, args.video_path, args.text) \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/nvclip/Dockerfile b/tools/inference_builder/builder/samples/nvclip/Dockerfile new file mode 100644 index 00000000..c1cd4ed7 --- /dev/null +++ b/tools/inference_builder/builder/samples/nvclip/Dockerfile @@ -0,0 +1,75 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Sample Dockerfile for an NVCLIP embedding model pipeline that uses +# DeepStream for hardware-accelerated video decoding and a +# TensorRT backend for inference. This is a reference for this specific +# sample, not a universal template for all deployments.em +FROM "nvcr.io/nvidia/deepstream:9.0-triton-multiarch" AS builder_base + +# Install Python dependencies required by THIS NVCLIP sample (CLIP +# model, TensorRT bindings, FastAPI server, etc.). Versions are pinned +# for reproducibility and can be adjusted for other pipelines. +RUN --mount=type=cache,target=/root/.cache/pip \ + pip install \ + requests==2.32.3 \ + python-multipart==0.0.9 \ + aiofiles==23.2.1 \ + aiohttp==3.11.0 \ + annotated-types==0.7.0 \ + python-magic==0.4.27 \ + Pillow==11.1.0 \ + open_clip_torch==3.2.0 \ + numpy==1.26.4 \ + omegaconf==2.3.0 \ + transformers==4.48.1 \ + fastapi==0.115.12 \ + uvicorn==0.29.0 \ + tensorrt==10.14.1.48.post1 --extra-index-url https://pypi.nvidia.com \ + polygraphy==0.49.24 +ENV TRANSFORMERS_CACHE=/tmp + +WORKDIR /workspace + +# Copy the inference flow package generated by Inference Builder. This +# tarball (here: nvclip.tgz) is typically produced when running the +# builder with tarball output enabled, and its name should match the +# generated pipeline artifact you want to deploy. +ADD nvclip.tgz /workspace + +RUN groupadd --gid 1000 --non-unique nvs && \ + useradd --create-home --shell /usr/sbin/nologin --uid 1000 --non-unique --gid 1000 nvs && \ + chown 1000.1000 /workspace + +USER nvs:1000 + +# Create the entrypoint script that runs the Inference Builder‑generated +# /workspace/__main__.py for this NVCLIP pipeline. Uncomment the +# while‑loop or alternative ENTRYPOINT for debugging if desired. +RUN touch /workspace/start_server.sh && \ + chmod a+rx /workspace/start_server.sh && \ + cat > /workspace/start_server.sh <<-EOF + #!/usr/bin/env bash + set -eu + + # SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + # SPDX-License-Identifier: LicenseRef-NvidiaProprietary + python3 /workspace/__main__.py +# while true; do sleep 5; done +EOF + +ENTRYPOINT ["/workspace/start_server.sh"] +#ENTRYPOINT ["/bin/bash"] +CMD [] \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/nvclip/README.md b/tools/inference_builder/builder/samples/nvclip/README.md new file mode 100644 index 00000000..586a3156 --- /dev/null +++ b/tools/inference_builder/builder/samples/nvclip/README.md @@ -0,0 +1,88 @@ +# Introduction + +This sample demonstrate how to create the inference microservice for an embedding model with TensorRT backend (polygrapy) and fastapi server. + +We provide a sample Dockerfile for the example, which you can use to build a Docker image and test the microservice on any x86 system with an NVIDIA Ampere, Hopper, and Blackwell GPU. + +# Prerequisites + +**Note:** Make sure you are in the root directory (`path/to/inference_builder`) to execute the commands in this README. All relative paths and commands assume you are running from the inference_builder root directory. Also ensure that your virtual environment is activated before running any commands. + +The model used in this sample can be found from NGC: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/nvclip_vit. Make sure you have the proper access right to the models, and download the checkpoints. + +Before downloading the model file, you need to set up your model repository: + +```bash +mkdir -p ~/.cache/model-repo && chmod 777 ~/.cache/model-repo +export MODEL_REPO=~/.cache/model-repo +``` + +## Download the model file using NGC CLI: + +```bash +ngc registry model download-version "nvidian/tao-nvaie/multi_modal_foundation_models:NVCLIP_224_700M_ViTH14" --dest /tmp +chmod 666 /tmp/multi_modal_foundation_models_vNVCLIP_224_700M_ViTH14/* +``` + +Run `ls /tmp/multi_modal_foundation_models_vNVCLIP_224_700M_ViTH14 -l` and be sure we have the checkpoint file `nvclip_clipa_vit_h14_700M.ckpt`. + +**Note:** If NGC commands fail, make sure you have access to the models you are trying to download. Some models require an active subscription. Ensure NGC is set up properly, or alternatively try using the NGC web UI to directly download the model from the links provided above within the prerequisites section. + +## Create the optimized TensorRT Engine: + +Prior to using the model for accelerated inference, we need to convert the checkpoints into onnx files and generate TensorRT engines accordingly. + +First build the container image for TensorRT optimizer: + +```bash +docker build -t trt-optimizer builder/samples/nvclip/optimizer +``` + +Then generate the engine files using the trt-optimizer + +```bash +docker login nvcr.io +docker run -it --rm --gpus all \ + -v $MODEL_REPO:/workspace/checkpoints/optimized \ + -v /tmp/multi_modal_foundation_models_vNVCLIP_224_700M_ViTH14:/workspace/checkpoints/baseline \ + -e CHECKPOINT_NAME=nvclip_clipa_vit_h14_700M.ckpt \ + trt-optimizer +``` + +Run `ls $MODEL_REPO/`, If the above process runs correctly, there'll be 2 folders created under model-repo directory: +- nvclip_clipa_vit_h14_700M_vision: for vision encoder +- nvclip_clipa_vit_h14_700M_text: for text encoder + +Go to inference_builder folder, adjust the permissions of each folder and copy the model config from source tree to the model folder: + +```bash +sudo chmod 777 $MODEL_REPO/nvclip_clipa_vit_h14_700M_text $MODEL_REPO/nvclip_clipa_vit_h14_700M_vision +cp builder/samples/nvclip/optimizer/configs/* $MODEL_REPO/nvclip_clipa_vit_h14_700M_text +``` + +# Build the inference flow + +Assume you've followed the [top level instructions](../../../README.md#getting-started) to set up the environment. + +```bash +source .venv/bin/activate +python builder/main.py builder/samples/nvclip/tensorrt_nvclip.yaml -a builder/samples/nvclip/openapi.yaml -c builder/samples/nvclip/processors.py -o builder/samples/nvclip --server-type fastapi -t +``` + +# Build and run the docker image + +```bash +cd builder/samples +docker compose up --build ms-nvclip +``` + +# Test the microservice with a client + +After the server is successfully started, open a new terminal in your inference_builder folder to launch the client with a jpeg or png file. The embeddings generated by the model will be returned in the json payload. + +**important:** ** is a place holder, please replace it with the actual image path** + +```bash +source .venv/bin/activate && cd builder/samples/nvclip +./test_client.sh +``` \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/nvclip/openapi.yaml b/tools/inference_builder/builder/samples/nvclip/openapi.yaml new file mode 100644 index 00000000..b75e748b --- /dev/null +++ b/tools/inference_builder/builder/samples/nvclip/openapi.yaml @@ -0,0 +1,487 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +openapi: 3.1.0 +info: + title: NVIDIA INFERENCE SERVICE API for nvidia/nvclip-vit-h-14 + description: NVIDIA INFERENCE SERVICE API + version: 1.0.0 + termsOfService: https://nvidia.com/legal/terms-of-use + contact: + name: NVIDIA Support + url: https://help.nvidia.com/ + license: + name: NVIDIA AI Foundation Models Community License + url: https://docs.nvidia.com/ai-foundation-models-community-license.pdf +tags: + - name: "NVIDIA NVCLIP API" + description: Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms. +paths: + "/v1/embeddings": + post: + tags: + - Embeddings + summary: Creates an embedding vector representing the input text or image. + description: "Invokes inference using the embedding parameters. If uploading + large images, this POST should be used in conjunction with the NVCF API which + allows for the upload of large assets. \nYou can find details on how to use + NVCF Asset APIs here: https://docs.api.nvidia.com/cloud-functions/reference/createasset." + operationId: create_embedding + requestBody: + content: + application/json: + schema: + "$ref": "#/components/schemas/EmbeddingsRequest" + required: true + x-nvai-meta: + name: "Create Text Embedding" + description: | + Generates an embedding vector from the provided text + using a specified model. The embedding can be returned + in either float array or base64-encoded format. + path: "create" + templates: + - title: "Synchronous requests" + requestEjs: + curl: | + curl -X POST https://integrate.api.nvidia.com/v1/embeddings \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $NVIDIA_API_KEY" \ + -d '{ + "input": ["Image of a dog", + 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"], + "model": "nvidia/nvclip-vit-h-14", + "encoding_format": "float" + }' + python: | + from openai import OpenAI + + client = OpenAI( + api_key="$NVIDIA_API_KEY", + base_url="https://integrate.api.nvidia.com/v1" + ) + + response = client.embeddings.create( + input=[ "Image of a dog", + 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"], + model="nvidia/nvclip-vit-h-14", + encoding_format="float" + ) + + print(response.data[0].embedding) + node: | + import OpenAI from 'openai'; + + const openai = new OpenAI({ + apiKey: '$NVIDIA_API_KEY', + baseURL: 'https://integrate.api.nvidia.com/v1/', + }) + + async function main() { + const response = await openai.embeddings.create({ + input: ["The quick brown fox jumped over the lazy dog", + 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" + ], + model: "nvidia/nvclip-vit-h-14", + encoding_format: "float" + }) + + const embedding = response.data[0].embedding; + process.stdout.write(`${embedding}\n`); + } + + main(); + + response: > + { + "object":"list", + 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+ "object":"embedding"}], + "usage":{"num_images":1,"prompt_tokens":77,"total_tokens":77}, + "model":"nvidia/nvclip-vit-h-14" + } + responses: + '200': + description: Invocation is fulfilled. + content: + application/json: + schema: + "$ref": "#/components/schemas/EmbeddingsResponse" + '402': + description: Payment Required. + content: + application/json: + schema: + properties: + detail: + type: string + maxLength: 256 + format: ^[a-zA-Z-]{1,64}$ + description: Contains specific information related to the error and why it occurred. + example: You have reached your limit of credits. + type: object + title: PaymentRequiredError + '422': + description: Validation failed, provided entity could not be processed. + content: + application/json: + schema: + "$ref": "#/components/schemas/Errors" + example: + type: urn:nvcf-worker-service:problem-details:unprocessable-entity + title: Unprocessable Entity + status: 422 + detail: string + instance: "/v2/nvcf/pexec/functions/bc205f8e-1740-40df-8d32-c4321763498a" + requestId: 3fa85f64-5717-4562-b3fc-2c963f66afa6 + '500': + description: The invocation ended with an error. + content: + application/json: + schema: + "$ref": "#/components/schemas/Errors" + example: + type: urn:nvcf-worker-service:problem-details:internal-server-error + title: Internal Server Error + status: 500 + detail: string + instance: "/v2/nvcf/pexec/functions/bc205f8e-1740-40df-8d32-c4321763498a" + requestId: 3fa85f64-5717-4562-b3fc-2c963f66afa6 +security: +- Token: [] +components: + securitySchemes: + Token: + type: http + scheme: bearer + schemas: + NIMAssetInfoResponse: + properties: + assetName: + type: string + title: Assetname + description: The name of the asset used by NIMs. + url: + type: string + title: Url + description: The url of the asset being used. + checksum: + type: string + title: Checksum + description: The unique identifier for asset. + type: object + required: + - assetName + - url + - checksum + title: NIMAssetInfoResponse + NIMHealthSuccessResponse: + properties: + object: + type: string + title: Object + default: health.response + message: + type: string + title: Message + type: object + required: + - message + title: NIMHealthSuccessResponse + NIMLicenseInfoResponse: + properties: + name: + type: string + title: Name + description: The name of the NIMs license. + path: + type: string + title: Path + description: The path of the NIMs license file inside the container. + sha: + type: string + title: Sha + description: The unique identifier of the license. + size: + type: integer + title: Size + description: The size of the license file in bytes. + url: + type: string + title: Url + description: The url of the NIMs license. + type: + type: string + title: Type + description: The content type of the license. + content: + type: string + title: Content + description: The content inside the NIMs license file. + type: object + required: + - name + - path + - sha + - size + - url + - type + - content + title: NIMLicenseInfoResponse + NIMMetadataResponse: + properties: + version: + type: string + title: version + description: The version of the NIM service. + modelInfo: + items: + $ref: '#/components/schemas/NIMModelInfoResponse' + type: array + title: Modelinfo + description: The list of information of models being served inside NIMs + container + default: [] + assetInfo: + items: + $ref: '#/components/schemas/NIMAssetInfoResponse' + type: array + title: Assetinfo + description: The list of different assets used by the NIMs + default: [] + licenseInfo: + allOf: + - $ref: '#/components/schemas/NIMLicenseInfoResponse' + description: The information of the license of released NIMs + type: object + required: + - version + - licenseInfo + title: NIMMetadataResponse + NIMModelInfoResponse: + properties: + shortName: + type: string + title: Shortname + description: The name of the model served by NIMs + modelUrl: + type: string + title: Modelurl + description: The URL from which the model is downloaded. + type: object + required: + - shortName + - modelUrl + title: NIMModelInfoResponse + EmbeddingsRequest: + additionalProperties: false + properties: + input: + oneOf: + - type: string + title: string + example: "This is a test." + pattern: ^.*$ + - type: array + items: + type: string + pattern: ^.*$ + maxItems: 64 + title: Input + description: 'The list of images or texts that you want to generate embeddings + for. Images should be in form of `data:image/{format};base64,{base64encodedimage}`. + If the size of an image is more than 200KB, it needs to be uploaded to + a presigned S3 bucket using NVCF Asset APIs. Once uploaded you can refer + to it using the following format: ``. Accepted formats are `jpg`, `png` and `jpeg`.' + examples: + - - The quick brown fox jumped over the lazy dog + - data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAIAAACQd1PeAAAAEElEQVR4nGK6HcwNCAAA//8DTgE8HuxwEQAAAABJRU5ErkJggg== + - - The quick brown fox jumped over the lazy dog + - data:image/png;asset_id,87b132b0-08f9-43ea-8fab-f107350a5d00 + encoding_format: + type: string + enum: + - float + - base64 + title: Encoding Format + description: "The format to return the embeddings in. Can be either `float` or `base64`." + default: float + model: + type: string + enum: [nvidia/nvclip-vit-h-14] + title: Model + description: ID of the embedding model. + examples: nvidia/nvclip-vit-h-14 + type: object + required: + - input + - model + title: EmbeddingsRequest + EmbeddingsResponse: + type: object + properties: + object: + description: 'The object type, which is always `list`.' + enum: + - list + title: Object + type: string + data: + items: + "$ref": "#/components/schemas/Embedding" + type: array + title: Data + description: The list of embeddings generated by the model. + maxItems: 128 + examples: + - - embedding: + - 0.1 + - 0.1 + - 0.1 + index: 0 + object: embedding + - embedding: + - 0.1 + - 0.1 + - 0.1 + index: 1 + object: embedding + model: + type: string + description: Model used to generate embeddings. + example: nvidia/nvclip-vit-h-14 + title: Model + const: nvidia/nvclip-vit-h-14 + usage: + allOf: + - "$ref": "#/components/schemas/Usage" + description: Usage statistics for the embeddings request. + examples: + - num_images: 1 + prompt_tokens: 80 + total_tokens: 80 + required: + - object + - data + - usage + - model + title: EmbeddingsResponse + Embedding: + type: object + description: Represents an embedding vector returned by embedding endpoint. + properties: + object: + description: 'The object type, which is always `embedding`.' + enum: + - embedding + title: Object + type: string + index: + type: integer + format: int32 + title: Index + description: The index of the embedding in the list of embeddings. + minimum: 0 + maximum: 127 + embedding: + oneOf: + - type: array + description: | + The embedding vector as a list of floats, The length of the vector depends on the model. + items: + - type: number + maxItems: 1024 + - type: string + description: | + The embedding vector as base64 string. The length of the vector depends on the model. + format: ^(?:[A-Za-z0-9+/]{4})*(?:[A-Za-z0-9+/]{4}|[A-Za-z0-9+/]{3}=|[A-Za-z0-9+/]{2}==)$ + title: Embedding + required: + - index + - object + - embedding + title: Embeddings + Usage: + properties: + num_images: + type: integer + format: int32 + title: Num Images + minimum: 0 + maximum: 64 + description: Number of images passed. + prompt_tokens: + type: integer + format: int32 + title: Prompt Tokens + description: Number of tokens in the prompt. + minimum: 77 + maximum: 4928 + total_tokens: + type: integer + format: int32 + title: Total Tokens + description: Total number of tokens used in the request. + minimum: 77 + maximum: 4928 + type: object + required: + - num_images + - prompt_tokens + - total_tokens + title: Usage + Errors: + properties: + type: + type: string + format: "^.{1, 128}$" + maxLength: 128 + description: Error type + title: + type: string + format: "^.{1, 128}$" + maxLength: 128 + description: Error title + status: + type: integer + format: int32 + minimum: 100 + maximum: 999 + description: Error status code + detail: + type: string + format: "^.{1, 1024}$" + maxLength: 1024 + description: Detailed information about the error + instance: + type: string + format: "^.{1, 256}$" + maxLength: 256 + description: Function instance used to invoke the request + requestId: + type: string + format: uuid + maxLength: 36 + description: UUID of the request + type: object + required: + - type + - title + - status + - detail + - instance + - requestId + title: InvokeError diff --git a/tools/inference_builder/builder/samples/nvclip/optimizer/CLIP.png b/tools/inference_builder/builder/samples/nvclip/optimizer/CLIP.png new file mode 100644 index 0000000000000000000000000000000000000000..a1b5ec9171fd7a51e36e845a02304eb837142ba1 GIT binary patch literal 252444 zcmeFZc{tSV`#&tQWUY|MnuLV16|yBuNp`X;jGgR^m{4i4rU==yFH?+tPg$}X>)65+ zW{e3lmSJX|x9;xycHf`x^ZS1Od5+_Gj^p{G_c2Gz^**ojdY!Lxy*$^yqs2teMNdIN 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index 00000000..30fd9fd1 --- /dev/null +++ b/tools/inference_builder/builder/samples/nvclip/optimizer/Dockerfile @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +FROM nvcr.io/nvidia/tritonserver:26.01-py3 + +WORKDIR /workspace/ + +# Environment variables +ENV PIP_NO_CACHE_DIR=off +ENV CUDA_MODULE_LOADING=LAZY +ENV PYTHONUNBUFFERED=1 + +RUN apt update +RUN apt-get install -y git +RUN pip install open_clip_torch==3.2.0 onnx==1.20.1 onnxscript==0.1.0 transformers==4.48.1 tokenizers>=0.21 +RUN pip install --no-cache-dir --extra-index-url https://pypi.ngc.nvidia.com polygraphy + +# Copy files into the container +COPY ./configs ./configs +COPY ./CLIP.png . +COPY ./export_to_onnx.py . +COPY ./optimize.sh . + +ENV MAX_BATCH_SIZE=64 +ENV INPUT_HEIGHT=224 +ENV INPUT_WIDTH=224 +ENV TEXT_LENGTH=77 + +# Make the script executable +RUN chmod +x /workspace/optimize.sh + + +# Set entrypoint to execute the installation script and then run optimize.sh +ENTRYPOINT ["/bin/bash", "-c", "/workspace/optimize.sh"] \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/nvclip/optimizer/configs/NVCLIP_224_700M_ViTH14.json b/tools/inference_builder/builder/samples/nvclip/optimizer/configs/NVCLIP_224_700M_ViTH14.json new file mode 100644 index 00000000..b34a5219 --- /dev/null +++ b/tools/inference_builder/builder/samples/nvclip/optimizer/configs/NVCLIP_224_700M_ViTH14.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 1024, + "init_logit_bias": -10, + "vision_cfg": { + "image_size": 224, + "layers": 32, + "width": 1280, + "head_width": 80, + "patch_size": 14, + "no_ln_pre": true, + "pool_type": "avg", + "final_ln_after_pool": true, + "pos_embed_type": "sin_cos_2d", + "patch_dropout": 0.0 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 32000, + "hf_tokenizer_name": "bert-base-uncased", + "tokenizer_kwargs": { + "strip_sep_token": true + }, + "width": 1024, + "heads": 16, + "layers": 24, + "pool_type": "last", + "no_causal_mask": true + } +} diff --git a/tools/inference_builder/builder/samples/nvclip/optimizer/export_to_onnx.py b/tools/inference_builder/builder/samples/nvclip/optimizer/export_to_onnx.py new file mode 100644 index 00000000..17a6d25a --- /dev/null +++ b/tools/inference_builder/builder/samples/nvclip/optimizer/export_to_onnx.py @@ -0,0 +1,81 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from pathlib import Path + +import open_clip +import torch +from PIL import Image +import os + +# Register the model config +open_clip.add_model_config("configs") + +MODEL = os.environ.get("MODEL_NAME", "NVCLIP_224_700M_ViTH14") +model, _, preprocess = open_clip.create_model_and_transforms( + MODEL, + pretrained=f"/workspace/checkpoints/baseline/{os.environ.get("CHECKPOINT_NAME")}", +) +tokenizer = open_clip.get_tokenizer(MODEL) + +text = tokenizer(["a diagram", "a dog", "a cat"]) +image = preprocess(Image.open("CLIP.png")).unsqueeze(0) + +# Export to onnx +npx = 224 +dummy_image = torch.randn(10, 3, npx, npx) +model.forward(dummy_image,text) # Original CLIP result (1) + +# Vision model +torch.onnx.export(model, (dummy_image, None), + f"/workspace/checkpoints/baseline/nvclip_clipa_vit_h14_700M_vision.onnx", + export_params=True, + input_names=["IMAGE"], + output_names=["LOGITS_PER_IMAGE"], + opset_version=19, + dynamic_axes={ + "IMAGE": { + 0: "image_batch_size", + }, + "LOGITS_PER_IMAGE": { + 0: "image_batch_size", + 1: "text_batch_size", + }, + }, + verbose=True, + dynamo=False +) + + +# Text model +torch.onnx.export(model, (None, text), + f"/workspace/checkpoints/baseline/nvclip_clipa_vit_h14_700M_text.onnx", + export_params=True, + input_names=["TEXT"], + output_names=["LOGITS_PER_TEXT"], + opset_version=19, + dynamic_axes={ + "TEXT": { + 0: "text_batch_size", + }, + "LOGITS_PER_TEXT": { + 0: "text_batch_size", + 1: "image_batch_size", + }, + }, + verbose=True, + dynamo=False +) diff --git a/tools/inference_builder/builder/samples/nvclip/optimizer/optimize.sh b/tools/inference_builder/builder/samples/nvclip/optimizer/optimize.sh new file mode 100644 index 00000000..f036f49f --- /dev/null +++ b/tools/inference_builder/builder/samples/nvclip/optimizer/optimize.sh @@ -0,0 +1,31 @@ +#!/usr/bin/env bash + +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +set -xe + +python3 export_to_onnx.py + +mkdir -p /workspace/checkpoints/optimized/nvclip_clipa_vit_h14_700M_vision +/usr/src/tensorrt/bin/trtexec --onnx=/workspace/checkpoints/baseline/nvclip_clipa_vit_h14_700M_vision.onnx --saveEngine=/workspace/checkpoints/optimized/nvclip_clipa_vit_h14_700M_vision/model.plan --optShapes=IMAGE:${MAX_BATCH_SIZE}x3x${INPUT_HEIGHT}x${INPUT_WIDTH} \ + --minShapes=IMAGE:1x3x${INPUT_HEIGHT}x${INPUT_WIDTH} --maxShapes=IMAGE:${MAX_BATCH_SIZE}x3x${INPUT_HEIGHT}x${INPUT_WIDTH} --fp16 + +mkdir -p /workspace/checkpoints/optimized/nvclip_clipa_vit_h14_700M_text +/usr/src/tensorrt/bin/trtexec --onnx=/workspace/checkpoints/baseline/nvclip_clipa_vit_h14_700M_text.onnx --saveEngine=/workspace/checkpoints/optimized/nvclip_clipa_vit_h14_700M_text/model.plan --optShapes=TEXT:${MAX_BATCH_SIZE}x${TEXT_LENGTH} \ + --minShapes=TEXT:1x${TEXT_LENGTH} --maxShapes=TEXT:${MAX_BATCH_SIZE}x${TEXT_LENGTH} --fp16 + + diff --git a/tools/inference_builder/builder/samples/nvclip/processors.py b/tools/inference_builder/builder/samples/nvclip/processors.py new file mode 100644 index 00000000..ab1deca8 --- /dev/null +++ b/tools/inference_builder/builder/samples/nvclip/processors.py @@ -0,0 +1,69 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import io +from PIL import Image +from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize, InterpolationMode + +class OpenclipTokenizer: + name = "openclip-tokenizer" + def __init__(self, config): + import open_clip + open_clip.add_model_config(config["model_home"]) + self._tokenizer = open_clip.get_tokenizer("NVCLIP_224_700M_ViTH14") + + def __call__(self, *args): + return self._tokenizer(args[0]) + +class VisionPreprocessor: + name = "nvclip-vision-preprocessor" + def __init__(self, config): + self._transform = Compose( + [ + Resize((224, 224), interpolation=InterpolationMode.BICUBIC), + CenterCrop(224), + lambda image: image.convert('RGB'), + ToTensor(), + Normalize( + (0.48145466, 0.4578275, 0.40821073), + (0.26862954, 0.26130258, 0.27577711), + ), + ] + ) + + def __call__(self, *args): + return self._transform(Image.open(io.BytesIO(args[0]))) + +class NvClipPostProcessor: + name = "nvclip-postprocessor" + def __init__(self, config): + self._config = config + + def __call__(self, *args): + text = args[0].tolist() + images = args[1].tolist() + indices = args[2].tolist() + total_tokens = sum(len(s) for s in text) + num_images = len(images) + embeddings = [] + for index in indices: + if index == "text": + embeddings.append(text.pop(0)) + elif index == "image": + embeddings.append(images.pop(0)) + return np.array(embeddings), np.array(total_tokens), np.array(num_images) + + diff --git a/tools/inference_builder/builder/samples/nvclip/tensorrt_nvclip.yaml b/tools/inference_builder/builder/samples/nvclip/tensorrt_nvclip.yaml new file mode 100644 index 00000000..8c7463ca --- /dev/null +++ b/tools/inference_builder/builder/samples/nvclip/tensorrt_nvclip.yaml @@ -0,0 +1,135 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: nvclip +model_repo: /workspace/model-repo/ + +# top level inference input specification +input: + - name: "text" + data_type: TYPE_STRING + dims: [ -1 ] + optional: true + - name: "image" + data_type: TYPE_CUSTOM_BINARY_BASE64 + dims: [ -1 ] + optional: true + - name: "indices" + data_type: TYPE_STRING + dims: [ -1 ] + optional: true + +# top level inference output specification +output: + - name: "embeddings" + data_type: TYPE_FP32 + dims: [-1, -1] + - name: "total_tokens" + data_type: TYPE_INT32 + dims: [ 1 ] + - name: "num_images" + data_type: TYPE_INT32 + dims: [ 1 ] + +server: + responders: + infer: + operation: create_embedding + requests: + # root template for generating input json from the request + EmbeddingsRequest: > + { + "text": [ + {% set text_items = request.input | reject('startswith', 'data:image') | list %} + {% for item in text_items %} + {{ item|tojson }}{% if not loop.last %}, {% endif %} + {% endfor %} + ], + "image": [ + {% set image_items = request.input | select('startswith', 'data:image') | map('replace', 'data:image\/[a-zA-Z0-9.+-]+;base64,', '') | list %} + {% for item in image_items %} + {{ item|tojson }}{% if not loop.last %}, {% endif %} + {% endfor %} + ], + "indices": [ + {% for item in request.input %} + "{{ 'image' if item.startswith('data:image') else 'text' }}"{% if not loop.last %}, {% endif %} + {% endfor %} + ] + } + responses: + EmbeddingsResponse: > + { + "data": [{% for item in response.embeddings %} {"index": {{ loop.index }}, "embedding": {{ item|tojson }}, "object": "embedding"}{% if not loop.last %}, {% endif %}{% endfor %}], + "usage": { "num_images": {{ response.num_images }}, "prompt_tokens": {{ response.total_tokens }}, "total_tokens": {{ response.total_tokens }}}, + "model": "nvidia/nvclip-vit-h-14", + "object": "list" + } + +# list of model specifications for inference +models: + - name: nvclip_clipa_vit_h14_700M_text + backend: "polygraphy" + max_batch_size: 64 + input: + - name: "TEXT" + data_type: TYPE_INT64 + dims: [-1, -1] + output: + - name: "LOGITS_PER_TEXT" + data_type: TYPE_FP32 + dims: [-1, -1] + parameters: + tensorrt_engine: "model.plan" + preprocessors: + - kind: "custom" + name: "openclip-tokenizer" + input: ["text"] + output: ["TEXT"] + - name: nvclip_clipa_vit_h14_700M_vision + backend: "polygraphy" + max_batch_size: 64 + input: + - name: "IMAGE" + data_type: TYPE_FP32 + dims: [-1, 3, -1, -1] + output: + - name: "LOGITS_PER_IMAGE" + data_type: TYPE_FP32 + dims: [-1, -1] + parameters: + tensorrt_engine: "model.plan" + preprocessors: + - kind: "custom" + name: "nvclip-vision-preprocessor" + input: ["image"] + output: ["IMAGE"] +postprocessors: + - kind: "custom" + name: "nvclip-postprocessor" + input: ["LOGITS_PER_TEXT", "LOGITS_PER_IMAGE", "indices"] + output: ["embeddings", "total_tokens", "num_images"] +# route map +# A route is defined as +# Source and destination should be defined as +# Model name is optional, so <:["TENSOR_NAME1", "TENSOR_NAME2", ...]> is valid +# Tensor list is optional too and is valid +routes: { + ':["text"]': 'nvclip_clipa_vit_h14_700M_text', + ':["image"]': 'nvclip_clipa_vit_h14_700M_vision', + ':["indices"]': ':', + 'nvclip_clipa_vit_h14_700M_text:["LOGITS_PER_TEXT"]': ':', + 'nvclip_clipa_vit_h14_700M_vision:["LOGITS_PER_IMAGE"]': ':' +} diff --git a/tools/inference_builder/builder/samples/nvclip/test_client.sh b/tools/inference_builder/builder/samples/nvclip/test_client.sh new file mode 100644 index 00000000..638afcee --- /dev/null +++ b/tools/inference_builder/builder/samples/nvclip/test_client.sh @@ -0,0 +1,32 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +image=$1 +image_b64=$( base64 $image ) + +url="http://localhost:8800/v1/embeddings" +echo '{ + "input": ["Image of a dog", + "data:image/png;base64,'${image_b64}'" + ], + "model": "nvidia/nvclip-vit-h-14", + "encoding_format": "float" + }' > payload.json + +curl -N -X POST $url \ + -H "Content-Type: application/json" \ + -H "$accept_header" \ + -d @payload.json + diff --git a/tools/inference_builder/builder/samples/qwen/Dockerfile b/tools/inference_builder/builder/samples/qwen/Dockerfile new file mode 100644 index 00000000..22c0d8ef --- /dev/null +++ b/tools/inference_builder/builder/samples/qwen/Dockerfile @@ -0,0 +1,83 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Sample Dockerfile for a Qwen multimodal pipeline using a TensorRT-LLM +# release image combined with DeepStream components for video input and +# hardware-accelerated decoding. +# +# Stage 1: pull in DeepStream libraries, binaries, and sample assets. +FROM "nvcr.io/nvidia/deepstream:9.0-triton-multiarch" AS ds_stage + +FROM nvcr.io/nvidia/tensorrt-llm/release:1.1.0 AS inference_base + +ENV NVIDIA_DRIVER_CAPABILITIES $NVIDIA_DRIVER_CAPABILITIES,video + +# Install GStreamer plugins and DeepStream runtime dependencies needed +# for hardware-accelerated decoding and RTSP support. +RUN apt update && apt install -y --no-install-recommends gstreamer1.0-plugins-good gstreamer1.0-plugins-bad \ + libyaml-cpp-dev libgstrtspserver-1.0-0 libjsoncpp25 libgles2 pkg-config + +COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/lib /opt/nvidia/deepstream/deepstream-9.0/lib +COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/bin /opt/nvidia/deepstream/deepstream-9.0/bin +COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/*.sh /opt/nvidia/deepstream/deepstream-9.0/ +COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/*.txt /opt/nvidia/deepstream/deepstream-9.0/ +COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/README* /opt/nvidia/deepstream/deepstream-9.0/ +COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/*.pdf /opt/nvidia/deepstream/deepstream-9.0/ +COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/service-maker /opt/nvidia/deepstream/deepstream-9.0/service-maker +COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/samples/streams/* /opt/nvidia/deepstream/deepstream-9.0/samples/streams/ + +RUN /opt/nvidia/deepstream/deepstream-9.0/install.sh +RUN ln -sf deepstream-9.0 /opt/nvidia/deepstream/deepstream || true + +# Install Python dependencies required by THIS Qwen sample. These +# versions are pinned for reproducibility and can be adjusted for +# other pipelines or environments. +RUN --mount=type=cache,target=/root/.cache/pip pip install qwen-vl-utils[decord]==0.0.8 \ + python-multipart==0.0.9 \ + aiofiles==23.2.1 \ + aiohttp==3.11.0 \ + annotated-types==0.7.0 \ + python-magic==0.4.27 \ + numpy==1.26.4 \ + omegaconf==2.3.0 + +WORKDIR /workspace + +# Copy the inference flow package generated by Inference Builder. +# This tarball (here: qwen.tgz) is typically produced when running the +# builder with tarball output enabled, and its name should match the +# generated pipeline artifact you want to deploy. +ADD qwen.tgz /workspace + +## Set Environment variables required for DeepStream+TensorRT-LLM runtime. +ENV LD_LIBRARY_PATH /opt/tritonserver/lib:/opt/nvidia/deepstream/deepstream/lib:/usr/local/tensorrt/lib/:/opt/tritonserver/backends/tensorrtllm:$LD_LIBRARY_PATH +ENV NVSTREAMMUX_ADAPTIVE_BATCHING=yes +ENV HF_HOME=/tmp + +# Create the entrypoint script that runs the Inference Builder-generated +# /workspace/__main__.py for this Qwen pipeline. +RUN touch /workspace/start_server.sh && \ + chmod a+rx /workspace/start_server.sh && \ + cat > /workspace/start_server.sh <<-EOF + #!/usr/bin/env bash + set -eu + # SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + # SPDX-License-Identifier: LicenseRef-NvidiaProprietary + python3 /workspace/__main__.py +# while true; do sleep 5; done +EOF + +ENTRYPOINT ["/workspace/start_server.sh"] +CMD [] diff --git a/tools/inference_builder/builder/samples/qwen/README.md b/tools/inference_builder/builder/samples/qwen/README.md new file mode 100644 index 00000000..58875de4 --- /dev/null +++ b/tools/inference_builder/builder/samples/qwen/README.md @@ -0,0 +1,129 @@ +## Introduction + +This example demonstrates how to build an inference pipeline and for the Qwen family of VLM models using the Inference Builder tool. Following models have been tested: +- Qwen/Qwen2.5-VL-3B-Instruct +- Qwen/Qwen2.5-VL-7B-Instruct +- Qwen/Qwen2.5-VL-32B-Instruct + +Three configurations are provided, allowing you to choose based on your specific software and hardware environment: + +1. pytorch_qwen.yaml: leveraging the transformer APIs and fits all the models +2. trtllm_qwen.yaml: leveraging TensorRT LLM APIs for better performance +3. trtllm_nvdec_qwen.yaml: leveraging h/w decoder and TensorRT LLM for the best performance + +We provide a sample Dockerfile for the example, which you can use to build a Docker image and test the microservice on any x86 system. + +**⚠️ System Requirements:** This model requires significant GPU resources and is optimized for high-performance systems. We recommend testing on H100 or B200 GPUs for optimal performance. Docker build may fail on less powerful hardware. + +## Prerequisites + +**Note:** Make sure you are in the root directory (`path/to/inference_builder`) to execute the commands in this README. All relative paths and commands assume you are running from the inference_builder root directory. Also ensure that your virtual environment is activated before running any commands. + +Before downloading the model file, you need to set up your model repository: + +```bash +mkdir -p ~/.cache/model-repo && chmod 777 ~/.cache/model-repo +export MODEL_REPO=~/.cache/model-repo +``` + +The model checkpoints can be downloaded from huggingface. First, install git-lfs for large model files: + +```bash +sudo apt install git-lfs +git lfs install +``` + +Then download the model: + +```bash +git clone https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct $MODEL_REPO/Qwen2.5-VL +``` + +Assume you've followed the [top level instructions](../../../README.md#getting-started) to set up the environment and be sure you're in the inference_builder folder, then activate your virtual environment: + +```bash +source .venv/bin/activate +``` + +## Build and Test the Qwen2.5-VL-7B-Instruct Inference Microservice + +### Generate the Inference Pipeline + +For Pytorch backend with transformers API, use the command below: + +```bash +python builder/main.py builder/samples/qwen/pytorch_qwen.yaml --api-spec builder/samples/qwen/openapi.yaml \ +-o builder/samples/qwen/ -c builder/samples/qwen/processors.py -t +``` + +For TensorRT-LLM backend with S/W decoder, use the command below: + +```bash +python builder/main.py builder/samples/qwen/trtllm_qwen.yaml --api-spec builder/samples/qwen/openapi.yaml \ +-o builder/samples/qwen/ -c builder/samples/qwen/processors.py -t +``` + +If you want to acheive the best performance with Deepstream and TensoRT-LLM, please use following command: + +```bash +# Enable hardware decoder +python builder/main.py builder/samples/qwen/trtllm_nvdec_qwen.yaml --api-spec builder/samples/qwen/openapi.yaml \ +-o builder/samples/qwen/ -c builder/samples/qwen/processors.py -t +``` + +### Build and Start the Inference Microservice: + +```bash +cd builder/samples && docker compose up ms-qwen --build +``` + +The build may take a while—images are pulled from NGC and speed depends on your network. ⌛ + +### Test the Inference Microservice with a client + +Wait for the server to start, then open a new terminal in your inference_builder folder. The sample includes a test OpenAI client. For image input, run the client with the path to an image file. + + +```bash +source .venv/bin/activate && cd builder/samples/qwen +python client.py --images # replace placeholder with an actual file +``` + +For video input, you need to first upload a test video file. + +**⚠️ Important:** **replace the placeholder in below command with an actual file path in your system**. + +```bash +export VIDEO_FILE= +curl -X "POST" \ + "http://localhost:8800/v1/files" \ + -H "accept: application/json" \ + -H "Content-Type: multipart/form-data" \ + -F "file=@$VIDEO_FILE;type=video/mp4" +``` + +You'll get a response 200 with a json body which includes the id and path of the uploaded asset like below: + +{ + "data": { + "id": "577a9f11-2b24-4db8-82c8-2601e0c2b6e4", + "path": "/tmp/assets/577a9f11-2b24-4db8-82c8-2601e0c2b6e4/its_1920_30s.mp4", + "size": 3472221, + "duration": 30000000000, + "contentType": "video/mp4" + } +} + +Run the client.py with the video path from your response if the inference pipeline is built from trtllm_qwen.yaml or pytorch_qwen.yaml without H/W decoder: + +```bash +# Please use the path from your file upload response +python client.py --videos /tmp/assets/577a9f11-2b24-4db8-82c8-2601e0c2b6e4/its_1920_30s.mp4 +``` + +OR run the client.py with the returned asset id if the inference pipeline is built from trtllm_nvdec_qwen.yaml with H/W decoder enabled: + +```bash +# Please replace 577a9f11-2b24-4db8-82c8-2601e0c2b6e4 with the id returned from your file upload response +python client.py --videos 577a9f11-2b24-4db8-82c8-2601e0c2b6e4?frames=8 +``` \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/qwen/client.py b/tools/inference_builder/builder/samples/qwen/client.py new file mode 100644 index 00000000..4c0a780b --- /dev/null +++ b/tools/inference_builder/builder/samples/qwen/client.py @@ -0,0 +1,137 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import time +import argparse +from openai import OpenAI +import base64 +import mimetypes +from concurrent.futures import ThreadPoolExecutor, as_completed +from urllib.parse import urlparse, parse_qs + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--images", type=str, help="Image paths to send for inference", default=None, nargs="*") + parser.add_argument("--videos", type=str, help="Video paths to send for inference", default=None, nargs="*") + parser.add_argument("--endpoint", type=str, help="Endpoint to send for inference", default="http://0.0.0.0:8800/v1", nargs="?") + parser.add_argument("-p", "--parallel", type=int, help="Number of parallel requests to send", default=1) + args = parser.parse_args() + + messages = [] + if args.images: + for image in args.images: + with open(image, "rb") as f: + data = base64.b64encode(f.read()).decode() + mime_type = mimetypes.guess_type(image)[0] + messages.append({ + "role": "user", + "content": [ + { + "type": "text", + "text": "Please describe the image in detail." + }, + { + "type": "image", + "image": f"data:{mime_type};base64,{data}" + } + ] + }) + elif args.videos: + for video in args.videos: + # Parse video path to check for ?nframes=x query parameter + # Only extract nframes if video starts with "/" (local file path) + if video.startswith('/') and '?' in video: + video_path, query_string = video.split('?', 1) + query_params = parse_qs(query_string) + + video_content = { + "type": "video", + "video": video_path + } + + # Add nframes parameter if present + if 'nframes' in query_params: + try: + nframes_value = int(query_params['nframes'][0]) + video_content["nframes"] = nframes_value + except (ValueError, IndexError): + pass # Ignore invalid nframes values + else: + video_content = { + "type": "video", + "video": video + } + + messages.append({ + "role": "user", + "content": [ + { + "type": "text", + "text": "Please describe the video in detail." + }, + video_content + ] + }) + else: + raise ValueError("No images or videos provided") + + client = OpenAI(base_url=args.endpoint, api_key="not-used") + + def make_request(request_id): + """Make a single request to the API""" + start_time = time.time() + chat_response = client.chat.completions.create( + model="nvidia/cosmos", + messages=messages, + max_tokens=512, + stream=False + ) + infer_time = time.time() - start_time + return request_id, infer_time, chat_response.choices[0].message + + # Send requests in parallel + overall_start_time = time.time() + if args.parallel == 1: + # Single request mode + request_id, infer_time, assistant_message = make_request(0) + print(f"Inference time: {infer_time:.4f} seconds") + print(assistant_message) + else: + # Parallel requests mode + with ThreadPoolExecutor(max_workers=args.parallel) as executor: + futures = [executor.submit(make_request, i) for i in range(args.parallel)] + + results = [] + for future in as_completed(futures): + request_id, infer_time, assistant_message = future.result() + results.append((request_id, infer_time, assistant_message)) + print(f"Request {request_id} completed in {infer_time:.4f} seconds") + + # Sort results by request_id for consistent output + results.sort(key=lambda x: x[0]) + + overall_time = time.time() - overall_start_time + avg_time = sum(r[1] for r in results) / len(results) + print(f"\n--- Summary ---") + print(f"Total requests: {args.parallel}") + print(f"Overall time: {overall_time:.4f} seconds") + print(f"Average inference time: {avg_time:.4f} seconds") + print(f"Throughput: {args.parallel / overall_time:.2f} requests/second") + + # Print all responses + print(f"\n--- Responses ---") + for req_id, _, assistant_message in results: + print(f"\nResponse (Request {req_id}):") + print(assistant_message) diff --git a/tools/inference_builder/builder/samples/qwen/openapi.yaml b/tools/inference_builder/builder/samples/qwen/openapi.yaml new file mode 100644 index 00000000..8f444d5f --- /dev/null +++ b/tools/inference_builder/builder/samples/qwen/openapi.yaml @@ -0,0 +1,1367 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +openapi: 3.1.0 +info: + title: NVIDIA NIM for VLMs + summary: Accelerated VLM inference for NVIDIA GPUs. + version: 1.1.2 +paths: + /v1/health/ready: + get: + tags: + - Health + summary: Health Ready + description: Provide your implementation of readiness to know when container ready to accept traffic + operationId: health_ready_v1_health_ready_get + responses: + '200': + description: Successful Response + content: + application/json: + schema: {} + /v1/chat/completions: + post: + summary: OpenAI-compatible chat endpoint + operationId: create_chat_completion_v1_chat_completions_post + parameters: + - name: content-type + in: header + required: false + schema: + anyOf: + - type: string + - type: 'null' + title: Content-Type + requestBody: + required: true + content: + application/json: + schema: + $ref: '#/components/schemas/NIMLLMChatCompletionRequest' + responses: + '200': + description: Successful Response + content: + application/json: + schema: + anyOf: + - $ref: '#/components/schemas/NIMLLMChatCompletionResponse' + - $ref: '#/components/schemas/NIMLLMChatCompletionStreamResponse' + title: Response Create Chat Completion V1 Chat Completions Post + '400': + description: Received an invalid request possibly containing unsupported + or out-of-range parameter values. + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + '404': + description: The requested model does not exist. + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + '500': + description: Internal Server Error + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + '415': + description: Unsupported Media Type + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + '422': + description: Validation Error + content: + application/json: + schema: + $ref: '#/components/schemas/HTTPValidationError' + /v1/files: + post: + tags: + - Files + summary: Upload a media file that can be used as inference input. + description: Upload media files to be used as inference input. Currently, only images upto 50 MB are supported. + operationId: add_media_file + requestBody: + required: true + content: + multipart/form-data: + schema: + $ref: '#/components/schemas/AddFileRequest' + responses: + "200": + description: Successful Response. + content: + application/json: + schema: + $ref: '#/components/schemas/AddFileResponse' + "500": + description: The invocation ended with an error. + content: + application/json: + schema: + $ref: '#/components/schemas/Errors' + get: + tags: + - Files + summary: Returns a list of the uploaded files. + description: Returns a list that includes ID, file name, size, purpose, file size, and media type for the uploaded files. + operationId: list_media_files + responses: + "200": + description: Successful Response. + content: + application/json: + schema: + $ref: '#/components/schemas/ListFilesResponse' + "500": + description: The invocation ended with an error. + content: + application/json: + schema: + $ref: '#/components/schemas/Errors' + /v1/files/{file_id}: + delete: + tags: + - Files + summary: Delete a uploaded file. + description: Deletes an uploaded file based on ID. + operationId: delete_media_file + parameters: + - name: file_id + in: path + required: true + schema: + type: string + maxLength: 36 + pattern: '[0-9a-fA-F]{8}-?[0-9a-fA-F]{4}-?4[0-9a-fA-F]{3}-?[89abAB][0-9a-fA-F]{3}-?[0-9a-fA-F]{12}' + description: File having 'file_id' to be deleted. + title: File Id + description: File having 'file_id' to be deleted. + responses: + "200": + description: Successful Response. + content: + application/json: + schema: + $ref: '#/components/schemas/DeleteFileResponse' + "500": + description: The invocation ended with an error. + content: + application/json: + schema: + $ref: '#/components/schemas/Errors' +components: + schemas: + BaseModel: + properties: {} + type: object + title: BaseModel + ChatCompletionLogProb: + properties: + token: + type: string + title: Token + logprob: + type: number + title: Logprob + default: -9999.0 + bytes: + anyOf: + - items: + type: integer + type: array + - type: 'null' + title: Bytes + additionalProperties: false + type: object + required: + - token + title: ChatCompletionLogProb + ChatCompletionLogProbs: + properties: + content: + anyOf: + - items: + $ref: '#/components/schemas/ChatCompletionLogProbsContent' + type: array + - type: 'null' + title: Content + additionalProperties: false + type: object + title: ChatCompletionLogProbs + ChatCompletionLogProbsContent: + properties: + token: + type: string + title: Token + logprob: + type: number + title: Logprob + default: -9999.0 + bytes: + anyOf: + - items: + type: integer + type: array + - type: 'null' + title: Bytes + top_logprobs: + items: + $ref: '#/components/schemas/ChatCompletionLogProb' + type: array + title: Top Logprobs + additionalProperties: false + type: object + required: + - token + title: ChatCompletionLogProbsContent + ChatCompletionNamedFunction: + properties: + name: + type: string + title: Name + additionalProperties: false + type: object + required: + - name + title: ChatCompletionNamedFunction + ChatCompletionNamedToolChoiceParam: + properties: + function: + $ref: '#/components/schemas/ChatCompletionNamedFunction' + type: + type: string + const: function + title: Type + default: function + additionalProperties: false + type: object + required: + - function + title: ChatCompletionNamedToolChoiceParam + ChatCompletionResponseChoice: + properties: + index: + type: integer + title: Index + message: + $ref: '#/components/schemas/ChatMessage' + logprobs: + anyOf: + - $ref: '#/components/schemas/ChatCompletionLogProbs' + - type: 'null' + finish_reason: + anyOf: + - type: string + enum: + - stop + - length + - tool_calls + - type: 'null' + title: Finish Reason + default: stop + stop_reason: + anyOf: + - type: integer + - type: string + - type: 'null' + title: Stop Reason + additionalProperties: false + type: object + required: + - index + - message + title: ChatCompletionResponseChoice + ChatCompletionResponseStreamChoice: + properties: + index: + type: integer + title: Index + delta: + $ref: '#/components/schemas/DeltaMessage' + logprobs: + anyOf: + - $ref: '#/components/schemas/ChatCompletionLogProbs' + - type: 'null' + finish_reason: + anyOf: + - type: string + enum: + - stop + - length + - tool_calls + - type: 'null' + title: Finish Reason + stop_reason: + anyOf: + - type: integer + - type: string + - type: 'null' + title: Stop Reason + additionalProperties: false + type: object + required: + - index + - delta + title: ChatCompletionResponseStreamChoice + ChatCompletionToolsParam: + properties: + type: + type: string + const: function + title: Type + default: function + function: + $ref: '#/components/schemas/FunctionDefinition' + additionalProperties: false + type: object + required: + - function + title: ChatCompletionToolsParam + ChatMessage: + properties: + role: + type: string + title: Role + content: + anyOf: + - type: string + - type: 'null' + title: Content + tool_calls: + anyOf: + - items: + $ref: '#/components/schemas/ToolCall' + type: array + - type: 'null' + title: Tool Calls + additionalProperties: false + type: object + required: + - role + title: ChatMessage + DeltaMessage: + properties: + role: + anyOf: + - type: string + - type: 'null' + title: Role + content: + anyOf: + - type: string + - type: 'null' + title: Content + tool_calls: + anyOf: + - items: + $ref: '#/components/schemas/ToolCall' + type: array + - type: 'null' + title: Tool Calls + additionalProperties: false + type: object + title: DeltaMessage + ErrorResponse: + properties: + object: + type: string + title: Object + default: error + message: + type: string + title: Message + type: + type: string + title: Type + param: + anyOf: + - type: string + - type: 'null' + title: Param + code: + type: integer + title: Code + additionalProperties: false + type: object + required: + - message + - type + - code + title: ErrorResponse + FunctionCall: + properties: + name: + anyOf: + - type: string + - type: 'null' + title: Name + default: '' + arguments: + anyOf: + - type: string + - type: 'null' + title: Arguments + default: '' + additionalProperties: false + type: object + title: FunctionCall + FunctionDefinition: + properties: + name: + type: string + title: Name + description: + anyOf: + - type: string + - type: 'null' + title: Description + parameters: + anyOf: + - type: object + - type: 'null' + title: Parameters + additionalProperties: false + type: object + required: + - name + title: FunctionDefinition + HTTPValidationError: + properties: + detail: + items: + $ref: '#/components/schemas/ValidationError' + type: array + title: Detail + type: object + title: HTTPValidationError + ImageURL: + properties: + url: + type: string + title: Url + type: object + required: + - url + title: ImageURL + Logprob: + properties: + logprob: + type: number + title: Logprob + rank: + anyOf: + - type: integer + - type: 'null' + title: Rank + decoded_token: + anyOf: + - type: string + - type: 'null' + title: Decoded Token + type: object + required: + - logprob + title: Logprob + NIMLLMChatCompletionMessage: + properties: + role: + $ref: '#/components/schemas/Role' + description: The role of the message's author. + content: + anyOf: + - type: string + minLength: 1 + - items: + anyOf: + - $ref: '#/components/schemas/NIMVLMChatCompletionContentPartImage' + - $ref: '#/components/schemas/NIMVLMChatCompletionContentPartText' + - $ref: '#/components/schemas/NIMVLMChatCompletionContentPartVideo' + - $ref: '#/components/schemas/NIMVLMChatCompletionContentPartVideoStreaming' + type: array + title: Content + description: "The contents of the message.\n
To pass images (only\ + \ with role=`user`):\n
- When content is a string, image\ + \ can be passed together with the text with `img` HTML tags that wraps\n\ + \ an image URL (``),\n base64 encoded\ + \ image data (``),\n or an NVCF asset ID (``)\n when the container is hosted in NVCF and the payload exceeds\ + \ 200KB.\n or http link (`https:{image_link}`)\n
\ + \ - When content is a list of objects, images can be passed as objects\ + \ with type=`image_url`.\n
- In both cases, images can\ + \ be PNG, JPG or JPEG.\n
To pass videos (only with role=`user`):\n\ + \
- When content is a string, video can be passed together\ + \ with the text with `video` HTML tags that wraps\n a video URL\ + \ (`&0n`apqhR)V<5#YOppV;UCdi{4g^bNwDo@ve>* zu<)n}=kVe_CYI+$f!wC(lysUbn)Ytqo zt>XD2cs33i18WsHotT7oC8^Z5Cd=(g8jTt5Rw&uESG^=PjwJ=i^lbd20 zNg3iGuki>vq@e-XWYhlQxJ6ieNH`AR%OEnaXUf8(^Jx?fqfNq9aoCc!uGqo)>})zu zuzKzJO)6g1e$9>0(W0z$%S4heywNYOn%Y1tOUVnq$vt?!zEwiz=TfIAM@)}(8`-DZs2RXMk(HRbx%0nwEY>kebuhoPE!^ByHk)9H_ zm*It_g;tK%bb4Gth6n|B`>nQ(ET7vlnpN8B7QUe}Z3OlPEx4Tu#p#|agk^Ff^Klu< zHg{QeSpai9zaRSRm9vw%U&|#%ly@~}iVoFheJ~m0C2qbdTKvlHa}opBOelrLPgstG zbwZl4*5*QpQ+JWgjB!L_3YTbQ%pT zp$~~i&vev7Lh;E$buOCa)(FYHk)5ckZS>6s-;Xd9t%Z97ZdHiLtavC;VJA?PeV_Qh zU25aP^)Mj-P2}80H5hvI1Y~ikk-`S9W>wufpXIpSZ?1HsV=WzZEHeX?O1hZQhvkCdE8V|01J&w_w)e7o&e~2N z95E*V7WDu12iPGLUUYPtp;!v8E4L|E*mc&WVf33RY$GZ!A&OcR7Ni0_F(_BV8{~Gm z|2U$|k;GBTyZ$P?ul0<|7TA5gO>H{t7$8024_Tb?3GmA&7FJkjX}ZB&8~ZMeN|k}l zsyoKhyd-{-NGF~_prSD=R3qT4agM%MM`?57+sb9ZU#_A8woH970J0L(l*J@yFc5+n5R2_y+SkDSI9^IUas4Z%$hB~rV 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b/tools/inference_builder/builder/samples/tao/validation/cls/expected.ILSVRC2012_val_00001362.json @@ -0,0 +1 @@ +{"data": [{"index": 0, "shape": [224, 224], "bboxes": [[0, 0, 224, 224]], "probs": [0.0], "labels": [["n13054560"]], "masks": [], "objects": [], "timestamp": 29306093786}], "model": "nvidia/nvdinov2", "usage": {"num_images": 1}} \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/tao/validation/cls/expected.ILSVRC2012_val_00002167.json b/tools/inference_builder/builder/samples/tao/validation/cls/expected.ILSVRC2012_val_00002167.json new file mode 100644 index 00000000..841e51ae --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/cls/expected.ILSVRC2012_val_00002167.json @@ -0,0 +1 @@ +{"data": [{"index": 0, "shape": [224, 224], "bboxes": [[0, 0, 224, 224]], "probs": [0.0], "labels": [["n13052670"]], "masks": [], "objects": [], "timestamp": 100464334485}], "model": "nvidia/nvdinov2", "usage": {"num_images": 1}} \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/tao/validation/cls/labels.txt b/tools/inference_builder/builder/samples/tao/validation/cls/labels.txt new file mode 100644 index 00000000..8972f79f --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/cls/labels.txt @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.generate.sh b/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.generate.sh new file mode 100644 index 00000000..3484ffb0 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.generate.sh @@ -0,0 +1,17 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +nim_workspace=/tmp/nim/generator/cv_nim +nim_generate_manifest --profiles-file model_manifest.profile.yaml --manifest-file model_manifest.yaml --nim-workspace $nim_workspace diff --git a/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.profile.yaml b/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.profile.yaml new file mode 100644 index 00000000..80da5367 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.profile.yaml @@ -0,0 +1,24 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +--- +schema_version: '1.0' +model: "eevaigoeixww/dev/tao_cv" +release: 'rc1' +simple: + profiles: + - model_version: cls.nvdino_pretrain_0114_final_scale0.8.engine_b1_fp16_A6000_trt10.9 + tags: + gpu: a6000 diff --git a/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.yaml b/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.yaml new file mode 100644 index 00000000..ceee76ac --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.yaml @@ -0,0 +1,34 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +schema_version: '2.0' +profile_selection_criteria: auto +model: eevaigoeixww/dev/tao_cv +release: rc1 +profiles: +- id: 425dcb3ef646e497d5a50bbd1eb1169c9f33f4253f36669bb428ef250e2447cd + tags: + gpu: a6000 + workspace: + files: + classifier.onnx_b1_gpu0_fp16.engine: + uri: ngc://eevaigoeixww/dev/tao_cv:cls.nvdino_pretrain_0114_final_scale0.8.engine_b1_fp16_A6000_trt10.9?file=classifier.onnx_b1_gpu0_fp16.engine + checksum: blake3:27621bbfdbba1538a1ee6b5ae527b233e841fd405887c8ac9ea46c6d509c5adb + labels.txt: + uri: ngc://eevaigoeixww/dev/tao_cv:cls.nvdino_pretrain_0114_final_scale0.8.engine_b1_fp16_A6000_trt10.9?file=labels.txt + checksum: blake3:7065fb33f25f05e562a0ac12acf084a95c2892ede4bec8b2af0b8c2df6f27172 + nvdsinfer_config.yaml: + uri: ngc://eevaigoeixww/dev/tao_cv:cls.nvdino_pretrain_0114_final_scale0.8.engine_b1_fp16_A6000_trt10.9?file=nvdsinfer_config.yaml + checksum: blake3:c406a73ece6aae6700606a937fb5b0ff38ed1ba0690c47fcd1182ffc772e9a10 diff --git a/tools/inference_builder/builder/samples/tao/validation/cls/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/tao/validation/cls/nvdsinfer_config.yaml new file mode 100644 index 00000000..86793ad4 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/cls/nvdsinfer_config.yaml @@ -0,0 +1,33 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +class-attrs-all: + pre-cluster-threshold: 0 + topk: 2 +property: + cluster-mode: 4 + gie-unique-id: 1 + infer-dims: 3;224;224 + maintain-aspect-ratio: 1 + net-scale-factor: 0.01508295626 # 1 / (255 * 0.26) + offsets: 122.7709;116.746;104.09373 # 255 * mean + network-mode: 2 # 0 FP32, 2 FP16 + network-type: 1 # classification + onnx-file: classifier.onnx + model-engine-file: ./classifier.onnx_b1_gpu0_fp16.engine + output-blob-names: output + workspace-size: 1048576 + classifier-threshold: 0 + labelfile-path: ./labels.txt diff --git a/tools/inference_builder/builder/samples/tao/validation/docker-compose.yaml b/tools/inference_builder/builder/samples/tao/validation/docker-compose.yaml new file mode 100644 index 00000000..d310821e --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/docker-compose.yaml @@ -0,0 +1,28 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +version: '3.8' + +services: + validation-client: + build: + context: . # Context is the validation directory + dockerfile: Dockerfile.validation + image: validation-client + # ipc: host + network_mode: "host" + environment: + - NIM_MODEL_NAME=${NIM_MODEL_NAME:-rtdetr} + - TEST_HOST=${TEST_HOST:-http://127.0.0.1:8800} diff --git 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a/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.generate.sh b/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.generate.sh new file mode 100644 index 00000000..3484ffb0 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.generate.sh @@ -0,0 +1,17 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +nim_workspace=/tmp/nim/generator/cv_nim +nim_generate_manifest --profiles-file model_manifest.profile.yaml --manifest-file model_manifest.yaml --nim-workspace $nim_workspace diff --git a/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.profile.yaml b/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.profile.yaml new file mode 100644 index 00000000..60694f86 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.profile.yaml @@ -0,0 +1,24 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +--- +schema_version: '1.0' +model: "eevaigoeixww/dev/tao_cv" +release: 'rc1' +simple: + profiles: + - model_version: gdino.engine_b1_fp16_A6000_trt10.9 + tags: + gpu: a6000 diff --git a/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.yaml b/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.yaml new file mode 100644 index 00000000..1d102d72 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.yaml @@ -0,0 +1,35 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +schema_version: '2.0' +profile_selection_criteria: auto +model: eevaigoeixww/dev/tao_cv +release: rc1 +profiles: +- id: 425dcb3ef646e497d5a50bbd1eb1169c9f33f4253f36669bb428ef250e2447cd + tags: + gpu: a6000 + workspace: + files: + grounding_dino_swin_tiny_commercial_deployable.onnx_b1_gpu0_fp16.engine: + uri: ngc://eevaigoeixww/dev/tao_cv:gdino.engine_b1_fp16_A6000_trt10.9?file=grounding_dino_swin_tiny_commercial_deployable.onnx_b1_gpu0_fp16.engine + checksum: blake3:092963900dd0b08fcdce6ab783fb7ab011051c51b92173a642dc82714af8d606 + nvdsinfer_config.yaml: + uri: ngc://eevaigoeixww/dev/tao_cv:gdino.engine_b1_fp16_A6000_trt10.9?file=nvdsinfer_config.yaml + checksum: blake3:a96518b01c399c0b22064ac73758b0555041badf26b11f20e29218e161f86c2c + nvdspreprocess_config.yaml: + uri: ngc://eevaigoeixww/dev/tao_cv:gdino.engine_b1_fp16_A6000_trt10.9?file=nvdspreprocess_config.yaml + checksum: blake3:a261def4031c9a7f068ecf144c4285d26b0767b9e143b83f48ea92b75e383041 diff --git a/tools/inference_builder/builder/samples/tao/validation/gdino/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/tao/validation/gdino/nvdsinfer_config.yaml new file mode 100644 index 00000000..54f07c11 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/gdino/nvdsinfer_config.yaml @@ -0,0 +1,36 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +class-attrs-all: + topk: 300 + pre-cluster-threshold: 0.4 +property: + gie-unique-id: 1 + net-scale-factor: 0.0078431372549 + model-engine-file: ./grounding_dino_swin_tiny_commercial_deployable.onnx_b1_gpu0_fp16.engine + onnx-file: ./grounding_dino_swin_tiny_commercial_deployable.onnx + # 0=FP32, 1=INT8, 2=FP16 mode + network-mode: 2 + # 0 detection; 1 cls; 2 seg; 3 instance seg; 100 no pp + network-type: 100 + offsets: 127.5;127.5;127.5 + output-tensor-meta: 1 + input-tensor-from-meta: 1 + infer-dims: 3;544;960 + custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so + # process-mode: 1 + # num-detected-classes: 256 + # batch-size: 1 + diff --git a/tools/inference_builder/builder/samples/tao/validation/gdino/nvdspreprocess_config.yaml b/tools/inference_builder/builder/samples/tao/validation/gdino/nvdspreprocess_config.yaml new file mode 100644 index 00000000..9d097d42 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/gdino/nvdspreprocess_config.yaml @@ -0,0 +1,64 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +# The values in the config file are overridden by values set through GObject properties. + +property: + tensor-name: inputs + processing-width: 960 + processing-height: 544 + network-input-shape: 1;3;544;960 + # Color format (0=RGB, 1=BGR, 2=GRAY) + network-color-format: 0 + + # Network input order (0=NCHW, 1=NHWC, 2=CUSTOM) + network-input-order: 0 + # Tensor data type (0=FP32, 1=UINT8, 2=INT8, 3=UINT32, 4=INT32, 5=FP16) + tensor-data-type: 0 + # Maintain aspect ratio while scaling + maintain-aspect-ratio: 1 + # Pad symmetrically with maintain-aspect-ratio enabled + symmetric-padding: 1 + # Memory type (0=NVBUF_MEM_DEFAULT, 1=NVBUF_MEM_CUDA_PINNED, 2=NVBUF_MEM_CUDA_DEVICE, 3=NVBUF_MEM_CUDA_UNIFIED) + scaling-pool-memory-type: 0 + # Compute hardware (0=Default, 1=GPU, 2=VIC) + scaling-pool-compute-hw: 0 + # Scaling interpolation method + # 0=Nearest, 1=Bilinear, 2=Algo1, 3=Algo2, 4=Algo3, 5=Algo4, 6=Default + scaling-filter: 0 + scaling-buf-pool-size: 6 + tensor-buf-pool-size: 6 + # List of component gie-id for which tensor is prepared + target-unique-ids: 1 + # Process type (0=process on objects, 1=process on frames) + process-on-frame: 1 + # Uniquely identify the metadata generated by this element + unique-id: 5 + # Custom library configuration + custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/gst-plugins/libcustom2d_preprocess.so + custom-tensor-preparation-function: CustomTensorPreparation + # Enable the component + enable: 1 +user-configs: + # Network scaling factor + pixel-normalization-factor: 0.007843137 + # Channel offsets + offsets: 127.5;127.5;127.5 +group: + - src-ids: -1 + custom-input-transformation-function: CustomAsyncTransformation + process-on-roi: 0 + diff --git a/tools/inference_builder/builder/samples/tao/validation/mgdino/expected.iva_0010_02_1280_0_181016_007207.json b/tools/inference_builder/builder/samples/tao/validation/mgdino/expected.iva_0010_02_1280_0_181016_007207.json new file mode 100644 index 00000000..2211607c --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/mgdino/expected.iva_0010_02_1280_0_181016_007207.json @@ -0,0 +1 @@ +{"data": [{"index": 0, "shape": [136, 240], "bboxes": [[76.01543426513672, 64.22148275375366, 96.24960422515869, 74.30086851119995], [117.1091365814209, 49.73912835121155, 126.87460899353027, 55.97859215736389], [149.55687046051025, 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a/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.generate.sh b/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.generate.sh new file mode 100644 index 00000000..e877c8a7 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.generate.sh @@ -0,0 +1,2 @@ +nim_workspace=/tmp/nim/generator/cv_nim +nim_generate_manifest --profiles-file model_manifest.profile.yaml --manifest-file model_manifest.yaml --nim-workspace $nim_workspace diff --git a/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.profile.yaml b/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.profile.yaml new file mode 100644 index 00000000..a493addc --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.profile.yaml @@ -0,0 +1,24 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +--- +schema_version: '1.0' +model: "eevaigoeixww/dev/tao_cv" +release: 'rc1' +simple: + profiles: + - model_version: mgdino.engine_b1_fp16_A6000_trt10.9 + tags: + gpu: a6000 diff --git a/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.yaml b/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.yaml new file mode 100644 index 00000000..e07aa2f8 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.yaml @@ -0,0 +1,35 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +schema_version: '2.0' +profile_selection_criteria: auto +model: eevaigoeixww/dev/tao_cv +release: rc1 +profiles: +- id: 425dcb3ef646e497d5a50bbd1eb1169c9f33f4253f36669bb428ef250e2447cd + tags: + gpu: a6000 + workspace: + files: + model_epoch_049.onnx_b1_gpu0_fp16.engine: + uri: ngc://eevaigoeixww/dev/tao_cv:mgdino.engine_b1_fp16_A6000_trt10.9?file=model_epoch_049.onnx_b1_gpu0_fp16.engine + checksum: blake3:947483a55c860eddea37cf1d930c6d92ab4a21d33d7d1dbc4c75e2aaa53e188c + nvdsinfer_config.yaml: + uri: ngc://eevaigoeixww/dev/tao_cv:mgdino.engine_b1_fp16_A6000_trt10.9?file=nvdsinfer_config.yaml + checksum: blake3:f957d513dff19d30f911d6c83416e61588cb1f652984fc9cd164a4eb72eedad3 + nvdspreprocess_config.yaml: + uri: ngc://eevaigoeixww/dev/tao_cv:mgdino.engine_b1_fp16_A6000_trt10.9?file=nvdspreprocess_config.yaml + checksum: blake3:a261def4031c9a7f068ecf144c4285d26b0767b9e143b83f48ea92b75e383041 diff --git a/tools/inference_builder/builder/samples/tao/validation/mgdino/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/tao/validation/mgdino/nvdsinfer_config.yaml new file mode 100644 index 00000000..aa6c12b4 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/mgdino/nvdsinfer_config.yaml @@ -0,0 +1,37 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +class-attrs-all: + topk: 300 + pre-cluster-threshold: 0.4 +property: + gie-unique-id: 1 + net-scale-factor: 0.0078431372549 + model-engine-file: ./model_epoch_049.onnx_b1_gpu0_fp16.engine + onnx-file: ./model_epoch_049.onnx + # 0=FP32, 1=INT8, 2=FP16 mode + network-mode: 2 + # 0 detection; 1 cls; 2 seg; 3 instance seg; 100 no pp + network-type: 100 + offsets: 127.5;127.5;127.5 + output-tensor-meta: 1 + input-tensor-from-meta: 1 + infer-dims: 3;544;960 + custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so + segmentation-threshold: 0.5 + # process-mode: 1 + # num-detected-classes: 256 + # batch-size: 1 + diff --git a/tools/inference_builder/builder/samples/tao/validation/mgdino/nvdspreprocess_config.yaml b/tools/inference_builder/builder/samples/tao/validation/mgdino/nvdspreprocess_config.yaml new file mode 100644 index 00000000..195f72ef --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/mgdino/nvdspreprocess_config.yaml @@ -0,0 +1,63 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +# The values in the config file are overridden by values set through GObject properties. + +property: + tensor-name: inputs + processing-width: 960 + processing-height: 544 + network-input-shape: 1;3;544;960 + # Color format (0=RGB, 1=BGR, 2=GRAY) + network-color-format: 0 + # Network input order (0=NCHW, 1=NHWC, 2=CUSTOM) + network-input-order: 0 + # Tensor data type (0=FP32, 1=UINT8, 2=INT8, 3=UINT32, 4=INT32, 5=FP16) + tensor-data-type: 0 + # Maintain aspect ratio while scaling + maintain-aspect-ratio: 1 + # Pad symmetrically with maintain-aspect-ratio enabled + symmetric-padding: 1 + # Memory type (0=NVBUF_MEM_DEFAULT, 1=NVBUF_MEM_CUDA_PINNED, 2=NVBUF_MEM_CUDA_DEVICE, 3=NVBUF_MEM_CUDA_UNIFIED) + scaling-pool-memory-type: 0 + # Compute hardware (0=Default, 1=GPU, 2=VIC) + scaling-pool-compute-hw: 0 + # Scaling interpolation method + # 0=Nearest, 1=Bilinear, 2=Algo1, 3=Algo2, 4=Algo3, 5=Algo4, 6=Default + scaling-filter: 0 + scaling-buf-pool-size: 6 + tensor-buf-pool-size: 6 + # List of component gie-id for which tensor is prepared + target-unique-ids: 1 + # Process type (0=process on objects, 1=process on frames) + process-on-frame: 1 + # Uniquely identify the metadata generated by this element + unique-id: 5 + # Custom library configuration + custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/gst-plugins/libcustom2d_preprocess.so + 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+34 +35 +36 +37 +38 +39 +40 +41 +42 +43 +44 +46 +47 +48 +49 +50 +51 +52 +53 +54 +55 +56 +57 +58 +59 +60 +61 +62 +63 +64 +65 +67 +70 +72 +73 +74 +75 +76 +77 +78 +79 +80 +81 +82 +84 +85 +86 +87 +88 +89 +90 diff --git a/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.generate.sh b/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.generate.sh new file mode 100644 index 00000000..3484ffb0 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.generate.sh @@ -0,0 +1,17 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +nim_workspace=/tmp/nim/generator/cv_nim +nim_generate_manifest --profiles-file model_manifest.profile.yaml --manifest-file model_manifest.yaml --nim-workspace $nim_workspace diff --git a/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.profile.yaml b/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.profile.yaml new file mode 100644 index 00000000..90cf3907 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.profile.yaml @@ -0,0 +1,24 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +--- +schema_version: '1.0' +model: "eevaigoeixww/dev/tao_cv" +release: 'rc1' +simple: + profiles: + - model_version: rtdetr.engine-b1_fp16_A6000_trt10.9 + tags: + gpu: a6000 diff --git a/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.yaml b/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.yaml new file mode 100644 index 00000000..4d65502d --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.yaml @@ -0,0 +1,34 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +schema_version: '2.0' +profile_selection_criteria: auto +model: eevaigoeixww/dev/tao_cv +release: rc1 +profiles: +- id: 425dcb3ef646e497d5a50bbd1eb1169c9f33f4253f36669bb428ef250e2447cd + tags: + gpu: a6000 + workspace: + files: + labels.txt: + uri: ngc://eevaigoeixww/dev/tao_cv:rtdetr.engine-b1_fp16_A6000_trt10.9?file=labels.txt + checksum: blake3:e50ee4f9ef6d7a17e8446eccc46748be8602629b9a4f729e3f959c80c7d6939a + model.onnx_b1_gpu0_fp16.engine: + uri: ngc://eevaigoeixww/dev/tao_cv:rtdetr.engine-b1_fp16_A6000_trt10.9?file=model.onnx_b1_gpu0_fp16.engine + checksum: blake3:a800796eaf366aa3eccb5c4151258c754ea27ddd2dfeb30844b0072bd9a641e1 + nvdsinfer_config.yaml: + uri: ngc://eevaigoeixww/dev/tao_cv:rtdetr.engine-b1_fp16_A6000_trt10.9?file=nvdsinfer_config.yaml + checksum: blake3:126064d8b7353208c0dfaf24ab9cea823bc80f772da44f6add8730840095a6f3 diff --git a/tools/inference_builder/builder/samples/tao/validation/rtdetr/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/tao/validation/rtdetr/nvdsinfer_config.yaml new file mode 100644 index 00000000..7b42b5b9 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/rtdetr/nvdsinfer_config.yaml @@ -0,0 +1,35 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +class-attrs-all: + pre-cluster-threshold: 0.5 +property: + gie-unique-id: 1 + cluster-mode: 4 + data-format: channels_first + infer-dims: 3;640;640 + maintain-aspect-ratio: 1 + model-color-format: 0 # RGB + net-scale-factor: 0.00392156863 # 1 / (255 x 1) + network-mode: 2 # 0 FP32, 2 FP16 + network-type: 0 + num-detected-classes: 80 + onnx-file: model.onnx + model-engine-file: ./model.onnx_b1_gpu0_fp16.engine + labelfile-path: ./labels.txt + parse-bbox-func-name: NvDsInferParseCustomDDETRTAO + custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so + workspace-size: 1048576 + # scaling-filter: 2 # 2 cubic diff --git a/tools/inference_builder/builder/samples/tao/validation/run_validation.sh b/tools/inference_builder/builder/samples/tao/validation/run_validation.sh new file mode 100644 index 00000000..27267dc2 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/run_validation.sh @@ -0,0 +1,30 @@ +#!/bin/bash + +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +set -e # Exit on any error +# Validate NIM_MODEL_NAME +valid_models=("rtdetr" "cls" "seg" "gdino" "mgdino") +if [[ ! " ${valid_models[@]} " =~ " ${NIM_MODEL_NAME} " ]]; then + echo "Error: NIM_MODEL_NAME must be one of: ${valid_models[*]}" + exit 1 +fi + +# Change to the appropriate test directory +cd "/app/validation/${NIM_MODEL_NAME}/.tmp" || exit 1 +echo $(pwd) +# Run the test +python test_runner.py diff --git a/tools/inference_builder/builder/samples/tao/validation/seg/expected.image_0.json b/tools/inference_builder/builder/samples/tao/validation/seg/expected.image_0.json new file mode 100644 index 00000000..878c383b --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/seg/expected.image_0.json @@ -0,0 +1 @@ +{"data": [{"index": 0, "shape": [224, 224], "bboxes": [], "probs": [], "labels": [], "masks": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 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+++ b/tools/inference_builder/builder/samples/tao/validation/seg/model_manifest.generate.sh @@ -0,0 +1,17 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +nim_workspace=/tmp/nim/generator/cv_nim +nim_generate_manifest --profiles-file model_manifest.profile.yaml --manifest-file model_manifest.yaml --nim-workspace $nim_workspace diff --git a/tools/inference_builder/builder/samples/tao/validation/seg/model_manifest.profile.yaml b/tools/inference_builder/builder/samples/tao/validation/seg/model_manifest.profile.yaml new file mode 100644 index 00000000..ffde05a2 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/seg/model_manifest.profile.yaml @@ -0,0 +1,24 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +--- +schema_version: '1.0' +model: "eevaigoeixww/dev/tao_cv" +release: 'rc1' +simple: + profiles: + - model_version: seg.cradiov2_b.engine_b1_fp16_A6000_tao2_trt10.9 + tags: + gpu: a6000 diff --git a/tools/inference_builder/builder/samples/tao/validation/seg/model_manifest.yaml b/tools/inference_builder/builder/samples/tao/validation/seg/model_manifest.yaml new file mode 100644 index 00000000..31398c30 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/seg/model_manifest.yaml @@ -0,0 +1,31 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +schema_version: '2.0' +profile_selection_criteria: auto +model: eevaigoeixww/dev/tao_cv +release: rc1 +profiles: +- id: 425dcb3ef646e497d5a50bbd1eb1169c9f33f4253f36669bb428ef250e2447cd + tags: + gpu: a6000 + workspace: + files: + nvdsinfer_config.yaml: + uri: ngc://eevaigoeixww/dev/tao_cv:seg.cradiov2_b.engine_b1_fp16_A6000_tao2_trt10.9?file=nvdsinfer_config.yaml + checksum: blake3:8e479d4f7fc8ac37b0b690040213f4cde8ac7c279ba120fa316e36d9d40ab41e + segformer.onnx_b1_gpu0_fp16.engine: + uri: ngc://eevaigoeixww/dev/tao_cv:seg.cradiov2_b.engine_b1_fp16_A6000_tao2_trt10.9?file=segformer.onnx_b1_gpu0_fp16.engine + checksum: blake3:13ce94093ac0f2365ccacc2ccc7fbf6be1aba981a2f3eed0422606346db6af3d diff --git a/tools/inference_builder/builder/samples/tao/validation/seg/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/tao/validation/seg/nvdsinfer_config.yaml new file mode 100644 index 00000000..fbfb7172 --- /dev/null +++ b/tools/inference_builder/builder/samples/tao/validation/seg/nvdsinfer_config.yaml @@ -0,0 +1,33 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +property: + gie-unique-id: 6 + process-mode: 1 + infer-dims: 3;224;224 + maintain-aspect-ratio: 1 + net-scale-factor: 0.007843137255 # 1 / (0.5 x 255) + offsets: 127.5;127.5;127.5 # 0.5 x 255 + network-mode: 2 + network-type: 2 # seg + num-detected-classes: 2 + onnx-file: segformer.onnx + model-engine-file: ./segformer.onnx_b1_gpu0_fp16.engine + output-blob-names: output + # output-tensor-meta: 1 + segmentation-output-order: 1 + workspace-size: 1048576 + parse-segmentation-func-name: NvDsInferParseCustomSegformerTAO2 + custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so diff --git a/tools/inference_builder/builder/samples/vllm/Dockerfile b/tools/inference_builder/builder/samples/vllm/Dockerfile new file mode 100644 index 00000000..27d932c1 --- /dev/null +++ b/tools/inference_builder/builder/samples/vllm/Dockerfile @@ -0,0 +1,88 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. + +# Sample Dockerfile for a vLLM-based vision-language pipeline using a +# Triton-enabled DeepStream base image for accelerated video decoding. +# This is specific to the vllm sample in this repository. +# Stage 1: pull in DeepStream libraries, binaries, and sample assets. +FROM "nvcr.io/nvidia/deepstream:9.0-triton-multiarch" AS ds_stage + +FROM nvcr.io/nvidia/vllm:26.02-py3 AS inference_base + +ENV NVIDIA_DRIVER_CAPABILITIES $NVIDIA_DRIVER_CAPABILITIES,video + +# Install GStreamer plugins and DeepStream runtime dependencies needed +# for hardware-accelerated decoding and RTSP support. +RUN apt update && apt install -y --no-install-recommends gstreamer1.0-plugins-good gstreamer1.0-plugins-bad \ + libyaml-cpp-dev libgstrtspserver-1.0-0 libjsoncpp25 libgles2 pkg-config + +COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/lib /opt/nvidia/deepstream/deepstream-9.0/lib +COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/bin /opt/nvidia/deepstream/deepstream-9.0/bin +COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/*.sh /opt/nvidia/deepstream/deepstream-9.0/ +COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/*.txt /opt/nvidia/deepstream/deepstream-9.0/ +COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/README* /opt/nvidia/deepstream/deepstream-9.0/ +COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/*.pdf /opt/nvidia/deepstream/deepstream-9.0/ +COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/service-maker /opt/nvidia/deepstream/deepstream-9.0/service-maker +COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/samples/streams/* /opt/nvidia/deepstream/deepstream-9.0/samples/streams/ + +# Copy CUDA NPP and nvjpeg libraries required by DeepStream GStreamer +# plugins (nvvideoconvert, nvdsgst_image, etc.) that are absent from +# the vLLM base image. +COPY --from=ds_stage /usr/local/cuda-13.1/targets/x86_64-linux/lib/libnpp*.so* /usr/local/lib/ +COPY --from=ds_stage /usr/local/cuda-13.1/targets/x86_64-linux/lib/libnvjpeg*.so* /usr/local/lib/ +RUN ldconfig + +RUN /opt/nvidia/deepstream/deepstream-9.0/install.sh +RUN ln -sf deepstream-9.0 /opt/nvidia/deepstream/deepstream || true + +# Install Python dependencies required by THIS Qwen sample. These +# versions are pinned for reproducibility and can be adjusted for +# other pipelines or environments. +RUN --mount=type=cache,target=/root/.cache/pip pip install qwen-vl-utils[decord]==0.0.8 \ + python-multipart==0.0.9 \ + aiofiles==23.2.1 \ + aiohttp==3.11.0 \ + annotated-types==0.7.0 \ + python-magic==0.4.27 \ + numpy==1.26.4 \ + omegaconf==2.3.0 + +WORKDIR /workspace + +# Copy the inference flow package generated by Inference Builder. +# This tarball (here: vllm.tgz) is typically produced when running the +# builder with tarball output enabled, and its name should match the +# generated pipeline artifact you want to deploy. +ADD vllm.tgz /workspace + +## Set Environment variables required for DeepStream+vLLM runtime. +# Ensure DeepStream libraries are on the library path and enable +# adaptive batching and appropriate CUDA architectures. +ENV LD_LIBRARY_PATH /opt/nvidia/deepstream/deepstream/lib +ENV NVSTREAMMUX_ADAPTIVE_BATCHING yes +ENV NVIDIA_DRIVER_CAPABILITIES=$NVIDIA_DRIVER_CAPABILITIES,video +ENV TORCH_CUDA_ARCH_LIST "7.5 8.0 8.6 9.0 10.0" +ENV CUDA_CACHE_DISABLE 0 + +# Create the entrypoint script that runs the Inference Builder-generated +# /workspace/__main__.py for this vLLM pipeline. +RUN touch /workspace/start_server.sh && \ + chmod a+rx /workspace/start_server.sh && \ + cat > /workspace/start_server.sh <<-EOF + #!/usr/bin/env bash + set -eu + # SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + # SPDX-License-Identifier: LicenseRef-NvidiaProprietary + python3 /workspace/__main__.py +# while true; do sleep 5; done +EOF + +ENTRYPOINT ["/workspace/start_server.sh"] +CMD [] diff --git a/tools/inference_builder/builder/samples/vllm/README.md b/tools/inference_builder/builder/samples/vllm/README.md new file mode 100644 index 00000000..de346aba --- /dev/null +++ b/tools/inference_builder/builder/samples/vllm/README.md @@ -0,0 +1,131 @@ +## Introduction + +This example demonstrates how to build an inference pipeline with Nvidia Deepstream MediaExtractor and vLLM Inference backends + +## Prerequisites + +The sample models including `Cosmos-Reason2-2B` and `Qwen3-VL-2B-Instruct` can all be downloaded from huggingface (Be sure to have git-lfs installed): + +```bash +git clone https://huggingface.co/nvidia/Cosmos-Reason2-2B ~/.cache/model-repo/Cosmos +``` + +or + +```bash +git clone https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct ~/.cache/model-repo/Qwen3-VL +``` + +## Generate the Inference Package + + +### Cosmos-Reason2-2B + +```bash +python builder/main.py builder/samples/vllm/vllm_nvdec_cosmos.yaml --api-spec builder/samples/vllm/openapi.yaml \ +-o builder/samples/vllm/ -c builder/samples/qwen/processors.py -t +``` + +### Qwen3-VL-2B-Instruct + +```bash +python builder/main.py builder/samples/vllm/vllm_nvdec_qwen3_vl.yaml --api-spec builder/samples/vllm/openapi.yaml \ +-o builder/samples/vllm/ -c builder/samples/qwen/processors.py -t +``` + +## Build and Start the Inference Microservice: + +The sample folder already contains all the ingredients for building the microservice, all you need is to run the command: + +```bash +cd builder/samples +docker compose up ms-vllm --build +``` + +## Test the model with a client + +There is an OpenAI client included in the sample for testing, and for image input, you can directly run the client with image path: + +```bash +cd builder/samples/qwen +python client.py --images +``` + +For video input, you need to first upload a test video file. + +**⚠️ Important:** **replace the placeholder in below command with an actual file path in your system**. + +```bash +export VIDEO_FILE= +curl -X "POST" \ + "http://localhost:8800/v1/files" \ + -H "accept: application/json" \ + -H "Content-Type: multipart/form-data" \ + -F "file=@$VIDEO_FILE;type=video/mp4" +``` + +You'll get a response 200 with a json body: +{ + "data": { + "id": "577a9f11-2b24-4db8-82c8-2601e0c2b6e4", + "path": "/tmp/assets/577a9f11-2b24-4db8-82c8-2601e0c2b6e4/its_1920_30s.mp4", + "size": 3472221, + "duration": 30000000000, + "contentType": "video/mp4" + } +} + +Similarly, for live stream input, you need to first add a live stream as an asset. + +**⚠️ Important:** **replace the placeholder in below command with an actual RTSP address**. + +```bash +export LIVE_STREAM= +curl -X "POST" \ + "http://localhost:8800/v1/streams" \ + -H "accept: application/json" \ + -H "Content-Type: application/json" \ + -d "{\"url\": \"$LIVE_STREAM\", \"name\": \"my-stream\"}" +``` + +Run the client.py with the returned asset id and specify the frames per chunk and number of chunks to extract: + +```bash +cd builder/samples/qwen +python client.py --videos "577a9f11-2b24-4db8-82c8-2601e0c2b6e4?frames=8&chunks=1" +``` + +You can also optionally specify the frame interval and scaling parameters: + +```bash +# Extract 1 chunk with 8 frames at the interval of 0.5s, and resize it to 640x260, then do summarization +python client.py --videos "577a9f11-2b24-4db8-82c8-2601e0c2b6e4?frames=8&chunks=1&interval=500000000&scaling=640x360" +``` + +Or use raw json payload with curl to get the result of all the chunks using "x-ndjson": + +```bash +curl -X 'POST' \ + 'http://localhost:8800/v1/chat/completions' \ + -H 'accept: application/x-ndjson' \ + -H 'Content-Type: application/json' \ + -d '{ + "model": "qwen3_vl", + "messages": [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "Please describe the video in detail." + }, + { + "type": "video", + "video": "577a9f11-2b24-4db8-82c8-2601e0c2b6e4?frames=8" + } + ] + } + ], + "max_tokens": 200 +}' +``` diff --git a/tools/inference_builder/builder/samples/vllm/openapi.yaml b/tools/inference_builder/builder/samples/vllm/openapi.yaml new file mode 100644 index 00000000..8f444d5f --- /dev/null +++ b/tools/inference_builder/builder/samples/vllm/openapi.yaml @@ -0,0 +1,1367 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +openapi: 3.1.0 +info: + title: NVIDIA NIM for VLMs + summary: Accelerated VLM inference for NVIDIA GPUs. + version: 1.1.2 +paths: + /v1/health/ready: + get: + tags: + - Health + summary: Health Ready + description: Provide your implementation of readiness to know when container ready to accept traffic + operationId: health_ready_v1_health_ready_get + responses: + '200': + description: Successful Response + content: + application/json: + schema: {} + /v1/chat/completions: + post: + summary: OpenAI-compatible chat endpoint + operationId: create_chat_completion_v1_chat_completions_post + parameters: + - name: content-type + in: header + required: false + schema: + anyOf: + - type: string + - type: 'null' + title: Content-Type + requestBody: + required: true + content: + application/json: + schema: + $ref: '#/components/schemas/NIMLLMChatCompletionRequest' + responses: + '200': + description: Successful Response + content: + application/json: + schema: + anyOf: + - $ref: '#/components/schemas/NIMLLMChatCompletionResponse' + - $ref: '#/components/schemas/NIMLLMChatCompletionStreamResponse' + title: Response Create Chat Completion V1 Chat Completions Post + '400': + description: Received an invalid request possibly containing unsupported + or out-of-range parameter values. + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + '404': + description: The requested model does not exist. + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + '500': + description: Internal Server Error + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + '415': + description: Unsupported Media Type + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + '422': + description: Validation Error + content: + application/json: + schema: + $ref: '#/components/schemas/HTTPValidationError' + /v1/files: + post: + tags: + - Files + summary: Upload a media file that can be used as inference input. + description: Upload media files to be used as inference input. Currently, only images upto 50 MB are supported. + operationId: add_media_file + requestBody: + required: true + content: + multipart/form-data: + schema: + $ref: '#/components/schemas/AddFileRequest' + responses: + "200": + description: Successful Response. + content: + application/json: + schema: + $ref: '#/components/schemas/AddFileResponse' + "500": + description: The invocation ended with an error. + content: + application/json: + schema: + $ref: '#/components/schemas/Errors' + get: + tags: + - Files + summary: Returns a list of the uploaded files. + description: Returns a list that includes ID, file name, size, purpose, file size, and media type for the uploaded files. + operationId: list_media_files + responses: + "200": + description: Successful Response. + content: + application/json: + schema: + $ref: '#/components/schemas/ListFilesResponse' + "500": + description: The invocation ended with an error. + content: + application/json: + schema: + $ref: '#/components/schemas/Errors' + /v1/files/{file_id}: + delete: + tags: + - Files + summary: Delete a uploaded file. + description: Deletes an uploaded file based on ID. + operationId: delete_media_file + parameters: + - name: file_id + in: path + required: true + schema: + type: string + maxLength: 36 + pattern: '[0-9a-fA-F]{8}-?[0-9a-fA-F]{4}-?4[0-9a-fA-F]{3}-?[89abAB][0-9a-fA-F]{3}-?[0-9a-fA-F]{12}' + description: File having 'file_id' to be deleted. + title: File Id + description: File having 'file_id' to be deleted. + responses: + "200": + description: Successful Response. + content: + application/json: + schema: + $ref: '#/components/schemas/DeleteFileResponse' + "500": + description: The invocation ended with an error. + content: + application/json: + schema: + $ref: '#/components/schemas/Errors' +components: + schemas: + BaseModel: + properties: {} + type: object + title: BaseModel + ChatCompletionLogProb: + properties: + token: + type: string + title: Token + logprob: + type: number + title: Logprob + default: -9999.0 + bytes: + anyOf: + - items: + type: integer + type: array + - type: 'null' + title: Bytes + additionalProperties: false + type: object + required: + - token + title: ChatCompletionLogProb + ChatCompletionLogProbs: + properties: + content: + anyOf: + - items: + $ref: '#/components/schemas/ChatCompletionLogProbsContent' + type: array + - type: 'null' + title: Content + additionalProperties: false + type: object + title: ChatCompletionLogProbs + ChatCompletionLogProbsContent: + properties: + token: + type: string + title: Token + logprob: + type: number + title: Logprob + default: -9999.0 + bytes: + anyOf: + - items: + type: integer + type: array + - type: 'null' + title: Bytes + top_logprobs: + items: + $ref: '#/components/schemas/ChatCompletionLogProb' + type: array + title: Top Logprobs + additionalProperties: false + type: object + required: + - token + title: ChatCompletionLogProbsContent + ChatCompletionNamedFunction: + properties: + name: + type: string + title: Name + additionalProperties: false + type: object + required: + - name + title: ChatCompletionNamedFunction + ChatCompletionNamedToolChoiceParam: + properties: + function: + $ref: '#/components/schemas/ChatCompletionNamedFunction' + type: + type: string + const: function + title: Type + default: function + additionalProperties: false + type: object + required: + - function + title: ChatCompletionNamedToolChoiceParam + ChatCompletionResponseChoice: + properties: + index: + type: integer + title: Index + message: + $ref: '#/components/schemas/ChatMessage' + logprobs: + anyOf: + - $ref: '#/components/schemas/ChatCompletionLogProbs' + - type: 'null' + finish_reason: + anyOf: + - type: string + enum: + - stop + - length + - tool_calls + - type: 'null' + title: Finish Reason + default: stop + stop_reason: + anyOf: + - type: integer + - type: string + - type: 'null' + title: Stop Reason + additionalProperties: false + type: object + required: + - index + - message + title: ChatCompletionResponseChoice + ChatCompletionResponseStreamChoice: + properties: + index: + type: integer + title: Index + delta: + $ref: '#/components/schemas/DeltaMessage' + logprobs: + anyOf: + - $ref: '#/components/schemas/ChatCompletionLogProbs' + - type: 'null' + finish_reason: + anyOf: + - type: string + enum: + - stop + - length + - tool_calls + - type: 'null' + title: Finish Reason + stop_reason: + anyOf: + - type: integer + - type: string + - type: 'null' + title: Stop Reason + additionalProperties: false + type: object + required: + - index + - delta + title: ChatCompletionResponseStreamChoice + ChatCompletionToolsParam: + properties: + type: + type: string + const: function + title: Type + default: function + function: + $ref: '#/components/schemas/FunctionDefinition' + additionalProperties: false + type: object + required: + - function + title: ChatCompletionToolsParam + ChatMessage: + properties: + role: + type: string + title: Role + content: + anyOf: + - type: string + - type: 'null' + title: Content + tool_calls: + anyOf: + - items: + $ref: '#/components/schemas/ToolCall' + type: array + - type: 'null' + title: Tool Calls + additionalProperties: false + type: object + required: + - role + title: ChatMessage + DeltaMessage: + properties: + role: + anyOf: + - type: string + - type: 'null' + title: Role + content: + anyOf: + - type: string + - type: 'null' + title: Content + tool_calls: + anyOf: + - items: + $ref: '#/components/schemas/ToolCall' + type: array + - type: 'null' + title: Tool Calls + additionalProperties: false + type: object + title: DeltaMessage + ErrorResponse: + properties: + object: + type: string + title: Object + default: error + message: + type: string + title: Message + type: + type: string + title: Type + param: + anyOf: + - type: string + - type: 'null' + title: Param + code: + type: integer + title: Code + additionalProperties: false + type: object + required: + - message + - type + - code + title: ErrorResponse + FunctionCall: + properties: + name: + anyOf: + - type: string + - type: 'null' + title: Name + default: '' + arguments: + anyOf: + - type: string + - type: 'null' + title: Arguments + default: '' + additionalProperties: false + type: object + title: FunctionCall + FunctionDefinition: + properties: + name: + type: string + title: Name + description: + anyOf: + - type: string + - type: 'null' + title: Description + parameters: + anyOf: + - type: object + - type: 'null' + title: Parameters + additionalProperties: false + type: object + required: + - name + title: FunctionDefinition + HTTPValidationError: + properties: + detail: + items: + $ref: '#/components/schemas/ValidationError' + type: array + title: Detail + type: object + title: HTTPValidationError + ImageURL: + properties: + url: + type: string + title: Url + type: object + required: + - url + title: ImageURL + Logprob: + properties: + logprob: + type: number + title: Logprob + rank: + anyOf: + - type: integer + - type: 'null' + title: Rank + decoded_token: + anyOf: + - type: string + - type: 'null' + title: Decoded Token + type: object + required: + - logprob + title: Logprob + NIMLLMChatCompletionMessage: + properties: + role: + $ref: '#/components/schemas/Role' + description: The role of the message's author. + content: + anyOf: + - type: string + minLength: 1 + - items: + anyOf: + - $ref: '#/components/schemas/NIMVLMChatCompletionContentPartImage' + - $ref: '#/components/schemas/NIMVLMChatCompletionContentPartText' + - $ref: '#/components/schemas/NIMVLMChatCompletionContentPartVideo' + - $ref: '#/components/schemas/NIMVLMChatCompletionContentPartVideoStreaming' + type: array + title: Content + description: "The contents of the message.\n
To pass images (only\ + \ with role=`user`):\n
- When content is a string, image\ + \ can be passed together with the text with `img` HTML tags that wraps\n\ + \ an image URL (``),\n base64 encoded\ + \ image data (``),\n or an NVCF asset ID (``)\n when the container is hosted in NVCF and the payload exceeds\ + \ 200KB.\n or http link (`https:{image_link}`)\n
\ + \ - When content is a list of objects, images can be passed as objects\ + \ with type=`image_url`.\n
- In both cases, images can\ + \ be PNG, JPG or JPEG.\n
To pass videos (only with role=`user`):\n\ + \
- When content is a string, video can be passed together\ + \ with the text with `video` HTML tags that wraps\n a video URL\ + \ (`

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output + data_type: TYPE_FP32 + dims: [ 1 ] + preprocessors: + - kind: "custom" + name: "frame-picker" + input: ["video_streams"] + output: ["media_frames"] + config: { "num_frames": 10 } + - kind: "custom" + name: "frame-picker" + input: ["video_files"] + output: ["media_frames"] + config: { "num_frames": 10 } + +routes: { + ':["video_files"]': 'dummy', + ':["video_streams"]': 'dummy', + 'dummy:["output"]': ':' +} \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/frame_sampling/processors.py b/tools/inference_builder/builder/tests/frame_sampling/processors.py new file mode 100644 index 00000000..07d5b12e --- /dev/null +++ b/tools/inference_builder/builder/tests/frame_sampling/processors.py @@ -0,0 +1,36 @@ +import torch +import logging + +logger = logging.getLogger(__name__) + +class FramePickerProcessor: + name = "frame-picker" + + def __init__(self, config): + self.num_frames = config['num_frames'] + # Eagerly initialize CUDA to avoid GIL deadlock with GStreamer threads + torch.cuda.init() + + def __call__(self, *args, **kwargs): + if len(args) != 1: + raise ValueError( + "FramePickerProcessor expects exactly one argument" + ) + frame_list = args[0] + if len(frame_list) != self.num_frames: + raise ValueError( + f"FramePickerProcessor expects a list of " + f"{self.num_frames} frames, while got {len(frame_list)}" + ) + torch_tensors = [ + torch.utils.dlpack.from_dlpack(frame.tensor) + for frame in frame_list + ] + + # Log frame dimensions for verification + if torch_tensors: + # Tensor shape is typically (H, W, C) from MediaExtractor + h, w = torch_tensors[0].shape[0], torch_tensors[0].shape[1] + logger.info(f"FramePickerProcessor: received {len(torch_tensors)} frames with dimensions {w}x{h}") + + return torch.stack(torch_tensors) diff --git a/tools/inference_builder/builder/tests/negative/df_1.yaml b/tools/inference_builder/builder/tests/negative/df_1.yaml new file mode 100644 index 00000000..824be9c9 --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/df_1.yaml @@ -0,0 +1,50 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +input: + - name: "base64" + data_type: TYPE_CUSTOM_BINARY_BASE64 + dims: [ -1 ] + +output: + - name: "output" + data_type: TYPE_FP32 + dims: [ 1 ] + +server: + responders: + healthy_ready: + operation: health_ready_v1_health_ready_get + infer: + operation: inference + requests: + InferenceRequest: > + { + "base64": ["hello"] + } + responses: + InferenceResponse: > + { + "data": {{ response.outputs | tojson }} + } + +models: +- name: dummy + backend: dummy + max_batch_size: 1 + input: + - name: inputs + data_type: TYPE_STRING + dims: [ -1] + output: + - name: output + data_type: TYPE_FP32 + dims: [ 1 ] + +routes: { + ':["base64"]': 'dummy:["inputs"]', + 'dummy:["output"]': ':' +} diff --git a/tools/inference_builder/builder/tests/negative/df_2.yaml b/tools/inference_builder/builder/tests/negative/df_2.yaml new file mode 100644 index 00000000..76db5efe --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/df_2.yaml @@ -0,0 +1,50 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +input: + - name: "base64" + data_type: TYPE_CUSTOM_BINARY_BASE64 + dims: [ -1 ] + +output: + - name: "output" + data_type: TYPE_FP32 + dims: [ 1 ] + +server: + responders: + healthy_ready: + operation: health_ready_v1_health_ready_get + infer: + operation: inference + requests: + InferenceRequest: > + { + "base64": [1, 2] + } + responses: + InferenceResponse: > + { + "data": {{ response.outputs | tojson }} + } + +models: +- name: dummy + backend: dummy + max_batch_size: 1 + input: + - name: inputs + data_type: TYPE_STRING + dims: [ -1] + output: + - name: output + data_type: TYPE_FP32 + dims: [ 1 ] + +routes: { + ':["base64"]': 'dummy:["inputs"]', + 'dummy:["output"]': ':' +} diff --git a/tools/inference_builder/builder/tests/negative/df_3 copy.yaml b/tools/inference_builder/builder/tests/negative/df_3 copy.yaml new file mode 100644 index 00000000..4b3d5cf7 --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/df_3 copy.yaml @@ -0,0 +1,33 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +input: + - name: "inputs" + data_type: TYPE_STRING + dims: [ -1 ] + +output: + - name: "output" + data_type: TYPE_FP32 + dims: [ 1 ] + +models: +- name: dummy + backend: dummy + max_batch_size: 1 + input: + - name: inputs + data_type: TYPE_STRING + dims: [ -1] + output: + - name: output + data_type: TYPE_FP32 + dims: [ 1 ] + postprocessors: + - kind: "custom" + name: "pp-output-2" + input: ["output"] + output: ["output_1", "output_2"] \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/df_3.yaml b/tools/inference_builder/builder/tests/negative/df_3.yaml new file mode 100644 index 00000000..a83bc7e8 --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/df_3.yaml @@ -0,0 +1,44 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +input: + - name: "video" + data_type: TYPE_CUSTOM_LONG_VIDEO_ASSETS + dims: [ -1 ] + optional: true + - name: "image" + data_type: TYPE_CUSTOM_IMAGE_ASSETS + dims: [ -1 ] + optional: true + - name: "video_chunk" + data_type: TYPE_CUSTOM_VIDEO_CHUNK_ASSETS + dims: [ -1 ] + optional: true + +output: + - name: "output" + data_type: TYPE_FP32 + dims: [ 1 ] + +models: +- name: dummy + backend: dummy + max_batch_size: 1 + input: + - name: inputs + data_type: TYPE_STRING + dims: [ -1 ] + output: + - name: output + data_type: TYPE_FP32 + dims: [ 1 ] + +routes: { + ':["video"]': 'dummy:["inputs"]', + ':["image"]': 'dummy:["inputs"]', + ':["video_chunk"]': 'dummy:["inputs"]', + 'dummy:["output"]': ':' +} \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/image.yaml b/tools/inference_builder/builder/tests/negative/image.yaml new file mode 100644 index 00000000..d9a4f0e6 --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/image.yaml @@ -0,0 +1,29 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +input: + - name: "image" + data_type: TYPE_CUSTOM_IMAGE_BASE64 + dims: [ -1 ] + optional: true + +output: + - name: "output" + data_type: TYPE_FP32 + dims: [ 1 ] + +models: +- name: dummy + backend: dummy + max_batch_size: 1 + input: + - name: image + data_type: TYPE_UINT8 + dims: [ -1, -1, -1, -1 ] + output: + - name: output + data_type: TYPE_FP32 + dims: [ 1 ] diff --git a/tools/inference_builder/builder/tests/negative/mo_1.yaml b/tools/inference_builder/builder/tests/negative/mo_1.yaml new file mode 100644 index 00000000..4b3d5cf7 --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/mo_1.yaml @@ -0,0 +1,33 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +input: + - name: "inputs" + data_type: TYPE_STRING + dims: [ -1 ] + +output: + - name: "output" + data_type: TYPE_FP32 + dims: [ 1 ] + +models: +- name: dummy + backend: dummy + max_batch_size: 1 + input: + - name: inputs + data_type: TYPE_STRING + dims: [ -1] + output: + - name: output + data_type: TYPE_FP32 + dims: [ 1 ] + postprocessors: + - kind: "custom" + name: "pp-output-2" + input: ["output"] + output: ["output_1", "output_2"] \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/mo_2.yaml b/tools/inference_builder/builder/tests/negative/mo_2.yaml new file mode 100644 index 00000000..f20dc58a --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/mo_2.yaml @@ -0,0 +1,34 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +server: + responders: + healthy_ready: + operation: health_ready_v1_health_ready_get + infer: + operation: inference + requests: + InferenceRequest: > + { + "inputs_not_found": ["hello"] + } + responses: + InferenceResponse: > + { + "output": {{ response.outputs | tojson }} + } +models: +- name: dummy + backend: dummy + max_batch_size: 1 + input: + - name: inputs + data_type: TYPE_STRING + dims: [ -1] + output: + - name: output + data_type: TYPE_FP32 + dims: [ 1 ] \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/model.yaml b/tools/inference_builder/builder/tests/negative/model.yaml new file mode 100644 index 00000000..ce03f7ca --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/model.yaml @@ -0,0 +1,28 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +input: + - name: "inputs" + data_type: TYPE_STRING + dims: [ -1 ] + +output: + - name: "output" + data_type: TYPE_FP32 + dims: [ 1 ] + +models: +- name: dummy + backend: dummy + max_batch_size: 1 + input: + - name: inputs + data_type: TYPE_STRING + dims: [ -1] + output: + - name: output + data_type: TYPE_FP32 + dims: [ 1 ] diff --git a/tools/inference_builder/builder/tests/negative/payloads/payloads.jsonl b/tools/inference_builder/builder/tests/negative/payloads/payloads.jsonl new file mode 100644 index 00000000..63702345 --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/payloads/payloads.jsonl @@ -0,0 +1 @@ +{"text": ["hello"]} diff --git a/tools/inference_builder/builder/tests/negative/pp_1.yaml b/tools/inference_builder/builder/tests/negative/pp_1.yaml new file mode 100644 index 00000000..698a2950 --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/pp_1.yaml @@ -0,0 +1,33 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +input: + - name: "inputs" + data_type: TYPE_STRING + dims: [ -1 ] + +output: + - name: "output" + data_type: TYPE_FP32 + dims: [ 1 ] + +models: +- name: dummy + backend: dummy + max_batch_size: 1 + input: + - name: inputs + data_type: TYPE_STRING + dims: [ -1] + output: + - name: output + data_type: TYPE_FP32 + dims: [ 1 ] + postprocessors: + - kind: "custom" + name: "pp-output-2" + input: ["output"] + output: ["output"] \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/pp_2.yaml b/tools/inference_builder/builder/tests/negative/pp_2.yaml new file mode 100644 index 00000000..2c1d58cb --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/pp_2.yaml @@ -0,0 +1,33 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +input: + - name: "inputs" + data_type: TYPE_STRING + dims: [ -1 ] + +output: + - name: "output" + data_type: TYPE_FP32 + dims: [ 1 ] + +models: +- name: dummy + backend: dummy + max_batch_size: 1 + input: + - name: inputs + data_type: TYPE_STRING + dims: [ -1] + output: + - name: output + data_type: TYPE_FP32 + dims: [ 1 ] + preprocessors: + - kind: "custom" + name: "pp-output-2" + input: ["inputs"] + output: ["inputs"] \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/pp_3.yaml b/tools/inference_builder/builder/tests/negative/pp_3.yaml new file mode 100644 index 00000000..fa869b99 --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/pp_3.yaml @@ -0,0 +1,33 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +input: + - name: "inputs" + data_type: TYPE_STRING + dims: [ -1 ] + +output: + - name: "output" + data_type: TYPE_FP32 + dims: [ 1 ] + +models: +- name: dummy + backend: dummy + max_batch_size: 1 + input: + - name: inputs + data_type: TYPE_STRING + dims: [ -1] + output: + - name: output + data_type: TYPE_FP32 + dims: [ 1 ] + preprocessors: + - kind: "custom" + name: "pp-nonexist" + input: ["inputs"] + output: ["inputs"] \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/processors.py b/tools/inference_builder/builder/tests/negative/processors.py new file mode 100644 index 00000000..9a35c1ab --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/processors.py @@ -0,0 +1,32 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +class InvalidProcessor1: + name = "pp-output-2" + + def __init__(self, config): + pass + + def __call__(self, *args, **kwargs): + return 0, 1 + +class InvalidProcessor2: + name = "pp-output-int" + + def __init__(self, config): + pass + + def __call__(self, *args, **kwargs): + return 0 diff --git a/tools/inference_builder/builder/tests/negative/route_1.yaml b/tools/inference_builder/builder/tests/negative/route_1.yaml new file mode 100644 index 00000000..465caf3e --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/route_1.yaml @@ -0,0 +1,33 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +input: + - name: "inputs" + data_type: TYPE_STRING + dims: [ -1 ] + +output: + - name: "output" + data_type: TYPE_FP32 + dims: [ 1 ] + +models: +- name: dummy + backend: dummy + max_batch_size: 1 + input: + - name: inputs + data_type: TYPE_STRING + dims: [ -1] + output: + - name: output + data_type: TYPE_FP32 + dims: [ 1 ] + +routes: { + ':["inputs"]:': 'dummy::', + 'dummy:["output"]': ':' +} \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/route_2.yaml b/tools/inference_builder/builder/tests/negative/route_2.yaml new file mode 100644 index 00000000..4123eb75 --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/route_2.yaml @@ -0,0 +1,33 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +input: + - name: "inputs" + data_type: TYPE_STRING + dims: [ -1 ] + +output: + - name: "output" + data_type: TYPE_FP32 + dims: [ 1 ] + +models: +- name: dummy + backend: dummy + max_batch_size: 1 + input: + - name: inputs + data_type: TYPE_STRING + dims: [ -1] + output: + - name: output + data_type: TYPE_FP32 + dims: [ 1 ] + +routes: { + ':["inputs"]': 'dummy:', + 'dummy:"output"': ':' +} \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/route_3.yaml b/tools/inference_builder/builder/tests/negative/route_3.yaml new file mode 100644 index 00000000..8b18d7b8 --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/route_3.yaml @@ -0,0 +1,33 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +input: + - name: "inputs" + data_type: TYPE_STRING + dims: [ -1 ] + +output: + - name: "output" + data_type: TYPE_FP32 + dims: [ 1 ] + +models: +- name: dummy + backend: dummy + max_batch_size: 1 + input: + - name: inputs + data_type: TYPE_STRING + dims: [ -1] + output: + - name: output + data_type: TYPE_FP32 + dims: [ 1 ] + +routes: { + ':["inputs"]': 'dummy:"inputs"', + 'dummy:["output"]': ':' +} \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/route_4.yaml b/tools/inference_builder/builder/tests/negative/route_4.yaml new file mode 100644 index 00000000..71029f9b --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/route_4.yaml @@ -0,0 +1,33 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +input: + - name: "inputs" + data_type: TYPE_STRING + dims: [ -1 ] + +output: + - name: "output" + data_type: TYPE_FP32 + dims: [ 1 ] + +models: +- name: dummy + backend: dummy + max_batch_size: 1 + input: + - name: inputs + data_type: TYPE_STRING + dims: [ -1] + output: + - name: output + data_type: TYPE_FP32 + dims: [ 1 ] + +routes: { + ':["inputs"]': 'dummy2', + 'dummy:["output"]': ':' +} \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/route_5.yaml b/tools/inference_builder/builder/tests/negative/route_5.yaml new file mode 100644 index 00000000..803c66c6 --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/route_5.yaml @@ -0,0 +1,33 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +input: + - name: "inputs" + data_type: TYPE_STRING + dims: [ -1 ] + +output: + - name: "output" + data_type: TYPE_FP32 + dims: [ 1 ] + +models: +- name: dummy + backend: dummy + max_batch_size: 1 + input: + - name: inputs + data_type: TYPE_STRING + dims: [ -1] + output: + - name: output + data_type: TYPE_FP32 + dims: [ 1 ] + +routes: { + ':["inputs"]': 'dummy', + 'dummy2:["output"]': ':' +} \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/video.yaml b/tools/inference_builder/builder/tests/negative/video.yaml new file mode 100644 index 00000000..c0987670 --- /dev/null +++ b/tools/inference_builder/builder/tests/negative/video.yaml @@ -0,0 +1,29 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +input: + - name: "video_chunk" + data_type: TYPE_CUSTOM_VIDEO_CHUNK_ASSETS + dims: [ -1 ] + optional: true + +output: + - name: "output" + data_type: TYPE_FP32 + dims: [ 1 ] + +models: +- name: dummy + backend: dummy + max_batch_size: 1 + input: + - name: video_chunk + data_type: TYPE_UINT8 + dims: [ -1, -1, -1, -1 ] + output: + - name: output + data_type: TYPE_FP32 + dims: [ 1 ] diff --git a/tools/inference_builder/builder/tests/output_types/app.yaml b/tools/inference_builder/builder/tests/output_types/app.yaml new file mode 100644 index 00000000..9759e4c5 --- /dev/null +++ b/tools/inference_builder/builder/tests/output_types/app.yaml @@ -0,0 +1,110 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +name: "test-app" +model_repo: "/workspace/models" + +# Input specification for testing various input types +input: + - name: "text" + data_type: TYPE_STRING + dims: [ -1 ] + optional: true + +# Output specification with multiple numpy array types +output: + - name: "outputs" + data_type: TYPE_STRING + dims: [ -1 ] + +server: + responders: + healthy_ready: + operation: health_ready_v1_health_ready_get + infer: + operation: inference + requests: + InferenceRequest: > + { + {% if request.text is not none %} + "text": [ + {% for item in request.text %} + {{ item|tojson }}{% if not loop.last %}, {% endif %} + {% endfor %} + ] + {% endif %} + } + responses: + InferenceResponse: > + { + "data": {{ response.outputs | tojson }} + } + +models: + - name: output_types_dummy + backend: "dummy" + max_batch_size: 1 + parameters: {} + input: + - name: text + data_type: TYPE_STRING + dims: [ -1 ] + output: + - name: int8_output + data_type: TYPE_INT8 + dims: [5, 4] + - name: int16_output + data_type: TYPE_INT16 + dims: [5, 4] + - name: int32_output + data_type: TYPE_INT32 + dims: [5, 4] + - name: int64_output + data_type: TYPE_INT64 + dims: [5, 4] + - name: uint8_output + data_type: TYPE_UINT8 + dims: [5, 4] + - name: uint16_output + data_type: TYPE_UINT16 + dims: [5, 4] + - name: uint32_output + data_type: TYPE_UINT32 + dims: [5, 4] + - name: uint64_output + data_type: TYPE_UINT64 + dims: [5, 4] + - name: fp16_output + data_type: TYPE_FP16 + dims: [5, 4] + - name: fp32_output + data_type: TYPE_FP32 + dims: [5, 4] + - name: fp64_output + data_type: TYPE_FP64 + dims: [5, 4] + - name: bool_output + data_type: TYPE_BOOL + dims: [5, 4] + - name: string_output + data_type: TYPE_STRING + dims: [5] + postprocessors: + - kind: "custom" + name: "output-types-validator" + input: + - int8_output + - int16_output + - int32_output + - int64_output + - uint8_output + - uint16_output + - uint32_output + - uint64_output + - fp16_output + - fp32_output + - fp64_output + - bool_output + - string_output + output: + - outputs diff --git a/tools/inference_builder/builder/tests/output_types/payloads/payloads.jsonl b/tools/inference_builder/builder/tests/output_types/payloads/payloads.jsonl new file mode 100644 index 00000000..79d79b3d --- /dev/null +++ b/tools/inference_builder/builder/tests/output_types/payloads/payloads.jsonl @@ -0,0 +1,4 @@ +{"text": ["hello-1"]} +{"text": ["hello-2"]} +{"text": ["hello-3"]} + diff --git a/tools/inference_builder/builder/tests/output_types/processors.py b/tools/inference_builder/builder/tests/output_types/processors.py new file mode 100644 index 00000000..dc977f08 --- /dev/null +++ b/tools/inference_builder/builder/tests/output_types/processors.py @@ -0,0 +1,68 @@ +from numpy import int64, uint, uint8 +import numpy as np + + +class Validator: + name = "output-types-validator" + + def __init__(self, config): + pass + + def __call__(self, *args): + int8_outputs = args[0] + int16_outputs = args[1] + int32_outputs = args[2] + int64_outputs = args[3] + uint8_outputs = args[4] + uint16_outputs = args[5] + uint32_outputs = args[6] + uint64_outputs = args[7] + fp16_outputs = args[8] + fp32_outputs = args[9] + fp64_outputs = args[10] + bool_outputs = args[11] + string_outputs = args[12] + + # Type assertions + assert isinstance(int8_outputs, np.ndarray), f"int8_outputs should be ndarray, got {type(int8_outputs)}" + assert int8_outputs.dtype == np.int8, f"int8_outputs should have dtype int8, got {int8_outputs.dtype}" + + assert isinstance(int16_outputs, np.ndarray), f"int16_outputs should be ndarray, got {type(int16_outputs)}" + assert int16_outputs.dtype == np.int16, f"int16_outputs should have dtype int16, got {int16_outputs.dtype}" + + assert isinstance(int32_outputs, np.ndarray), f"int32_outputs should be ndarray, got {type(int32_outputs)}" + assert int32_outputs.dtype == np.int32, f"int32_outputs should have dtype int32, got {int32_outputs.dtype}" + + assert isinstance(int64_outputs, np.ndarray), f"int64_outputs should be ndarray, got {type(int64_outputs)}" + assert int64_outputs.dtype == np.int64, f"int64_outputs should have dtype int64, got {int64_outputs.dtype}" + + assert isinstance(uint8_outputs, np.ndarray), f"uint8_outputs should be ndarray, got {type(uint8_outputs)}" + assert uint8_outputs.dtype == np.uint8, f"uint8_outputs should have dtype uint8, got {uint8_outputs.dtype}" + + assert isinstance(uint16_outputs, np.ndarray), f"uint16_outputs should be ndarray, got {type(uint16_outputs)}" + assert uint16_outputs.dtype == np.uint16, f"uint16_outputs should have dtype uint16, got {uint16_outputs.dtype}" + + assert isinstance(uint32_outputs, np.ndarray), f"uint32_outputs should be ndarray, got {type(uint32_outputs)}" + assert uint32_outputs.dtype == np.uint32, f"uint32_outputs should have dtype uint32, got {uint32_outputs.dtype}" + + assert isinstance(uint64_outputs, np.ndarray), f"uint64_outputs should be ndarray, got {type(uint64_outputs)}" + assert uint64_outputs.dtype == np.uint64, f"uint64_outputs should have dtype uint64, got {uint64_outputs.dtype}" + + assert isinstance(fp16_outputs, np.ndarray), f"fp16_outputs should be ndarray, got {type(fp16_outputs)}" + assert fp16_outputs.dtype == np.float16, f"fp16_outputs should have dtype float16, got {fp16_outputs.dtype}" + + assert isinstance(fp32_outputs, np.ndarray), f"fp32_outputs should be ndarray, got {type(fp32_outputs)}" + assert fp32_outputs.dtype == np.float32, f"fp32_outputs should have dtype float32, got {fp32_outputs.dtype}" + + assert isinstance(fp64_outputs, np.ndarray), f"fp64_outputs should be ndarray, got {type(fp64_outputs)}" + assert fp64_outputs.dtype == np.float64, f"fp64_outputs should have dtype float64, got {fp64_outputs.dtype}" + + assert isinstance(bool_outputs, np.ndarray), f"bool_outputs should be ndarray, got {type(bool_outputs)}" + assert bool_outputs.dtype == np.bool_, f"bool_outputs should have dtype bool_, got {bool_outputs.dtype}" + + assert isinstance(string_outputs, np.ndarray), f"string_outputs should be ndarray, got {type(string_outputs)}" + # String arrays can be np.bytes_ (|S*), np.str_ (U*), or np.object_ + assert np.issubdtype(string_outputs.dtype, np.bytes_) or np.issubdtype(string_outputs.dtype, np.str_) or string_outputs.dtype == np.object_, \ + f"string_outputs should have string-like dtype, got {string_outputs.dtype}" + + return [], diff --git a/tools/inference_builder/builder/tests/run_tests.sh b/tools/inference_builder/builder/tests/run_tests.sh new file mode 100644 index 00000000..8ecb80e4 --- /dev/null +++ b/tools/inference_builder/builder/tests/run_tests.sh @@ -0,0 +1,194 @@ +#!/bin/bash + +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +# Test script wrapper for Docker container builds +# This script provides easy-to-use commands for testing Docker builds + +set -e + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +TEST_SCRIPT="$SCRIPT_DIR/test_docker_builds.py" + +# Colors for output +RED='\033[0;31m' +GREEN='\033[0;32m' +YELLOW='\033[1;33m' +BLUE='\033[0;34m' +NC='\033[0m' # No Color + +# Function to print colored output +print_info() { + echo -e "${BLUE}[INFO]${NC} $1" +} + +print_success() { + echo -e "${GREEN}[SUCCESS]${NC} $1" +} + +print_warning() { + echo -e "${YELLOW}[WARNING]${NC} $1" +} + +print_error() { + echo -e "${RED}[ERROR]${NC} $1" +} + +# Function to show usage +show_usage() { + cat << EOF +Usage: $0 [OPTIONS] + +Run Docker build tests using a configuration file (defaults to test_configs.json). + +Options: + --no-cleanup Don't cleanup Docker images after testing + --output FILE Save test report to specified file + -c, --config FILE Use configuration file (optional, defaults to test_configs.json) + --log-dir DIR Directory to save container logs (default: logs) + -t, --test-case NAME Run only test cases matching this name (partial match). Use '*' for all. + --gpus DEVICES GPU devices to use (default: 'all'). Examples: 'all', 'device=0', 'device=0,1' + +Examples: + $0 + $0 -c my_config.json --output results.json + $0 --no-cleanup --output full_results.json --log-dir test_logs + $0 -t "Frame Sampling" + $0 -t "*" + $0 --test-case "FastAPI" + $0 --gpus "device=0" + $0 --gpus "device=0,1" -t "*" + +EOF +} + +# Function to check prerequisites +check_prerequisites() { + print_info "Checking prerequisites..." + + # Check if Docker is installed and running + if ! command -v docker &> /dev/null; then + print_error "Docker is not installed or not in PATH" + exit 1 + fi + + if ! docker info &> /dev/null; then + print_error "Docker is not running or you don't have permissions" + exit 1 + fi + + # Check if Python script exists + if [ ! -f "$TEST_SCRIPT" ]; then + print_error "Test script not found: $TEST_SCRIPT" + exit 1 + fi + + # Check if Python script is executable + if [ ! -x "$TEST_SCRIPT" ]; then + print_warning "Making test script executable..." + chmod +x "$TEST_SCRIPT" + fi + + print_success "Prerequisites check passed" +} + +# Parse command line arguments +NO_CLEANUP="false" +OUTPUT_FILE="" +CONFIG_FILE="" +LOG_DIR="logs" +TEST_CASE="" +GPUS="all" + +while [[ $# -gt 0 ]]; do + case $1 in + -h|--help) + show_usage + exit 0 + ;; + --no-cleanup) + NO_CLEANUP="true" + shift + ;; + --output) + OUTPUT_FILE="$2" + shift 2 + ;; + -c|--config) + CONFIG_FILE="$2" + shift 2 + ;; + --log-dir) + LOG_DIR="$2" + shift 2 + ;; + -t|--test-case) + TEST_CASE="$2" + shift 2 + ;; + --gpus) + GPUS="$2" + shift 2 + ;; + *) + print_error "Unknown option: $1" + show_usage + exit 1 + ;; + esac +done + +# Change to script directory +cd "$SCRIPT_DIR" + +# Check prerequisites +check_prerequisites + +# Determine configuration file (default to test_configs.json) +if [ -z "$CONFIG_FILE" ]; then + CONFIG_FILE="test_configs.json" + print_info "No configuration file provided, using default: $CONFIG_FILE" +fi + +if [ ! -f "$CONFIG_FILE" ]; then + print_error "Configuration file not found: $CONFIG_FILE" + exit 1 +fi + +print_info "Running tests with configuration: $CONFIG_FILE" + +args=("--dockerfile" "Dockerfile" "--base-dir" "." "--config-file" "$CONFIG_FILE" "--log-dir" "$LOG_DIR") + +if [ "$NO_CLEANUP" = "true" ]; then + args+=("--no-cleanup") +fi + +if [ -n "$OUTPUT_FILE" ]; then + args+=("--output" "$OUTPUT_FILE") +fi + +if [ -n "$TEST_CASE" ]; then + args+=("--test-case" "$TEST_CASE") +fi + +if [ -n "$GPUS" ]; then + args+=("--gpus" "$GPUS") +fi + +python3 "$TEST_SCRIPT" "${args[@]}" + +print_success "Test execution completed" \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/setup_rtsp_server.sh b/tools/inference_builder/builder/tests/setup_rtsp_server.sh new file mode 100644 index 00000000..6f359e5b --- /dev/null +++ b/tools/inference_builder/builder/tests/setup_rtsp_server.sh @@ -0,0 +1,376 @@ +#!/bin/bash + +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +# RTSP Server Setup Script for Testing +# This script sets up an RTSP server using cvlc to stream MP4 videos for testing +# Supports both MP4 files directly and JPEG images (transcoded to MP4) + +set -e + +# Colors for output +RED='\033[0;31m' +GREEN='\033[0;32m' +YELLOW='\033[1;33m' +BLUE='\033[0;34m' +NC='\033[0m' # No Color + +# Function to print colored output +print_info() { + echo -e "${BLUE}[INFO]${NC} $1" +} + +print_success() { + echo -e "${GREEN}[SUCCESS]${NC} $1" +} + +print_warning() { + echo -e "${YELLOW}[WARNING]${NC} $1" +} + +print_error() { + echo -e "${RED}[ERROR]${NC} $1" +} + +# Default configuration +RTSP_PORT=8554 +RTSP_MOUNT_POINT="video-stream" +INPUT_FILE="" +CACHE_TIME=1500 +FRAME_RATE=1 +DURATION=5 +TEMP_DIR="/tmp/rtsp_server" +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +PID_FILE="${SCRIPT_DIR}/logs/rtsp_server.pid" +LOG_FILE="${SCRIPT_DIR}/logs/rtsp_server.log" + +# Function to show usage +show_usage() { + cat << EOF +Usage: $0 [OPTIONS] + +Options: + -p, --port PORT RTSP port (default: 8554) + -m, --mount MOUNT_POINT RTSP mount point (default: video-stream) + -c, --cache CACHE_TIME Network caching time in ms (default: 1500) + -f, --fps FRAME_RATE Frame rate for JPEG transcoding (default: 1) + -d, --duration DURATION Duration per frame in seconds for JPEG (default: 5) + --daemon Run as daemon in background + -k, --kill Kill existing RTSP server + -s, --status Check RTSP server status + -h, --help Show this help message + +Examples: + $0 sample_video.mp4 + $0 sample_image.jpg + $0 -p 8555 -m test-stream sample_image.jpg + $0 --daemon -f 2 -d 3 sample_image.jpg + $0 --kill + $0 --status + +Environment Variables: + RTSP_PORT Default RTSP port + RTSP_MOUNT_POINT Default mount point + INPUT_FILE Default input file path + FRAME_RATE Default frame rate for JPEG transcoding + DURATION Default duration per frame for JPEG + +EOF +} + +# Function to check prerequisites +check_prerequisites() { + print_info "Checking prerequisites..." + + # Check if cvlc is installed + if ! command -v cvlc &> /dev/null; then + print_error "cvlc (VLC command line) is not installed" + print_info "Install VLC: sudo apt-get install vlc" + exit 1 + fi + + # Check if ffmpeg is installed (for JPEG transcoding) + if ! command -v ffmpeg &> /dev/null; then + print_error "ffmpeg is not installed" + print_info "Install ffmpeg: sudo apt-get install ffmpeg" + exit 1 + fi + + # Check if port is available + if netstat -tuln 2>/dev/null | grep -q ":$RTSP_PORT "; then + print_warning "Port $RTSP_PORT is already in use" + print_info "Use --kill to stop existing server or specify different port" + fi + + # Create temp directory + mkdir -p "$TEMP_DIR" + + print_success "Prerequisites check passed" +} + +# Function to transcode JPEG to MP4 +transcode_jpeg_to_mp4() { + local jpeg_file="$1" + local output_mp4="$2" + local frame_rate="$3" + local duration="$4" + + print_info "Transcoding JPEG to MP4..." + print_info "Input: $jpeg_file" + print_info "Output: $output_mp4" + print_info "Frame rate: $frame_rate fps" + print_info "Duration per frame: $duration seconds" + + # Create a video from JPEG image with specified frame rate and duration + ffmpeg -loop 1 -i "$jpeg_file" -c:v libx264 -tune stillimage -crf 18 -preset ultrafast \ + -r "$frame_rate" -t "$duration" -y "$output_mp4" > /dev/null 2>&1 + + if [ $? -eq 0 ] && [ -f "$output_mp4" ]; then + print_success "JPEG transcoded to MP4 successfully" + return 0 + else + print_error "Failed to transcode JPEG to MP4" + return 1 + fi +} + +# Function to get file type +get_file_type() { + local file="$1" + local file_info=$(file "$file" 2>/dev/null) + + if echo "$file_info" | grep -q "JPEG\|JFIF"; then + echo "jpeg" + elif echo "$file_info" | grep -q "MP4\|MPEG\|AVI\|MOV\|WebM\|video"; then + echo "video" + else + echo "unknown" + fi +} + +# Function to kill existing RTSP server +kill_rtsp_server() { + if [ -f "$PID_FILE" ]; then + local pid=$(cat "$PID_FILE") + if kill -0 "$pid" 2>/dev/null; then + print_info "Killing existing RTSP server (PID: $pid)" + kill "$pid" + sleep 2 + if kill -0 "$pid" 2>/dev/null; then + print_warning "Server still running, force killing..." + kill -9 "$pid" + fi + print_success "RTSP server stopped" + else + print_warning "PID file exists but process not running" + fi + rm -f "$PID_FILE" + else + print_info "No RTSP server PID file found" + fi +} + +# Function to check server status +check_status() { + if [ -f "$PID_FILE" ]; then + local pid=$(cat "$PID_FILE") + if kill -0 "$pid" 2>/dev/null; then + print_success "RTSP server is running (PID: $pid)" + print_info "RTSP URL: rtsp://localhost:$RTSP_PORT/$RTSP_MOUNT_POINT" + return 0 + else + print_warning "PID file exists but process not running" + rm -f "$PID_FILE" + return 1 + fi + else + print_info "RTSP server is not running" + return 1 + fi +} + +# Function to cleanup temp files +cleanup_temp_files() { + if [ -d "$TEMP_DIR" ]; then + rm -rf "$TEMP_DIR" + print_info "Cleaned up temporary files" + fi +} + +# Function to start RTSP server +start_rtsp_server() { + local input_file="$1" + local daemon_mode="$2" + + # Validate input file + if [ ! -f "$input_file" ]; then + print_error "Input file not found: $input_file" + exit 1 + fi + + # Get file type + local file_type=$(get_file_type "$input_file") + local video_file="$input_file" + + if [ "$file_type" = "jpeg" ]; then + print_info "JPEG file detected, transcoding to MP4..." + local output_mp4="$TEMP_DIR/transcoded_$(basename "$input_file" .jpg).mp4" + + if ! transcode_jpeg_to_mp4 "$input_file" "$output_mp4" "$FRAME_RATE" "$DURATION"; then + exit 1 + fi + + video_file="$output_mp4" + print_info "Using transcoded MP4: $video_file" + elif [ "$file_type" = "video" ]; then + print_info "Video file detected, using directly" + else + print_warning "Unknown file type: $input_file" + print_info "Attempting to use as video file..." + fi + + # Kill existing server + kill_rtsp_server + + # Create cvlc command for video streaming + # Use loop to continuously stream the video + # Add headless flags for Docker/CI environments + local cvlc_cmd="vlc-wrapper -I dummy --no-video-title-show --no-xlib --loop \"$video_file\" \":sout=#gather:rtp{sdp=rtsp://:$RTSP_PORT/$RTSP_MOUNT_POINT}\" :network-caching=$CACHE_TIME :sout-all :sout-keep" + + print_info "Starting RTSP server..." + print_info "Input file: $input_file" + print_info "Video file: $video_file" + print_info "RTSP URL: rtsp://localhost:$RTSP_PORT/$RTSP_MOUNT_POINT" + print_info "Command: $cvlc_cmd" + + if [ "$daemon_mode" = "true" ]; then + # Run as daemon + print_info "Running in daemon mode" + nohup bash -c "$cvlc_cmd" > "$LOG_FILE" 2>&1 & + local pid=$! + echo "$pid" > "$PID_FILE" + sleep 2 + + if kill -0 "$pid" 2>/dev/null; then + print_success "RTSP server started in background (PID: $pid)" + print_info "Log file: $LOG_FILE" + print_info "PID file: $PID_FILE" + else + print_error "Failed to start RTSP server" + if [ -f "$LOG_FILE" ]; then + print_error "Log file contents:" + cat "$LOG_FILE" + fi + exit 1 + fi + else + # Run in foreground + print_info "Running in foreground mode (Ctrl+C to stop)" + print_info "RTSP server will be available at: rtsp://localhost:$RTSP_PORT/$RTSP_MOUNT_POINT" + echo "$$" > "$PID_FILE" + + # Set up cleanup on exit + trap cleanup_temp_files EXIT + + exec bash -c "$cvlc_cmd" + fi +} + +# Parse command line arguments +DAEMON_MODE="false" +KILL_SERVER="false" +CHECK_STATUS="false" + +while [[ $# -gt 0 ]]; do + case $1 in + -p|--port) + RTSP_PORT="$2" + shift 2 + ;; + -m|--mount) + RTSP_MOUNT_POINT="$2" + shift 2 + ;; + -c|--cache) + CACHE_TIME="$2" + shift 2 + ;; + -f|--fps) + FRAME_RATE="$2" + shift 2 + ;; + -d|--duration) + DURATION="$2" + shift 2 + ;; + --daemon) + DAEMON_MODE="true" + shift + ;; + -k|--kill) + KILL_SERVER="true" + shift + ;; + -s|--status) + CHECK_STATUS="true" + shift + ;; + -h|--help) + show_usage + exit 0 + ;; + -*) + print_error "Unknown option: $1" + show_usage + exit 1 + ;; + *) + if [ -z "$INPUT_FILE" ]; then + INPUT_FILE="$1" + else + print_error "Multiple input files specified" + exit 1 + fi + shift + ;; + esac +done + +# Handle special commands +if [ "$KILL_SERVER" = "true" ]; then + kill_rtsp_server + cleanup_temp_files + exit 0 +fi + +if [ "$CHECK_STATUS" = "true" ]; then + check_status + exit $? +fi + +# Check if input file is provided +if [ -z "$INPUT_FILE" ]; then + print_error "Input file (JPEG or MP4) is required" + show_usage + exit 1 +fi + +# Check prerequisites +check_prerequisites + +# Start RTSP server +start_rtsp_server "$INPUT_FILE" "$DAEMON_MODE" \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/test_configs.json b/tools/inference_builder/builder/tests/test_configs.json new file mode 100644 index 00000000..a3e697a0 --- /dev/null +++ b/tools/inference_builder/builder/tests/test_configs.json @@ -0,0 +1,504 @@ +[ + { + "name": "frame_sampling_on_files", + "description": "Test with default TensorRT and CUDA versions", + "build_args": { + "TEST_APP_NAME": "frame_sampling", + "CACHE_BUSTER": "default_test" + }, + "test_config": { + "default_enable": true, + "timeout": 30, + "env": { + "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes" + }, + "volumes": { + "frame_sampling/models": "/workspace/models" + }, + "cmd": [ + "--video-files", "file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h265.mp4?frames=10&start=0&duration=10000000000" + ], + "prerequisite_script": null + } + }, + { + "name": "frame_sampling_on_streams", + "description": "Test with default TensorRT and CUDA versions", + "build_args": { + "TEST_APP_NAME": "frame_sampling", + "CACHE_BUSTER": "default_test" + }, + "test_config": { + "default_enable": false, + "timeout": 30, + "env": { + "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes" + }, + "volumes": { + "frame_sampling/models": "/workspace/models" + }, + "cmd": [ + "--video-streams", "rtsp://localhost:8554/test?frames=10&interval=100000000&chunks=3&scale=640x360" + ], + "prerequisite_script": "./setup_rtsp_server.sh frame.jpg -m test -f 30 --daemon" + } + }, + { + "name": "asset_upload_test", + "description": "Test asset upload", + "build_args": { + "TEST_APP_NAME": "asset_management", + "CACHE_BUSTER": "asset_management", + "SERVER_TYPE": "fastapi", + "OPENAPI_SPEC": "builder/samples/dummy/openapi.yaml" + }, + "test_config": { + "default_enable": true, + "timeout": 5, + "env": { + "MODEL_REPO": "/workspace/model-repo" + }, + "volumes": { + "asset_management/models": "/workspace/models" + }, + "ports": { + "8000": "8000" + }, + "test_requests": [ + { + "payload_path": "asset_management/payloads/assets.jsonl", + "method": "POST", + "headers": { + "Content-Type": "multipart/form-data" + } + }, + { + "method": "GET", + "headers": { + "Content-Type": "application/json" + } + } + ], + "endpoint": "/v1/files" + } + }, + { + "name": "Output Types Compatibility Test", + "description": "Test numpy array output type compatibility with simplified dummy backend", + "build_args": { + "TEST_APP_NAME": "output_types", + "CACHE_BUSTER": "output_types_simplified", + "SERVER_TYPE": "fastapi", + "OPENAPI_SPEC": "builder/samples/dummy/openapi.yaml" + }, + + "test_config": { + "default_enable": true, + "timeout": 10, + "volumes": { + "output_types/models": "/workspace/models" + }, + "env": { + "DEBUG": "0" + }, + "cmd": [] + } + }, + { + "name": "fastapi_concurrency_test", + "description": "Build concurrency app and run threaded client", + "build_args": { + "TEST_APP_NAME": "concurrency", + "CACHE_BUSTER": "concurrency_fastapi", + "SERVER_TYPE": "fastapi", + "OPENAPI_SPEC": "builder/samples/dummy/openapi.yaml" + }, + "test_config": { + "default_enable": true, + "timeout": 30, + "volumes": { + "concurrency/models": "/workspace/models" + }, + "cmd": [] + } + }, + { + "name": "negative_invalid_route_config_1", + "description": "Negative test: Invalid route configuration should fail with proper error", + "build_args": { + "TEST_APP_NAME": "negative", + "CACHE_BUSTER": "negative_route_test", + "APP_YAML_PATH": "./negative/route_1.yaml" + }, + "test_config": { + "default_enable": true, + "timeout": 5, + "expected_error": "ERR_ROUTE_001", + "volumes": { + "concurrency/models": "/workspace/models" + }, + "cmd": [ "--inputs", "string" ] + } + }, + { + "name": "negative_invalid_route_config_2", + "description": "Negative test: Invalid route configuration should fail with proper error", + "build_args": { + "TEST_APP_NAME": "negative", + "CACHE_BUSTER": "negative_route_test", + "APP_YAML_PATH": "./negative/route_2.yaml" + }, + "test_config": { + "default_enable": true, + "timeout": 5, + "expected_error": "ERR_ROUTE_001", + "volumes": { + "concurrency/models": "/workspace/models" + }, + "cmd": [ "--inputs", "string" ] + } + }, + { + "name": "negative_invalid_route_config_3", + "description": "Negative test: Invalid route configuration should fail with proper error", + "build_args": { + "TEST_APP_NAME": "negative", + "CACHE_BUSTER": "negative_route_test", + "APP_YAML_PATH": "./negative/route_3.yaml" + }, + "test_config": { + "default_enable": true, + "timeout": 5, + "expected_error": "ERR_ROUTE_001", + "volumes": { + "concurrency/models": "/workspace/models" + }, + "cmd": [ "--inputs", "string" ] + } + }, + { + "name": "negative_invalid_route_config_4", + "description": "Negative test: Invalid route configuration should fail with proper error", + "build_args": { + "TEST_APP_NAME": "negative", + "CACHE_BUSTER": "negative_route_test", + "APP_YAML_PATH": "./negative/route_4.yaml" + }, + "test_config": { + "default_enable": true, + "timeout": 5, + "expected_error": "ERR_ROUTE_002", + "volumes": { + "concurrency/models": "/workspace/models" + }, + "cmd": [ "--inputs", "string" ] + } + }, + { + "name": "negative_invalid_route_config_5", + "description": "Negative test: Invalid route configuration should fail with proper error", + "build_args": { + "TEST_APP_NAME": "negative", + "CACHE_BUSTER": "negative_route_test", + "APP_YAML_PATH": "./negative/route_5.yaml" + }, + "test_config": { + "default_enable": true, + "timeout": 5, + "expected_error": "ERR_ROUTE_002", + "volumes": { + "concurrency/models": "/workspace/models" + }, + "cmd": [ "--inputs", "string" ] + } + }, + { + "name": "negative_invalid_model_repo", + "description": "Negative test: Invalid model repository", + "build_args": { + "TEST_APP_NAME": "negative", + "CACHE_BUSTER": "negative_model_test", + "APP_YAML_PATH": "./negative/model.yaml" + }, + "test_config": { + "default_enable": true, + "timeout": 5, + "expected_error": "ERR_SYS_001", + "volumes": {}, + "cmd": [ "--inputs", "string" ] + } + }, + { + "name": "negative_invalid_processor_1", + "description": "Negative test: Invalid post-processor", + "build_args": { + "TEST_APP_NAME": "negative", + "CACHE_BUSTER": "negative_pp_test", + "APP_YAML_PATH": "./negative/pp_1.yaml" + }, + "test_config": { + "default_enable": true, + "timeout":10, + "expected_error": "ERR_PROC_001", + "volumes": { + "concurrency/models": "/workspace/models" + }, + "cmd": [ "--inputs", "string" ] + } + }, + { + "name": "negative_invalid_processor_2", + "description": "Negative test: Invalid post-processor", + "build_args": { + "TEST_APP_NAME": "negative", + "CACHE_BUSTER": "negative_pp_test", + "APP_YAML_PATH": "./negative/pp_2.yaml" + }, + "test_config": { + "default_enable": true, + "timeout": 10, + "expected_error": "ERR_PROC_001", + "volumes": { + "concurrency/models": "/workspace/models" + }, + "cmd": [ "--inputs", "string" ] + } + }, + { + "name": "negative_invalid_processor_3", + "description": "Negative test: Invalid post-processor", + "build_args": { + "TEST_APP_NAME": "negative", + "CACHE_BUSTER": "negative_pp_test", + "APP_YAML_PATH": "./negative/pp_3.yaml" + }, + "test_config": { + "default_enable": true, + "timeout": 10, + "expected_error": "ERR_PROC_004", + "volumes": { + "concurrency/models": "/workspace/models" + }, + "cmd": [ "--inputs", "string" ] + } + }, + { + "name": "negative_dataflow_test_1", + "description": "Negative test: Invalid base64 input", + "build_args": { + "TEST_APP_NAME": "negative", + "CACHE_BUSTER": "negative_df_test", + "APP_YAML_PATH": "./negative/df_1.yaml", + "SERVER_TYPE": "fastapi", + "OPENAPI_SPEC": "builder/samples/dummy/openapi.yaml" + }, + + "test_config": { + "default_enable": true, + "timeout": 5, + "volumes": { + "output_types/models": "/workspace/models" + }, + "expected_error": "ERR_DF_002", + "cmd": [] + } + }, + { + "name": "negative_dataflow_test_2", + "description": "Negative test: Invalid base64 input", + "build_args": { + "TEST_APP_NAME": "negative", + "CACHE_BUSTER": "negative_df_test", + "APP_YAML_PATH": "./negative/df_2.yaml", + "SERVER_TYPE": "fastapi", + "OPENAPI_SPEC": "builder/samples/dummy/openapi.yaml" + }, + + "test_config": { + "default_enable": true, + "timeout": 5, + "volumes": { + "output_types/models": "/workspace/models" + }, + "expected_error": "ERR_DF_002", + "cmd": [] + } + }, + { + "name": "negative_dataflow_test_3", + "description": "Negative test: Invalid asset", + "build_args": { + "TEST_APP_NAME": "negative", + "CACHE_BUSTER": "negative_df_test", + "APP_YAML_PATH": "./negative/df_3.yaml" + }, + + "test_config": { + "default_enable": true, + "timeout": 10, + "volumes": { + "output_types/models": "/workspace/models" + }, + "expected_error": "ERR_DF_003", + "cmd": [ "--video", "hello" ] + } + }, + { + "name": "negative_dataflow_test_4", + "description": "Negative test: Invalid video frame sampling parameters", + "build_args": { + "TEST_APP_NAME": "negative", + "CACHE_BUSTER": "negative_df_test", + "APP_YAML_PATH": "./negative/video.yaml" + }, + + "test_config": { + "default_enable": true, + "timeout": 10, + "volumes": { + "output_types/models": "/workspace/models" + }, + "expected_error": "ERR_VIDEO_001", + "cmd": [ + "--video-chunk", + "/opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h265.mp4?frames=10&duration=10000000000", + "/opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h265.mp4?frames=10&duration=20000000000" + ] + } + }, + { + "name": "negative_dataflow_test_5", + "description": "Negative test: Invalid video frame sampling parameters", + "build_args": { + "TEST_APP_NAME": "negative", + "CACHE_BUSTER": "negative_df_test", + "APP_YAML_PATH": "./negative/video.yaml" + }, + + "test_config": { + "default_enable": true, + "timeout": 10, + "volumes": { + "output_types/models": "/workspace/models" + }, + "expected_error": "ERR_VIDEO_001", + "cmd": [ + "--video-chunk", + "/opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h265.mp4?frames=10&duration=10000000000", + "/opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h265.mp4?frames=1&duration=10000000000" + ] + } + }, + { + "name": "negative_dataflow_test_6", + "description": "Negative test: Invalid base64 string", + "build_args": { + "TEST_APP_NAME": "negative", + "CACHE_BUSTER": "negative_df_test", + "APP_YAML_PATH": "./negative/image.yaml" + }, + + "test_config": { + "default_enable": true, + "timeout": 10, + "volumes": { + "output_types/models": "/workspace/models" + }, + "expected_error": "ERR_DF_002", + "cmd": [ "--image", "hello" ] + } + }, + { + "name": "negative_model_output_incomplete", + "description": "Negative test: Invalid model output", + "build_args": { + "TEST_APP_NAME": "negative", + "CACHE_BUSTER": "negative_mo_test", + "APP_YAML_PATH": "./negative/mo_1.yaml" + }, + "test_config": { + "default_enable": true, + "timeout": 10, + "expected_error": "ERR_MO_002", + "volumes": { + "concurrency/models": "/workspace/models" + }, + "cmd": [ "--inputs", "string" ] + } + }, + { + "name": "negative_model_input_incomplete", + "description": "Negative test: Invalid base64 input", + "build_args": { + "TEST_APP_NAME": "negative", + "CACHE_BUSTER": "negative_df_test", + "APP_YAML_PATH": "./negative/mo_2.yaml", + "SERVER_TYPE": "fastapi", + "OPENAPI_SPEC": "builder/samples/dummy/openapi.yaml" + }, + + "test_config": { + "default_enable": true, + "timeout": 5, + "volumes": { + "output_types/models": "/workspace/models" + }, + "expected_error": "ERR_DF_006", + "cmd": [] + } + }, + { + "name": "live_stream_graceful_deletion_test", + "description": "Test graceful deletion of RTSP stream during processing", + "build_args": { + "TEST_APP_NAME": "asset_management", + "CACHE_BUSTER": "live_stream_deletion", + "SERVER_TYPE": "fastapi", + "OPENAPI_SPEC": "builder/tests/asset_management/openapi.yaml" + }, + "test_config": { + "default_enable": false, + "timeout": 120, + "env": { + "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes", + "DEBUG": "0", + "HTTP_PORT": "8000" + }, + "volumes": { + "asset_management/models": "/workspace/models" + }, + "ports": { + "8000": "8000" + }, + "assets": [ + { + "assetId": "00000000-0000-4000-8000-000000000001", + "path": "rtsp://localhost:8554/test", + "fileName": "", + "mimeType": "", + "duration": 0, + "username": "", + "password": "", + "description": "Test RTSP stream for graceful deletion", + "size": 0, + "live": true + } + ], + "test_requests": [ + { + "scheduled_time": 10, + "method": "POST", + "endpoint": "/v1/inference", + "payload_path": "asset_management/payloads/start_stream.jsonl", + "async": true + }, + { + "scheduled_time": 15, + "method": "DELETE", + "endpoint": "/v1/live-streams/00000000-0000-4000-8000-000000000001" + } + ], + "prerequisite_script": "./setup_rtsp_server.sh frame.jpg -m test -f 30 --daemon" + } + } +] diff --git a/tools/inference_builder/builder/tests/test_docker_builds.py b/tools/inference_builder/builder/tests/test_docker_builds.py new file mode 100644 index 00000000..fe2d6014 --- /dev/null +++ b/tools/inference_builder/builder/tests/test_docker_builds.py @@ -0,0 +1,2971 @@ +#!/usr/bin/env python3 + +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +Test script for Docker container builds with different arguments. +This script tests the Dockerfile in the tests directory with various configurations. + +Security Features: +- Input validation for app names and script commands to prevent command injection +- File path validation to prevent directory traversal attacks +- Safe subprocess calls using argument lists instead of shell string interpolation +- Logging of all executed commands for audit trails +""" + +import subprocess +import sys +import os +import json +import time +import argparse +from pathlib import Path +import shutil +import socket +from typing import Dict, List, Optional, Tuple +import logging +import re +import urllib.request +import urllib.error +from huggingface_hub import snapshot_download + +# Set up logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + + +def validate_app_name(app_name: str) -> bool: + """Validate app name to prevent command injection.""" + # Allow only alphanumeric characters, underscores, and hyphens + return bool(re.match(r'^[a-zA-Z0-9_-]+$', app_name)) + + +def validate_script_command(script_command: str) -> bool: + """Validate script command using a flexible approach that allows legitimate arguments.""" + if not isinstance(script_command, str) or not script_command.strip(): + return False + + script_command = script_command.strip() + + # Split command into parts for analysis + try: + import shlex + parts = shlex.split(script_command) + except ValueError: + # Invalid shell syntax + return False + + if not parts: + return False + + # Check the main command/script + main_command = parts[0] + + # Allow specific known scripts + allowed_script_names = [ + "setup_rtsp_server.sh", + "./setup_rtsp_server.sh", + "prepare_engine.sh", + "./prepare_engine.sh", + ] + + allowed_shell_commands = ["bash", "sh"] + + # Validate main command + if main_command not in allowed_script_names and main_command not in allowed_shell_commands: + return False + + # If it's a shell command, check the script being executed + if main_command in allowed_shell_commands and len(parts) > 1: + script_name = parts[1] + if script_name not in allowed_script_names: + return False + + # Validate all arguments + for arg in parts[1:]: + if not validate_script_arg(arg): + return False + + # Check for dangerous patterns in the full command + dangerous_patterns = [ + r'[;&|`]', # Shell metacharacters (but allow some like spaces) + r'\$\(', # Command substitution + r'`', # Backticks + r'>>?', # Redirections + r'\|\|', # OR operator + r'&&', # AND operator + r'<', # Input redirection + r'\x00', # Null bytes + ] + + for pattern in dangerous_patterns: + if re.search(pattern, script_command): + return False + + return True + + +def validate_script_arg(arg: str) -> bool: + """Validate individual script argument.""" + if not isinstance(arg, str): + return False + + # Check for null bytes and control characters + if '\x00' in arg or any(ord(c) < 32 for c in arg if c not in ['\t']): + return False + + # Allow common script arguments + dangerous_chars = ['`', '$', ';', '|', '>', '<', '(', ')'] + if any(char in arg for char in dangerous_chars): + return False + + # Allow legitimate flags and file arguments + if arg.startswith('-'): + # Allow common flag patterns + if not re.match(r'^-{1,2}[a-zA-Z0-9][a-zA-Z0-9_-]*$', arg): + return False + + # Prevent excessively long arguments + if len(arg) > 1024: + return False + + return True + + +def validate_safe_path(path: str) -> bool: + """Validate file path to prevent directory traversal and command injection.""" + if not path or not isinstance(path, str): + return False + + # Check for double slashes + if '//' in path: + return False + + # Check for absolute paths that could access system directories + if os.path.isabs(path): + system_dirs = ['/etc', '/sys', '/proc', '/dev', '/boot', '/usr/bin', '/usr/sbin', '/root'] + for sys_dir in system_dirs: + if path.startswith(sys_dir): + return False + + # Check for invalid characters + invalid_chars = ['<', '>', ':', '"', '|', '?', '*', '\\'] + if any(char in path for char in invalid_chars): + return False + + return True + +def validate_config_file_path(config_file: str) -> bool: + """Validate configuration file path for security.""" + if not validate_safe_path(config_file): + return False + + # Ensure the filename ends with .json + filename = os.path.basename(config_file) + if not filename.lower().endswith('.json'): + return False + + return True + +def validate_dockerfile_path(dockerfile: str) -> bool: + """Validate Dockerfile path for security.""" + if not validate_safe_path(dockerfile): + return False + + # Ensure the filename is Dockerfile or has .dockerfile extension + filename = os.path.basename(dockerfile) + if not (filename.lower() == 'dockerfile' or filename.lower().endswith('.dockerfile')): + return False + + return True + +def validate_log_directory(log_dir: str) -> bool: + """Validate log directory path for security.""" + if not validate_safe_path(log_dir): + return False + + # Prevent access to system directories + if log_dir.startswith('/') and not log_dir.startswith('./'): + return False + + return True + +def validate_gitlab_token(token: str) -> bool: + """Validate GitLab token format.""" + if not token: + return True # Empty token is allowed + + # Basic validation for GitLab token format + if not isinstance(token, str) or len(token) < 10: + return False + + # Check for suspicious patterns + suspicious_patterns = [' bool: + """Validate docker argument to prevent command injection while allowing legitimate flags.""" + if not isinstance(arg, str): + return False + + # Check for null bytes and control characters + if '\x00' in arg or any(ord(c) < 32 for c in arg if c not in ['\t', '\n', '\r']): + return False + + # Check for dangerous characters and patterns (but allow legitimate usage) + dangerous_patterns = [ + r'[;|`$()]', # Shell metacharacters (removed & for now) + r'\$\(', # Command substitution + r'`', # Backticks + r'>>?', # Redirections + r'\|\|', # OR operator + r'&&', # AND operator for command chaining + r'[\x00-\x08\x0B\x0C\x0E-\x1F\x7F]', # Control characters + ] + + for pattern in dangerous_patterns: + if re.search(pattern, arg): + return False + + # Check for dangerous & usage (but allow in URL query parameters) + if '&' in arg: + # Allow & in URL-like contexts (after ?) + if '?' not in arg: + # & without ? suggests command chaining, not URL parameter + return False + # Check for command chaining patterns even with ? + if ' &' in arg or '& ' in arg or arg.endswith('&'): + return False + + # Additional dangerous characters for docker contexts (be selective) + # Note: Removed '?' to allow URL query parameters, '*' and '!' for legitimate use + dangerous_chars = ['[', ']', '{', '}', '\\'] + if any(char in arg for char in dangerous_chars): + return False + + # Check for wildcard patterns that could be dangerous in specific contexts + if '*' in arg and any(pattern in arg for pattern in ['*.*', '*.sh', '*.py', '*/', '/*']): + return False + + # Allow legitimate command-line flags but prevent injection attempts + if arg.strip().startswith('-'): + # Allow common legitimate patterns + legitimate_flag_patterns = [ + r'^--[a-zA-Z0-9][a-zA-Z0-9_-]*$', # --flag-name + r'^--[a-zA-Z0-9][a-zA-Z0-9_-]*=.*$', # --flag=value + r'^-[a-zA-Z0-9]$', # -f + r'^-[a-zA-Z0-9][a-zA-Z0-9]*$', # -abc + ] + + # Check if it matches any legitimate pattern + is_legitimate = any(re.match(pattern, arg) for pattern in legitimate_flag_patterns) + + if not is_legitimate: + return False + + # Additional checks for flag values (after =) + if '=' in arg: + flag_value = arg.split('=', 1)[1] + # Check flag value for dangerous patterns + if any(char in flag_value for char in ['`', '$', ';', '&', '|', '(', ')']): + return False + + # Prevent excessively long arguments (potential DoS) + if len(arg) > 8192: # Reasonable limit for docker arguments + return False + + # Check for potential escape sequences in non-flag arguments + if not arg.startswith('-') and '\\' in arg: + if any(seq in arg for seq in ['\\n', '\\r', '\\t', '\\x', '\\u']): + return False + + return True + + +def validate_env_var_name(name: str) -> bool: + """Validate environment variable name.""" + if not isinstance(name, str) or not name: + return False + + # Environment variable names should be alphanumeric + underscore, starting with letter or underscore + return bool(re.match(r'^[a-zA-Z_][a-zA-Z0-9_]*$', name)) + + +def validate_env_var_value(value: str) -> bool: + """Validate environment variable value.""" + if not isinstance(value, str): + return False + + # Check for null bytes + if '\x00' in value: + return False + + # Check for dangerous shell characters + dangerous_chars = ['`', '$', ';', '&', '|', '(', ')', '>', '<'] + if any(char in value for char in dangerous_chars): + return False + + return True + + +def validate_http_method(method: str) -> bool: + """Validate HTTP method.""" + if not isinstance(method, str): + return False + + # Allow common HTTP methods + allowed_methods = ['GET', 'POST', 'PUT', 'PATCH', 'DELETE', 'HEAD', 'OPTIONS'] + return method.upper() in allowed_methods + + +def validate_http_header_name(name: str) -> bool: + """Validate HTTP header name.""" + if not isinstance(name, str) or not name: + return False + + # Header names should be alphanumeric with hyphens + # Follow RFC 7230 field-name = token + if not re.match(r'^[a-zA-Z0-9!#$%&\'*+\-.^_`|~]+$', name): + return False + + # Prevent excessively long header names + if len(name) > 256: + return False + + return True + + +def validate_http_header_value(value: str) -> bool: + """Validate HTTP header value.""" + if not isinstance(value, str): + return False + + # Check for null bytes and control characters (except tab and space) + if '\x00' in value or any(ord(c) < 32 for c in value if c not in ['\t', ' ']): + return False + + # Check for newlines that could cause header injection + if '\n' in value or '\r' in value: + return False + + # Prevent excessively long header values + if len(value) > 8192: + return False + + return True + + +def validate_volume_path(path: str) -> bool: + """Validate volume mount path to prevent injection attacks. + + Note: Allows legitimate relative paths with .. for test configs. + """ + if not isinstance(path, str) or not path: + return False + + # Check for null bytes and control characters + if '\x00' in path or any(ord(c) < 32 for c in path if c not in ['\t']): + return False + + # Check for dangerous path patterns (but allow relative paths) + # Allow: ../tao/, ./models, ../../shared + # Reject: /../../../etc/passwd (absolute path traversal attacks) + + # Check for double slashes (suspicious) + if '//' in path: + return False + + # Check for Windows-style backslashes (not needed in Docker contexts) + if '\\' in path: + return False + + # Check for absolute path traversal attacks (starting with / and going up) + # This would be trying to escape from an absolute path to system directories + if path.startswith('/') and '/../' in path: + # Check if trying to access system directories + system_dirs = ['/../etc', '/../root', '/../sys', '/../proc', '/../dev'] + if any(path.startswith(sys_dir) for sys_dir in system_dirs): + return False + + # Check for dangerous shell characters and command injection attempts + # Allow ~ for home directory expansion at the start of path + dangerous_chars = ['`', '$', ';', '&', '|', '(', ')', '>', '<', '*', '?', '!', '[', ']', '{', '}'] + if any(char in path for char in dangerous_chars): + return False + + # Allow ~ only at the start of path (for home directory expansion) + if '~' in path and not path.startswith('~'): + return False + + # Check for spaces at beginning/end (could be injection attempts) + if path.startswith(' ') or path.endswith(' '): + return False + + # Check for argument injection (starting with dash) + if path.startswith('-'): + return False + + # Ensure path doesn't contain colon (except for Windows drive letters or container paths) + colon_count = path.count(':') + if colon_count > 1: # Allow one colon for Windows drive letters or container paths + return False + if colon_count == 1: + # Allow Windows drive letters (C:) or absolute container paths (/app:) + if not (re.match(r'^[A-Za-z]:', path) or ':' in path[1:]): + return False + + # Additional check: ensure reasonable path length to prevent buffer overflow attacks + if len(path) > 4096: # Most systems limit paths to 4096 characters + return False + + return True + + +def validate_build_arg_name(name: str) -> bool: + """Validate Docker build argument name to prevent injection.""" + if not isinstance(name, str) or not name: + return False + + # Build arg names should be alphanumeric + underscore, no dashes at start + if name.startswith('-'): + return False + + # Allow standard environment variable naming convention + return bool(re.match(r'^[a-zA-Z_][a-zA-Z0-9_]*$', name)) + + +def validate_build_arg_value(value: str) -> bool: + """Validate Docker build argument value to prevent injection.""" + if not isinstance(value, str): + return False + + # Check for null bytes + if '\x00' in value: + return False + + # Check for dangerous shell characters that could cause issues + dangerous_chars = ['`', '$', ';', '&', '|', '(', ')', '>', '<', '\n', '\r'] + if any(char in value for char in dangerous_chars): + return False + + # Check for argument injection attempts + if value.strip().startswith('-'): + return False + + return True + + +def validate_image_name(image_name: str) -> bool: + """Validate Docker image name to prevent injection.""" + if not isinstance(image_name, str) or not image_name: + return False + + # Check for null bytes + if '\x00' in image_name: + return False + + # Check for dangerous characters + dangerous_chars = ['`', '$', ';', '&', '|', '(', ')', '>', '<', ' ', '\n', '\r'] + if any(char in image_name for char in dangerous_chars): + return False + + # Basic docker image name validation (simplified) + # Allow alphanumeric, hyphens, underscores, slashes, colons, dots + if not re.match(r'^[a-zA-Z0-9._/-]+(?::[a-zA-Z0-9._-]+)?$', image_name): + return False + + return True + + +def validate_test_config(test_config: dict) -> Tuple[bool, str]: + """Validate test configuration to prevent command injection.""" + if not isinstance(test_config, dict): + return False, "Test config must be a dictionary" + + # Validate environment variables + if "env" in test_config: + if not isinstance(test_config["env"], dict): + return False, "env must be a dictionary" + + for key, value in test_config["env"].items(): + if not validate_env_var_name(key): + return False, f"Invalid environment variable name: {key}" + if not validate_env_var_value(str(value)): + return False, f"Invalid environment variable value for {key}: {value}" + + # Validate volume mounts + if "volumes" in test_config: + if not isinstance(test_config["volumes"], dict): + return False, "volumes must be a dictionary" + + for host_path, container_path in test_config["volumes"].items(): + if not validate_volume_path(host_path): + return False, f"Invalid host path in volume mount: {host_path}" + if not validate_volume_path(container_path): + return False, f"Invalid container path in volume mount: {container_path}" + + # Validate command arguments + if "cmd" in test_config: + if not isinstance(test_config["cmd"], list): + return False, "cmd must be a list" + + for arg in test_config["cmd"]: + if not validate_docker_arg(str(arg)): + return False, f"Invalid command argument: {arg}" + + # Validate timeout + if "timeout" in test_config: + if not isinstance(test_config["timeout"], (int, float)) or test_config["timeout"] <= 0: + return False, "timeout must be a positive number" + if test_config["timeout"] > 3600: # Max 1 hour + return False, "timeout cannot exceed 3600 seconds" + + # Validate prerequisite script + if "prerequisite_script" in test_config: + script = test_config["prerequisite_script"] + if script and not validate_script_command(script): + return False, f"Invalid prerequisite script: {script}" + + # Validate auto_validation configuration + if "auto_validation" in test_config: + if not isinstance(test_config["auto_validation"], str): + return False, "auto_validation must be a string path" + if not validate_safe_path(test_config["auto_validation"]): + return False, f"Invalid auto_validation path: {test_config['auto_validation']}" + # Validate test_requests (must be a list of objects with optional payload_path, method, headers) + if "test_requests" in test_config: + test_requests = test_config["test_requests"] + if not isinstance(test_requests, list): + return False, "test_requests must be a list" + + for item in test_requests: + # Must be dict format with payload_path (optional), method (optional), and headers (optional) + if not isinstance(item, dict): + return False, "test_requests items must be dictionaries" + + # Validate payload_path if present (optional for GET requests without body) + if "payload_path" in item: + payload_path = item["payload_path"] + if not isinstance(payload_path, str): + return False, "test_requests 'payload_path' must be a string" + if not validate_safe_path(payload_path): + return False, f"Invalid test_requests payload_path: {payload_path}" + + # Validate method if specified (optional, defaults to POST) + if "method" in item: + method = item["method"] + if not validate_http_method(method): + return False, f"Invalid HTTP method in payload_config: {method}" + + # Validate headers if specified (optional) + if "headers" in item: + headers = item["headers"] + if not isinstance(headers, dict): + return False, "payload_config headers must be a dictionary" + for header_name, header_value in headers.items(): + if not validate_http_header_name(header_name): + return False, f"Invalid HTTP header name in payload_config: {header_name}" + if not validate_http_header_value(str(header_value)): + return False, f"Invalid HTTP header value in payload_config for {header_name}: {header_value}" + + # Validate scheduled_time if specified (optional, for sequential execution) + if "scheduled_time" in item: + if not isinstance(item["scheduled_time"], (int, float)) or item["scheduled_time"] < 0: + return False, "test_requests 'scheduled_time' must be a non-negative number" + + # Validate async if specified (optional, for non-blocking requests) + if "async" in item: + if not isinstance(item["async"], bool): + return False, "test_requests 'async' must be a boolean" + + # Validate endpoint + if "endpoint" in test_config: + endpoint = test_config["endpoint"] + if not isinstance(endpoint, str): + return False, "endpoint must be a string" + # Basic validation for endpoint path format + if not endpoint.startswith("/"): + return False, "endpoint must start with /" + # Check for dangerous characters + if any(char in endpoint for char in ['<', '>', '"', '{', '}', '|', '\\', '^', '`', ' ']): + return False, f"Invalid characters in endpoint: {endpoint}" + + # Validate expected_error for negative tests + if "expected_error" in test_config: + expected_error = test_config["expected_error"] + if not isinstance(expected_error, str): + return False, "expected_error must be a string" + if not expected_error: + return False, "expected_error cannot be empty" + # Basic validation - should be an error code pattern (e.g., ERR_ROUTE_001) + if len(expected_error) > 100: + return False, "expected_error is too long (max 100 characters)" + + return True, "" + + +class DockerBuildTester: + def __init__(self, dockerfile_path: str, base_dir: str, log_dir: Optional[str] = None): + self.dockerfile_path = Path(dockerfile_path) + self.base_dir = Path(base_dir) + self.log_dir = Path(log_dir) if log_dir else Path("logs") + self.test_results = [] + + # Create log directory if it doesn't exist + self.log_dir.mkdir(exist_ok=True) + + def download_models(self, models_config: Dict, config_dir: Path) -> Tuple[bool, str]: + """Download models from NGC based on the models configuration. + + Args: + models_config: Dictionary with model configurations + config_dir: Directory containing the test config (for resolving relative paths) + + Returns: + Tuple of (success, message) + """ + if not models_config: + return True, "No models to download" + + for model_name, model_info in models_config.items(): + try: + source = model_info.get("source", "NGC") + + if source == "HF": + # Handle Hugging Face model download + target_dir = Path(model_info["target"]).expanduser() + model_path = model_info["path"] # e.g., "Qwen/Qwen2.5-VL-3B-Instruct" + + # Final destination + final_model_dir = target_dir / model_name + + # Check if model already exists + if final_model_dir.exists(): + logger.info(f"✅ Model '{model_name}' already exists at {final_model_dir}, skipping download") + continue + + logger.info(f"📥 Downloading model '{model_name}' from Hugging Face...") + logger.info(f" HF path: {model_path}") + logger.info(f" Target: {final_model_dir}") + + # Create target directory + target_dir.mkdir(parents=True, exist_ok=True) + + # Download using huggingface_hub + hf_token = os.environ.get("HF_TOKEN") + try: + snapshot_download( + model_path, + local_dir=str(final_model_dir), + token=hf_token, + ) + except Exception as e: + error_msg = f"Failed to download model '{model_name}' from HF: {e}" + logger.error(f"❌ {error_msg}") + return False, error_msg + + # Execute post-script if specified (for HF models) + post_script = model_info.get("post_script") + if post_script: + logger.info(f"🔧 Executing post-script for '{model_name}': {post_script}") + try: + script_result = subprocess.run( + post_script, + shell=True, + capture_output=True, + text=True, + cwd=str(final_model_dir), + timeout=600 + ) + if script_result.returncode == 0: + logger.info(f"✅ Post-script executed successfully for '{model_name}'") + if script_result.stdout.strip(): + logger.info(f" Output: {script_result.stdout.strip()}") + else: + error_msg = ( + f"Post-script failed for '{model_name}' " + f"(exit code {script_result.returncode}): " + f"{script_result.stderr.strip()}" + ) + logger.error(f"❌ {error_msg}") + return False, error_msg + except subprocess.TimeoutExpired: + error_msg = f"Post-script timed out for '{model_name}' after 600 seconds" + logger.error(f"❌ {error_msg}") + return False, error_msg + except Exception as e: + error_msg = f"Failed to execute post-script for '{model_name}': {str(e)}" + logger.error(f"❌ {error_msg}") + return False, error_msg + + logger.info(f"✅ Successfully downloaded model '{model_name}' from Hugging Face") + continue + + if source != "NGC": + logger.warning(f"⚠️ Unsupported model source '{source}' for {model_name}, skipping") + continue + + # NGC model download logic + target_dir = Path(model_info["target"]).expanduser() + model_path = model_info["path"] + version = model_info["version"] + configs_path = model_info.get("configs", "") + + # Construct the full NGC path + ngc_path = f"{model_path}:{version}" + + # Determine the downloaded folder name (NGC naming convention) + # e.g., "nvidia/tao/grounding_dino:grounding_dino_swin_tiny_commercial_deployable_v1.0" + # becomes "grounding_dino_vgrounding_dino_swin_tiny_commercial_deployable_v1.0" + model_base_name = model_path.split('/')[-1] # e.g., "grounding_dino" + downloaded_folder = f"{model_base_name}_v{version}" + + # Final destination + final_model_dir = target_dir / model_name + + # Check if model already exists + if final_model_dir.exists(): + logger.info(f"✅ Model '{model_name}' already exists at {final_model_dir}, skipping download") + continue + + logger.info(f"📥 Downloading model '{model_name}' from NGC...") + logger.info(f" NGC path: {ngc_path}") + logger.info(f" Target: {final_model_dir}") + + # Create target directory + target_dir.mkdir(parents=True, exist_ok=True) + + # Download from NGC + download_cmd = ["ngc", "registry", "model", "download-version", ngc_path] + logger.info(f" Running: {' '.join(download_cmd)}") + + result = subprocess.run( + download_cmd, + capture_output=True, + text=True, + timeout=600 # 10 minute timeout for download + ) + + if result.returncode != 0: + error_msg = f"Failed to download model '{model_name}': {result.stderr}" + logger.error(f"❌ {error_msg}") + return False, error_msg + + # Move the downloaded folder to the target location + downloaded_path = Path(downloaded_folder) + if not downloaded_path.exists(): + error_msg = f"Downloaded folder not found: {downloaded_path}" + logger.error(f"❌ {error_msg}") + return False, error_msg + + logger.info(f" Moving {downloaded_path} to {final_model_dir}") + shutil.move(str(downloaded_path), str(final_model_dir)) + + # Set permissions + try: + os.chmod(final_model_dir, 0o777) + logger.info(f" Set permissions: chmod 777 {final_model_dir}") + except Exception as e: + logger.warning(f"⚠️ Failed to set permissions on {final_model_dir}: {e}") + + # Copy config files if specified + if configs_path: + # Resolve configs_path relative to test config directory + source_configs = (config_dir / configs_path).resolve() + if source_configs.exists(): + logger.info(f" Copying configs from {source_configs} to {final_model_dir}") + # Copy all files from source_configs to final_model_dir + for item in source_configs.iterdir(): + dest = final_model_dir / item.name + if item.is_file(): + shutil.copy2(str(item), str(dest)) + elif item.is_dir(): + shutil.copytree(str(item), str(dest), dirs_exist_ok=True) + logger.info(f" ✅ Copied config files") + else: + logger.warning(f"⚠️ Config path not found: {source_configs}") + + # Execute post-script if specified + post_script = model_info.get("post_script") + if post_script: + logger.info(f"🔧 Executing post-script for '{model_name}': {post_script}") + try: + script_result = subprocess.run( + post_script, + shell=True, + capture_output=True, + text=True, + cwd=str(final_model_dir), + timeout=600 + ) + if script_result.returncode == 0: + logger.info(f"✅ Post-script executed successfully for '{model_name}'") + if script_result.stdout.strip(): + logger.info(f" Output: {script_result.stdout.strip()}") + else: + error_msg = ( + f"Post-script failed for '{model_name}' " + f"(exit code {script_result.returncode}): " + f"{script_result.stderr.strip()}" + ) + logger.error(f"❌ {error_msg}") + return False, error_msg + except subprocess.TimeoutExpired: + error_msg = f"Post-script timed out for '{model_name}' after 600 seconds" + logger.error(f"❌ {error_msg}") + return False, error_msg + except Exception as e: + error_msg = f"Failed to execute post-script for '{model_name}': {str(e)}" + logger.error(f"❌ {error_msg}") + return False, error_msg + + logger.info(f"✅ Successfully downloaded and setup model '{model_name}'") + + except KeyError as e: + error_msg = f"Missing required field in model config for '{model_name}': {e}" + logger.error(f"❌ {error_msg}") + return False, error_msg + except subprocess.TimeoutExpired: + error_msg = f"Model download timed out for '{model_name}'" + logger.error(f"❌ {error_msg}") + return False, error_msg + except Exception as e: + error_msg = f"Exception during model download for '{model_name}': {str(e)}" + logger.error(f"❌ {error_msg}") + return False, error_msg + + return True, "All models downloaded successfully" + + def get_service_host(self) -> str: + """ + Determine the appropriate host to use for connecting to Docker + containers. In CI environments (like GitLab CI with + Docker-in-Docker), 127.0.0.1 won't work because each container has + its own localhost. Use Docker gateway IP instead. + """ + # Check if running in CI environment + if os.environ.get('CI') or os.environ.get('GITLAB_CI'): + logger.info( + "🔍 CI environment detected, using Docker gateway IP " + "for service connectivity" + ) + # Try to get docker gateway IP from bridge network + try: + result = subprocess.run( + [ + "docker", "network", "inspect", "bridge", "-f", + "{{range .IPAM.Config}}{{.Gateway}}{{end}}" + ], + capture_output=True, + text=True, + timeout=5 + ) + if result.returncode == 0 and result.stdout.strip(): + gateway_ip = result.stdout.strip() + logger.info(f"📡 Using Docker gateway IP: {gateway_ip}") + return gateway_ip + except Exception as e: + logger.warning(f"⚠️ Failed to get Docker gateway IP: {e}") + + # Fallback to default Docker bridge gateway + logger.info( + "📡 Using default Docker bridge gateway: 172.17.0.1" + ) + return "172.17.0.1" + + # Local development environment - use localhost + logger.info("📡 Using localhost for service connectivity") + return "127.0.0.1" + + def generate_inference_code(self, build_args: Dict[str, str], test_config: Dict = None) -> Tuple[bool, str]: + """Generate inference code (codegen) without building or testing Docker images. + + This runs the same pre-build code generation step used by build_image(), + including optional OPENAPI_SPEC staging, but skips Docker build. + + Supports flexible path configuration via build_args: + - APP_YAML_PATH: Custom path to app.yaml (overrides {TEST_APP_NAME}/app.yaml) + - OUTPUT_DIR: Custom output directory (overrides {TEST_APP_NAME}) + - PROCESSORS_PATH: Custom processors.py path (overrides auto-detection) + + Args: + build_args: Build arguments for code generation + test_config: Optional test configuration. If present and contains 'auto_validation', + the validation directory will be passed to code generation via -v flag. + """ + try: + # Validate all build arguments to prevent command injection + for key, value in build_args.items(): + if not validate_build_arg_name(key): + error_msg = f"Invalid build arg name: {key}. Only alphanumeric characters and underscores allowed." + logger.error(f"❌ {error_msg}") + return False, error_msg + if not validate_build_arg_value(str(value)): + error_msg = f"Invalid build arg value for {key}: {value}. Value contains dangerous characters." + logger.error(f"❌ {error_msg}") + return False, error_msg + + # Determine parameters for code generation + test_app_name = build_args.get("TEST_APP_NAME", "frame_sampling") + if not validate_app_name(test_app_name): + error_msg = ( + f"Invalid app name: {test_app_name}. Only alphanumeric characters, underscores, and hyphens are allowed." + ) + logger.error(f"❌ {error_msg}") + return False, error_msg + + server_type = build_args.get("SERVER_TYPE", "serverless") + openapi_spec = build_args.get("OPENAPI_SPEC") + + # Support flexible paths via build_args + app_yaml_path = build_args.get("APP_YAML_PATH", f"{test_app_name}/app.yaml") + output_dir = build_args.get("OUTPUT_DIR", test_app_name) + processors_path_arg = build_args.get("PROCESSORS_PATH", "") + + # Find main.py relative to this script (in builder/main.py) + main_py_path = Path(__file__).parent.parent / "main.py" + if not main_py_path.exists(): + error_msg = f"Cannot find main.py at {main_py_path}" + logger.error(f"❌ {error_msg}") + return False, error_msg + + pre_build_command = [ + "python", str(main_py_path), app_yaml_path, + "-o", output_dir + ] + + # Add processors.py if specified or auto-detect + if processors_path_arg: + processors_path = Path(processors_path_arg) + if processors_path.exists(): + pre_build_command.extend(["-c", processors_path_arg]) + else: + logger.warning(f"⚠️ Specified PROCESSORS_PATH not found: {processors_path_arg}") + else: + # Auto-detect in output directory + processors_path = Path(f"{test_app_name}/processors.py") + if processors_path.exists(): + pre_build_command.extend(["-c", f"{test_app_name}/processors.py"]) + + # Add validation directory if specified in test_config + if test_config and "auto_validation" in test_config: + validation_folder = test_config["auto_validation"] + # Resolve path relative to config directory if it's relative + config_dir = test_config.get("_config_dir") + if config_dir: + validation_path = (Path(config_dir) / validation_folder).resolve() + else: + validation_path = Path(validation_folder).resolve() + + if validation_path.exists(): + pre_build_command.extend(["--validation-dir", str(validation_path), "--no-docker"]) + logger.info(f"📁 Adding validation directory for build: {validation_path}") + else: + logger.warning(f"⚠️ Auto validation folder not found: {validation_path}") + + pre_build_command.extend(["--server-type", server_type, "-t"]) + + if openapi_spec: + # Resolve provided spec relative to project root, copy into local app folder to avoid unsafe paths + project_root = Path(__file__).resolve().parents[2] + resolved_spec = None + candidates = [] + spec_path = Path(openapi_spec) + if spec_path.is_absolute(): + candidates.append(spec_path) + else: + candidates.append((project_root / openapi_spec).resolve()) + candidates.append((project_root / "builder" / openapi_spec).resolve()) + + for cand in candidates: + try: + if cand.exists() and str(cand).startswith(str(project_root)): + resolved_spec = cand + break + except Exception: + continue + + if not resolved_spec: + error_msg = f"Invalid OPENAPI_SPEC path: {openapi_spec}" + logger.error(f"❌ {error_msg}") + return False, error_msg + + local_spec_path = Path(output_dir) / "openapi.yaml" + + # Only copy if source and destination are different + if resolved_spec.resolve() != local_spec_path.resolve(): + try: + shutil.copyfile(str(resolved_spec), str(local_spec_path)) + logger.info(f"📄 Staged OPENAPI_SPEC: {resolved_spec} -> {local_spec_path}") + except Exception as e: + error_msg = f"Failed to stage OPENAPI_SPEC: {e}" + logger.error(f"❌ {error_msg}") + return False, error_msg + else: + logger.info(f"📄 OPENAPI_SPEC already in output directory: {local_spec_path}") + + pre_build_command[3:3] = ["-a", str(local_spec_path)] + + logger.info(f"🔧 Executing codegen command: {' '.join(pre_build_command)}") + pre_build_result = subprocess.run( + pre_build_command, + capture_output=True, + text=True, + timeout=600 + ) + + if pre_build_result.returncode != 0: + error_msg = f"Code generation failed: {pre_build_result.stderr}" + logger.error(f"❌ {error_msg}") + return False, error_msg + + logger.info("✅ Code generation completed successfully") + if pre_build_result.stdout: + logger.info(f"Codegen output: {pre_build_result.stdout}") + return True, pre_build_result.stdout + + except subprocess.TimeoutExpired: + error_msg = "Code generation timed out" + logger.error(f"❌ {error_msg}") + return False, error_msg + except Exception as e: + error_msg = f"Exception during code generation: {str(e)}" + logger.error(f"❌ {error_msg}") + return False, error_msg + + def build_image(self, build_args: Dict[str, str], image_name: str, dockerfile: Optional[str] = None, base_dir: Optional[str] = None, test_config: Dict = None) -> Tuple[bool, str]: + """Build Docker image with given arguments. + + Supports flexible path configuration via build_args: + - APP_YAML_PATH: Custom path to app.yaml (overrides {TEST_APP_NAME}/app.yaml) + - OUTPUT_DIR: Custom output directory (overrides {TEST_APP_NAME}) + - PROCESSORS_PATH: Custom processors.py path (overrides auto-detection) + + Args: + build_args: Build arguments for the Docker image + image_name: Name for the Docker image + dockerfile: Optional path to Dockerfile (overrides self.dockerfile_path) + base_dir: Optional Docker build context directory (overrides self.base_dir) + test_config: Optional test configuration. If present and contains 'auto_validation', + the validation directory will be passed to code generation via -v flag. + """ + try: + # Use provided paths or fall back to instance defaults + dockerfile_path = Path(dockerfile) if dockerfile else self.dockerfile_path + build_context = Path(base_dir) if base_dir else self.base_dir + # Validate image name to prevent command injection + if not validate_image_name(image_name): + error_msg = f"Invalid image name: {image_name}. Image name contains invalid characters." + logger.error(f"❌ {error_msg}") + return False, error_msg + + # Validate all build arguments to prevent command injection + for key, value in build_args.items(): + if not validate_build_arg_name(key): + error_msg = f"Invalid build arg name: {key}. Only alphanumeric characters and underscores allowed." + logger.error(f"❌ {error_msg}") + return False, error_msg + if not validate_build_arg_value(str(value)): + error_msg = f"Invalid build arg value for {key}: {value}. Value contains dangerous characters." + logger.error(f"❌ {error_msg}") + return False, error_msg + + # Execute pre-build command using TEST_APP_NAME + test_app_name = build_args.get("TEST_APP_NAME", "frame_sampling") + + # Validate app name to prevent command injection + if not validate_app_name(test_app_name): + error_msg = f"Invalid app name: {test_app_name}. Only alphanumeric characters, underscores, and hyphens are allowed." + logger.error(f"❌ {error_msg}") + return False, error_msg + + # Determine server type from build args (fallback to 'serverless') + server_type = build_args.get("SERVER_TYPE", "serverless") + + # Optional: OpenAPI spec path (to pass -a) + openapi_spec = build_args.get("OPENAPI_SPEC") + + # Require OpenAPI spec for non-serverless builds + if server_type != "serverless" and not openapi_spec: + error_msg = ( + "OPENAPI_SPEC is required in build_args when SERVER_TYPE is not 'serverless'" + ) + logger.error(f"❌ {error_msg}") + return False, error_msg + + # Support flexible paths via build_args + app_yaml_path = build_args.get("APP_YAML_PATH", f"{test_app_name}/app.yaml") + output_dir = build_args.get("OUTPUT_DIR", test_app_name) + processors_path_arg = build_args.get("PROCESSORS_PATH", "") + + # Find main.py relative to this script (in builder/main.py) + main_py_path = Path(__file__).parent.parent / "main.py" + if not main_py_path.exists(): + error_msg = f"Cannot find main.py at {main_py_path}" + logger.error(f"❌ {error_msg}") + return False, error_msg + + # Use safer subprocess call without shell=True + pre_build_command = [ + "python", str(main_py_path), app_yaml_path, + "-o", output_dir + ] + + # Add processors.py if specified or auto-detect + if processors_path_arg: + processors_path = Path(processors_path_arg) + if processors_path.exists(): + pre_build_command.extend(["-c", processors_path_arg]) + else: + logger.warning(f"⚠️ Specified PROCESSORS_PATH not found: {processors_path_arg}") + else: + # Auto-detect in test_app_name directory + processors_path = Path(f"{test_app_name}/processors.py") + if processors_path.exists(): + pre_build_command.extend(["-c", f"{test_app_name}/processors.py"]) + + # Add validation directory if specified in test_config + if test_config and "auto_validation" in test_config: + validation_folder = test_config["auto_validation"] + # Resolve path relative to config directory if it's relative + config_dir = test_config.get("_config_dir") + if config_dir: + validation_path = (Path(config_dir) / validation_folder).resolve() + else: + validation_path = Path(validation_folder).resolve() + + if validation_path.exists(): + pre_build_command.extend(["--validation-dir", str(validation_path), "--no-docker"]) + logger.info(f"📁 Adding validation directory for build: {validation_path}") + else: + logger.warning(f"⚠️ Auto validation folder not found: {validation_path}") + + pre_build_command.extend(["--server-type", server_type, "-t"]) + + if openapi_spec: + # Resolve provided spec relative to project root, copy into local app folder to avoid unsafe paths + project_root = Path(__file__).resolve().parents[2] + resolved_spec = None + candidates = [] + spec_path = Path(openapi_spec) + if spec_path.is_absolute(): + candidates.append(spec_path) + else: + candidates.append((project_root / openapi_spec).resolve()) + candidates.append((project_root / "builder" / openapi_spec).resolve()) + + for cand in candidates: + try: + if cand.exists() and str(cand).startswith(str(project_root)): + resolved_spec = cand + break + except Exception: + continue + + if not resolved_spec: + error_msg = f"Invalid OPENAPI_SPEC path: {openapi_spec}" + logger.error(f"❌ {error_msg}") + return False, error_msg + + local_spec_path = Path(output_dir) / "openapi.yaml" + + # Only copy if source and destination are different + if resolved_spec.resolve() != local_spec_path.resolve(): + try: + shutil.copyfile(str(resolved_spec), str(local_spec_path)) + logger.info(f"📄 Staged OPENAPI_SPEC: {resolved_spec} -> {local_spec_path}") + except Exception as e: + error_msg = f"Failed to stage OPENAPI_SPEC: {e}" + logger.error(f"❌ {error_msg}") + return False, error_msg + else: + logger.info(f"📄 OPENAPI_SPEC already in output directory: {local_spec_path}") + + pre_build_command[3:3] = ["-a", str(local_spec_path)] + + logger.info(f"🔧 Executing pre-build command: {' '.join(pre_build_command)}") + pre_build_result = subprocess.run( + pre_build_command, + capture_output=True, + text=True, + timeout=600 # 10 minute timeout for pre-build + ) + + if pre_build_result.returncode != 0: + error_msg = f"Pre-build command failed: {pre_build_result.stderr}" + logger.error(f"❌ {error_msg}") + return False, error_msg + + logger.info("✅ Pre-build command completed successfully") + if pre_build_result.stdout: + logger.info(f"Pre-build output: {pre_build_result.stdout}") + + cmd = [ + "docker", "build", + "-f", str(dockerfile_path), + "-t", image_name + ] + + # Add build arguments + for key, value in build_args.items(): + cmd.extend(["--build-arg", f"{key}={value}"]) + + # Add context directory + cmd.append(str(build_context)) + + logger.info(f"Building image: {image_name}") + logger.info(f"Command: {' '.join(cmd)}") + + result = subprocess.run( + cmd, + capture_output=True, + text=True, + timeout=3600 # 1 hour timeout + ) + + if result.returncode == 0: + logger.info(f"✅ Successfully built image: {image_name}") + return True, result.stdout + else: + logger.error(f"❌ Failed to build image: {image_name}") + logger.error(f"Error: {result.stderr}") + return False, result.stderr + + except subprocess.TimeoutExpired: + error_msg = f"Build timed out for image: {image_name}" + logger.error(f"❌ {error_msg}") + return False, error_msg + except Exception as e: + error_msg = f"Exception during build: {str(e)}" + logger.error(f"❌ {error_msg}") + return False, error_msg + + def run_prerequisite_script(self, script_command: str, test_id: int, image_name: str = "", cwd: str = None) -> Tuple[bool, str]: + """Run a prerequisite script before testing the docker container. + + Supports {image_name} placeholder in script_command, which is replaced + with the built Docker image name before execution. + """ + if not script_command: + return True, "No prerequisite script specified" + + # Substitute {image_name} placeholder + if image_name: + script_command = script_command.replace("{image_name}", image_name) + + # Validate script command to prevent command injection + if not validate_script_command(script_command): + error_msg = f"Invalid script command: {script_command}. Command contains potentially dangerous characters." + logger.error(f"❌ {error_msg}") + return False, error_msg + + log_file = self.log_dir / f"prerequisite_{test_id}.log" + + try: + logger.info(f"🔧 Running prerequisite script: {script_command}") + logger.info(f"📄 Prerequisite logs will be saved to: {log_file}") + + # Use safer command execution - split command into arguments when possible + import shlex + try: + # Attempt to parse command safely first + cmd_args = shlex.split(script_command) + if len(cmd_args) == 1 and cmd_args[0].endswith('.sh'): + # Single script file - use direct execution without shell + result = subprocess.run( + cmd_args, + capture_output=True, + text=True, + timeout=1800, + cwd=cwd, + shell=False # Safer execution without shell + ) + else: + # Complex command - use shell with additional validation + # Double-check validation before shell execution + if not validate_script_command(script_command): + raise ValueError("Command failed security validation") + + result = subprocess.run( + script_command, + shell=True, # Only when necessary and after validation + capture_output=True, + text=True, + timeout=1800, + cwd=cwd + ) + except ValueError as e: + if "failed security validation" in str(e): + raise e + # Fall back to shell execution for complex commands (with validation) + result = subprocess.run( + script_command, + shell=True, + capture_output=True, + text=True, + timeout=1800, + cwd=cwd + ) + + # Save prerequisite script logs + with open(log_file, 'w') as f: + f.write("=== Prerequisite Script Execution ===\n") + f.write(f"Command: {script_command}\n") + f.write(f"Return Code: {result.returncode}\n") + f.write(f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}\n") + f.write("\n=== STDOUT ===\n") + f.write(result.stdout) + f.write("\n=== STDERR ===\n") + f.write(result.stderr) + f.write("\n=== END LOG ===\n") + + if result.returncode == 0: + logger.info(f"✅ Prerequisite script completed successfully") + if result.stdout: + logger.info(f"Prerequisite output: {result.stdout}") + return True, result.stdout + else: + error_msg = f"Prerequisite script failed with return code {result.returncode}" + logger.error(f"❌ {error_msg}") + logger.error(f"Prerequisite stderr: {result.stderr}") + return False, error_msg + + except subprocess.TimeoutExpired: + error_msg = f"Prerequisite script timed out after 1800 seconds" + logger.error(f"❌ {error_msg}") + + # Save timeout log + with open(log_file, 'w') as f: + f.write("=== Prerequisite Script Execution ===\n") + f.write(f"Command: {script_command}\n") + f.write(f"Status: TIMEOUT\n") + f.write(f"Timeout: 1800 seconds\n") + f.write(f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}\n") + f.write("\n=== ERROR ===\n") + f.write(error_msg) + f.write("\n=== END LOG ===\n") + + return False, error_msg + except Exception as e: + error_msg = f"Exception during prerequisite script execution: {str(e)}" + logger.error(f"❌ {error_msg}") + + # Save exception log + with open(log_file, 'w') as f: + f.write("=== Prerequisite Script Execution ===\n") + f.write(f"Command: {script_command}\n") + f.write(f"Status: EXCEPTION\n") + f.write(f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}\n") + f.write("\n=== ERROR ===\n") + f.write(error_msg) + f.write("\n=== END LOG ===\n") + + return False, error_msg + + def test_image(self, image_name: str, test_config: Dict, test_id: int, gpus: str = "all") -> Tuple[bool, str, str]: + """Test the built image by running it and capture logs. + + Args: + image_name: Name of the Docker image to test + test_config: Test configuration dictionary + test_id: Unique test identifier + gpus: GPU devices to use (default: 'all') + """ + log_file = self.log_dir / f"test_{test_id}_{image_name.replace(':', '_')}.log" + + # Validate image name to prevent command injection + if not validate_image_name(image_name): + error_msg = f"Invalid image name: {image_name}. Image name contains invalid characters." + logger.error(f"❌ {error_msg}") + return False, error_msg, "" + + # Validate test configuration to prevent command injection + config_valid, config_error = validate_test_config(test_config) + if not config_valid: + error_msg = f"Invalid test configuration: {config_error}" + logger.error(f"❌ {error_msg}") + return False, error_msg, "" + + # Get timeout from test config, default to 10 seconds + timeout = test_config.get("timeout", 10) + + try: + # Check if image exists + result = subprocess.run( + ["docker", "image", "inspect", image_name], + capture_output=True, + text=True + ) + + if result.returncode != 0: + return False, f"Image {image_name} not found", "" + + # Run prerequisite script if specified + prerequisite_script = test_config.get("prerequisite_script") + if prerequisite_script: + config_dir = str(Path(test_config.get("_config_dir", ".")).resolve()) + prerequisite_success, prerequisite_output = self.run_prerequisite_script( + prerequisite_script, test_id, image_name=image_name, cwd=config_dir + ) + if not prerequisite_success: + return False, f"Prerequisite script failed: {prerequisite_output}", "" + + # Detect server type to choose test strategy + server_type = test_config.get("SERVER_TYPE", "serverless") + is_serverless = server_type == "serverless" + + # Prepare assets if specified + asset_ids = [] + asset_temp_dir = None + if "assets" in test_config: + import tempfile + + assets_config = test_config["assets"] + logger.info(f"📦 Preparing {len(assets_config)} asset(s)...") + + # Create temp directory for asset info.json files + asset_temp_dir = Path(tempfile.mkdtemp(prefix=f"test_assets_{test_id}_")) + + for idx, asset_data in enumerate(assets_config): + # Use the provided assetId + asset_id = asset_data["assetId"] + asset_ids.append(asset_id) + + # Create asset directory structure + asset_dir = asset_temp_dir / asset_id + asset_dir.mkdir(parents=True) + + # Write asset data to info.json inside the directory + info_file = asset_dir / "info.json" + with info_file.open("w") as f: + json.dump(asset_data, f, indent=2) + + logger.info(f"✅ Created asset {idx}: {asset_id} ({asset_data['path']})") + + # Run the container with test configuration + # Use host network for serverless; use port mapping for non-serverless to avoid port conflicts + # Use --ipc=host to share host's /dev/shm with the container; + # Docker's default 64MB /dev/shm is insufficient for PyTorch/TensorRT-LLM + # multiprocessing which shares tensors via shared memory, causing SIGBUS + # (Bus error: nonexistent physical address) when /dev/shm is exhausted. + cmd = ["docker", "run", "--ipc=host", "--gpus", gpus] + + # Add environment variables if specified + if "env" in test_config: + for key, value in test_config["env"].items(): + # Double-check validation to prevent injection + if not validate_env_var_name(key) or not validate_env_var_value(str(value)): + error_msg = f"Invalid environment variable: {key}={value}" + logger.error(f"❌ {error_msg}") + return False, error_msg, "" + cmd.extend(["-e", f"{key}={value}"]) + + # Get the test config directory for resolving relative paths + config_dir = Path(test_config.get("_config_dir", ".")).resolve() + + # Automatically add ERROR_EXPORT_PATH for error file collection + # Use unique error file names for different test cases + error_export_dir = Path("/tmp/error") + error_export_dir.mkdir(parents=True, exist_ok=True) + error_filename = f"inference_errors_{test_id}_{int(time.time())}.json" + error_export_file = error_export_dir / error_filename + container_error_path = f"/tmp/error/{error_filename}" + cmd.extend(["-e", f"ERROR_EXPORT_PATH={container_error_path}"]) + cmd.extend(["-v", f"{error_export_dir}:{error_export_dir}"]) + logger.info(f"📄 Error export: {error_export_file} -> {container_error_path}") + + # Add volume mounts if specified + if "volumes" in test_config: + logger.info(f"📁 Processing volumes: {test_config['volumes']}") + + for host_path, container_path in test_config["volumes"].items(): + # Double-check validation to prevent injection + if not validate_volume_path(host_path) or not validate_volume_path(container_path): + error_msg = f"Invalid volume path: {host_path}:{container_path}" + logger.error(f"❌ {error_msg}") + return False, error_msg, "" + + # Resolve host path to absolute path + # Priority: 1) Expand ~ for home directory, 2) Resolve relative to config dir, 3) Use as-is if absolute + expanded_path = os.path.expanduser(host_path) + + if not os.path.isabs(expanded_path): + # Relative path - resolve relative to test config directory + abs_host_path = str((config_dir / expanded_path).resolve()) + logger.info(f"📁 Resolved relative path: {host_path} -> {abs_host_path} (relative to config dir)") + else: + # Already absolute path + abs_host_path = expanded_path + logger.info(f"📁 Using absolute path: {host_path} -> {abs_host_path}") + + # Verify the resolved path exists (optional warning, not error) + if not os.path.exists(abs_host_path): + logger.warning(f"⚠️ Volume path does not exist yet: {abs_host_path}") + logger.warning(f"⚠️ This is OK if the path will be created by model download or other setup") + + volume_arg = f"{abs_host_path}:{container_path}" + cmd.extend(["-v", volume_arg]) + logger.info(f"📁 Adding volume mount: {volume_arg}") + else: + logger.info("📁 No additional volumes specified in test config") + + # Give the container a name for logging/cleanup purposes + expected_error = test_config.get("expected_error") + container_name = f"test-{test_id}-{int(time.time())}" + cmd.extend(["--name", container_name]) + logger.info(f"🏷️ Container name: {container_name}") + + # For FastAPI (or any non-serverless), run detached on host network (DinD friendly) + if not is_serverless: + cmd.insert(2, "--network=host") + cmd.insert(2, "-d") # run detached + else: + cmd.insert(2, "--network=host") + + # For serverless non-negative tests, save inference results for validation + result_export_file = None + if is_serverless and not expected_error: + result_export_dir = Path("/tmp/result") + result_export_dir.mkdir(parents=True, exist_ok=True) + result_filename = f"result_{test_id}_{int(time.time())}.json" + result_export_file = result_export_dir / result_filename + container_result_path = f"/tmp/result/{result_filename}" + cmd.extend(["-v", f"{result_export_dir}:{result_export_dir}"]) + logger.info(f"📄 Result export: {result_export_file} -> {container_result_path}") + + cmd.append(image_name) + + # Add command arguments if specified (serverless only) + if server_type == "serverless" and "cmd" in test_config: + cmd_args = test_config["cmd"] + + # Double-check validation for each command argument + for arg in cmd_args: + if not validate_docker_arg(str(arg)): + error_msg = f"Invalid command argument: {arg}" + logger.error(f"❌ {error_msg}") + return False, error_msg, "" + cmd.extend(cmd_args) + + # Append -s flag for serverless non-negative tests + if result_export_file: + cmd.extend(["-s", container_result_path]) + + logger.info(f"Testing image: {image_name}") + logger.info(f"Command: {' '.join(cmd)}") + logger.info(f"Timeout: {timeout} seconds") + logger.info(f"Logs will be saved to: {log_file}") + + # Log the complete command for debugging + logger.info("🔍 Complete docker run command:") + logger.info(f" {' '.join(cmd)}") + + if is_serverless: + # For serverless with assets, we need to start detached, copy assets, then wait + if asset_temp_dir: + # Insert -d flag to run detached + cmd.insert(2, "-d") + logger.info("🔍 Starting container in detached mode to copy assets...") + result = subprocess.run( + cmd, + capture_output=True, + text=True, + timeout=30 + ) + if result.returncode != 0: + logger.error(f"❌ Failed to start container: {result.stderr}") + return False, f"Container start failed: {result.stderr}", "" + + # Container ID is in stdout + container_id = result.stdout.strip() + logger.info(f"✅ Container started: {container_id[:12]}") + + # Create /tmp/assets directory in container + logger.info("📁 Creating /tmp/assets directory in container...") + mkdir_result = subprocess.run( + ["docker", "exec", container_name, "mkdir", "-p", "/tmp/assets"], + capture_output=True, + text=True, + timeout=10 + ) + if mkdir_result.returncode != 0: + logger.error(f"❌ Failed to create /tmp/assets: {mkdir_result.stderr}") + subprocess.run(["docker", "stop", container_name], capture_output=True, timeout=10) + subprocess.run(["docker", "rm", container_name], capture_output=True, timeout=10) + return False, f"Failed to create /tmp/assets: {mkdir_result.stderr}", "" + + # Copy assets into container + for idx, asset_id in enumerate(asset_ids): + src_path = asset_temp_dir / asset_id + dest_path = f"{container_name}:/tmp/assets/{asset_id}" + + logger.info(f"📦 Copying asset {idx} to container: {asset_id}") + copy_result = subprocess.run( + ["docker", "cp", str(src_path), dest_path], + capture_output=True, + text=True, + timeout=30 + ) + if copy_result.returncode != 0: + logger.error(f"❌ Failed to copy asset: {copy_result.stderr}") + subprocess.run(["docker", "stop", container_name], capture_output=True, timeout=10) + subprocess.run(["docker", "rm", container_name], capture_output=True, timeout=10) + return False, f"Asset copy failed: {copy_result.stderr}", "" + logger.info(f"✅ Asset {idx} copied successfully") + + # Now wait for container to complete + logger.info("⏳ Waiting for container to complete...") + wait_result = subprocess.run( + ["docker", "wait", container_name], + capture_output=True, + text=True, + timeout=timeout + ) + + # Get logs + logs_result = subprocess.run( + ["docker", "logs", container_name], + capture_output=True, + text=True, + timeout=30 + ) + stdout_output = logs_result.stdout + stderr_output = logs_result.stderr + + # Clean up the temp asset directory + import shutil as shutil_module + shutil_module.rmtree(asset_temp_dir, ignore_errors=True) + logger.info("🧹 Cleaned up temporary asset directory") + else: + # Run container normally and capture output + logger.info("🔍 Running container and capturing output...") + result = subprocess.run( + cmd, + capture_output=True, + text=True, + timeout=timeout + ) + stdout_output = result.stdout + stderr_output = result.stderr + + # Clean up the container + subprocess.run(["docker", "rm", container_name], capture_output=True, text=True, timeout=10) + + # Check if error export file was created + error_copy_failed = False + error_copy_msg = "" + if error_export_file.exists(): + file_size = error_export_file.stat().st_size + logger.info(f"📄 Found error export file: {error_export_file} (size: {file_size} bytes)") + else: + error_copy_failed = True + error_copy_msg = f"Error export file not found: {error_export_file}" + logger.warning(f"⚠️ {error_copy_msg}") + else: + # FastAPI-like server flow: start container detached, poll readiness, run client, then stop and collect logs + logger.info("🚀 Starting server container in detached mode") + start_proc = subprocess.run( + cmd, + capture_output=True, + text=True + ) + if start_proc.returncode != 0: + error_msg = f"Failed to start server container: {start_proc.stderr}" + logger.error(f"❌ {error_msg}") + return False, error_msg, "" + container_id = start_proc.stdout.strip() + logger.info(f"🆔 Container ID: {container_id}") + + # Copy assets into container if specified + if asset_temp_dir: + # Create /tmp/assets directory in container + logger.info("📁 Creating /tmp/assets directory in container...") + mkdir_result = subprocess.run( + ["docker", "exec", container_name, "mkdir", "-p", "/tmp/assets"], + capture_output=True, + text=True, + timeout=10 + ) + if mkdir_result.returncode != 0: + logger.error(f"❌ Failed to create /tmp/assets: {mkdir_result.stderr}") + subprocess.run(["docker", "stop", container_name], capture_output=True, timeout=10) + subprocess.run(["docker", "rm", container_name], capture_output=True, timeout=10) + return False, f"Failed to create /tmp/assets: {mkdir_result.stderr}", "" + + for idx, asset_id in enumerate(asset_ids): + src_path = asset_temp_dir / asset_id + dest_path = f"{container_name}:/tmp/assets/{asset_id}" + + logger.info(f"📦 Copying asset {idx} to container: {asset_id}") + copy_result = subprocess.run( + ["docker", "cp", str(src_path), dest_path], + capture_output=True, + text=True, + timeout=30 + ) + if copy_result.returncode != 0: + logger.error(f"❌ Failed to copy asset: {copy_result.stderr}") + subprocess.run(["docker", "stop", container_name], capture_output=True, timeout=10) + subprocess.run(["docker", "rm", container_name], capture_output=True, timeout=10) + return False, f"Asset copy failed: {copy_result.stderr}", "" + logger.info(f"✅ Asset {idx} copied successfully") + + # Clean up the temp asset directory + import shutil as shutil_module + shutil_module.rmtree(asset_temp_dir, ignore_errors=True) + logger.info("🧹 Cleaned up temporary asset directory") + + # Readiness probe + ready = False + http_error_status = None # Track if we got an HTTP error + ready_deadline = time.time() + max(1, timeout) + service_host = self.get_service_host() + # Get HTTP port from test config env, default to 8000 + http_port = test_config.get("env", {}).get("HTTP_PORT", "8000") + health_url = f"http://{service_host}:{http_port}/v1/health/ready" + logger.info(f"🔎 Probing readiness: {health_url}") + while time.time() < ready_deadline: + try: + with urllib.request.urlopen(health_url, timeout=2) as resp: + if resp.status == 200: + ready = True + break + else: + logger.info(f"Health probe returned non-200 status: {resp.status}") + except urllib.error.HTTPError as e: + http_error_status = e.code + logger.error(f"❌ Health probe HTTPError: status={e.code} - server returned error, stopping retry") + break # No retry on HTTP errors - server is responding but not healthy + except urllib.error.URLError as e: + if isinstance(e.reason, socket.timeout): + logger.warning("Health probe request timed out") + else: + logger.info(f"Health probe URLError: {e.reason}, retrying...") + except (TimeoutError, socket.timeout): + logger.warning("Health probe request timed out, retrying...") + time.sleep(2) + + client_stdout = "" + client_stderr = "" + client_rc = 1 + + if not ready: + if http_error_status: + logger.error(f"❌ Server health check failed with HTTP error {http_error_status}") + else: + logger.error("❌ Server did not become ready within timeout") + # Collect logs to see what went wrong during initialization + logger.info("📋 Collecting container logs for failed readiness...") + logs_proc = subprocess.run(["docker", "logs", container_id], capture_output=True, text=True) + if logs_proc.stdout: + logger.info("🔍 Container initialization logs:") + for line in logs_proc.stdout.split('\n')[-20:]: # Show last 20 lines + if line.strip(): + logger.info(f" {line}") + if logs_proc.stderr: + logger.error("🔍 Container error logs:") + for line in logs_proc.stderr.split('\n')[-10:]: # Show last 10 error lines + if line.strip(): + logger.error(f" {line}") + else: + # Check if auto_validation is specified - run directly on host + auto_validation_path = test_config.get("auto_validation") + if auto_validation_path: + logger.info("✅ Server is ready. Running validation script on host...") + + try: + # Resolve validation folder path relative to config directory + config_dir = Path(test_config.get("_config_dir", ".")).resolve() + validation_folder = (config_dir / auto_validation_path).resolve() + + # Fixed script name is test_runner.py (generated during build via -v flag) + # It's located in the validation folder subdirectories (e.g., gdino/.tmp/test_runner.py) + # Determine which subdirectory based on TAO_MODEL_NAME + tao_model_name = test_config.get("env", {}).get("TAO_MODEL_NAME", "") + if not tao_model_name: + logger.error("❌ TAO_MODEL_NAME not specified in test_config.env") + client_rc = 1 + client_stdout = "" + client_stderr = "TAO_MODEL_NAME not specified" + else: + validation_script_path = validation_folder / ".tmp" / "test_runner.py" + + if not validation_script_path.exists(): + logger.error(f"❌ Validation script not found: {validation_script_path}") + client_rc = 1 + client_stdout = "" + client_stderr = f"Validation script not found: {validation_script_path}" + else: + logger.info(f"🔧 Running validation script: {validation_script_path}") + + # Set up environment variables for validation script + validation_env = os.environ.copy() + if "env" in test_config: + validation_env.update({k: str(v) for k, v in test_config["env"].items()}) + + # Add service host for validation script to connect to server + # The validation script expects TEST_HOST environment variable + # Use HTTP_PORT from test config env, default to 8000 + validation_env["TEST_HOST"] = f"http://{service_host}:{http_port}" + + # Run validation script with Python + validation_proc = subprocess.run( + ["python", str(validation_script_path)], + capture_output=True, + text=True, + timeout=max(60, timeout), + cwd=str(validation_script_path.parent), + env=validation_env + ) + client_rc = validation_proc.returncode + client_stdout = validation_proc.stdout + client_stderr = validation_proc.stderr + + if client_rc == 0: + logger.info("✅ Validation script completed successfully") + if client_stdout: + logger.info(f"Validation output:\n{client_stdout}") + else: + logger.error(f"❌ Validation script failed with return code {client_rc}") + if client_stderr: + logger.error(f"Validation stderr:\n{client_stderr}") + if client_stdout: + logger.error(f"Validation stdout:\n{client_stdout}") + except subprocess.TimeoutExpired: + logger.error("❌ Validation script timed out") + client_rc = 1 + client_stdout = "" + client_stderr = "Validation script timed out" + except Exception as e: + logger.error(f"❌ Exception running validation script: {e}") + client_rc = 1 + client_stdout = "" + client_stderr = str(e) + else: + logger.info("✅ Server is ready. Launching concurrent curl requests...") + # Read NDJSON files and launch curl for each line + # Get test_requests from test_config, with a default fallback based on app name + test_app_name = test_config.get("TEST_APP_NAME") or None + default_test_requests = [{"payload_path": f"{test_app_name}/payloads/payloads.jsonl"}] + test_requests_list = test_config.get("test_requests", default_test_requests) + + # Ensure test_requests is a list + if not isinstance(test_requests_list, list): + logger.error(f"❌ test_requests must be a list, got: {type(test_requests_list)}") + client_rc = 1 + client_stdout = "" + client_stderr = "test_requests must be a list" + else: + # Check if any request has scheduled_time (for sequential execution) + has_scheduled = any("scheduled_time" in item for item in test_requests_list) + + # Resolve payloads_path relative to config directory + config_dir = Path(test_config.get("_config_dir", ".")).resolve() + + # Get endpoint from test_config, default to /v1/inference + endpoint = test_config.get("endpoint", "/v1/inference") + logger.info(f"🎯 Using endpoint: {endpoint}") + + if has_scheduled: + # Sequential execution with timing + logger.info("⏰ Using sequential execution with scheduled times") + + # Sort requests by scheduled_time + sorted_requests = sorted(test_requests_list, key=lambda x: x.get("scheduled_time", 0)) + + start_time = time.time() + all_succeeded = True + async_processes = [] # Track async processes for later status check + + for request_item in sorted_requests: + scheduled_time = request_item.get("scheduled_time", 0) + elapsed = time.time() - start_time + + # Wait until scheduled time + if scheduled_time > elapsed: + wait_time = scheduled_time - elapsed + logger.info(f"⏳ Waiting {wait_time:.1f}s until scheduled time {scheduled_time}s...") + time.sleep(wait_time) + + # Extract request config + http_method = request_item.get("method", "POST").upper() + http_headers = request_item.get("headers", {"Content-Type": "application/json"}) + request_endpoint = request_item.get("endpoint", endpoint) + + # Get payload + payload_data = None + if "payload_path" in request_item: + payload_path = (config_dir / request_item["payload_path"]).resolve() + if payload_path.exists(): + with payload_path.open("r") as f: + first_line = f.readline().strip() + if first_line: + payload_data = first_line + else: + logger.error(f"❌ Payload file not found: {payload_path}") + all_succeeded = False + continue + + # Build curl command + url = f"http://{service_host}:{http_port}{request_endpoint}" + curl_cmd = ["curl", "-sS", "-X", http_method, "-w", "\\n%{http_code}"] + + for header_name, header_value in http_headers.items(): + curl_cmd.extend(["-H", f"{header_name}: {header_value}"]) + + if payload_data and http_method in ["POST", "PUT", "PATCH", "DELETE"]: + curl_cmd.extend(["-d", payload_data]) + + curl_cmd.append(url) + + logger.info(f"🌐 [{scheduled_time}s] {http_method} {request_endpoint}") + if payload_data: + logger.info(f"📦 Payload: {payload_data[:200]}{'...' if len(payload_data) > 200 else ''}") + + # Check if this is an async request + is_async = request_item.get("async", False) + + if is_async: + # Launch async request without waiting for response + logger.info(f"🚀 Launching async request") + try: + proc = subprocess.Popen( + curl_cmd, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True + ) + async_processes.append((proc, http_method, request_endpoint)) + logger.info(f"✅ Async request launched (PID: {proc.pid})") + except Exception as e: + logger.error(f"❌ Failed to launch async request: {e}") + all_succeeded = False + else: + # Execute synchronous request + try: + result = subprocess.run(curl_cmd, capture_output=True, text=True, timeout=30) + response_output = result.stdout.strip() + + # Split response body and status code + lines = response_output.split('\n') + status_code = int(lines[-1]) if lines and lines[-1].isdigit() else 0 + response_body = '\n'.join(lines[:-1]) if len(lines) > 1 else "" + + logger.info(f"📡 Status: {status_code}") + if response_body: + logger.info(f"✅ Response: {response_body[:300]}{'...' if len(response_body) > 300 else ''}") + + if status_code < 200 or status_code >= 300: + logger.error(f"❌ Request failed with status {status_code}") + all_succeeded = False + except subprocess.TimeoutExpired: + logger.error(f"❌ Request timed out") + all_succeeded = False + except Exception as e: + logger.error(f"❌ Request failed: {e}") + all_succeeded = False + + # Wait for async processes and check their results + if async_processes: + logger.info(f"\n⏳ Waiting for {len(async_processes)} async request(s) to complete...") + for proc, method, endpoint in async_processes: + try: + stdout, stderr = proc.communicate(timeout=30) + response_output = stdout.strip() + + # Split response body and status code + lines = response_output.split('\n') + status_code = int(lines[-1]) if lines and lines[-1].isdigit() else 0 + response_body = '\n'.join(lines[:-1]) if len(lines) > 1 else "" + + logger.info(f"📡 Async {method} {endpoint} - Status: {status_code}") + if response_body: + logger.info(f" Response: {response_body[:200]}{'...' if len(response_body) > 200 else ''}") + + if status_code < 200 or status_code >= 300: + logger.error(f"❌ Async request failed with status {status_code}") + all_succeeded = False + except subprocess.TimeoutExpired: + logger.error(f"❌ Async {method} {endpoint} timed out") + proc.kill() + all_succeeded = False + except Exception as e: + logger.error(f"❌ Async {method} {endpoint} failed: {e}") + all_succeeded = False + + # Set final result + if all_succeeded: + logger.info("\n✅ All scheduled requests completed successfully") + client_rc = 0 + client_stdout = "All requests succeeded" + client_stderr = "" + else: + logger.error("\n❌ Some requests failed") + client_rc = 1 + client_stdout = "" + client_stderr = "One or more requests failed" + else: + # Parallel execution (existing logic) + logger.info("🚀 Using parallel execution") + + # Collect all payload lines from all files with their configs + # Each entry: (payload_line, method, headers) + all_payloads = [] + for request_item in test_requests_list: + # Parse test request (must be dict) + if not isinstance(request_item, dict): + logger.error(f"❌ Invalid test_requests item (must be dict): {request_item}") + continue + + # Extract config with defaults + http_method = request_item.get("method", "POST").upper() + http_headers = request_item.get("headers", {"Content-Type": "application/json"}) + + # Check if payload_path is provided + if "payload_path" not in request_item: + # No payload_path - single request with no payload (useful for GET) + logger.info(f"📄 No payload file specified") + logger.info(f" Method: {http_method}, Headers: {http_headers}") + all_payloads.append(("", http_method, http_headers)) + else: + # Path provided - load payloads from file + payload_path_str = request_item["payload_path"] + payload_path = (config_dir / payload_path_str).resolve() + logger.info(f"📄 Resolved payload path: {payload_path_str} -> {payload_path}") + logger.info(f" Method: {http_method}, Headers: {http_headers}") + + if not payload_path.exists(): + logger.error(f"❌ Payload file not found: {payload_path}") + else: + with payload_path.open("r") as f: + payload_lines = [line.strip() for line in f if line.strip()] + # Associate each payload with its method and headers + for line in payload_lines: + all_payloads.append((line, http_method, http_headers)) + logger.info(f"📋 Loaded {len(payload_lines)} payloads from {payload_path.name}") + + if not all_payloads: + logger.error(f"❌ No payloads found in any file") + client_rc = 1 + client_stdout = "" + client_stderr = "No payloads found" + else: + logger.info(f"📋 Total payloads to process: {len(all_payloads)}") + + procs: List[subprocess.Popen] = [] + for payload_line, method, headers in all_payloads: + # Check if this is a multipart/form-data upload + content_type = headers.get("Content-Type", "").lower() + is_multipart = "multipart/form-data" in content_type + + # Build curl command with method and headers from payload config + curl_cmd = ["curl", "-sS", "-X", method, "-w", "%{http_code}"] + + if is_multipart and method in ["POST", "PUT", "PATCH"] and payload_line: + # Multipart upload: parse JSON to get file field and use -F + try: + payload_data = json.loads(payload_line) + + # Add non-Content-Type headers (curl -F sets Content-Type automatically) + for header_name, header_value in headers.items(): + if header_name.lower() != "content-type": + curl_cmd.extend(["-H", f"{header_name}: {header_value}"]) + + # Add form fields with -F + for field_name, field_value in payload_data.items(): + if isinstance(field_value, str): + # Try to resolve as a file path (relative to config dir) + file_path = Path(field_value) + if not file_path.is_absolute(): + # Try relative to config dir + resolved_path = (config_dir / field_value).resolve() + else: + # Already absolute + resolved_path = file_path.resolve() + + # Check if the resolved path exists and is a file + if resolved_path.exists() and resolved_path.is_file(): + # Use @ prefix for file upload + curl_cmd.extend(["-F", f"{field_name}=@{resolved_path}"]) + logger.info(f" 📎 Uploading file: {field_name}={resolved_path}") + else: + # Not a valid file path, send as string value + curl_cmd.extend(["-F", f"{field_name}={field_value}"]) + if field_value: + logger.debug(f" 📝 Form field: {field_name}={field_value}") + else: + # Non-string value (number, bool, etc.) + curl_cmd.extend(["-F", f"{field_name}={field_value}"]) + except json.JSONDecodeError as e: + logger.error(f"❌ Failed to parse payload as JSON for multipart upload: {e}") + logger.error(f" Payload: {payload_line}") + continue + else: + # Regular JSON request + # Add headers + for header_name, header_value in headers.items(): + curl_cmd.extend(["-H", f"{header_name}: {header_value}"]) + + # Add data for methods that support body (only if payload is not empty) + if method in ["POST", "PUT", "PATCH"] and payload_line: + curl_cmd.extend(["--data", payload_line]) + + # Add URL + curl_cmd.append(f"http://{service_host}:{http_port}{endpoint}") + + procs.append(subprocess.Popen( + curl_cmd, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True + )) + + outs: List[str] = [] + errs: List[str] = [] + client_rc = 0 + http_status_errors = [] + for i, p in enumerate(procs): + out, err = p.communicate(timeout=max(5, timeout)) + outs.append(out or "") + errs.append(err or "") + if p.returncode != 0: + client_rc = p.returncode + else: + # For non-serverless, check HTTP status is 200 + # curl -w "%{http_code}" appends status to stdout + if out and len(out) >= 3: + # Extract last 3 chars as HTTP status code + http_status = out[-3:] + if http_status != "200": + http_status_errors.append( + f"Request {i+1}: HTTP {http_status}" + ) + client_rc = 1 # Mark as failed + + client_stdout = "\n".join(outs) + client_stderr = "\n".join(errs) + + # Log HTTP status errors for non-serverless + if http_status_errors: + error_msg = ( + f"Non-serverless server returned non-200 status " + f"codes: {'; '.join(http_status_errors)}" + ) + logger.error("❌ %s", error_msg) + client_stderr += f"\n{error_msg}" + + # Always attempt to stop and collect logs + logger.info("🛑 Stopping server container") + + # Collect logs BEFORE stopping container (while it's still running) + logger.info("📋 Collecting container logs...") + logs_proc = subprocess.run(["docker", "logs", container_id], capture_output=True, text=True) + server_logs = logs_proc.stdout + if logs_proc.stderr: + server_logs += "\n=== STDERR ===\n" + logs_proc.stderr + + # Check if error export file was created + error_copy_failed = False + error_copy_msg = "" + if error_export_file.exists(): + file_size = error_export_file.stat().st_size + logger.info(f"📄 Found error export file: {error_export_file} (size: {file_size} bytes)") + else: + error_copy_failed = True + error_copy_msg = f"Error export file not found: {error_export_file}" + logger.warning(f"⚠️ {error_copy_msg}") + + # Now stop and remove container + subprocess.run(["docker", "stop", container_id], capture_output=True, text=True) + subprocess.run(["docker", "rm", container_id], capture_output=True, text=True) + + # Prepare a Result-like object + class Result: + def __init__(self, returncode, stdout, stderr): + self.returncode = returncode + self.stdout = stdout + self.stderr = stderr + + combined_stdout = "".join([ + "=== SERVER LOGS ===\n", server_logs or "", + "\n=== CLIENT STDOUT ===\n", client_stdout or "", + ]) + combined_stderr = client_stderr or "" + # For non-serverless flows, we care about HTTP responses and validation status, + # not the container's own exit code. Track client failure separately and + # keep Result.returncode neutral (0) so final status can branch by server type. + client_failed = not (ready and client_rc == 0) + result = Result(0, combined_stdout, combined_stderr) + + # Save logs to file + with open(log_file, 'w') as f: + f.write("=== Test Configuration ===\n") + f.write(f"Image: {image_name}\n") + f.write(f"Command: {' '.join(cmd)}\n") + f.write(f"Return Code: {result.returncode}\n") + f.write(f"Timeout: {timeout} seconds\n") + f.write("Error Detection: Enabled (via JSON export)\n") + f.write(f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}\n") + f.write("\n=== STDOUT ===\n") + f.write(result.stdout) + f.write("\n=== STDERR ===\n") + f.write(result.stderr) + f.write("\n=== END LOG ===\n") + + # Check for expected_error (negative test) using exported error JSON + expected_error = test_config.get("expected_error") + if expected_error: + # This is a negative test - fail immediately if error file not found + if error_copy_failed or error_export_file is None: + error_msg = f"Negative test FAILED: Could not find error export file: {error_copy_msg}" + logger.error(f"❌ {error_msg}") + logger.info(f"📄 Logs saved to: {log_file}") + return False, error_msg, str(log_file) + + # Try to read the exported error file + matched_error = None # Will hold the error that matches expected_error exactly + + if error_export_file.exists(): + try: + with open(error_export_file, 'r') as f: + error_data = json.load(f) + + stats = error_data.get("stats", {}) + total_errors = stats.get("total_errors", 0) + errors = error_data.get("errors", []) + collected_codes = [err.get("error_code") for err in errors] + + # Search for EXACT match of expected error code + for err in errors: + if err.get("error_code") == expected_error: + matched_error = err + break + + if matched_error is not None: + # EXACT match found - test passes + logger.info(f"✅ Negative test PASSED: Error code '{expected_error}' matches exactly") + logger.info(f" Error message: {matched_error.get('message', 'N/A')[:200]}") + logger.info(f" Component: {matched_error.get('component', 'N/A')}") + logger.info(f" Operation: {matched_error.get('operation', 'N/A')}") + logger.info(f" Total errors collected: {total_errors}") + logger.info(f"📄 Logs saved to: {log_file}") + return True, result.stdout, str(log_file) + else: + # No exact match - test fails + error_msg = f"Negative test FAILED: Expected error '{expected_error}' not found" + logger.error(f"❌ {error_msg}") + logger.error(f" Collected error codes: {collected_codes}") + logger.error(f" Total errors: {total_errors}") + logger.info(f"📄 Logs saved to: {log_file}") + return False, error_msg, str(log_file) + except json.JSONDecodeError as e: + error_msg = f"Negative test FAILED: Failed to parse error export file: {e}" + logger.error(f"❌ {error_msg}") + logger.info(f"📄 Logs saved to: {log_file}") + return False, error_msg, str(log_file) + except Exception as e: + error_msg = f"Negative test FAILED: Failed to read error export file: {e}" + logger.error(f"❌ {error_msg}") + logger.info(f"📄 Logs saved to: {log_file}") + return False, error_msg, str(log_file) + else: + # Error export file doesn't exist + error_msg = f"Negative test FAILED: Error export file not found: {error_export_file}" + logger.error(f"❌ {error_msg}") + logger.info(f"📄 Logs saved to: {log_file}") + return False, error_msg, str(log_file) + + # For serverless non-negative tests, check that the result file exists and is non-empty + if result_export_file: + if not result_export_file.exists(): + error_msg = f"Result file not found: {result_export_file}" + logger.error(f"❌ {error_msg}") + logger.info(f"📄 Logs saved to: {log_file}") + return False, error_msg, str(log_file) + file_size = result_export_file.stat().st_size + if file_size == 0: + error_msg = f"Result file is empty: {result_export_file}" + logger.error(f"❌ {error_msg}") + logger.info(f"📄 Logs saved to: {log_file}") + return False, error_msg, str(log_file) + logger.info(f"📄 Result file OK: {result_export_file} ({file_size} bytes)") + + # Check for errors using the exported error JSON file (if ERROR_EXPORT_PATH was configured) + has_errors = False + error_details = [] + + if error_export_file and error_export_file.exists(): + try: + with open(error_export_file, 'r') as f: + error_data = json.load(f) + + stats = error_data.get("stats", {}) + total_errors = stats.get("total_errors", 0) + errors = error_data.get("errors", []) + + if total_errors > 0: + has_errors = True + for err in errors: + error_details.append({ + "code": err.get("error_code", "UNKNOWN"), + "message": err.get("message", "N/A"), + "severity": err.get("severity", "unknown"), + "component": err.get("component", "N/A") + }) + except json.JSONDecodeError as e: + logger.warning(f"⚠️ Failed to parse error export file: {e}") + except Exception as e: + logger.warning(f"⚠️ Failed to read error export file: {e}") + + # Determine overall success: + # - For serverless flows, rely on container return code and error exports. + # - For non-serverless HTTP server flows, rely on HTTP/client status (client_failed) + # and error exports; ignore the container's own exit code. + if is_serverless: + rc_failed = result.returncode < 0 + else: + # client_failed is only set in non-serverless branch; default to False otherwise + rc_failed = 'client_failed' in locals() and client_failed + + if not rc_failed and not has_errors: + logger.info(f"✅ Successfully tested image: {image_name}") + logger.info(f"📄 Logs saved to: {log_file}") + return True, result.stdout, str(log_file) + elif has_errors: + error_msg = f"Test failed: {len(error_details)} error(s) detected in image: {image_name}" + logger.error(f"❌ {error_msg}") + logger.error("Errors found:") + for err in error_details[:10]: # Show first 10 errors + logger.error(f" [{err['severity'].upper()}] {err['code']}: {err['message'][:100]}") + if len(error_details) > 10: + logger.error(f" ... and {len(error_details) - 10} more errors") + logger.info(f"📄 Logs saved to: {log_file}") + logger.info(f"📄 Error export: {error_export_file}") + return False, error_msg, str(log_file) + else: + if is_serverless: + logger.error( + f"❌ Test failed with negative return code for image: {image_name}" + ) + logger.error(f"Return code: {result.returncode}") + failure_reason = f"Test failed with negative return code {result.returncode}" + else: + logger.error( + f"❌ Test failed due to client/HTTP errors for image: {image_name}" + ) + failure_reason = "Test failed due to client/HTTP errors" + logger.info(f"📄 Logs saved to: {log_file}") + return False, failure_reason, str(log_file) + + except subprocess.TimeoutExpired as timeout_ex: + error_msg = f"Test timed out after {timeout} seconds for image: {image_name}" + logger.error(f"❌ {error_msg}") + + # Try to collect container logs if this was a detached container + container_logs = "" + if server_type != "serverless": + # For non-serverless, we have a detached container that needs cleanup + try: + # Check if container_id was defined (container was started) + if 'container_id' in locals(): + logger.info(f"📋 Collecting logs from timed-out container: {container_id}") + logs_proc = subprocess.run( + ["docker", "logs", container_id], + capture_output=True, + text=True, + timeout=10 + ) + container_logs = logs_proc.stdout + if logs_proc.stderr: + container_logs += "\n=== STDERR ===\n" + logs_proc.stderr + + # Stop and remove the container + logger.info(f"🛑 Stopping timed-out container: {container_id}") + subprocess.run(["docker", "stop", container_id], capture_output=True, text=True, timeout=30) + subprocess.run(["docker", "rm", container_id], capture_output=True, text=True, timeout=10) + except Exception as cleanup_error: + logger.warning(f"⚠️ Failed to collect logs or cleanup container: {cleanup_error}") + container_logs += f"\n\n[Error during log collection: {cleanup_error}]\n" + + # Save timeout log with container logs if available + with open(log_file, 'w') as f: + f.write("=== Test Configuration ===\n") + f.write(f"Image: {image_name}\n") + f.write(f"Command: {' '.join(cmd)}\n") + f.write(f"Status: TIMEOUT\n") + f.write(f"Timeout: {timeout} seconds\n") + f.write(f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}\n") + f.write("\n=== ERROR ===\n") + f.write(error_msg) + if container_logs: + f.write("\n\n=== CONTAINER LOGS ===\n") + f.write(container_logs) + f.write("\n=== END LOG ===\n") + + return False, error_msg, str(log_file) + except Exception as e: + error_msg = f"Exception during test: {str(e)}" + logger.error(f"❌ {error_msg}") + + # Add full traceback for debugging + import traceback + full_traceback = traceback.format_exc() + logger.error(f"Traceback:\n{full_traceback}") + + # Try to collect container logs if this was a detached container + container_logs = "" + if server_type != "serverless": + # For non-serverless, we have a detached container that needs cleanup + try: + # Check if container_id was defined (container was started) + if 'container_id' in locals(): + logger.info(f"📋 Collecting logs from failed container: {container_id}") + logs_proc = subprocess.run( + ["docker", "logs", container_id], + capture_output=True, + text=True, + timeout=10 + ) + container_logs = logs_proc.stdout + if logs_proc.stderr: + container_logs += "\n=== STDERR ===\n" + logs_proc.stderr + + # Stop and remove the container + logger.info(f"🛑 Stopping failed container: {container_id}") + subprocess.run(["docker", "stop", container_id], capture_output=True, text=True, timeout=30) + subprocess.run(["docker", "rm", container_id], capture_output=True, text=True, timeout=10) + except Exception as cleanup_error: + logger.warning(f"⚠️ Failed to collect logs or cleanup container: {cleanup_error}") + container_logs += f"\n\n[Error during log collection: {cleanup_error}]\n" + + # Save exception log + with open(log_file, 'w') as f: + f.write("=== Test Configuration ===\n") + f.write(f"Image: {image_name}\n") + f.write(f"Status: EXCEPTION\n") + f.write(f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}\n") + f.write("\n=== ERROR ===\n") + f.write(error_msg) + f.write("\n\n=== TRACEBACK ===\n") + f.write(full_traceback) + if container_logs: + f.write("\n\n=== CONTAINER LOGS ===\n") + f.write(container_logs) + f.write("\n=== END LOG ===\n") + + return False, error_msg, str(log_file) + + def cleanup_prerequisite_script(self, test_config: Dict, test_id: int) -> bool: + """Clean up resources created by prerequisite scripts.""" + prerequisite_script = test_config.get("prerequisite_script") + if not prerequisite_script: + return True + + try: + # Check if the script is the RTSP server setup script + if "setup_rtsp_server.sh" in prerequisite_script: + logger.info("🧹 Cleaning up RTSP server...") + # Use secure command execution for cleanup + cleanup_command = ["./setup_rtsp_server.sh", "--kill"] + cleanup_result = subprocess.run( + cleanup_command, + shell=False, # Safer execution without shell + capture_output=True, + text=True, + timeout=30 + ) + + if cleanup_result.returncode == 0: + logger.info("✅ RTSP server cleanup completed") + return True + else: + logger.warning(f"⚠️ RTSP server cleanup failed: {cleanup_result.stderr}") + return False + + # Add more cleanup logic for other prerequisite scripts here + logger.info("🧹 No specific cleanup needed for prerequisite script") + return True + + except Exception as e: + logger.warning(f"⚠️ Exception during prerequisite cleanup: {str(e)}") + return False + + def cleanup_image(self, image_name: str) -> bool: + """Remove the test image.""" + try: + result = subprocess.run( + ["docker", "rmi", image_name], + capture_output=True, + text=True + ) + + if result.returncode == 0: + logger.info(f"🧹 Cleaned up image: {image_name}") + return True + else: + logger.warning(f"⚠️ Failed to cleanup image: {image_name}") + return False + + except Exception as e: + logger.warning(f"⚠️ Exception during cleanup: {str(e)}") + return False + + def run_test_suite(self, test_configs: List[Dict], cleanup: bool = True, gitlab_token: Optional[str] = None, force_full_flow: bool = False, gpus: str = "all") -> Dict: + """Run a suite of tests with different configurations. + + Args: + test_configs: List of test configurations + cleanup: Whether to cleanup Docker images after testing + gitlab_token: GitLab token for authentication + force_full_flow: Force full build+test flow even for disabled tests + gpus: GPU devices to use (default: 'all') + """ + results = { + "total_tests": len(test_configs), + "passed": 0, + "failed": 0, + "results": [] + } + + for i, config in enumerate(test_configs, 1): + logger.info(f"\n{'='*60}") + logger.info(f"Running test {i}/{len(test_configs)}") + logger.info(f"{'='*60}") + + # Generate unique image name + image_name = f"test-inference_builder-{i}-{int(time.time())}" + + build_args = config.get("build_args", {}).copy() + test_cfg = config.get("test_config", {}).copy() + + # Add _config_dir to test_cfg so paths can be resolved correctly + if "_config_dir" in config: + test_cfg["_config_dir"] = config["_config_dir"] + + # Note: auto_validation is passed directly to build_image via test_config + # No need to add it to build_args + + # Debug logging for flow decision + default_enable = test_cfg.get("default_enable", True) + logger.info(f"🔍 Test flow decision: default_enable={default_enable}, force_full_flow={force_full_flow}") + + # If default_enable is False, skip Docker build/test but run codegen, + # unless full flow is forced by selection (e.g., --test-case provided) + if not default_enable and not force_full_flow: + logger.info("⚙️ Test disabled via default_enable=false. Running code generation only (skipping model download)...") + codegen_success, codegen_output = self.generate_inference_code(build_args, test_cfg) + + status = "SKIPPED" + test_result = { + "test_id": i, + "config": config, + "status": status, + "build_success": False, + "test_success": False, + "build_output": codegen_output, + "test_output": "", + "log_file": "", + "image_name": image_name + } + results["results"].append(test_result) + logger.info(f"Test {i} result: {status} (codegen_only)") + # Do not increment passed/failed counters for skipped tests + continue + + # Download models if specified in test config (only for full build+test flow) + models_config = test_cfg.get("models", {}) + if models_config: + logger.info(f"📦 Checking and downloading models (full build+test flow)...") + config_dir = Path(config.get("_config_dir", ".")) + download_success, download_msg = self.download_models(models_config, config_dir) + if not download_success: + logger.error(f"❌ Model download failed: {download_msg}") + results["failed"] += 1 + test_result = { + "test_id": i, + "config": config, + "status": "FAILED", + "build_success": False, + "test_success": False, + "build_output": "", + "test_output": download_msg, + "log_file": "", + "image_name": image_name + } + results["results"].append(test_result) + logger.info(f"Test {i} result: FAILED (model download)") + continue + + # Build image with resolved dockerfile and base_dir + dockerfile_to_use = config.get("_resolved_dockerfile") + base_dir_to_use = config.get("_resolved_base_dir") + build_success, build_output = self.build_image(build_args, image_name, dockerfile_to_use, base_dir_to_use, test_cfg) + + if build_success: + # Test image - pass server type and config dir to test_config + test_config = config.get("test_config", {}).copy() + if "SERVER_TYPE" in build_args: + test_config["SERVER_TYPE"] = build_args["SERVER_TYPE"] + # Pass config directory for relative path resolution + if "_config_dir" in config: + test_config["_config_dir"] = config["_config_dir"] + if "TEST_APP_NAME" in build_args: + test_config["TEST_APP_NAME"] = build_args["TEST_APP_NAME"] + test_success, test_output, log_file = self.test_image( + image_name, + test_config, + i, + gpus=gpus + ) + + if test_success: + results["passed"] += 1 + status = "PASSED" + else: + results["failed"] += 1 + status = "FAILED" + else: + results["failed"] += 1 + test_success = False + test_output = "" + log_file = "" + status = "FAILED" + + # Cleanup (run cleanup for full-flow tests; skip if codegen-only path was taken) + if cleanup: + self.cleanup_image(image_name) + # Clean up prerequisite scripts + self.cleanup_prerequisite_script(config.get("test_config", {}), i) + + # Store result + test_result = { + "test_id": i, + "config": config, + "status": status, + "build_success": build_success, + "test_success": test_success, + "build_output": build_output, + "test_output": test_output, + "log_file": log_file, + "image_name": image_name + } + + results["results"].append(test_result) + + logger.info(f"Test {i} result: {status}") + + return results + + def generate_report(self, results: Dict, output_file: Optional[str] = None): + """Generate a test report.""" + report = { + "summary": { + "total_tests": results["total_tests"], + "passed": results["passed"], + "failed": results["failed"], + "success_rate": f"{(results['passed'] / results['total_tests'] * 100):.1f}%" if results["total_tests"] > 0 else "0%" + }, + "results": results["results"] + } + + if output_file: + with open(output_file, 'w') as f: + json.dump(report, f, indent=2) + logger.info(f"📄 Report saved to: {output_file}") + + # Print summary + logger.info(f"\n{'='*60}") + logger.info("TEST SUMMARY") + logger.info(f"{'='*60}") + logger.info(f"Total tests: {results['total_tests']}") + logger.info(f"Passed: {results['passed']}") + logger.info(f"Failed: {results['failed']}") + logger.info(f"Success rate: {report['summary']['success_rate']}") + + # Print test results with log file locations + logger.info(f"\n{'='*60}") + logger.info("TEST RESULTS") + logger.info(f"{'='*60}") + for result in results["results"]: + test_name = result["config"].get("name", f"Test {result['test_id']}") + status = result.get("status", "UNKNOWN") + log_file = result.get("log_file", "") + + if status == "PASSED": + status_icon = "✅" + elif status == "FAILED": + status_icon = "❌" + elif status == "SKIPPED": + status_icon = "⏭️" + else: + status_icon = "❓" + + if log_file: + logger.info(f"{status_icon} [{result['test_id']}] {test_name}: {log_file}") + else: + logger.info(f"{status_icon} [{result['test_id']}] {test_name}") + + return report + + +def main(): + parser = argparse.ArgumentParser(description="Test Docker builds with different arguments") + parser.add_argument("--dockerfile", default="Dockerfile", help="Path to Dockerfile") + parser.add_argument("--base-dir", default=".", help="Base directory for Docker build context") + parser.add_argument("--config-file", required=True, help="JSON file with test configurations") + parser.add_argument("--output", help="Output file for test report") + parser.add_argument("--log-dir", default="logs", help="Directory to save container logs") + parser.add_argument("--no-cleanup", action="store_true", help="Don't cleanup images after testing") + parser.add_argument("--gitlab-token", help="GitLab token for authentication") + parser.add_argument("--test-case", help="Run only the test case with this name (partial match supported). Supplying this forces full flow (build+test) even if disabled.") + parser.add_argument("--gpus", default="all", help="GPU devices to use for Docker containers (default: 'all'). Examples: 'all', 'device=0', 'device=0,1', '\"device=0,1\"'") + + # Parse arguments with security validation + try: + args = parser.parse_args() + except Exception as e: + logger.error(f"❌ Argument parsing failed: {str(e)}") + sys.exit(1) + + # Comprehensive security validation + validation_errors = [] + + # Validate dockerfile path + if not validate_dockerfile_path(args.dockerfile): + validation_errors.append(f"Invalid Dockerfile path: {args.dockerfile}") + + # Validate base directory + if not validate_safe_path(args.base_dir): + validation_errors.append(f"Invalid base directory path: {args.base_dir}") + + # Validate config file + if not validate_config_file_path(args.config_file): + validation_errors.append(f"Invalid config file path: {args.config_file}") + + # Validate output file if provided + if args.output and not validate_safe_path(args.output): + validation_errors.append(f"Invalid output file path: {args.output}") + + # Validate log directory + if not validate_log_directory(args.log_dir): + validation_errors.append(f"Invalid log directory path: {args.log_dir}") + + # Validate GitLab token if provided + if args.gitlab_token and not validate_gitlab_token(args.gitlab_token): + validation_errors.append("Invalid GitLab token format") + + # Exit if any validation errors + if validation_errors: + logger.error("❌ Security validation failed:") + for error in validation_errors: + logger.error(f" - {error}") + sys.exit(1) + + # Additional file existence checks with security + try: + # Validate config file first (required) + if not os.path.isfile(args.config_file): + logger.error(f"❌ Config file not found: {args.config_file}") + sys.exit(1) + + # Dockerfile and base-dir are optional - they'll be resolved from config file if not found + # Only warn if they're explicitly specified but don't exist + dockerfile_exists = os.path.isfile(args.dockerfile) + base_dir_exists = os.path.isdir(args.base_dir) + + if not dockerfile_exists: + logger.info(f"ℹ️ Dockerfile not found at command-line path: {args.dockerfile}") + logger.info(f"ℹ️ Will use Dockerfile from test config or config directory") + + if not base_dir_exists: + logger.info(f"ℹ️ Base directory not found at command-line path: {args.base_dir}") + logger.info(f"ℹ️ Will use base directory from test config or config directory") + + # Validate log directory path (prevent directory traversal) + log_dir_path = Path(args.log_dir).resolve() + current_dir = Path.cwd().resolve() + try: + log_dir_path.relative_to(current_dir) + except ValueError: + logger.error(f"❌ Log directory path is outside current directory: {args.log_dir}") + sys.exit(1) + + except Exception as e: + logger.error(f"❌ File validation failed: {str(e)}") + sys.exit(1) + + # Initialize tester with validated arguments + try: + tester = DockerBuildTester(args.dockerfile, args.base_dir, args.log_dir) + except Exception as e: + logger.error(f"❌ Failed to initialize DockerBuildTester: {str(e)}") + sys.exit(1) + + # Load test configurations from file with security validation + try: + with open(args.config_file, 'r') as f: + test_configs = json.load(f) + + # Validate JSON structure + if not isinstance(test_configs, list): + logger.error("❌ Config file must contain a list of test configurations") + sys.exit(1) + + # Get the directory containing the config file for resolving relative paths + config_dir = Path(args.config_file).parent.resolve() + logger.info(f"📁 Config file directory: {config_dir}") + + # Validate each test configuration for security + for i, config in enumerate(test_configs): + if not isinstance(config, dict): + logger.error(f"❌ Test configuration {i+1} must be a dictionary") + sys.exit(1) + + # Handle dockerfile specification + # Priority: 1) dockerfile in test_config, 2) Dockerfile in config dir, 3) command-line arg + if "dockerfile" in config: + # User explicitly specified dockerfile in config + dockerfile_path = (config_dir / config["dockerfile"]).resolve() + if not dockerfile_path.exists(): + logger.error(f"❌ Dockerfile specified in test config not found: {dockerfile_path}") + sys.exit(1) + config["_resolved_dockerfile"] = str(dockerfile_path) + logger.info(f"🔗 Using Dockerfile from config: {config['dockerfile']} -> {dockerfile_path}") + else: + # Check for Dockerfile in same directory as config + default_dockerfile = config_dir / "Dockerfile" + if default_dockerfile.exists(): + config["_resolved_dockerfile"] = str(default_dockerfile) + logger.info(f"🔗 Using default Dockerfile from config directory: {default_dockerfile}") + else: + # Fall back to command-line specified dockerfile + config["_resolved_dockerfile"] = args.dockerfile + logger.info(f"🔗 Using Dockerfile from command-line: {args.dockerfile}") + + # Base directory is ALWAYS the directory containing the test config JSON + # This makes test configs self-contained and portable + config["_resolved_base_dir"] = str(config_dir) + config["_config_dir"] = str(config_dir) # Store for model downloads + logger.info(f"🔗 Using base_dir (config directory): {config_dir}") + + # Validate build_args if present + if "build_args" in config: + if not isinstance(config["build_args"], dict): + logger.error(f"❌ build_args in test configuration {i+1} must be a dictionary") + sys.exit(1) + + # Resolve paths relative to config file directory + path_args = ["APP_YAML_PATH", "OUTPUT_DIR", "PROCESSORS_PATH", "OPENAPI_SPEC"] + for path_arg in path_args: + if path_arg in config["build_args"]: + original_path = config["build_args"][path_arg] + # Resolve relative to config file directory + resolved_path = (config_dir / original_path).resolve() + # Convert back to relative path from current working directory + try: + rel_path = resolved_path.relative_to(Path.cwd().resolve()) + config["build_args"][path_arg] = str(rel_path) + logger.info(f"🔗 Resolved {path_arg}: {original_path} -> {rel_path}") + except ValueError: + # If can't make relative, use absolute path + config["build_args"][path_arg] = str(resolved_path) + logger.info(f"🔗 Resolved {path_arg}: {original_path} -> {resolved_path}") + + for key, value in config["build_args"].items(): + if not validate_build_arg_name(key): + logger.error(f"❌ Invalid build arg name in test configuration {i+1}: {key}") + sys.exit(1) + if not isinstance(value, str) or not validate_build_arg_value(value): + logger.error(f"❌ Invalid build arg value in test configuration {i+1}: {value}") + sys.exit(1) + + # Validate test_config if present + if "test_config" in config: + test_config = config["test_config"] + config_valid, config_error = validate_test_config(test_config) + if not config_valid: + logger.error(f"❌ Test configuration {i+1} validation failed: {config_error}") + sys.exit(1) + + # Filter test configurations by test case name if specified + if args.test_case: + original_count = len(test_configs) + if args.test_case == "*": + logger.info("--test-case '*' specified: selecting all tests (including disabled).") + else: + test_configs = [ + config for config in test_configs + if args.test_case.lower() in config.get("name", "").lower() + ] + + if not test_configs: + logger.error(f"❌ No test cases found matching '{args.test_case}'") + logger.info("Available test cases:") + # Reload original configs to show available names + with open(args.config_file, 'r') as f: + original_configs = json.load(f) + for i, config in enumerate(original_configs, 1): + logger.info(f" {i}. {config.get('name', f'Unnamed test {i}')}") + sys.exit(1) + + logger.info(f"Filtered {original_count} test configurations to {len(test_configs)} matching '{args.test_case}'") + for i, config in enumerate(test_configs, 1): + logger.info(f" {i}. {config.get('name', f'Test {i}')}") + else: + # Include disabled tests; they will run codegen-only path unless -c/--test-case forces full flow. + logger.info("Including disabled tests (default_enable=false). They will run codegen-only unless --test-case is provided.") + + except json.JSONDecodeError as e: + logger.error(f"❌ Invalid JSON in config file: {str(e)}") + sys.exit(1) + except Exception as e: + logger.error(f"❌ Failed to load config file: {str(e)}") + sys.exit(1) + + # Run tests with error handling + try: + logger.info(f"Starting test suite with {len(test_configs)} configurations") + logger.info(f"Logs will be saved to: {args.log_dir}") + logger.info(f"Using GPU devices: {args.gpus}") + # Force full flow if --test-case provided (including "*") + force_full_flow = args.test_case is not None and len(args.test_case) > 0 + logger.info(f"🔍 Test suite settings: test_case={args.test_case}, force_full_flow={force_full_flow}") + results = tester.run_test_suite(test_configs, cleanup=not args.no_cleanup, gitlab_token=args.gitlab_token, force_full_flow=force_full_flow, gpus=args.gpus) + except Exception as e: + logger.error(f"❌ Test suite execution failed: {str(e)}") + sys.exit(1) + + # Generate report with error handling + try: + tester.generate_report(results, args.output) + except Exception as e: + logger.error(f"❌ Report generation failed: {str(e)}") + sys.exit(1) + + # Exit with appropriate code + if results["failed"] > 0: + sys.exit(1) + else: + sys.exit(0) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tools/inference_builder/builder/triton/__init__.py b/tools/inference_builder/builder/triton/__init__.py new file mode 100644 index 00000000..3159bfe6 --- /dev/null +++ b/tools/inference_builder/builder/triton/__init__.py @@ -0,0 +1,14 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tools/inference_builder/builder/triton/model_config_pb2.py b/tools/inference_builder/builder/triton/model_config_pb2.py new file mode 100644 index 00000000..fb8e904e --- /dev/null +++ b/tools/inference_builder/builder/triton/model_config_pb2.py @@ -0,0 +1,195 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# NO CHECKED-IN PROTOBUF GENCODE +# source: model_config.proto +# Protobuf Python Version: 5.27.3 +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import runtime_version as _runtime_version +from google.protobuf import symbol_database as _symbol_database +from google.protobuf.internal import builder as _builder +_runtime_version.ValidateProtobufRuntimeVersion( + _runtime_version.Domain.PUBLIC, + 5, + 27, + 3, + '', + 'model_config.proto' +) +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + + + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x12model_config.proto\x12\tinference\"\x96\x01\n\x10ModelRateLimiter\x12\x37\n\tresources\x18\x01 \x03(\x0b\x32$.inference.ModelRateLimiter.Resource\x12\x10\n\x08priority\x18\x02 \x01(\r\x1a\x37\n\x08Resource\x12\x0c\n\x04name\x18\x01 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_globals['_MODELCONFIG']._serialized_end=8368 + _globals['_MODELCONFIG_CCMODELFILENAMESENTRY']._serialized_start=8163 + _globals['_MODELCONFIG_CCMODELFILENAMESENTRY']._serialized_end=8218 + _globals['_MODELCONFIG_METRICTAGSENTRY']._serialized_start=8220 + _globals['_MODELCONFIG_METRICTAGSENTRY']._serialized_end=8269 + _globals['_MODELCONFIG_PARAMETERSENTRY']._serialized_start=8271 + _globals['_MODELCONFIG_PARAMETERSENTRY']._serialized_end=8347 +# @@protoc_insertion_point(module_scope) diff --git a/tools/inference_builder/builder/triton/utils.py b/tools/inference_builder/builder/triton/utils.py new file mode 100644 index 00000000..27aa8e1b --- /dev/null +++ b/tools/inference_builder/builder/triton/utils.py @@ -0,0 +1,80 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .model_config_pb2 import DataType, ModelConfig, ModelParameter, ModelTensorReshape +from google.protobuf import text_format +from typing import Dict + +datatype_mapping = { + "TYPE_INVALID": DataType.TYPE_INVALID, + "TYPE_BOOL": DataType.TYPE_BOOL, + "TYPE_UINT8": DataType.TYPE_UINT8, + "TYPE_UINT16": DataType.TYPE_UINT16, + "TYPE_UINT32": DataType.TYPE_UINT32, + "TYPE_UINT64": DataType.TYPE_UINT64, + "TYPE_INT8": DataType.TYPE_INT8, + "TYPE_INT16": DataType.TYPE_INT16, + "TYPE_INT32": DataType.TYPE_INT32, + "TYPE_INT64": DataType.TYPE_INT64, + "TYPE_FP16": DataType.TYPE_FP16, + "TYPE_FP32": DataType.TYPE_FP32, + "TYPE_FP64": DataType.TYPE_FP64, + "TYPE_STRING": DataType.TYPE_STRING, + "TYPE_BF16": DataType.TYPE_BF16 +} + +def generate_pbtxt(model_config: Dict, backend:str): + triton_model_config = ModelConfig() + for key, value in model_config.items(): + if hasattr(triton_model_config, key): + # only pick the standard triton model configuration items to the pbtxt + if key == "input" or key == "output": + for i in value: + entry = triton_model_config.input.add() if key == "input" else triton_model_config.output.add() + for k, v in i.items(): + if hasattr(entry, k): + if k == "data_type": + setattr(entry, k, datatype_mapping[v]) + elif k == "dims": + for dim in v: + entry.dims.append(dim) + elif k == "reshape": + if len(v['shape']) == 0: + # WAR: force generating reshape + entry.reshape.shape.append(-1) + else: + for s in v['shape']: + entry.reshape.shape.append(s) + else: + setattr(entry, k, v) + elif key == "parameters": + for k, p in value.items(): + triton_model_config.parameters[k].string_value = p + elif key == "instance_group": + for i in value: + entry = triton_model_config.instance_group.add() + for k, v in i.items(): + if k == "gpus": + entry.gpus.extend(v) + else: + setattr(entry, k, v) + elif key == "model_transaction_policy": + for k, v in value.items(): + setattr(triton_model_config.model_transaction_policy, k, v) + else: + setattr(triton_model_config, key, value) + # set the triton backend + setattr(triton_model_config, "backend", backend) + return text_format.MessageToString(triton_model_config, use_short_repeated_primitives=True).replace('shape: [-1]', 'shape: [ ]') \ No newline at end of file diff --git a/tools/inference_builder/builder/utils.py b/tools/inference_builder/builder/utils.py new file mode 100644 index 00000000..f0fd292a --- /dev/null +++ b/tools/inference_builder/builder/utils.py @@ -0,0 +1,172 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from pathlib import Path +import os +import shutil +from typing import Callable +import tarfile +import mimetypes +import base64 + +class PayloadBuilder: + """ + Builder class for constructing the payload with required and optional fields + + Example: + >>> builder = PayloadBuilder(files=["image1.jpg"], model="nvdino-v2") + >>> payload = builder.add_text(["cat,dog"]).build() + { + "input": ["data:image/jpeg;base64,/9j/4AAQSkZJRg..."], + "model": "nvidia/nvdino-v2", + "text": [["cat", "dog"]] + } + """ + @staticmethod + def prepare_image_inputs(files): + """ + Prepare base64 encoded image inputs for the payload + Args: + files: List of file paths, e.g. ["path/to/image1.jpg", "path/to/image2.png"] + Returns: + list: List of encoded image strings in format "data:mime_type;base64,..." + + Example: + >>> prepare_image_inputs(["image1.jpg", "image2.png"]) + [ + "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/4QBmRX...", + "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA7..." + ] + """ + encoded_inputs = [] + for file in files: + mime_type = mimetypes.guess_type(file)[0] + with open(file, "rb") as f: + b64_image = base64.b64encode(f.read()).decode() + encoded_inputs.append(f"data:{mime_type};base64,{b64_image}") + return encoded_inputs + + @staticmethod + def prepare_text_input(text): + """ + Prepare text input for the payload + Args: + text: List of comma-separated text strings, e.g. ["cat,dog", "person,car"] + Returns: + list: List of text arrays split by comma, or None if text is None + + Example: + >>> prepare_text_input(["cat,dog", "person,car"]) + [["cat", "dog"], ["person", "car"]] + + >>> prepare_text_input(None) + None + """ + return [t.split(",") for t in text] if text else None + + @staticmethod + def prepare_text_input_from_file(txt_file_path): + """ + Prepare text input from a .txt file + Args: + txt_file_path: Path to .txt file containing comma-separated text + Returns: + list: List of text arrays split by comma, or None if file not found/empty + + Example: + >>> prepare_text_input_from_file("labels.txt") + # For a file containing "cat,car,truck" + [["cat", "car", "truck"]] + + >>> prepare_text_input_from_file("nonexistent.txt") + None + """ + try: + with open(txt_file_path, 'r') as f: + content = f.read().strip() + return PayloadBuilder.prepare_text_input([content]) if content else None + except FileNotFoundError: + print(f"Warning: Text file not found: {txt_file_path}") + return None + except Exception as e: + print(f"Warning: Error reading text file: {e}") + return None + + @staticmethod + def prepare_model_name(model): + """ + Prepare model name for the payload + Args: + model: Base model name, e.g. "nvdino-v2" + Returns: + str: Full model name with nvidia prefix + + Example: + >>> prepare_model_name("nvdino-v2") + "nvidia/nvdino-v2" + """ + return f"nvidia/{model}" + + def __init__(self, files, model): + """Initialize with required fields""" + self.payload = { + "input": self.prepare_image_inputs(files), + "model": self.prepare_model_name(model) + } + + def add_text(self, text): + """Add optional text field if provided""" + if text: + text_input = self.prepare_text_input(text) + if text_input: + self.payload["text"] = text_input + return self + + def add_more_field(self, key, value): + """Add more field to the payload""" + if key is not None and value is not None: + self.payload[key] = value + return self + + def build(self): + """Return the constructed payload""" + return self.payload + +def get_resource_path(relative_path): + """ Get absolute path to resource, works for dev and for PyInstaller """ + base_path = Path(__file__).absolute().parent.parent + return str(Path(base_path, relative_path)) + +def copy_files(source_dir, destination_dir, filter: Callable[[str], bool]=None): + # Ensure destination directory exists + if not os.path.exists(destination_dir): + os.makedirs(destination_dir) + + # List all files in the source directory + files = os.listdir(source_dir) + + for file in files: + if filter and not filter(file): + continue + # Full path to the source file + full_file_name = os.path.join(source_dir, file) + + # Check if it's a file (not a directory) + if os.path.isfile(full_file_name): + shutil.copy(full_file_name, destination_dir) + +def create_tar_gz(output_filename, source_dir): + with tarfile.open(output_filename, "w:gz") as tar: + tar.add(source_dir, arcname=".") \ No newline at end of file diff --git a/tools/inference_builder/builder/validate.py b/tools/inference_builder/builder/validate.py new file mode 100644 index 00000000..d05819b7 --- /dev/null +++ b/tools/inference_builder/builder/validate.py @@ -0,0 +1,1012 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import json +import math +import os +import sys +import subprocess +import base64 +import importlib +from pathlib import Path +from typing import List, Dict, Union, Any +from jinja2 import Environment, FileSystemLoader +from utils import get_resource_path +import shutil +import yaml +from utils import PayloadBuilder + +# Common paths - single source of truth +OUT_DIR = ".tmp" +GENERATED_CLIENT_DIR = "generated_client" +TEST_CASES_FILE = "test_cases.yaml" + +# Setup paths and generate OpenAPI client +def check_client_exists() -> bool: + """Check if OpenAPI client exists and is importable. + + Returns: + bool: True if client exists and can be imported, False otherwise + """ + # Check if the module can be imported + try: + importlib.import_module('openapi_client') + print("Able to import OpenAPI client") + return True + except ImportError: + print("Not able to import OpenAPI client") + return False + +def setup_environment(generated_client_path: Path) -> bool: + """Setup environment for client import.""" + try: + # First check if client directory exists + if not generated_client_path.exists(): + print(f"Generated client directory not found at: {generated_client_path}") + return False + + # Add to PYTHONPATH if needed + if str(generated_client_path) not in sys.path: + sys.path.insert(0, str(generated_client_path)) # Insert at beginning of path + print(f"✓ Added to PYTHONPATH: {generated_client_path}") + + # Try importing to verify setup + importlib.import_module('openapi_client') + print("✓ Successfully imported OpenAPI client") + return True + + except ImportError as e: + print(f"Failed to import OpenAPI client: {e}") + print(f"Directory contents: {os.listdir(generated_client_path)}") + return False + except Exception as e: + print(f"Unexpected error setting up environment: {e}") + return False + + +def check_client_valid(client_dir: Path) -> bool: + """Check if existing OpenAPI client is valid. + + Args: + client_dir: Path to generated client directory + + Returns: + bool: True if client exists and is valid, False otherwise + """ + try: + # Check if directory exists and has key files + if not client_dir.exists(): + return False + + required_files = [ + "setup.py", + "openapi_client/__init__.py", + "openapi_client/api/nvidiametropolisinferenceapi_api.py", + "openapi_client/models/inference_request.py", + "openapi_client/models/inference_response.py" + ] + + for file in required_files: + if not (client_dir / file).exists(): + print(f"Missing required file: {file}") + return False + + # Try importing to validate + sys_path_modified = False + if str(client_dir) not in sys.path: + sys.path.insert(0, str(client_dir)) + sys_path_modified = True + + try: + import openapi_client + from openapi_client.models.inference_request import InferenceRequest + from openapi_client.models.inference_response import InferenceResponse + print(f"✓ Found valid existing client at: {client_dir}") + return True + except ImportError as e: + print(f"Client validation failed: {e}") + return False + finally: + if sys_path_modified: + sys.path.remove(str(client_dir)) + + except Exception as e: + print(f"Error checking client: {e}") + return False + +def generate_openapi_client(openapi_spec_path: Path, output_dir: Path, use_docker: bool = True) -> bool: + """Generate OpenAPI client using Docker or local OpenAPI Generator. + + Args: + openapi_spec_path: Path to OpenAPI specification file + output_dir: Target output directory for generated client + use_docker: Whether to use Docker for generation (default: True) + """ + try: + # Check if valid client already exists + if check_client_valid(output_dir): + print("Using existing OpenAPI client") + return True + + print("Generating new OpenAPI client...") + # Get absolute paths + abs_spec_path = openapi_spec_path.resolve() + abs_output_dir = output_dir.resolve() + + tmp_client = "tmp_generated_client" + tmp_client_path = abs_spec_path.parent / tmp_client + + if use_docker: + # Construct docker command + # docker run --rm -v "${PWD}:/local" openapitools/openapi-generator-cli generate \ + # -i /local/openapi.yaml \ + # -g python \ + # -o /local/generated_client + generator_cmd = [ + "docker", "run", "--rm", + "-v", f"{abs_spec_path.parent}:/local", + "--user", f"{os.getuid()}:{os.getgid()}", # Use current user's UID/GID + "openapitools/openapi-generator-cli", "generate", + "-i", f"/local/{abs_spec_path.name}", + "-g", "python", + "-o", f"/local/{tmp_client}" + ] + print(f"Generating OpenAPI client using Docker...") + else: + # Use local OpenAPI Generator + generator_cmd = [ + "openapi-generator-cli", "generate", + "-i", str(abs_spec_path), + "-g", "python", + "-o", str(tmp_client_path) + ] + print(f"Generating OpenAPI client using local OpenAPI Generator...") + print(f"Using OpenAPI spec: {abs_spec_path}") + print(f"Temporary directory: {tmp_client_path}") + print(f"Final output directory: {output_dir.resolve()}") + + result = subprocess.run(generator_cmd, + check=True, + capture_output=True, + text=True) + + if result.stdout: + print(result.stdout) + + # Move generated files to target directory + output_dir.mkdir(parents=True, exist_ok=True) + try: + # Use shutil.copytree to copy the contents + shutil.copytree(tmp_client_path, output_dir, dirs_exist_ok=True) + # Clean up temp directory + shutil.rmtree(tmp_client_path) + return True + except Exception as e: + print(f"Failed to move generated client: {e}") + return False + + return True + + except subprocess.CalledProcessError as e: + print(f"✗ Docker command failed: {e}") + if e.stdout: + print("stdout:", e.stdout) + if e.stderr: + print("stderr:", e.stderr) + return False + except Exception as e: + print(f"✗ Failed to generate client: {e}") + return False + finally: + # Ensure temp directory is cleaned up + if 'temp_client_dir' in locals() and tmp_client_path.exists(): + shutil.rmtree(tmp_client_path) + + + + +def prepare_image_input(image_path: Path) -> str: + """Prepare image for inference by converting to base64 format. + + Examples: + image_input = tester.prepare_image_input("path/to/image.jpg") + # image_input = "data:image/jpg;base64,/9j/4AAQSkZJRg..." + + Args: + image_path: Path to image file (jpg, jpeg, or png) + + Returns: + str: Base64 encoded image with data URI scheme + + Raises: + ValueError: If file doesn't exist or has unsupported format + IOError: If file can't be read + """ + if not os.path.exists(image_path): + raise ValueError(f"Image file not found: {image_path}") + + ext = os.path.splitext(image_path)[1][1:].lower() + if ext not in ['jpg', 'jpeg', 'png']: + raise ValueError(f"Unsupported image format: {ext}. Use jpg, jpeg or png") + + with open(image_path, "rb") as f: + image_data = f.read() + b64_image = base64.b64encode(image_data).decode() + return f"data:image/{ext};base64,{b64_image}" + + +def get_test_cases(validation_dir: Path) -> List[Dict[str, str]]: + """Discover test cases by scanning validation directory for images. + + Args: + validation_dir: Path to validation directory + + Returns: + List of test cases with image and expected response paths + """ + test_cases = [] + supported_formats = {'.jpg', '.jpeg', '.png'} + + # Find all image files + for idx, file_path in enumerate(validation_dir.iterdir()): + if file_path.suffix.lower() in supported_formats: + image_name = file_path.stem + expected_path = validation_dir / f"expected.{image_name}.json" + text_path = validation_dir / f"{image_name}.txt" + + if expected_path.exists(): + test_cases.append({ + "name": image_name + f"_{idx}", + "input": file_path.name, + "text": text_path.name if text_path.exists() else None, + "expected": expected_path.name, + }) + else: + print(f"Warning: Found image {file_path.name} but missing expected response: {expected_path.name}") + + print(f"Discovered {len(test_cases)} test cases:") + for test in test_cases: + print(f" • {test['name']}: {test['input']} → {test['expected']}") + + return test_cases + +def generate_default_test_cases(validation_dir: Path) -> List[Dict[str, str]]: + """Generate default test cases when no test cases are found in validation directory. + + Creates a test case with: + - A predefined base64 encoded image data (saved as a temporary PNG file) + - A predefined text "describe the picture" + - An empty expected result + + Args: + validation_dir: Path to validation directory + + Returns: + List of default test cases with predefined image and text + """ + # Predefined base64 encoded PNG image (1x1 transparent pixel) + # This is the same as used in lib/codec.py + predefined_image_data = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAIAAACQd1PeAAAAEElEQVR4nGK6HcwNCAAA//8DTgE8HuxwEQAAAABJRU5ErkJggg==" + + # Create the image file with predefined base64 data + image_path = validation_dir / "default_test.png" + if not image_path.exists(): + try: + # Decode the base64 data and write to file + image_data = base64.b64decode(predefined_image_data) + with open(image_path, "wb") as f: + f.write(image_data) + print(f"✓ Created default image file: {image_path}") + except Exception as e: + print(f"Warning: Failed to create default image file: {e}") + return [] + + # Create the expected result file (empty result) + expected_path = validation_dir / "expected.default_test.json" + if not expected_path.exists(): + with open(expected_path, "w") as f: + json.dump({}, f, indent=2) + print(f"✓ Created default expected result file: {expected_path}") + + # Create the text file + text_path = validation_dir / "default_test.txt" + if not text_path.exists(): + with open(text_path, "w") as f: + f.write("describe the picture") + print(f"✓ Created default text file: {text_path}") + + # Create the test case structure + test_cases = [{ + "name": "default_test_0", + "input": "default_test.png", + "text": "default_test.txt", + "expected": "expected.default_test.json" + }] + + print(f"Generated {len(test_cases)} default test cases:") + for test in test_cases: + print(f" • {test['name']}: {test['input']} → {test['expected']}") + + return test_cases + + +def get_request_template(client_dir: Path) -> Dict: + """Generate request template from OpenAPI client models. + + Args: + client_dir: Path to generated OpenAPI client directory + + Returns: + Dict containing request template + """ + try: + # Temporarily add client to path + if str(client_dir) not in sys.path: + sys.path.insert(0, str(client_dir)) + + # Import the request model + try: + from openapi_client.models.input_inner import InputInner + + # Create request using the model class + input_data = [InputInner("")] # Create Input instance first + except ImportError: + from openapi_client.models.inference_request_input_inner import InferenceRequestInputInner + input_data = [InferenceRequestInputInner("")] + from openapi_client.models.inference_request import InferenceRequest + request = InferenceRequest( + model='nvidia/nvdino-v2', # First allowed value from model_validate_enum + input=input_data + ) + + # Convert to dict for JSON serialization + template = request.to_dict() + print("Generated request template from OpenAPI client models") + return template + + except ImportError as e: + print(f"Failed to import OpenAPI client models: {e}") + raise + except Exception as e: + print(f"Failed to generate request template: {e}") + raise + finally: + # Clean up sys.path + if str(client_dir) in sys.path: + sys.path.remove(str(client_dir)) + + +def build_requests(validation_dir: Path, out_dir: Path, client_dir: Path, test_cases_abs_path: bool = False) -> bool: + """Build request payloads from template and config. + + Args: + validation_dir: Directory containing validation data + out_dir: Directory to write generated files + client_dir: Directory containing OpenAPI client + test_cases_abs_path: Whether to use absolute paths in test_cases.yaml (default: False) + """ + try: + print("\nBuilding request payloads...") + # Get request template from OpenAPI client models + template = get_request_template(client_dir) + + # Get test cases from directory scan + test_cases = get_test_cases(validation_dir) + if not test_cases: + test_cases = generate_default_test_cases(validation_dir) + + # Store request-response mapping + request_response_map = [] + + # Generate request for each test case + for test in test_cases: + request = template.copy() + image_path = validation_dir / test["input"] + request["input"] = PayloadBuilder.prepare_image_inputs([image_path]) + if test["text"]: + request["text"] = PayloadBuilder.prepare_text_input_from_file(validation_dir / test["text"]) + + # write request file + request_path = out_dir / f"request.{test['name']}.json" + with open(request_path, "w") as f: + json.dump(request, f, indent=2) + print(f"✓ Generated request for test '{test['name']}': {request_path}") + + expected_path = validation_dir / test["expected"] + + # Add to mapping with either absolute or relative paths + if test_cases_abs_path: + request_path_str = str(request_path.resolve()) + expected_path_str = str(expected_path.resolve()) + else: + # Make paths relative to validation directory + request_path_str = os.path.relpath(request_path, out_dir) + expected_path_str = os.path.relpath(expected_path, out_dir) + + # Add to mapping + request_response_map.append({ + "name": test["name"], + "request": request_path_str, + "expected": expected_path_str + }) + + # Write request-response mapping + test_cases_file_path = out_dir / TEST_CASES_FILE + with open(test_cases_file_path, "w") as f: + yaml.safe_dump(request_response_map, f, default_flow_style=False) + print(f"✓ Generated test cases file: {test_cases_file_path}") + print("✓ Successfully built all request payloads") + return True + except Exception as e: + print(f"✗ Failed to build requests: {e}") + return False + +def build_test_runner(out_dir: Path) -> bool: + """Generate test runner from template.""" + try: + print("\nBuilding test runner...") + + tpl_dir = get_resource_path("templates") + print(f"Using template directory: {tpl_dir}") + + jinja_env = Environment(loader=FileSystemLoader(tpl_dir)) + test_tpl = jinja_env.get_template("client/test_runner.jinja.py") + output_path = out_dir / "test_runner.py" + with open(output_path, 'w') as f: + validator_dir = Path(__file__).parent.resolve() + f.write(test_tpl.render(validator_dir=validator_dir)) + print(f"✓ Generated test runner: {output_path}") + return True + except Exception as e: + print(f"✗ Failed to build test runner: {e}") + return False + +def copy_test_runner_dep_tree(out_dir: Path) -> bool: + """Copy test runner dependency tree.""" + try: + print("\nCopying test runner dependency tree...") + # Copy all local dependencies + builder_dir = Path(__file__).parent + files_to_copy = { + builder_dir / "validate.py": out_dir / "validate.py", + builder_dir / "utils.py": out_dir / "utils.py", + # Add any other dependencies here + } + + for src, dest in files_to_copy.items(): + shutil.copy2(src, dest) + print(f"✓ Copied {src.name} to: {dest}") + return True + except Exception as e: + print(f"✗ Failed to copy test runner dependency tree: {e}") + return False + +def build_validation(openapi_spec_path: Path, validation_dir: Path, use_docker: bool = True, test_cases_abs_path: bool = False) -> bool: + """Generate validation components.""" + try: + print("\n=== Building Validator ===") + + # Create temp directory + out_dir = validation_dir / OUT_DIR + out_dir.mkdir(parents=True, exist_ok=True) + print(f"Created temporary directory: {out_dir}") + + # 1. Generate OpenAPI client + print("\n1. Generating OpenAPI client...") + client_dir = out_dir / GENERATED_CLIENT_DIR + if not generate_openapi_client(openapi_spec_path, client_dir, use_docker): + raise Exception("Failed to generate OpenAPI client") + + # 2. Build request payloads + print("\n2. Building request payloads...") + if not build_requests(validation_dir, out_dir, client_dir, test_cases_abs_path): + raise Exception("Failed to build requests") + + # 3. Generate test runner + print("\n3. Generating test runner...") + if not build_test_runner(out_dir): + raise Exception("Failed to build test runner") + + # 4. Copy test runner deps like validate.py to out_dir (.tmp) + print("\n4. Copying validate.py to out_dir...") + if not copy_test_runner_dep_tree(out_dir): + raise Exception("Failed to copy test runner dependency tree") + + print("\n✓ Successfully built all validation components") + return True + except Exception as e: + print(f"\n✗ Failed to build validation: {e}") + return False + + +class CvValidator: + """Class for validating CV inference results""" + + @staticmethod + def is_float_equal(a: float, b: float, tolerance: float = 1e-5) -> bool: + """Compare floats with relative and absolute tolerance, similar to numpy.isclose + + Uses both relative and absolute tolerance for robust comparison: + - Relative tolerance scales with the magnitude of the values + - Absolute tolerance handles cases where values are close to zero + - With large tolerance values, most comparisons will pass + + Formula: abs(a - b) <= (atol + rtol * max(abs(a), abs(b))) + + Args: + a: First float value + b: Second float value + tolerance: Relative tolerance parameter (default: 1e-5). + Also used as absolute tolerance scaled down by 1000x + + Returns: + bool: True if values are close within tolerance + + Examples: + # Basic usage + is_float_equal(1.0, 1.00001, 1e-4) # True + + # Scales with magnitude (relative tolerance) + is_float_equal(1000.0, 1000.01, 1e-4) # True + is_float_equal(0.001, 0.00100001, 1e-4) # True + + # Handles near-zero values (absolute tolerance) + is_float_equal(1e-10, 2e-10, 1e-5) # True + + # Large tolerance passes most cases + is_float_equal(1.0, 2.0, 1.0) # True + """ + # Use tolerance as relative tolerance (rtol) + rtol = tolerance + # Use much smaller absolute tolerance to handle near-zero values + atol = tolerance * 1e-3 + + return abs(a - b) <= (atol + rtol * max(abs(a), abs(b))) + + @staticmethod + def compute_iou(box1: List, box2: List) -> float: + """Compute Intersection over Union between two bounding boxes. + + Args: + box1: First bounding box as [x1, y1, x2, y2] + box2: Second bounding box as [x1, y1, x2, y2] + + Returns: + float: IoU value in [0.0, 1.0] + """ + x1 = max(box1[0], box2[0]) + y1 = max(box1[1], box2[1]) + x2 = min(box1[2], box2[2]) + y2 = min(box1[3], box2[3]) + + intersection = max(0, x2 - x1) * max(0, y2 - y1) + + area1 = (box1[2] - box1[0]) * (box1[3] - box1[1]) + area2 = (box2[2] - box2[0]) * (box2[3] - box2[1]) + union = area1 + area2 - intersection + + if union == 0: + return 0.0 + return intersection / union + + @staticmethod + def tolerance_to_iou_params(tolerance: float) -> tuple: + """Project numeric tolerance to IoU matching parameters. + + Uses two mathematical projections: + + 1. iou_threshold = 1 / (1 + sqrt(tolerance)) + Inverse-sqrt decay — maps tolerance to the (0, 1] range. + The square root softens the curve so that moderate tolerances + still produce workable IoU thresholds for small objects, where + even 1-pixel shifts cause large IoU drops. + At tolerance=1.0 this yields 0.5, the standard PASCAL VOC threshold. + + 2. max_unmatched = floor(log2(1 + tolerance)) + Logarithmic growth — allows more unmatched detections as tolerance + increases, but grows slowly to prevent over-permissiveness. + + Reference table: + tolerance iou_threshold max_unmatched + 0 1.000 0 + 1e-5 ≈1.000 0 + 0.01 ≈0.909 0 + 0.1 ≈0.760 0 + 0.5 ≈0.586 0 + 1.0 0.500 1 + 3.0 ≈0.366 2 + 7.0 ≈0.274 3 + + Args: + tolerance: The numeric float-comparison tolerance + + Returns: + Tuple of (iou_threshold, max_unmatched) + """ + iou_threshold = 1.0 / (1.0 + math.sqrt(tolerance)) + max_unmatched = int(math.floor(math.log2(1.0 + tolerance))) if tolerance > 0 else 0 + return iou_threshold, max_unmatched + + @classmethod + def compare_lists(cls, list1: List[Any], list2: List[Any], tolerance: float) -> bool: + """Compare two lists with tolerance for float values. + For nested lists (list of lists): + - Outer list order doesn't matter (will be sorted) + - Inner list order DOES matter (will not be sorted) + For regular lists (non-nested): + - Order doesn't matter (will be sorted) + + Examples: + # These are equal (outer order doesn't matter): + list1 = [[1, 2], [3, 4]] + list2 = [[3, 4], [1, 2]] # True + + # These are NOT equal (inner order matters): + list1 = [[1, 2], [3, 4]] + list2 = [[2, 1], [3, 4]] # False + + # These are equal (floats within tolerance): + list1 = [[1.001, 2.002], [3.003, 4.004]] + list2 = [[3.004, 4.003], [1.002, 2.001]] # True + + # Regular lists (order doesn't matter): + list1 = [1.0, 2.0, 3.0] + list2 = [3.0, 1.0, 2.0] # True + + # Regular lists with tolerance: + list1 = [1.001, 2.002] + list2 = [2.001, 1.000] # True (within tolerance) + + Args: + list1: First list to compare + list2: Second list to compare + tolerance: Float tolerance for comparing float values + + Returns: + bool: True if lists are equal within tolerance, False otherwise + """ + if len(list1) != len(list2): + return False + + # If lists contain more lists/tuples, keep inner items as tuples but don't sort them + if all(isinstance(x, (list, tuple)) for x in list1 + list2): + tuples1 = [tuple(x) for x in list1] + tuples2 = [tuple(x) for x in list2] + # Sort the outer list only + sorted1 = sorted(tuples1) + sorted2 = sorted(tuples2) + + # Use tolerance-aware matching instead of simple pairing + # This handles cases where sorting order might differ slightly + # due to float precision + used = [False] * len(sorted2) + for item1 in sorted1: + matched = False + for i, item2 in enumerate(sorted2): + if used[i]: + continue + # Check if all elements match within tolerance + if len(item1) == len(item2): + all_match = all( + cls.is_float_equal(a, b, tolerance) + if isinstance(a, (float, int)) and + isinstance(b, (float, int)) + else a == b + for a, b in zip(item1, item2) + ) + if all_match: + used[i] = True + matched = True + break + if not matched: + return False + return True + + # For lists of floats, compare with tolerance + if all(isinstance(x, (float, int)) for x in list1 + list2): + sorted1 = sorted(list1) + sorted2 = sorted(list2) + return all( + cls.is_float_equal(float(a), float(b), tolerance) + for a, b in zip(sorted1, sorted2) + ) + + # For other types, use regular comparison + return sorted(list1) == sorted(list2) + + @classmethod + def match_detections(cls, actual_bboxes: List[List], expected_bboxes: List[List], + iou_threshold: float = 0.5, + actual_labels: List = None, + expected_labels: List = None) -> tuple: + """Match actual detections to expected detections using greedy IoU matching. + + Pairs detections by highest IoU first. A pair is only accepted if + IoU >= iou_threshold. When labels are provided, pairs must also + have matching labels — this prevents cross-matching at locations + where multiple classes are detected on the same bbox. + + Args: + actual_bboxes: List of actual bounding boxes [x1, y1, x2, y2] + expected_bboxes: List of expected bounding boxes [x1, y1, x2, y2] + iou_threshold: Minimum IoU to consider a valid match + actual_labels: Optional per-detection labels for actual results + expected_labels: Optional per-detection labels for expected results + + Returns: + Tuple of (matches, unmatched_actual, unmatched_expected) where + matches is a list of (actual_idx, expected_idx, iou) tuples + """ + use_labels = actual_labels is not None and expected_labels is not None + + candidates = [] + for i, a_box in enumerate(actual_bboxes): + for j, e_box in enumerate(expected_bboxes): + if use_labels and actual_labels[i] != expected_labels[j]: + continue + iou = cls.compute_iou(a_box, e_box) + if iou >= iou_threshold: + candidates.append((iou, i, j)) + + candidates.sort(reverse=True) + + matched_actual = set() + matched_expected = set() + matches = [] + + for iou, i, j in candidates: + if i not in matched_actual and j not in matched_expected: + matches.append((i, j, iou)) + matched_actual.add(i) + matched_expected.add(j) + + unmatched_actual = set(range(len(actual_bboxes))) - matched_actual + unmatched_expected = set(range(len(expected_bboxes))) - matched_expected + + return matches, unmatched_actual, unmatched_expected + + @classmethod + def _compare_detections_iou(cls, actual: Dict, expected: Dict, tolerance: float, + iou_threshold: float, max_unmatched: int) -> bool: + """Compare detection results using IoU-based matching. + + Matches detections holistically — each detection's bbox, label, and + probability are validated together as a unit. + + Args: + actual: Actual inference result dict (must contain 'bboxes') + expected: Expected inference result dict (must contain 'bboxes') + tolerance: Float tolerance for probability comparison + iou_threshold: Minimum IoU for a bbox match + max_unmatched: Max total unmatched detections allowed (actual + expected) + + Returns: + bool: True if detection results match within constraints + """ + per_detection_keys = {'bboxes', 'probs', 'labels', 'masks', 'objects'} + + for key in actual: + if key in per_detection_keys: + continue + actual_value = actual[key] + expected_value = expected[key] + + if key == 'shape': + if actual_value != expected_value: + print(f" ✗ {key} mismatch: {actual_value} vs {expected_value}") + return False + print(f" ✓ {key} passed") + continue + + if isinstance(actual_value, list): + if not cls.compare_lists(actual_value, expected_value, tolerance): + print(f" ✗ {key} mismatch") + return False + print(f" ✓ {key} passed") + continue + + if actual_value != expected_value: + print(f" ✗ {key} mismatch: {actual_value} vs {expected_value}") + return False + + actual_labels = actual.get('labels') + expected_labels = expected.get('labels') + matches, unmatched_actual, unmatched_expected = cls.match_detections( + actual['bboxes'], expected['bboxes'], iou_threshold, + actual_labels, expected_labels + ) + + total_unmatched = len(unmatched_actual) + len(unmatched_expected) + if total_unmatched > max_unmatched: + print(f" ✗ Too many unmatched detections: " + f"{len(unmatched_actual)} extra in actual + " + f"{len(unmatched_expected)} missing from expected " + f"= {total_unmatched} (max allowed: {max_unmatched})") + for idx in sorted(unmatched_actual): + prob = actual['probs'][idx] if 'probs' in actual else 'N/A' + label = actual['labels'][idx] if 'labels' in actual else 'N/A' + print(f" Extra actual[{idx}]: bbox={actual['bboxes'][idx]}, " + f"prob={prob}, label={label}") + for idx in sorted(unmatched_expected): + prob = expected['probs'][idx] if 'probs' in expected else 'N/A' + label = expected['labels'][idx] if 'labels' in expected else 'N/A' + print(f" Missing expected[{idx}]: bbox={expected['bboxes'][idx]}, " + f"prob={prob}, label={label}") + return False + + if unmatched_actual or unmatched_expected: + print(f" ⚠ Unmatched detections within tolerance: " + f"{len(unmatched_actual)} extra in actual, " + f"{len(unmatched_expected)} missing from expected") + + for actual_idx, expected_idx, iou in matches: + if 'probs' in actual and 'probs' in expected: + a_prob = actual['probs'][actual_idx] + e_prob = expected['probs'][expected_idx] + if isinstance(a_prob, (int, float)) and isinstance(e_prob, (int, float)): + if not cls.is_float_equal(float(a_prob), float(e_prob), tolerance): + print(f" ✗ prob mismatch at IoU={iou:.3f}: " + f"actual[{actual_idx}]={a_prob} vs expected[{expected_idx}]={e_prob}") + return False + + if 'labels' in actual and 'labels' in expected: + a_label = actual['labels'][actual_idx] + e_label = expected['labels'][expected_idx] + if a_label != e_label: + print(f" ✗ label mismatch at IoU={iou:.3f}: " + f"actual[{actual_idx}]={a_label} vs expected[{expected_idx}]={e_label}") + return False + + if 'masks' in actual and 'masks' in expected: + a_masks = actual['masks'] + e_masks = expected['masks'] + if a_masks and e_masks and \ + len(a_masks) > actual_idx and len(e_masks) > expected_idx: + a_mask = a_masks[actual_idx] + e_mask = e_masks[expected_idx] + if isinstance(a_mask, list) and isinstance(e_mask, list): + if not cls.compare_lists(a_mask, e_mask, tolerance): + print(f" ✗ mask mismatch at IoU={iou:.3f}: " + f"actual[{actual_idx}] vs expected[{expected_idx}]") + return False + + print(f" ✓ detections passed: {len(matches)} matched by IoU " + f"(iou_threshold={iou_threshold}, prob_tolerance={tolerance}, " + f"unmatched={total_unmatched}/{max_unmatched})") + return True + + @classmethod + def compare_inference_result(cls, actual: Dict, expected: Dict, tolerance: float, + iou_threshold: float = None, + max_unmatched: int = None) -> bool: + """Compare inference result dictionaries with special handling for different field types. + + When bboxes are present, uses IoU-based matching to pair detections + holistically (bbox + label + prob as a unit). Otherwise falls back + to independent list comparison. + + If iou_threshold or max_unmatched are not provided, they are + automatically derived from tolerance via tolerance_to_iou_params(). + + Args: + actual: Dictionary containing actual inference results + expected: Dictionary containing expected inference results + tolerance: Float tolerance for comparing numeric values + iou_threshold: Minimum IoU to consider a bbox match (default: derived from tolerance) + max_unmatched: Max allowed unmatched detections (default: derived from tolerance) + + Returns: + bool: True if dictionaries match within tolerance + """ + actual.pop('timestamp', None) + expected.pop('timestamp', None) + + if actual.keys() != expected.keys(): + return False + + if 'bboxes' in actual and actual['bboxes'] and expected.get('bboxes'): + if iou_threshold is None or max_unmatched is None: + derived_iou, derived_max = cls.tolerance_to_iou_params(tolerance) + if iou_threshold is None: + iou_threshold = derived_iou + if max_unmatched is None: + max_unmatched = derived_max + print(f" IoU params (from tolerance={tolerance}): " + f"iou_threshold={iou_threshold:.4f}, max_unmatched={max_unmatched}") + return cls._compare_detections_iou( + actual, expected, tolerance, iou_threshold, max_unmatched + ) + + for key, actual_value in actual.items(): + expected_value = expected[key] + + if key == 'shape': + if actual_value != expected_value: + return False + continue + + if isinstance(actual_value, list): + if not cls.compare_lists(actual_value, expected_value, tolerance): + return False + else: + print(f" ✓ {key} passed") + continue + + if actual_value != expected_value: + return False + + return True + + @classmethod + def compare_responses(cls, actual: Dict, expected: Dict, tolerance: float = 1e-5, + iou_threshold: float = None, max_unmatched: int = None) -> bool: + """Compare actual and expected inference API responses. + + If iou_threshold or max_unmatched are not provided, they are + automatically derived from tolerance via tolerance_to_iou_params(). + + Args: + actual: Dictionary containing actual API response + expected: Dictionary containing expected API response + tolerance: Float tolerance for comparing numeric values + iou_threshold: Minimum IoU for bbox matching (default: derived from tolerance) + max_unmatched: Max allowed unmatched detections (default: derived from tolerance) + + Returns: + bool: True if responses match within tolerance + """ + if actual.keys() != expected.keys(): + return False + + for key, value in actual.items(): + if key == 'data': + if len(actual['data']) != len(expected['data']): + return False + for actual_item, expected_item in zip(actual['data'], expected['data']): + if not cls.compare_inference_result(actual_item, expected_item, tolerance, + iou_threshold, max_unmatched): + return False + else: + if actual[key] != expected[key]: + return False + return True + + + +def main(): + parser = argparse.ArgumentParser(description='Build validator components') + parser.add_argument('--api-spec', + type=str, + required=True, + help='Path to OpenAPI specification file') + parser.add_argument('--base-dir', + type=str, + default='.', + help='Base directory for validation') + + args = parser.parse_args() + + with open(args.api_spec) as f: + api_schema = f.read() + + if build_validation(api_schema, Path(args.base_dir)): + print("✓ Successfully built validation") + else: + print("✗ Failed to build validation") + sys.exit(1) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/tools/inference_builder/cursor-mcp-config.json b/tools/inference_builder/cursor-mcp-config.json new file mode 100644 index 00000000..8dc6aaca --- /dev/null +++ b/tools/inference_builder/cursor-mcp-config.json @@ -0,0 +1,13 @@ +{ + "mcpServers": { + "deepstream-inference-builder": { + "command": ".venv/bin/python", + "args": ["mcp/mcp_server.py"], + "cwd": ".", + "env": { + "PYTHONPATH": "." + } + } + } +} + diff --git a/tools/inference_builder/doc/architecture.md b/tools/inference_builder/doc/architecture.md new file mode 100644 index 00000000..8cc994c0 --- /dev/null +++ b/tools/inference_builder/doc/architecture.md @@ -0,0 +1,38 @@ +# Inference Builder Architecture + +## Overview + +Inference Builder generates the Python package that includes both the inference logic and server integration. The package is constructed from a standardized inference flow combined with a variety of well tested source code templates—including server templates and inference templates. + +The generated code follows the architecture illustrated in the diagram below. + +![Architecture](assets/architecture.png) + +## Inference Flow + +The inference flow is standardized by the base class `InferenceBase`, which supports inference through multiple models. Each model is managed by a `ModelOperator` instance, and is connected with one another through data flows. Each data flow is managed by various implementations of `DataFlow`, handling various types of data, such as images, videos, speech, etc. +The inference flow is standardized by the base class `InferenceBase`, which supports execution of inference across multiple models. Each model is managed by a dedicated `ModelOperator` instance, and models are connected through well-defined data flows. The output data from one model is passed to other models through data flows, using Python dictionaries that map names to tensors. This allows each model to receive its required inputs in a structured and consistent format. + +## Inference Backend + +The inference backend can vary depending on the model type, user requirements, and hardware platform. To support a wide range of SDKs and frameworks, Inference Builder defines a standard interface: 'ModelBackend'. This interface allows the ModelOperator to interact with models regardless of their underlying implementation. + +## API Server + +The API server handles incoming requests from end users and returns responses. It supports communication protocols such as HTTP and gRPC, and conforms to standards like OpenAPI specifications or Protocol Buffers (protobuf) definitions. + +Multiple server implementations are supported, provided they comply with the API specification and interact with the inference pipeline through a defined abstract interface. + +Additionally, Inference Builder supports a server type called `serverless`, which enables users to run inference as a standalone command-line interface without deploying a server. + +The interactions among the components described above are illustrated in the diagram below: + +![Block Diagram](assets/block_digram.png) + +### Integration with Customized Code + +Custom logic can be integrated into the inference flow through the Processor Interface. In the inference configuration file, multiple named pre-processors and post-processors can be assigned to each `ModelOperator` instance. Corresponding Python code snippets implementing these processors must be provided prior to code generation. + +When custom processors are configured, the `ModelOperator` sequentially passes named tensors from the input data flow through the assigned pre-processors before invoking the model backend. Similarly, after receiving output tensors from the backend, the `ModelOperator` passes them through the configured post-processors in sequence. + +Users can define a processor in the configuration file in a way similar to declaring a function interface—by specifying input names and return value names in order. 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GB>6vu=#b0+ literal 0 HcmV?d00001 diff --git a/tools/inference_builder/doc/platform-guide.md b/tools/inference_builder/doc/platform-guide.md new file mode 100644 index 00000000..0c1c0ec4 --- /dev/null +++ b/tools/inference_builder/doc/platform-guide.md @@ -0,0 +1,99 @@ +# Platform Guide: Choosing the Right Dockerfile and Base Image + +Use this guide to select the correct Dockerfile template and DeepStream base image +for your target hardware **before** writing any Dockerfile or pipeline config. + +--- + +## Quick Selection Table + +| Platform | Architecture | Example Hardware | Dockerfile Template | DeepStream Base Image | +|---|---|---|---|---| +| **x86_64 datacenter** | x86_64 | A100, H100, B200, RTX | `samples://dockerfile/ds_app/Dockerfile` | `nvcr.io/nvidia/deepstream:9.0-triton-multiarch` | +| **Jetson / Tegra** | aarch64 | Orin, AGX Orin, Thor | `samples://dockerfile/ds_app/Dockerfile.tegra` | `nvcr.io/nvidia/deepstream:9.0-triton-multiarch` | +| **arm-sbsa server** | aarch64 | GB10, GB300, DGX Spark | `samples://dockerfile/ds_app/Dockerfile.dgxspark` | `nvcr.io/nvidia/deepstream:9.0-triton-sbsa-dgx-spark` | + +--- + +## How to Identify Your Platform + +### Step 1 — Check architecture +```bash +uname -m +``` +- `x86_64` → use the **x86_64 datacenter** Dockerfile +- `aarch64` → continue to Step 2 + +### Step 2 — Distinguish Tegra (Jetson) from arm-sbsa (server) +```bash +# Tegra devices expose a device-tree model string: +cat /proc/device-tree/model 2>/dev/null +# e.g. "NVIDIA Jetson AGX Orin" → Tegra + +# Server ARM64 (GB10, GB300, DGX Spark) report GPU via nvidia-smi: +nvidia-smi -L +# e.g. "GPU 0: NVIDIA GB10" → arm-sbsa server +``` + +If `/proc/device-tree/model` contains "Jetson" → use **Dockerfile.tegra**. +If `nvidia-smi` reports GB10 / GB300 / GH200 / Grace and no Jetson device-tree → use **Dockerfile.dgxspark**. + +--- + +## Key Differences by Platform + +### x86_64 (`Dockerfile`) +- PyTorch installed via pip from `https://download.pytorch.org/whl/cu130` +- TRT OSS GPU archs: `80;86;90;100` (Ampere, Ada, Hopper, Blackwell-x86) +- CUDA headers at `/usr/local/cuda-/include` + +### Jetson / Tegra (`Dockerfile.tegra`) +- PyTorch **copied** from `nvcr.io/nvidia/pytorch:25.08-py3` (no pip wheel for Tegra) +- TRT OSS GPU archs: `110;120;121` (Ampere/Orin, Blackwell/Thor) +- Tegra BSP libraries (`libnvbufsurface.so`, etc.) **mounted at runtime** by the + NVIDIA Container Toolkit — container will fail without `--runtime nvidia` on + actual Jetson hardware +- Base image: Tegra multiarch variant (`-ma` suffix) + +### arm-sbsa server (`Dockerfile.dgxspark`) +- PyTorch **copied** from `nvcr.io/nvidia/pytorch:25.08-py3` (same as Tegra) +- TRT OSS GPU archs: `110;120;121` (same as Tegra) +- CUDA headers present in the arm-sbsa base image (no Tegra BSP dependency) +- Base image: `arm-sbsa-spark` variant — **not** the `-ma` Tegra image +- Requires `apt-get install cmake libyaml-cpp-dev pkg-config` before TRT OSS build + +--- + +## Common Mistakes to Avoid + +| Mistake | Symptom | Fix | +|---|---|---| +| Using Tegra base image on GB10/GB300 | `libnvbufsurface.so.1.0.0: cannot open shared object file` | Switch to `Dockerfile.dgxspark` and the `arm-sbsa-spark` base image | +| Installing PyTorch via pip on Tegra/arm-sbsa | No compatible wheel found, or wrong CUDA variant | Copy torch from `nvcr.io/nvidia/pytorch:25.08-py3` | +| Missing `cmake` before TRT OSS build | `cmake: command not found` | Add `apt-get install cmake` (or skip TRT OSS build if not needed) | +| Using wrong GPU arch flags | TRT plugins fail to load or slow JIT compilation | Match `-DGPU_ARCHS` to the GPU table above | +| Tegra container without `--runtime nvidia` | Missing Tegra BSP `.so` files at runtime | Run with `--runtime nvidia` via NVIDIA Container Toolkit on Jetson | + +--- + +## TAO Post-Processor Library (`libnvds_infercustomparser_tao.so`) + +Required for TAO-trained detection models (e.g. PeopleNet Transformer) that use +`NvDsInferParseCustomDDETRTAO`. It is **not** bundled in any base image and must +be built from source: + +```bash +# Inside the container (or in the Dockerfile RUN step): +git clone https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git +cd deepstream_tao_apps/post_processor +CUDA_VER=. make DS_SRC_PATH=/opt/nvidia/deepstream/deepstream +cp libnvds_infercustomparser_tao.so /opt/nvidia/deepstream/deepstream/lib/ +``` + +CUDA version by platform: +- x86_64 datacenter: `CUDA_VER=13.1` +- Jetson / arm-sbsa server: `CUDA_VER=13.0` + +On arm-sbsa (GB10/GB300), if CUDA headers are not in the container, mount them +from the host at `/usr/local/cuda-13.0` or pre-build the `.so` on the host and +`COPY` it into the Dockerfile. diff --git a/tools/inference_builder/doc/usage.md b/tools/inference_builder/doc/usage.md new file mode 100644 index 00000000..48253ab8 --- /dev/null +++ b/tools/inference_builder/doc/usage.md @@ -0,0 +1,204 @@ +# Inference Builder Usage + +## Command Line Arguments + +For generating the inference code with the corresponding server implementation, you can run the following command: + +```bash +python builder/main.py -h +usage: Inference Builder [-h] [--server-type [{triton,fastapi,serverless}]] [-o [OUTPUT_DIR]] [-a [API_SPEC]] [-c [CUSTOM_MODULE ...]] [-x] [-t] [-v VALIDATION_DIR] [--no-docker] [--test-cases-abs-path] config + +positional arguments: + config Path the the configuration + +options: + -h, --help show this help message and exit + --server-type [{triton,fastapi,serverless}] + Choose the server type + -o [OUTPUT_DIR], --output-dir [OUTPUT_DIR] + Output directory + -a [API_SPEC], --api-spec [API_SPEC] + File for OpenAPI specification + -c [CUSTOM_MODULE ...], --custom-module [CUSTOM_MODULE ...] + Custom python modules + -x, --exclude-lib Do not include common lib to the generated code. + -t, --tar-output Zip the output to a single file + -v, --version show program's version number and exit + --validation-dir VALIDATION_DIR + valid validation directory path to build validator + --no-docker Use local OpenAPI Generator instead of Docker for OpenAPI client generation + --test-cases-abs-path + Use absolute paths in generated test_cases.yaml +``` +## Configuration File + +Before using the tool, you must prepare a YAML configuration file to define the inference flow. If server integration is required, you also need to provide an OpenAPI specification that defines the server and update the configuration with server templates based on the OpenAPI specification. + +For comprehensive documentation on configuration schemas, including validation, IDE support, and detailed backend specifications, see [schemas/README.md](../schemas/README.md). + +The configuration file is a YAML file that defines the inference flow and server implementation. It contains the following sections: + +- **name**: The name of the inference pipeline, which will be also used as the name of the folder for saving the generated inference code or the name of the tarball if '-t' is specified. +- **model_repo**: Specifies the path to the model repository from which the inference pipeline searches and loads the required model files. Each model has a separate folder for their files. +- **models**: List of model definitions. A inference flow can incorporate multiple models, each might be implemented with different backends to achieve optimal performance. +- **input** (optional): Defines the top-level inputs of the inference flow. This field is required only when the pipeline includes multiple models or when at least one input is a custom type—such as in cases that require standard preprocessing like video decoding before the input data is passed to the model. +- **output** (optional): Defines the top-level outputs of the inference flow. This field is required only when the pipeline includes multiple models or when at least one output is custom type—such as in cases that require standard preprocessing like video encoding before the output data is passed out. +- **server** (optional): Defines the endpoint templates for the server implementation. This field is not required if the server type is set to "serverless". +- **routes** (optional): Defines the routing rules for the inference flow when multiple models are involved. +- **postprocessors** (optional): Defines the top-level post-processors for the inference flow. This field is required only when the pipeline includes multiple models and the output of these models need to be consolidated. + +A configuration file can be as simple as the following example: + +```yaml + +name: "detector" +model_repo: "/workspace/models" + +models: +- name: rtdetr + backend: deepstream/nvinfer + max_batch_size: 4 + input: + - name: media_url + data_type: TYPE_CUSTOM_DS_MEDIA_URL + dims: [ -1 ] + - name: mime + data_type: TYPE_CUSTOM_DS_MIME + dims: [ -1 ] + output: + - name: output + data_type: TYPE_CUSTOM_DS_METADATA + dims: [ -1 ] + parameters: + infer_config_path: + - nvdsinfer_config.yaml +``` + +With the above configuration file, you can generate inference code for the RT-DETR object detection model using the DeepStream backend, which takes image or video url as input and produces bounding boxes as output. (Step by step guide can be found in the [detection README](./builder/samples/ds_app/detection/README.md)) + +Breakdown of the configuration: + +- The inference package is named `detector`, and the model search path is set to `/workspace/models`. The pipeline uses one model and will look for the model file (in this case, an ONNX file and a yaml config for Deepstream) under the `rtdetr` directory under `/workspace/models`, as specified by the model name and model_repo. +- Inference is performed using nvinfer from the NVIDIA DeepStream SDK. A corresponding configuration file named `nvdsinfer_config.yaml` must be present under `/workspace/models/rtdetr`. +- The pipeline supports two inputs: + - media_url: the path or URL to the input media. + - mime: the media type (e.g., "video/mp4" or "image/jpeg"). +- The pipeline supports batch processing with up to 4 media items at a time as indicated by max_batch_size, and the number can be adjusted to suit the capabilities of the hardware platform and the requirements of the model. +- The output is a custom DeepStream metadata dictionary (`TYPE_CUSTOM_DS_METADATA`), which carries per-frame information such as detected bounding boxes, confidence scores, labels, segmentation maps, tracking IDs, and timestamps. The structure is flexible and returned as a dictionary with fields like `shape`, `bboxes`, `probs`, `labels`, `seg_maps`, `objects`, and `timestamp`. For complete structure details, see the [DeepStream-Specific Types section in schemas/README.md](../schemas/README.md#type_custom_ds_metadata). + +### Input and Output Definition + +Input and output definitions are required at the model level, and in some cases also at the top level of the pipeline. Each definition specifies the name, data type, dimensions, and optional flags for tensors flowing through the pipeline. + +For complete documentation on input/output specifications, supported data types (including custom types like TYPE_CUSTOM_IMAGE_BASE64, TYPE_CUSTOM_LONG_VIDEO_ASSETS, etc.), and field descriptions, see the [Data Types section in schemas/README.md](../schemas/README.md#data-types). + +### Model Definition + +The model definition specifies the inference backend, input/output tensors, batch size, and optional preprocessors/postprocessors. The definition is derived from the Triton model configuration and extended to support various backends including DeepStream, TensorRT, TensorRT-LLM, vLLM, and others. + +For complete documentation on model specifications, including: +- Supported backend types and their hierarchical structure (e.g., `deepstream/nvinfer`, `triton/tensorrt`, `tensorrtllm/pytorch`, `vllm`) +- Backend-specific parameters and configuration options +- Input/output tensor definitions +- Preprocessor and postprocessor specifications + +See the [Backend-Specific Schemas section in schemas/README.md](../schemas/README.md#backend-specific-schemas). + + +### Custom Preprocessors and Postprocessors + +Custom preprocessors and postprocessors integrate user-defined Python code into the inference flow, offering a new paradigm for programming with neural network models. + +Both custom preprocessors and postprocessors are defined using the following specification: +- **kind**: The kind of the processor: "custom" or "auto". Only "custom" processors are supported in the current release. +- **name**: The name of the processor. It must match the "name" defined in the user-implemented processor class. +- **input**: Specifies the names of the processor's inputs in order. This defines how tensors from the inference flow are passed to the processor. +- **output**: Specifies the names of the processor's outputs in order. This defines how the inference flow extracts the tensors from the processor. +- **config**: Defines the processor's configuration as a dictionary. The contents are implementation-specific. + +For a list of common preprocessors (image normalizers, tokenizers, VLM loaders) and postprocessors (masking, embedding processors) with their configurations, see the [Preprocessors and Postprocessors section in schemas/README.md](../schemas/README.md#preprocessors-and-postprocessors). + +#### Custom Preprocessor/Postprocessor Implementation Requirements + +When implementing a custom preprocessor or postprocessor, the class must adhere to the following specification: +- A class variable named `"name"` must be defined to uniquely identify the processor. +- An `__init__` method must be implemented to accept a single argument: a dictionary containing the processor's configuration (`config`). +- A `__call__` method must be defined to accept multiple positional arguments. The number and order of these arguments must match the input definition of the processor in the configuration file. +- The `__call__` method must return a tuple of tensors, in the order specified by the output definition of the processor in the configuration file. +- Input and output data are expected to be either NumPy arrays or PyTorch tensors, unless a custom data type is explicitly specified. + +### Server Definition + +The server definition is required at the top level unless the server type is set to "serverless". It specifies the server implementation along with the corresponding request and response templates. Both templates must be written in Jinja2, and are used to: +- Extract the necessary inputs from the server request. +- Format the inference outputs into the desired server response. + +The "responders" section allows users to map server implementations to operations defined in the OpenAPI specification. Each responder corresponds to a specific endpoint or operation. +For detailed information about responders, operations, and examples, see the [Server Configuration section in schemas/README.md](../schemas/README.md#server-configuration). + +### Routing + +The routes section is optional and typically used for multi-model inference flows. It defines custom routing rules that control how data flows between different models in the pipeline. + +The routes definition is a map where each entry specifies a connection between a source and destination: +- Format: `: : :` +- If the model name is omitted (e.g., `:`), the tensors are at the top level of the inference flow. +- Tensor lists are optional and specified as JSON arrays (e.g., `["tensor1", "tensor2"]`). + +For detailed examples and explanation of routing logic, see the [Routes section in schemas/README.md](../schemas/README.md#routes). + +## Runtime Environment Variables + +The following environment variables can be used to control runtime behavior of the generated inference pipelines. + +### Logging & Debugging + +| Variable | Default | Description | +|----------|---------|-------------| +| `DEBUG` | `0` | Enable debug mode (sets log level to DEBUG when non-zero) | +| `LOG_LEVEL` | `INFO` | Log verbosity level: `DEBUG`, `INFO`, `WARNING`, `ERROR`, `CRITICAL` | + +### Inference Performance + +| Variable | Default | Description | +|----------|---------|-------------| +| `N_CODEC_INSTANCES` | `1` | Number of video codec instances for parallel decoding. Increase for higher throughput when processing multiple video streams. | +| `MAX_BATCH_SIZE` | `16` | Maximum batch size for live stream frame collection. Controls memory usage vs throughput trade-off. | + +### Server Configuration + +| Variable | Default | Description | +|----------|---------|-------------| +| `HTTP_PORT` | `8000` | HTTP port for FastAPI server when using `fastapi` server type. | + +### Testing & Validation + +| Variable | Default | Description | +|----------|---------|-------------| +| `TEST_HOST` | `http://127.0.0.1:8800` | Target host URL for the test runner to connect to. | +| `TEST_TOLERANCE` | `1e-5` | Numerical tolerance for floating-point comparisons in test assertions. | +| `ERROR_EXPORT_PATH` | `/tmp/inference_errors.json` | File path to export collected errors for test validation and debugging. | + +### Dynamic Configuration Override + +The generated configuration module (`config.py`) supports dynamic environment variable substitution using the pattern `$ENV_VAR|default_value`. This allows any configuration value to be overridden at runtime. + +**Format:** `$VARIABLE_NAME|default_value` + +**Example configuration:** +```yaml +models: +- name: my_model + parameters: + batch_size: $MODEL_BATCH_SIZE|8 + precision: $MODEL_PRECISION|fp16 +``` + +In this example: +- `batch_size` will use the value of `MODEL_BATCH_SIZE` environment variable if set, otherwise defaults to `8` +- `precision` will use `MODEL_PRECISION` if set, otherwise defaults to `fp16` + +Type conversion is automatic based on the default value: +- `"true"`/`"false"` → boolean +- Numeric strings → int or float +- Everything else → string \ No newline at end of file diff --git a/tools/inference_builder/lib/__init__.py b/tools/inference_builder/lib/__init__.py new file mode 100644 index 00000000..3159bfe6 --- /dev/null +++ b/tools/inference_builder/lib/__init__.py @@ -0,0 +1,14 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tools/inference_builder/lib/asset_manager.py b/tools/inference_builder/lib/asset_manager.py new file mode 100644 index 00000000..38ee5957 --- /dev/null +++ b/tools/inference_builder/lib/asset_manager.py @@ -0,0 +1,363 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from .utils import get_logger +import os +from dataclasses import dataclass, field +from typing import Dict, TYPE_CHECKING +import json +import uuid +import shutil + +if TYPE_CHECKING: + from fastapi import UploadFile +from pyservicemaker.utils import MediaInfo +import tempfile +from urllib.parse import urlparse +import threading + +logger = get_logger(__name__) + +DEFAULT_ASSET_DIR = "/tmp/assets" +@dataclass +class Asset: + id: str + file_name: str + mime_type: str + size: int + duration: int + path: str + use_count: int + asset_dir: str + description: str + username: str + password: str + live: bool + managed: bool + _lock: threading.Lock = field(init=False, repr=False, compare=False, default=None) + _condition: threading.Condition = field(init=False, repr=False, compare=False, default=None) + _marked_for_deletion: bool = field(init=False, repr=False, compare=False, default=False) + + def __post_init__(self): + self._lock = threading.Lock() + self._condition = threading.Condition(self._lock) + self._marked_for_deletion = False + + def to_dict(self): + """Convert Asset to dictionary, excluding non-serializable fields.""" + return { + "id": self.id, + "file_name": self.file_name, + "mime_type": self.mime_type, + "size": self.size, + "duration": self.duration, + "path": self.path, + "use_count": self.use_count, + "asset_dir": self.asset_dir, + "description": self.description, + "username": self.username, + "password": self.password, + "live": self.live, + "managed": self.managed + } + + def lock(self): + with self._condition: + self.use_count += 1 + + def unlock(self): + with self._condition: + if self.use_count <= 0: + logger.warning("Attempted to unlock asset %s with use_count %d", + self.id, self.use_count) + return + self.use_count -= 1 + if self.use_count == 0: + # Notify waiting threads (e.g., delete_asset) + self._condition.notify_all() + + # Safety net: cleanup unmanaged assets when object is destroyed + if not self.managed and os.path.exists(self.path): + try: + os.remove(self.path) + logger.info("Removed unmanaged asset %s from %s", + self.id, self.path) + except OSError as e: + logger.exception( + "Failed to cleanup asset %s: %s", self.id, e) + + def is_marked_for_deletion(self): + """Check if this asset is marked for deletion.""" + with self._condition: + return self._marked_for_deletion + + def mark_for_deletion(self): + """Mark this asset for deletion, signaling processing loops to stop.""" + with self._condition: + self._marked_for_deletion = True + logger.info("Marked asset %s for deletion", self.id) + + @classmethod + def fromdir(cls, asset_dir): + with open(os.path.join(asset_dir, "info.json")) as f: + info = json.load(f) + + return Asset(id=info["assetId"], + path=info["path"], + file_name=info["fileName"], + mime_type=info["mimeType"], + duration=info["duration"], + username=info["username"], + password=info["password"], + description=info["description"], + asset_dir=asset_dir, + use_count=0, + size=info["size"], + live=info["live"], + managed=True) + + +class AssetManager: + _instance = None + + def __new__(cls): + if cls._instance is None: + cls._instance = super(AssetManager, cls).__new__(cls) + cls._instance._initialized = False + return cls._instance + + def __init__(self): + if self._initialized: + return + + self._asset_dir = DEFAULT_ASSET_DIR + self._asset_map:Dict[str, Asset] = dict() + os.makedirs(self._asset_dir, exist_ok=True) + asset_ids = self._get_existing_asset_ids() + self._asset_map: dict[str, Asset] = { + asset_id: Asset.fromdir(os.path.join(self._asset_dir, asset_id)) + for asset_id in asset_ids + } + self._initialized = True + + def save_file(self, file: "UploadFile", file_name: str, mime_type: str) -> Asset | None: + asset_id = str(uuid.uuid4()) + while asset_id in self._asset_map: + asset_id = str(uuid.uuid4()) + asset_dir = os.path.join(self._asset_dir, asset_id) + try: + os.makedirs(asset_dir) + except: + logger.error(f"Failed to create asset directory: {asset_dir}") + return None + + with open(os.path.join(asset_dir, file_name), "wb") as f: + shutil.copyfileobj(file, f) + try: + size = os.path.getsize(os.path.join(asset_dir, file_name)) + except: + size = 0 + mediainfo = MediaInfo.discover(os.path.join(asset_dir, file_name)) + + with open(os.path.join(asset_dir, "info.json"), "w") as f: + json.dump( + { + "assetId": asset_id, + "path": os.path.join(asset_dir, file_name), + "fileName": file_name, + "mimeType": mime_type, + "duration": mediainfo.duration, + "username": "", + "password": "", + "description": "", + "size": size, + "live": False, + }, f) + + self._asset_map[asset_id] = Asset.fromdir(asset_dir) + + logger.info(f"Saved file - asset-id: {asset_id} name: {file_name}") + + return self._asset_map[asset_id] + + def add_live_stream(self, url: str, description="", username="", password="") -> Asset | None: + asset_id = str(uuid.uuid4()) + while asset_id in self._asset_map: + asset_id = str(uuid.uuid4()) + asset_dir = os.path.join(self._asset_dir, asset_id) + try: + os.makedirs(asset_dir) + except Exception as e: + logger.error(f"Failed to create asset directory: {asset_dir}") + return None + + with open(os.path.join(asset_dir, "info.json"), "w") as f: + json.dump( + { + "assetId": asset_id, + "path": url, + "fileName": "", + "mimeType": "", + "duration": 0, + "username": username, + "password": password, + "description": description, + "size": 0, + "live": True + }, f) + + self._asset_map[asset_id] = Asset.fromdir(asset_dir) + + logger.info(f"Added live stream - asset-id: {asset_id} url: {url}") + + return self._asset_map[asset_id] + + + def list_assets(self): + return list(self._asset_map.values()) + + def get_asset(self, asset_id): + if asset_id in self._asset_map: + return self._asset_map[asset_id] + else: + # check if the asset is an id or a path + # Handle file:// URIs by converting them to paths + file_path = asset_id + if asset_id.startswith("file://"): + parsed = urlparse(asset_id) + file_path = parsed.path + + if file_path.startswith("/") and os.path.exists(file_path): + # Check if this path already exists in the asset map + for existing_asset in self._asset_map.values(): + if existing_asset.path == file_path: + return existing_asset + + mediainfo = MediaInfo.discover(file_path) + asset = Asset(id=str(uuid.uuid4()), + path=file_path, + file_name=os.path.basename(file_path), + mime_type="", + duration=mediainfo.duration, + username="", + password="", + description="", + asset_dir=file_path, + use_count=0, + size=os.path.getsize(file_path), + live=False, + managed=True) + self._asset_map[asset.id] = asset + return asset + elif (asset_id.startswith("http://") or + asset_id.startswith("https://")): + # Download the video from the web + try: + # Extract filename from URL + parsed_url = urlparse(asset_id) + file_name = os.path.basename(parsed_url.path) + if not file_name: + file_name = "downloaded_video" + + # Create a temporary directory + temp_dir = tempfile.mkdtemp(prefix="web_asset_") + temp_file_path = os.path.join(temp_dir, file_name) + + # Download the file + logger.info(f"Downloading video from {asset_id}") + import requests # lazy import to avoid GIL error + response = requests.get( + asset_id, stream=True, timeout=30) + response.raise_for_status() + + with open(temp_file_path, "wb") as f: + for chunk in response.iter_content(chunk_size=8192): + f.write(chunk) + + # Get media info + mediainfo = MediaInfo.discover(temp_file_path) + file_size = os.path.getsize(temp_file_path) + + logger.info(f"Downloaded video to {temp_file_path}") + + return Asset(id=str(uuid.uuid4()), + path=temp_file_path, + file_name=file_name, + mime_type="", + duration=mediainfo.duration, + username="", + password="", + description=f"Downloaded from {asset_id}", + asset_dir=temp_dir, + use_count=0, + size=file_size, + live=False, + managed=False) + except Exception as e: + logger.exception( + "Failed to download video from %s: %s", asset_id, e) + return None + elif asset_id.startswith("rtsp://"): + return self.add_live_stream(asset_id) + else: + logger.error(f"Invalid asset ID: {asset_id}") + return None + + def delete_asset(self, asset_id, timeout=None): + """ + Delete an asset, waiting for it to be released if currently in use. + + Args: + asset_id: The ID of the asset to delete + timeout: Maximum time to wait in seconds (None for indefinite wait) + + Returns: + bool: True if asset was deleted, False if asset not found or timeout occurred + """ + if asset_id not in self._asset_map: + return False + + asset = self._asset_map[asset_id] + + # Mark asset for deletion first (signals processing loops to stop) + logger.info(f"Marking asset {asset_id} for deletion") + asset.mark_for_deletion() + logger.info(f"Marked asset {asset_id} for deletion") + + # Wait for asset to be released + with asset._condition: + if asset.use_count > 0: + logger.info(f"Asset {asset_id} is in use (use_count={asset.use_count}), waiting for release...") + # Wait until use_count becomes 0 + if not asset._condition.wait_for(lambda: asset.use_count == 0, timeout=timeout): + logger.error(f"Timeout waiting for asset {asset_id} to be released") + return False + logger.info(f"Asset {asset_id} released, proceeding with deletion") + + # Now safe to delete + asset_dir = os.path.join(self._asset_dir, asset_id) + if os.path.exists(asset_dir): + shutil.rmtree(asset_dir) + self._asset_map.pop(asset_id) + logger.info(f"Removed asset {asset_id} and cleaned up associated resources") + return True + + def _get_existing_asset_ids(self): + entries = os.listdir(self._asset_dir) + return [ + entry for entry in entries if os.path.isdir(os.path.join(self._asset_dir, entry)) + and os.path.isfile(os.path.join(self._asset_dir, entry, "info.json")) + ] diff --git a/tools/inference_builder/lib/codec.py b/tools/inference_builder/lib/codec.py new file mode 100644 index 00000000..e46e4659 --- /dev/null +++ b/tools/inference_builder/lib/codec.py @@ -0,0 +1,100 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from pyservicemaker import BufferProvider, Buffer, Pipeline, Flow, BufferRetriever, as_tensor, ColorFormat +from queue import Queue, Empty, Full +import numpy as np +from .utils import get_logger +from typing import List +import base64 +import torch + +logger = get_logger(__name__) + +png_data = base64.b64decode("iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAIAAACQd1PeAAAAEElEQVR4nGK6HcwNCAAA//8DTgE8HuxwEQAAAABJRU5ErkJggg==") +jpg_data = base64.b64decode("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") + + +class ImageInput(BufferProvider): + DEFAULT_HEIGHT = 1080 + DEFAULT_WIDTH = 1920 + + def __init__(self, format): + super().__init__() + self.format = format + self.height = ImageInput.DEFAULT_HEIGHT + self.width = ImageInput.DEFAULT_WIDTH + self.framerate = 1 + self.device = 'cpu' + self.queue = Queue(maxsize=1) + + def generate(self, size): + tensor = self.queue.get() + return tensor.wrap(ColorFormat.I420) + + def send(self, data): + self.queue.put(data) + +class ImageOutput(BufferRetriever): + def __init__(self, max_queue_size: int=1): + super().__init__() + self._timeout = 10 + self._output = Queue(maxsize=max_queue_size) + + def consume(self, buffer): + try: + tensor = buffer.extract(0).clone() + torch_tensor = torch.utils.dlpack.from_dlpack(tensor) + self._output.put(torch_tensor) + except Full: + logger.error(f"ImageOutput queue is full, buffer dropped") + return 1 + + def get(self): + try: + data = self._output.get(timeout=self._timeout) + except Empty: + logger.error("ImageOutput timeout, failed to decode the image, input data may be corrupted") + return None + return data + + +class ImageDecoder: + def __init__(self, formats: List[str], device_id: int=0): + self._piplines = [Pipeline(f"image_decoder_{format}") for format in formats] + self._image_inputs = {format: ImageInput(format) for format in formats} + self._image_output = ImageOutput() + self._flows = [] + for pipline, format in zip(self._piplines, formats): + image_input = self._image_inputs[format] + flow = Flow(pipline).inject([image_input]).decode().retrieve(self._image_output, gpu_id=device_id) + self._flows.append(flow) + pipline.start() + + if "PNG" in formats: + logger.info("PNG decoder warmup:") + warmup_data = np.frombuffer(png_data, dtype=np.uint8) + self.decode(as_tensor(warmup_data.copy(), "PNG"), "PNG") + if "JPEG" in formats: + logger.info("JPEG decoder warmup:") + warmup_data = np.frombuffer(jpg_data, dtype=np.uint8) + self.decode(as_tensor(warmup_data.copy(), "JPEG"), "JPEG") + logger.info("Image decoder initialized") + + + def decode(self, tensor, format: str): + self._image_inputs[format].send(tensor) + return self._image_output.get() diff --git a/tools/inference_builder/lib/errors.py b/tools/inference_builder/lib/errors.py new file mode 100644 index 00000000..cc4c37a8 --- /dev/null +++ b/tools/inference_builder/lib/errors.py @@ -0,0 +1,1163 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Enhanced Error Handling Module for Inference Builder + +This module provides rich error context, categorization, and troubleshooting guidance +to make debugging easier for both humans and AI tools. +""" + +import traceback +import time +import uuid +import json +import threading +import inspect +import os +from dataclasses import dataclass, field, asdict +from typing import ClassVar, Dict, List, Optional, Any, Union +from enum import Enum +import numpy as np + +# Try to import torch, but make it optional +try: + import torch + TORCH_AVAILABLE = True +except ImportError: + TORCH_AVAILABLE = False + + +class ErrorCategory(Enum): + """Categorize errors for easier filtering and analysis""" + CONFIGURATION = "configuration" + DATA_VALIDATION = "data_validation" + ASSET_NOT_FOUND = "asset_not_found" + PROCESSING = "processing" + BACKEND = "backend" + TIMEOUT = "timeout" + RESOURCE = "resource" + NETWORK = "network" + INTERNAL = "internal" + CODEC = "codec" + QUEUE = "queue" + + +class ErrorSeverity(Enum): + """Error severity levels""" + CRITICAL = "critical" # Service-breaking + ERROR = "error" # Request-breaking + WARNING = "warning" # Degraded but continuing + INFO = "info" # Informational only + + +@dataclass +class Error: + """ + Base Error class for simple error messages. + + This is the base class that EnhancedError inherits from. + Use this for simple errors that don't need rich context. + Use EnhancedError (via ErrorFactory) for structured errors with full context. + """ + message: str + + def __bool__(self): + """Return False so error objects evaluate to False in boolean context""" + return False + + def __str__(self): + """Human-readable string representation""" + return self.message + + +@dataclass +class EnhancedError(Error): + """ + Enhanced error class with rich context for AI-powered troubleshooting + + This class provides comprehensive error information including: + - Categorization and severity + - Contextual information (component, operation, model) + - Technical details (stack traces, error codes) + - Data context (inputs, expected vs actual) + - Troubleshooting guidance and remediation steps + - Error chaining for root cause analysis + + Inherits from Error base class, which provides: + - message field + - __bool__() override (returns False for boolean checks) + """ + + # Core error information (message inherited from Error) + category: ErrorCategory + severity: ErrorSeverity = ErrorSeverity.ERROR + + # Contextual information + component: str = "" # e.g., "DataFlow", "ModelOperator", "ImageInputDataFlow" + operation: str = "" # e.g., "bind_input", "_process_custom_data" + request_id: str = field(default_factory=lambda: str(uuid.uuid4())) + timestamp: float = field(default_factory=time.time) + + # Technical details + error_code: str = "" # e.g., "ERR_DF_001", "ERR_MO_005" + stack_trace: Optional[str] = None + exception_type: Optional[str] = None + + # Data context + input_data: Dict[str, Any] = field(default_factory=dict) + expected_data: Dict[str, Any] = field(default_factory=dict) + actual_data: Dict[str, Any] = field(default_factory=dict) + + # Pipeline context + model_name: Optional[str] = None + dataflow_names: List[str] = field(default_factory=list) + tensor_names: List[str] = field(default_factory=list) + + # Troubleshooting guidance + troubleshooting_hints: List[str] = field(default_factory=list) + related_config: Dict[str, Any] = field(default_factory=dict) + remediation_steps: List[str] = field(default_factory=list) + + # Error chain for root cause analysis + caused_by: Optional['EnhancedError'] = None + + # Additional metadata + metadata: Dict[str, Any] = field(default_factory=dict) + + def __str__(self): + """Human-readable string representation""" + parts = [f"[{self.error_code or 'ERROR'}]"] + if self.component: + parts.append(f"{self.component}") + if self.operation: + parts.append(f".{self.operation}()") + parts.append(f": {self.message}") + return " ".join(parts) + + def to_dict(self, include_stack_trace: bool = True, sanitize_data: bool = True) -> Dict: + """ + Convert to dictionary for structured logging + + Args: + include_stack_trace: Whether to include full stack trace (can be verbose) + sanitize_data: Whether to sanitize large data structures + + Returns: + Dictionary representation suitable for JSON serialization + """ + result = { + "error_id": self.request_id, + "timestamp": self.timestamp, + "iso_timestamp": time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(self.timestamp)), + "category": self.category.value, + "severity": self.severity.value, + "component": self.component, + "operation": self.operation, + "message": self.message, + "error_code": self.error_code, + "exception_type": self.exception_type, + } + + if include_stack_trace and self.stack_trace: + result["stack_trace"] = self.stack_trace + + # Add data context + if sanitize_data: + result["input_data"] = self._sanitize_data(self.input_data) + result["actual_data"] = self._sanitize_data(self.actual_data) + else: + result["input_data"] = self.input_data + result["actual_data"] = self.actual_data + + result["expected_data"] = self.expected_data + + # Add pipeline context + if self.model_name: + result["model_name"] = self.model_name + if self.dataflow_names: + result["dataflow_names"] = self.dataflow_names + if self.tensor_names: + result["tensor_names"] = self.tensor_names + + # Add troubleshooting information + if self.troubleshooting_hints: + result["troubleshooting_hints"] = self.troubleshooting_hints + if self.remediation_steps: + result["remediation_steps"] = self.remediation_steps + if self.related_config: + result["related_config"] = self._sanitize_config(self.related_config) + + # Add metadata + if self.metadata: + result["metadata"] = self.metadata + + # Add error chain + if self.caused_by: + result["caused_by"] = self.caused_by.to_dict( + include_stack_trace=False, # Don't include nested stack traces + sanitize_data=sanitize_data + ) + + return result + + def _sanitize_data(self, data: Dict) -> Dict: + """Sanitize large tensors/arrays for logging""" + if not data: + return {} + + sanitized = {} + for k, v in data.items(): + try: + if isinstance(v, np.ndarray): + sanitized[k] = { + "type": "numpy.ndarray", + "shape": str(v.shape), + "dtype": str(v.dtype), + "sample": self._safe_sample(v) + } + elif TORCH_AVAILABLE and isinstance(v, torch.Tensor): + sanitized[k] = { + "type": "torch.Tensor", + "shape": str(list(v.shape)), + "dtype": str(v.dtype), + "device": str(v.device), + "sample": self._safe_sample(v.cpu().numpy() if v.is_cuda else v.numpy()) + } + elif isinstance(v, (list, tuple)): + if len(v) > 10: + sanitized[k] = { + "type": type(v).__name__, + "length": len(v), + "sample": str(v[:3]) + " ... " + str(v[-1:]) + } + else: + sanitized[k] = v + elif isinstance(v, dict): + sanitized[k] = self._sanitize_data(v) + elif isinstance(v, str) and len(v) > 500: + sanitized[k] = v[:500] + "... [truncated]" + else: + sanitized[k] = v + except Exception as e: + sanitized[k] = f"" + + return sanitized + + def _safe_sample(self, arr: np.ndarray, max_elements: int = 5) -> str: + """Get a safe sample from array""" + try: + flat = arr.flatten() + if len(flat) <= max_elements: + return str(flat.tolist()) + else: + sample = flat[:max_elements].tolist() + return f"{sample} ... (total: {len(flat)} elements)" + except Exception: + return "" + + def _sanitize_config(self, config: Dict) -> Dict: + """Sanitize configuration data""" + if not config: + return {} + + sanitized = {} + for k, v in config.items(): + if isinstance(v, dict): + sanitized[k] = self._sanitize_config(v) + elif isinstance(v, (list, tuple)) and len(v) > 20: + sanitized[k] = f"{type(v).__name__}(length={len(v)})" + else: + sanitized[k] = v + return sanitized + + def to_json(self, **kwargs) -> str: + """Convert to JSON string""" + return json.dumps(self.to_dict(**kwargs), indent=2, default=str) + + def log(self, logger, as_json: bool = True): + """ + Log error with appropriate level + + Args: + logger: Logger instance + as_json: If True, log as structured JSON; otherwise log as formatted text + """ + if as_json: + error_dict = self.to_dict() + log_msg = json.dumps(error_dict, indent=2, default=str) + else: + log_msg = self._format_text() + + if self.severity == ErrorSeverity.CRITICAL: + logger.critical(log_msg) + elif self.severity == ErrorSeverity.ERROR: + logger.error(log_msg) + elif self.severity == ErrorSeverity.WARNING: + logger.warning(log_msg) + else: + logger.info(log_msg) + + def _format_text(self) -> str: + """Format error as human-readable text""" + lines = [ + "=" * 80, + f"ERROR: {self.error_code or 'UNKNOWN'}", + f"Severity: {self.severity.value.upper()}", + f"Category: {self.category.value}", + "=" * 80, + f"Message: {self.message}", + ] + + if self.component or self.operation: + lines.append(f"Location: {self.component}.{self.operation}()") + + if self.model_name: + lines.append(f"Model: {self.model_name}") + + if self.dataflow_names: + lines.append(f"Dataflow: {', '.join(self.dataflow_names)}") + + if self.tensor_names: + lines.append(f"Tensors: {', '.join(self.tensor_names)}") + + if self.exception_type: + lines.append(f"Exception Type: {self.exception_type}") + + if self.input_data: + sanitized_input = self._sanitize_data(self.input_data) + lines.append(f"\nInput Data: {sanitized_input}") + + if self.expected_data: + lines.append(f"\nExpected Data: {self.expected_data}") + + if self.actual_data: + sanitized_actual = self._sanitize_data(self.actual_data) + lines.append(f"\nActual Data: {sanitized_actual}") + + if self.related_config: + lines.append(f"\nRelated Config: {self.related_config}") + + if self.troubleshooting_hints: + lines.append("\nTroubleshooting Hints:") + for i, hint in enumerate(self.troubleshooting_hints, 1): + lines.append(f" {i}. {hint}") + + if self.remediation_steps: + lines.append("\nRemediation Steps:") + for i, step in enumerate(self.remediation_steps, 1): + lines.append(f" {i}. {step}") + + if self.stack_trace: + lines.append("\nStack Trace:") + lines.append(self.stack_trace) + + if self.caused_by: + lines.append("\nCaused by:") + lines.append(f" {self.caused_by}") + + lines.append("=" * 80) + + return "\n".join(lines) + + +class ErrorFactory: + """ + Factory for creating standardized errors with consistent codes and hints + + This factory maintains an error catalog with predefined error codes, categories, + and troubleshooting guidance. It ensures consistent error handling across the codebase. + + The factory can automatically extract component and operation names from context: + - Pass `caller=self` to auto-extract class name as component + - Pass `auto_operation=True` to auto-detect calling method name + """ + + # Error code definitions with troubleshooting hints + ERROR_CATALOG: ClassVar[Dict[str, Dict[str, Any]]] = { + # DataFlow errors (DF) + "ERR_DF_001": { + "message": "Required tensor not found in dataflow input", + "category": ErrorCategory.DATA_VALIDATION, + "severity": ErrorSeverity.ERROR, + "hints": [ + "Check that all required input tensors are provided", + "Verify the tensor names match the configuration", + "Consider marking the tensor as optional if it's not always needed", + "Review the model input configuration" + ], + "remediation": [ + "Add the missing tensor to the input data", + "Update configuration to mark tensor as optional", + "Check upstream processor output", + "Verify route configuration" + ] + }, + "ERR_DF_002": { + "message": "Invalid data type in custom data processing", + "category": ErrorCategory.DATA_VALIDATION, + "severity": ErrorSeverity.ERROR, + "hints": [ + "Ensure base64 binary data is provided as bytes or string", + "Check data encoding format", + "Verify MIME type matches data format", + "Review data type conversions" + ], + "remediation": [ + "Convert data to expected format (bytes or string)", + "Verify base64 encoding is correct", + "Check data type in request payload" + ] + }, + "ERR_DF_003": { + "message": "Asset not found", + "category": ErrorCategory.ASSET_NOT_FOUND, + "severity": ErrorSeverity.ERROR, + "hints": [ + "Verify asset ID exists in AssetManager", + "Check if asset was properly uploaded/registered", + "Ensure asset hasn't been deleted or expired", + "Verify asset path and permissions" + ], + "remediation": [ + "Upload the asset before referencing it", + "Check asset ID spelling/format", + "Verify asset lifecycle management", + "Check AssetManager logs for registration issues" + ] + }, + "ERR_DF_004": { + "message": "Multiple result queues not supported in single dataflow", + "category": ErrorCategory.CONFIGURATION, + "severity": ErrorSeverity.ERROR, + "hints": [ + "Restructure dataflow to have single queue output", + "Split into multiple dataflows if needed" + ], + "remediation": [ + "Refactor to use single queue per dataflow", + "Create separate dataflows for multiple queues" + ] + }, + "ERR_DF_005": { + "message": "Queue full, dropping frames", + "category": ErrorCategory.QUEUE, + "severity": ErrorSeverity.WARNING, + "hints": [ + "Consumer is too slow, increase processing speed", + "Increase queue size if appropriate", + "Check for bottlenecks in pipeline" + ], + "remediation": [ + "Optimize downstream processing", + "Increase queue maxsize", + "Add backpressure handling" + ] + }, + "ERR_DF_006": { + "message": "Failed to inject tensors into input flows", + "category": ErrorCategory.DATA_VALIDATION, + "severity": ErrorSeverity.ERROR, + "hints": [ + "No matching input tensors found in the request" + ], + "remediation": [ + "Provide at least one valid tensor in the request", + "Check input tensor names against model configuration", + "Verify request payload format", + "Review input flow configuration" + ] + }, + + # Model Operator errors (MO) + "ERR_MO_001": { + "message": "Empty result from preprocessing", + "category": ErrorCategory.PROCESSING, + "severity": ErrorSeverity.ERROR, + "hints": [ + "Check preprocessor logic and return values", + "Verify input data is valid", + "Review preprocessor configuration", + "Ensure preprocessor returns non-empty data" + ], + "remediation": [ + "Debug preprocessor implementation", + "Verify input data format", + "Check preprocessor output configuration", + "Review preprocessing chain" + ] + }, + "ERR_MO_002": { + "message": "Model output incomplete", + "category": ErrorCategory.PROCESSING, + "severity": ErrorSeverity.ERROR, + "hints": [ + "Verify model generates all required outputs", + "Check if postprocessor is needed", + "Review model configuration", + "Ensure all output tensors are generated" + ], + "remediation": [ + "Add missing output tensors", + "Implement required postprocessor", + "Update model output configuration", + "Check model implementation" + ] + }, + "ERR_MO_003": { + "message": "No output dataflow bound to model", + "category": ErrorCategory.CONFIGURATION, + "severity": ErrorSeverity.CRITICAL, + "hints": [ + "Check route configuration", + "Ensure model output is connected to downstream", + "Review pipeline setup" + ], + "remediation": [ + "Add output binding in route configuration", + "Connect model to output dataflow", + "Review inference pipeline setup" + ] + }, + "ERR_MO_004": { + "message": "Invalid result from preprocessing", + "category": ErrorCategory.PROCESSING, + "severity": ErrorSeverity.ERROR, + "hints": [ + "Preprocessor must return valid data structure", + "Check for errors in preprocessing chain", + "Verify data format compatibility" + ] + }, + + # Processor errors (PROC) + "ERR_PROC_001": { + "message": "Processor output count mismatch", + "category": ErrorCategory.CONFIGURATION, + "severity": ErrorSeverity.WARNING, + "hints": [ + "Verify processor returns correct number of outputs", + "Check processor configuration matches implementation", + "Review processor output specification in the configuration", + "Ensure output list length matches config" + ], + "remediation": [ + "Update processor to return correct number of outputs", + "Fix processor configuration", + "Review processor implementation" + ] + }, + "ERR_PROC_002": { + "message": "Failed to load AutoProcessor", + "category": ErrorCategory.CONFIGURATION, + "severity": ErrorSeverity.CRITICAL, + "hints": [ + "Verify model home path is correct", + "Check if model supports AutoProcessor", + "Ensure transformers library is installed", + "Verify model files are present" + ], + "remediation": [ + "Check model_home path", + "Download model files", + "Use custom processor instead", + "Install required dependencies" + ] + }, + "ERR_PROC_003": { + "message": "Custom processor module invalid", + "category": ErrorCategory.CONFIGURATION, + "severity": ErrorSeverity.CRITICAL, + "hints": [ + "Custom module must have create_instance() method", + "Check custom processor implementation", + "Verify module imports correctly" + ], + "remediation": [ + "Implement create_instance() in custom module", + "Fix custom processor module", + "Check module path and imports" + ] + }, + "ERR_PROC_004": { + "message": "Failed to create custom processor", + "category": ErrorCategory.CONFIGURATION, + "severity": ErrorSeverity.CRITICAL, + "hints": [ + "Check processor configuration", + "Verify processor class exists", + "Verify the processor name in the configuration matches the name in the processor class" + ] + }, + + # Image processing errors (IMG) + "ERR_IMG_001": { + "message": "Unsupported image format", + "category": ErrorCategory.DATA_VALIDATION, + "severity": ErrorSeverity.ERROR, + "hints": [ + "Supported formats: JPEG, PNG", + "Check MIME type of uploaded image", + "Convert image to supported format", + "Verify image encoding" + ], + "remediation": [ + "Convert image to JPEG or PNG", + "Check image MIME type", + "Re-encode image in supported format" + ] + }, + "ERR_IMG_002": { + "message": "Invalid base64 image data", + "category": ErrorCategory.DATA_VALIDATION, + "severity": ErrorSeverity.ERROR, + "hints": [ + "Base64 image must be bytes or string", + "Check data encoding", + "Verify base64 format: data:image/[type];base64,[data]" + ], + "remediation": [ + "Encode image as valid base64 string", + "Check data URL format", + "Verify data type (bytes/string)" + ] + }, + "ERR_IMG_003": { + "message": "Image decoder not initialized", + "category": ErrorCategory.INTERNAL, + "severity": ErrorSeverity.CRITICAL, + "hints": [ + "Image decoder failed to initialize", + "Check codec dependencies", + "Verify system libraries" + ] + }, + + # Video processing errors (VIDEO) + "ERR_VIDEO_001": { + "message": "Invalid video frame sampling parameters", + "category": ErrorCategory.CONFIGURATION, + "severity": ErrorSeverity.WARNING, + "hints": [ + "Frames or chunks parameters differ across assets", + "Ensure consistent parameters for batch processing", + "Review asset query parameters" + ] + }, + + # Route configuration errors (ROUTE) + "ERR_ROUTE_001": { + "message": "Invalid route configuration", + "category": ErrorCategory.CONFIGURATION, + "severity": ErrorSeverity.CRITICAL, + "hints": [ + "Check route format: 'model:[tensor, ...]' -> 'model:[tensor, ...]'", + "Verify JSON syntax in data specifications", + "Review route configuration documentation" + ], + "remediation": [ + "Fix route configuration format", + "Validate JSON in route data", + "Review routing examples" + ] + }, + "ERR_ROUTE_002": { + "message": "Model not found in route configuration", + "category": ErrorCategory.CONFIGURATION, + "severity": ErrorSeverity.CRITICAL, + "hints": [ + "Model referenced in route doesn't exist", + "Check model name spelling", + "Verify model is defined in configuration" + ], + "remediation": [ + "Add model to configuration", + "Fix model name in route", + "Review model definitions" + ] + }, + + # AsyncDispatcher errors (ASYNC) + "ERR_ASYNC_001": { + "message": "AsyncDispatcher failed to start - collector is None", + "category": ErrorCategory.INTERNAL, + "severity": ErrorSeverity.CRITICAL, + "hints": [ + "Collector not properly initialized", + "Check pipeline setup", + "Review initialization order" + ] + }, + "ERR_ASYNC_002": { + "message": "AsyncDispatcher failed to start - event loop is None", + "category": ErrorCategory.INTERNAL, + "severity": ErrorSeverity.CRITICAL, + "hints": [ + "Event loop not properly initialized", + "Check async setup", + "Review initialization order" + ] + }, + "ERR_ASYNC_003": { + "message": "Consumer queue reached maximum size", + "category": ErrorCategory.QUEUE, + "severity": ErrorSeverity.CRITICAL, + "hints": [ + "Too many concurrent requests", + "Increase queue size", + "Add rate limiting", + "Check consumer processing speed" + ], + "remediation": [ + "Increase n_max_async_queues parameter", + "Implement request throttling", + "Optimize consumer processing" + ] + }, + + # System errors (SYS) + "ERR_SYS_001": { + "message": "Model repository does not exist", + "category": ErrorCategory.CONFIGURATION, + "severity": ErrorSeverity.CRITICAL, + "hints": [ + "Check model_repo path in configuration", + "Verify directory exists and is accessible", + "Check file permissions" + ], + "remediation": [ + "Create the model repository directory and correctly mount it into the container.", + "Fix model_repo path in configuration", + "Check directory permissions" + ] + }, + "ERR_SYS_002": { + "message": "Inference pipeline incomplete", + "category": ErrorCategory.CONFIGURATION, + "severity": ErrorSeverity.CRITICAL, + "hints": [ + "Either input or output is empty", + "Check route configuration", + "Review pipeline setup" + ], + "remediation": [ + "Add input/output bindings", + "Fix route configuration", + "Review pipeline configuration" + ] + }, + } + + @classmethod + def _get_caller_info(cls, caller=None, auto_operation: bool = False): + """ + Extract component and operation names from caller context + + Args: + caller: The calling object (usually `self`). If provided, extracts class name. + auto_operation: If True, extracts the calling method name from stack + + Returns: + tuple: (component_name, operation_name) + """ + component = None + operation = None + + # Extract component from caller object + if caller is not None: + component = caller.__class__.__name__ + + # Extract operation from call stack + if auto_operation: + # Get the calling frame (skip this method and the create/from_exception method) + frame = inspect.currentframe() + try: + # Go up the stack: _get_caller_info -> create/from_exception -> actual caller + caller_frame = frame.f_back.f_back + if caller_frame: + operation = caller_frame.f_code.co_name + finally: + del frame # Avoid reference cycles + + return component, operation + + @classmethod + def create( + cls, + error_code: str, + message: Optional[str] = None, + caller: Optional[Any] = None, + auto_operation: bool = True, + **kwargs + ) -> EnhancedError: + """ + Create error from catalog with additional context + + Args: + error_code: Error code from ERROR_CATALOG + message: Custom message (overrides catalog message) + caller: The calling object (usually `self`). Auto-extracts class name as component. + auto_operation: If True, auto-detects calling method name as operation. + **kwargs: Additional context fields for EnhancedError + + Returns: + EnhancedError instance with all context + + Example: + # Automatic component/operation detection: + error = ErrorFactory.create( + "ERR_DF_001", + caller=self, # Extracts "DataFlow" from self.__class__.__name__ + auto_operation=True, # Extracts "put" from calling method + tensor_names=["image"] + ) + + # Manual specification (still supported): + error = ErrorFactory.create( + "ERR_DF_001", + component="DataFlow", + operation="put", + tensor_names=["image"] + ) + """ + # Auto-detect component and operation if requested + auto_component, auto_operation_name = cls._get_caller_info(caller, auto_operation) + + # Use auto-detected values if not explicitly provided + if auto_component and 'component' not in kwargs: + kwargs['component'] = auto_component + if auto_operation_name and 'operation' not in kwargs: + kwargs['operation'] = auto_operation_name + + if error_code not in cls.ERROR_CATALOG: + # Remove conflicting keys from kwargs + final_category = kwargs.pop('category', ErrorCategory.INTERNAL) + final_severity = kwargs.pop('severity', ErrorSeverity.ERROR) + error = EnhancedError( + message=message or f"Unknown error code: {error_code}", + category=final_category, + severity=final_severity, + error_code=error_code, + troubleshooting_hints=[ + "This error code is not in the catalog", + "Check error code spelling", + "Review error handling implementation" + ], + **kwargs + ) + # Add to global collector if enabled + cls._add_to_collector(error) + return error + + template = cls.ERROR_CATALOG[error_code] + + # Use custom message or template message + final_message = message or template["message"] + + # If custom message provided, append template message as hint + hints = list(template.get("hints", [])) + if message and message != template["message"]: + hints.insert(0, f"Base error: {template['message']}") + + # Merge catalog values with provided kwargs + # User-provided kwargs override catalog defaults + final_category = kwargs.pop('category', template.get("category", ErrorCategory.INTERNAL)) + final_severity = kwargs.pop('severity', template.get("severity", ErrorSeverity.ERROR)) + + error = EnhancedError( + message=final_message, + category=final_category, + severity=final_severity, + error_code=error_code, + troubleshooting_hints=hints, + remediation_steps=list(template.get("remediation", [])), + **kwargs + ) + # Add to global collector if enabled + cls._add_to_collector(error) + return error + + @classmethod + def _add_to_collector(cls, error: EnhancedError): + """Add error to global collector if enabled""" + global _global_error_collector + if _global_error_collector is not None: + _global_error_collector.add(error) + + @classmethod + def from_exception( + cls, + exc: Exception, + component: Optional[str] = None, + operation: Optional[str] = None, + caller: Optional[Any] = None, + auto_operation: bool = True, + error_code: Optional[str] = None, + category: ErrorCategory = ErrorCategory.INTERNAL, + severity: ErrorSeverity = ErrorSeverity.ERROR, + **kwargs + ) -> EnhancedError: + """ + Create EnhancedError from Python exception + + Args: + exc: The exception object + component: Component where error occurred (or use caller for auto-detection) + operation: Operation that failed (or use auto_operation=True) + caller: The calling object (usually `self`). Auto-extracts class name as component. + auto_operation: If True, auto-detects calling method name as operation. + error_code: Optional error code + category: Error category + severity: Error severity + **kwargs: Additional context + + Returns: + EnhancedError with exception details + + Example: + # Automatic component/operation detection: + except Exception as e: + error = ErrorFactory.from_exception( + e, + caller=self, # Extracts class name + auto_operation=True # Extracts method name + ) + + # Manual specification (still supported): + except Exception as e: + error = ErrorFactory.from_exception( + e, + component="ModelOperator", + operation="run" + ) + """ + # Auto-detect component and operation if requested + auto_component, auto_operation_name = cls._get_caller_info(caller, auto_operation) + + # Use auto-detected values if not explicitly provided + if component is None and auto_component: + component = auto_component + if operation is None and auto_operation_name: + operation = auto_operation_name + + # Remove conflicting keys from kwargs to avoid duplicate parameter errors + # (kwargs might contain category/severity if passed by caller) + kwargs.pop('category', None) + kwargs.pop('severity', None) + kwargs.pop('component', None) + kwargs.pop('operation', None) + kwargs.pop('error_code', None) + + error = EnhancedError( + message=str(exc), + category=category, + severity=severity, + component=component, + operation=operation, + error_code=error_code or "ERR_EXCEPTION", + exception_type=type(exc).__name__, + stack_trace=traceback.format_exc(), + troubleshooting_hints=[ + f"Exception type: {type(exc).__name__}", + f"Exception message: {str(exc)}", + "Review stack trace for details" + ], + **kwargs + ) + # Add to global collector if enabled + cls._add_to_collector(error) + return error + + @classmethod + def get_catalog_summary(cls) -> Dict[str, Any]: + """Get summary of error catalog for documentation""" + summary = { + "total_errors": len(cls.ERROR_CATALOG), + "by_category": {}, + "by_severity": {}, + "errors": {} + } + + for code, info in cls.ERROR_CATALOG.items(): + category = info.get("category", ErrorCategory.INTERNAL).value + severity = info.get("severity", ErrorSeverity.ERROR).value + + summary["by_category"][category] = summary["by_category"].get(category, 0) + 1 + summary["by_severity"][severity] = summary["by_severity"].get(severity, 0) + 1 + + summary["errors"][code] = { + "message": info["message"], + "category": category, + "severity": severity, + "hints_count": len(info.get("hints", [])), + "remediation_count": len(info.get("remediation", [])) + } + + return summary + + +class ErrorCollector: + """ + Collect and analyze errors for patterns and statistics + + This class maintains a thread-safe collection of errors and provides + statistics and analysis capabilities for monitoring and debugging. + """ + + def __init__(self, max_errors: int = 1000): + """ + Initialize error collector + + Args: + max_errors: Maximum number of errors to keep in memory + """ + self._errors: List[EnhancedError] = [] + self._error_counts: Dict[str, int] = {} + self._max_errors = max_errors + self._lock = threading.RLock() # RLock allows same thread to acquire multiple times + self._start_time = time.time() + + def add(self, error: EnhancedError): + """Add error to collection (thread-safe)""" + with self._lock: + self._errors.append(error) + self._error_counts[error.error_code] = \ + self._error_counts.get(error.error_code, 0) + 1 + + # Trim if exceeds max size (keep most recent) + if len(self._errors) > self._max_errors: + self._errors = self._errors[-self._max_errors:] + + def get_stats(self, include_recent: int = 10) -> Dict: + """ + Get error statistics for monitoring + + Args: + include_recent: Number of recent errors to include + + Returns: + Dictionary with error statistics + """ + with self._lock: + stats = { + "collection_period_seconds": time.time() - self._start_time, + "total_errors": len(self._errors), + "unique_error_codes": len(self._error_counts), + "by_category": self._count_by("category"), + "by_severity": self._count_by("severity"), + "by_component": self._count_by("component"), + "by_model": self._count_by("model_name"), + "by_code": dict(sorted( + self._error_counts.items(), + key=lambda x: x[1], + reverse=True + )[:20]), # Top 20 error codes + } + + if include_recent > 0: + stats["recent_errors"] = [ + e.to_dict(include_stack_trace=False) + for e in self._errors[-include_recent:] + ] + + return stats + + def get_errors_by_category(self, category: ErrorCategory) -> List[EnhancedError]: + """Get all errors of a specific category""" + with self._lock: + return [e for e in self._errors if e.category == category] + + def get_errors_by_code(self, error_code: str) -> List[EnhancedError]: + """Get all errors with specific error code""" + with self._lock: + return [e for e in self._errors if e.error_code == error_code] + + def get_critical_errors(self) -> List[EnhancedError]: + """Get all critical errors""" + with self._lock: + return [e for e in self._errors if e.severity == ErrorSeverity.CRITICAL] + + def clear(self): + """Clear all collected errors""" + with self._lock: + self._errors.clear() + self._error_counts.clear() + self._start_time = time.time() + + def _count_by(self, field: str) -> Dict[str, int]: + """Count errors by field value""" + counts = {} + for error in self._errors: + value = getattr(error, field, None) + if value is None or value == "": + continue + + # Handle enum values + if hasattr(value, 'value'): + key = value.value + # Handle lists + elif isinstance(value, list): + if len(value) == 0: + continue + key = ", ".join(str(v) for v in value[:3]) # First 3 items + if len(value) > 3: + key += "..." + else: + key = str(value) + + counts[key] = counts.get(key, 0) + 1 + + return dict(sorted(counts.items(), key=lambda x: x[1], reverse=True)[:20]) + + def export_to_json(self, filepath: str, include_stack_traces: bool = False): + """Export all errors to JSON file""" + with self._lock: + data = { + "exported_at": time.time(), + "stats": self.get_stats(include_recent=0), + "errors": [ + e.to_dict(include_stack_trace=include_stack_traces) + for e in self._errors + ] + } + + with open(filepath, 'w') as f: + json.dump(data, f, indent=2, default=str) + f.flush() + os.fsync(f.fileno()) + + +# Global error collector instance (optional) +_global_error_collector = None + +def get_global_error_collector() -> ErrorCollector: + """Get or create global error collector""" + global _global_error_collector + if _global_error_collector is None: + _global_error_collector = ErrorCollector() + return _global_error_collector + + +def enable_global_error_collection(max_errors: int = 1000): + """Enable global error collection""" + global _global_error_collector + _global_error_collector = ErrorCollector(max_errors=max_errors) + + +def disable_global_error_collection(): + """Disable global error collection""" + global _global_error_collector + _global_error_collector = None + diff --git a/tools/inference_builder/lib/inference.py b/tools/inference_builder/lib/inference.py new file mode 100644 index 00000000..9b25f29e --- /dev/null +++ b/tools/inference_builder/lib/inference.py @@ -0,0 +1,2051 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import atexit +import base64 +from concurrent.futures import ThreadPoolExecutor, Future +import os +import threading +from queue import Queue, Empty, Full +from abc import ABC, abstractmethod +from config import global_config +from .utils import get_logger, split_tensor_in_dict, FutureConsumer, QueueConsumer +from .codec import ImageDecoder +import custom +from omegaconf import OmegaConf +from dataclasses import dataclass +from typing import Dict, List, Tuple, Union, Generator +import numpy as np +from collections import namedtuple +import json +from pyservicemaker import Pipeline, as_tensor +from pyservicemaker.utils import MediaExtractor, MediaChunk +import torch +from .asset_manager import AssetManager +import time +import asyncio +import inspect +from .errors import ( + Error, + EnhancedError, + ErrorFactory, + ErrorCategory, + ErrorSeverity, + enable_global_error_collection, + get_global_error_collector +) + + +logger = get_logger(__name__) + +py_datatype_mapping = { + "TYPE_UINT8": int, + "TYPE_UINT16": int, + "TYPE_UINT32": int, + "TYPE_UINT64": int, + "TYPE_INT8": int, + "TYPE_INT16": int, + "TYPE_INT32": int, + "TYPE_INT64": int, + "TYPE_FP16": float, + "TYPE_FP32": float, + "TYPE_FP64": float, + "TYPE_STRING": str, + "TYPE_CUSTOM_DS_IMAGE": str, + "TYPE_CUSTOM_DS_MIME": str, + "TYPE_CUSTOM_LIST": str, + "TYPE_CUSTOM_DS_MEDIA_URL": str, + "TYPE_CUSTOM_DS_SOURCE_CONFIG": str, + "TYPE_CUSTOM_BINARY_BASE64": str, + "TYPE_CUSTOM_VIDEO_CHUNK_ASSETS": str, + "TYPE_CUSTOM_LONG_VIDEO_ASSETS": str, + "TYPE_CUSTOM_IMAGE_ASSETS": str, + "TYPE_CUSTOM_IMAGE_BASE64": str +} + +np_datatype_mapping = { + "TYPE_INVALID": None, + "TYPE_BOOL": np.bool_, + "TYPE_UINT8": np.ubyte, + "TYPE_UINT16": np.uint16, + "TYPE_UINT32": np.uint32, + "TYPE_UINT64": np.uint64, + "TYPE_INT8": np.byte, + "TYPE_INT16": np.int16, + "TYPE_INT32": np.int32, + "TYPE_INT64": np.int64, + "TYPE_FP16": np.float16, + "TYPE_FP32": np.float32, + "TYPE_FP64": np.float64, + "TYPE_STRING": np.string_, + "TYPE_CUSTOM_DS_IMAGE": np.ubyte, + "TYPE_CUSTOM_DS_MIME": np.string_, + "TYPE_CUSTOM_DS_MEDIA_URL": np.string_, + "TYPE_CUSTOM_DS_SOURCE_CONFIG": str, + "TYPE_BF16": None, + "TYPE_CUSTOM_OBJECT": None +} + +torch_datatype_mapping = { + "TYPE_INVALID": None, + "TYPE_BOOL": torch.bool, + "TYPE_UINT8": torch.uint8, + "TYPE_UINT16": torch.int16, + "TYPE_UINT32": torch.int32, + "TYPE_UINT64": torch.int64, + "TYPE_INT8": torch.int8, + "TYPE_INT16": torch.int16, + "TYPE_INT32": torch.int32, + "TYPE_INT64": torch.int64, + "TYPE_FP16": torch.float16, + "TYPE_FP32": torch.float32, + "TYPE_FP64": torch.float64, + "TYPE_STRING": None, + "TYPE_CUSTOM_DS_IMAGE": torch.int8, + "TYPE_CUSTOM_DS_MIME": None, + "TYPE_CUSTOM_DS_MEDIA_URL": None, + "TYPE_CUSTOM_DS_SOURCE_CONFIG": str, + "TYPE_BF16": None, + "TYPE_CUSTOM_OBJECT": None +} + +@dataclass +class Stop: + reason: str + + def __bool__(self): + return False + +Path = namedtuple('Path', ['source', 'target']) +Route = namedtuple('Route', ['model', 'data']) + +class DataFlow: + """A single data flow from or to a model""" + def __init__( + self, + configs: List[Dict], + tensor_names: List[Tuple[str, str]], + inbound: bool = False, + outbound: bool = False, + timeout=1.0 + ): + self._configs = configs + self._tensor_names = tensor_names + self._inbound = inbound + self._outbound = outbound + self._timeout = timeout + self._queue = Queue() + self._optional = False + self._stop_event = threading.Event() + if self._inbound or self._outbound: + self._optional = all([ + config["optional"] if "optional" in config else False + for config in self._configs + ]) + self._queue_consumer = None + + def _process_custom_data(self, tensor: np.ndarray, data_type: str) -> Union[List, np.ndarray, Queue, EnhancedError]: + """ + Process custom data types that are not standard numpy types. + + Args: + tensor: Input tensor data + data_type: The custom data type string (e.g., "TYPE_CUSTOM_BINARY_BASE64") + + Returns: + Union[List, np.ndarray, Queue, EnhancedError]: + - List: For processed data like base64 decoded arrays + - np.ndarray: For tensor data + - Queue: For streaming data (in subclasses) + - EnhancedError: If processing fails (e.g., decoder not initialized) + + Note: + Subclasses override this method to handle specific data types. + The return value is assigned to collected[o_name] in the put() method. + """ + processed = tensor + if self._inbound: + if data_type == "TYPE_CUSTOM_LIST": + logger.debug("DataFlow _process_custom_data: %s", data_type) + processed = [input for input in tensor] + elif data_type == "TYPE_CUSTOM_BINARY_BASE64": + logger.debug("DataFlow _process_custom_data: %s", data_type) + processed = [] + for input in tensor: + if not isinstance(input, np.str_): + error = ErrorFactory.create( + "ERR_DF_002", + message=f"base64 binary must be bytes or string, got {type(input).__name__}", + caller=self, + input_data={"input_type": type(input).__name__}, + expected_data={"valid_types": ["bytes", "np.bytes_", "str", "np.str_"]} + ) + error.log(logger, as_json=False) + return error + try: + decoded = base64.b64decode(input) + except Exception as e: + error = ErrorFactory.create( + "ERR_DF_002", + message=f"Failed to decode base64 string: {e}", + caller=self, + input_data={"input": input}, + expected_data={"valid_encoding": "base64"} + ) + error.log(logger, as_json=False) + return error + processed.append(np.frombuffer(decoded, dtype=np.uint8)) + return processed + + def _is_collected_valid(self, collected: Dict): + if not collected: + logger.info("No data collected from the dataflow: %s", self.in_names) + return False + # check output data integrity + if self._outbound: + for config in self._configs: + name = config["name"] + optional = "optional" in config and config["optional"] + if name not in collected and not optional: + return False + return True + + @property + def in_names(self): + return [i[0] for i in self._tensor_names] + + @property + def o_names(self): + return [i[1] for i in self._tensor_names] + + @property + def optional(self): + return self._optional + + def get_config(self, name: str): + if not self._configs: + return None + return next((config for config in self._configs if config["name"] == name), None) + + def put(self, item: Union[Dict, Error, Stop]): + logger.debug("[DataFlow.put] in_names=%s, o_names=%s, item_type=%s", self.in_names, self.o_names, type(item).__name__) + if not item: + if self._queue_consumer is None: + # pass Error or Stop to the downstream + self._queue.put(item, timeout=self._timeout) + return + # check input data integrity + if self._inbound: + for config in self._configs: + name = config["name"] + optional = "optional" in config and config["optional"] + if name not in item and not optional: + error = ErrorFactory.create( + "ERR_DF_001", + message=f"{name} is not optional and not found from the dataflow input configs", + caller=self, + dataflow_names=self.in_names, + tensor_names=[name], + expected_data={"required_tensors": [c["name"] for c in self._configs if not c.get("optional", False)]}, + actual_data={"provided_tensors": list(item.keys())}, + related_config={"config": config, "all_configs": self._configs} + ) + error.log(logger, as_json=False) + return + # collect data and deposit it to the queue + collected = {} + for i_name, o_name in self._tensor_names: + if i_name not in item: + continue + tensor = item[i_name] + # handling custom data type + if self._inbound or self._outbound: + config = self.get_config(i_name) + if config and not config["data_type"] in np_datatype_mapping: + result = self._process_custom_data(tensor, config["data_type"]) + # Check if processing returned an error + if isinstance(result, EnhancedError): + self._queue.put(result) + return + collected[o_name] = result + else: + collected[o_name] = tensor + else: + collected[o_name] = tensor + # check output data integrity + if not self._is_collected_valid(collected): + error = ErrorFactory.create( + "ERR_DF_007", + message=f"Invalid data collected from the dataflow: {self.in_names}", + caller=self, + severity=ErrorSeverity.WARNING, + dataflow_names=self.in_names, + actual_data={"collected_keys": list(collected.keys()) if collected else []} + ) + error.log(logger, as_json=False) + self._queue.put(error) + return + + # deposit the collected data to the queue + values = {} + results_queues = {} + for k, v in collected.items(): + if isinstance(v, Queue): + results_queues[k] = v + else: + values[k] = v + + if results_queues: + if len(results_queues) > 1: + error = ErrorFactory.create( + "ERR_DF_004", + message=f"Multiple result queues are not supported in one dataflow yet, got {len(results_queues)} queues", + caller=self, + dataflow_names=self.in_names, + actual_data={"n_queues": len(results_queues), "queue_keys": list(results_queues.keys())} + ) + error.log(logger, as_json=False) + self._queue.put(error) + return + q_key, q = next(iter(results_queues.items())) + def on_result_queue_result(item, user_data): + if not item: + # end of the inference + self._queue.put(item, timeout=self._timeout) + return + result_values = user_data[1] + result_values[q_key] = item + self._queue.put(result_values, timeout=self._timeout) + if self._queue_consumer is None: + self._queue_consumer = QueueConsumer("dataflow_queue_consumer", self._stop_event) + collected.pop(q_key) + self._queue_consumer.append_queue(q, on_result_queue_result, (q_key, collected)) + else: + self._queue.put(values, timeout=self._timeout) + return + + def get(self): + logger.debug("[DataFlow.get] Attempting get from queue: in_names=%s, o_names=%s, qsize=%s", self.in_names, self.o_names, self._queue.qsize()) + result = self._queue.get(timeout=self._timeout) + logger.debug("[DataFlow.get] Got result: in_names=%s, item_type=%s", self.in_names, type(result).__name__) + return result + + def stop(self): + self._stop_event.set() + self._queue.put(Stop("Shutdown")) + + def is_stopped(self): + return self._stop_event.is_set() + + def parse_asset_string(self, asset: str): + pieces = asset.split("?") + asset = pieces[0] + params = {} + if len(pieces) > 1: + query_string = pieces[1] + query_params = query_string.split("&") + for param in query_params: + key, value = param.split("=") + params[key] = value + return asset, params + +class VideoInputDataFlow(DataFlow): + """A data flow for live stream data""" + def __init__(self, configs: List[Dict], tensor_names: List[Tuple[str, str]], key_tensor_type: str, timeout=1.0): + super().__init__(configs, tensor_names, True, False, timeout) + self._video_tensor_type = key_tensor_type + self._video_tensor_names = [] + for tensor_name in tensor_names: + config = next((c for c in configs if c["name"] == tensor_name[0]), None) + if config and config["data_type"] == key_tensor_type: + self._video_tensor_names.append(tensor_name[1]) + # Thread pool for collecting frames in background + n_codec_instances = int(os.getenv("N_CODEC_INSTANCES", 1)) + self._frame_collector = ThreadPoolExecutor(max_workers=n_codec_instances) + logger.info(f"VideoInputDataFlow initialized with {n_codec_instances} codec instances") + + def _process_custom_data(self, tensor: np.ndarray, data_type: str): + """ + Process custom video data types. + + Returns: + Union[Queue, np.ndarray, EnhancedError]: + - Queue: Results queue for live video streams (non-blocking) + - np.ndarray: Fallback to parent processing + - EnhancedError: If asset not found or processing fails + """ + if data_type == self._video_tensor_type: + return self._process_video_assets(tensor) + else: + return super()._process_custom_data(tensor, data_type) + + def _collect_live_stream_frames(self, asset_list, chunk_params, media_chunks, results_queue, scaling=None): + """ + Collect frames from live streams in a background thread. + + Args: + asset_list: List of locked assets + chunk_params: List of dicts with n_frames and max_chunks for each asset + media_chunks: List of MediaChunk configurations + results_queue: Queue to put results into + scaling: Optional target resolution as (width, height) tuple + """ + unlocked = False + try: + max_batch_size = int(os.getenv("MAX_BATCH_SIZE", 16)) + batch_size = min(max_batch_size, len(media_chunks)) + extractor_kwargs = {"n_thread": 1, "batch_size": batch_size} + if scaling is not None: + extractor_kwargs["scaling"] = scaling + with MediaExtractor(media_chunks, **extractor_kwargs) as media_extractor: + qs = media_extractor() + eos_flags = [False] * len(qs) + chunks_yielded = [0] * len(qs) # Track chunks per stream + + while not all(eos_flags): + results = [] + for idx, q in enumerate(qs): + # Check if this specific asset is marked for deletion + if asset_list[idx].is_marked_for_deletion(): + logger.info(f"Stream {idx} asset marked for deletion, stopping processing for this stream") + eos_flags[idx] = True + continue + + # Check if this stream has reached its maximum chunks limit + max_chunks = chunk_params[idx].get("max_chunks") + if max_chunks is not None and chunks_yielded[idx] >= max_chunks: + logger.info("Stream %s reached maximum chunks limit: %s", idx, max_chunks) + eos_flags[idx] = True + continue + + n_frames = chunk_params[idx]["n_frames"] + frames = [] + frame_count = 0 + + while not eos_flags[idx]: + try: + frame = q.get(timeout=5.0) + if frame is None: + logger.info("Frame extraction completed for stream %s", idx) + eos_flags[idx] = True + break + frames.append(frame) + frame_count += 1 + + # Put to results queue when we reach n_frames + if n_frames and frame_count >= n_frames: + results.append(frames) + chunks_yielded[idx] += 1 + frames = [] + frame_count = 0 + break # Exit inner loop + + except Empty: + logger.info("EOS on live stream %s", idx) + eos_flags[idx] = True + break + + # TODO WAR on pyservicemaker error + time.sleep(0) + + if results: # Only put if we have collected frames + try: + results_queue.put(results, timeout=10.0) + except Full: + logger.warning(f"Results queue is full, dropping frame batch") + break + + # Unlock assets BEFORE MediaExtractor cleanup to avoid blocking on __exit__ + results_queue.put(Stop("end")) + for asset in asset_list: + asset.unlock() + unlocked = True + + logger.info("Media processing completed") + except Exception as e: + logger.exception(f"Error in live stream frame collection: {e}") + error = ErrorFactory.from_exception(e, caller=self) + error.log(logger, as_json=False) + results_queue.put(error) + finally: + # Fallback: ensure assets are unlocked if exception occurred before unlocking + if not unlocked: + results_queue.put(Stop("end")) + for asset in asset_list: + asset.unlock() + + def _process_video_assets(self, assets: np.ndarray): + """ + Process video assets using Queue-based approach (non-blocking). + + Args: + assets: Array of asset strings with query parameters + + Returns: + Union[Queue, EnhancedError]: Results queue or error + """ + media_chunks = [] + chunk_params = [] # Store n_frames and max_chunks for each asset + asset_list = [] + expected_n_frames = None + expected_scaling = None + + for a in assets: + asset_manager = AssetManager() + asset_id, params = self.parse_asset_string(a) + asset = asset_manager.get_asset(asset_id) + if asset: + asset_list.append(asset) + n = int(params.get("frames", None)) + max_chunks = params.get("chunks", None) + if max_chunks is not None: + max_chunks = int(max_chunks) + + # Validate that all assets have the same n_frames + if expected_n_frames is None: + expected_n_frames = n + elif expected_n_frames != n: + error = ErrorFactory.create( + "ERR_VIDEO_001", + message=f"frames mismatch on multiple assets: {expected_n_frames} != {n}", + caller=self, + severity=ErrorSeverity.WARNING, + actual_data={"n_frames_first": expected_n_frames, "n_frames_current": n} + ) + error.log(logger, as_json=False) + return error + + interval = int(params.get("interval", 0)) + + # Parse scaling parameter (format: widthxheight, e.g., 1920x1080) + scaling = None + scale_param = params.get("scale", None) + if scale_param: + try: + width, height = scale_param.split("x") + scaling = (int(width), int(height)) + except (ValueError, AttributeError) as e: + error = ErrorFactory.create( + "ERR_VIDEO_001", + message=f"Invalid scale parameter format: {scale_param}. Expected format: widthxheight (e.g., 1920x1080)", + caller=self, + severity=ErrorSeverity.WARNING, + input_data={"scale_param": scale_param} + ) + error.log(logger, as_json=False) + return error + + # Validate that all assets have the same scaling + if expected_scaling is None: + expected_scaling = scaling + elif expected_scaling != scaling: + error = ErrorFactory.create( + "ERR_VIDEO_001", + message=f"scale mismatch on multiple assets: {expected_scaling} != {scaling}", + caller=self, + severity=ErrorSeverity.WARNING, + actual_data={"scale_first": expected_scaling, "scale_current": scaling} + ) + error.log(logger, as_json=False) + return error + + media_chunks.append(MediaChunk( + asset.path, + start_pts=0, + interval=interval + )) + chunk_params.append({"n_frames": n, "max_chunks": max_chunks}) + else: + error = ErrorFactory.create( + "ERR_DF_003", + message=f"Asset not found: {asset_id}", + caller=self, + input_data={ + "asset_id": asset_id, + "asset_string": a, + "params": params + }, + metadata={ + "n_frames": params.get("frames"), + "interval": params.get("interval") + } + ) + error.log(logger, as_json=False) + return error + + # Create results queue and submit background thread + results_queue = Queue(maxsize=32) + for asset in asset_list: + asset.lock() + + self._frame_collector.submit( + self._collect_live_stream_frames, asset_list, chunk_params, media_chunks, results_queue, expected_scaling + ) + return results_queue + + def _is_collected_valid(self, collected: Dict): + result = super()._is_collected_valid(collected) + if not result: + return False + return all([n in collected for n in self._video_tensor_names]) + + def stop(self): + super().stop() + if hasattr(self, '_frame_collector') and self._frame_collector: + logger.info("VideoInputDataFlow: shutting down frame collector") + self._frame_collector.shutdown(wait=True) + logger.info("VideoInputDataFlow: frame collector shut down") + self._frame_collector = None + + +class VideoFrameSamplingDataFlow(DataFlow): + """A data flow for video frame sampling - for short videos without chunking""" + def __init__(self, configs: List[Dict], tensor_names: List[Tuple[str, str]], key_tensor_type: str, timeout=1.0): + super().__init__(configs, tensor_names, True, False, timeout) + n_codec_instances = int(os.getenv("N_CODEC_INSTANCES", 1)) + self._media_extractor = MediaExtractor(chunks=[], n_thread=n_codec_instances) + self._video_tensor_type = key_tensor_type + self._video_tensor_names = [] + for tensor_name in tensor_names: + config = next((c for c in configs if c["name"] == tensor_name[0]), None) + if config and config["data_type"] == key_tensor_type: + self._video_tensor_names.append(tensor_name[1]) + self._media_extractor() + self._frame_collector = ThreadPoolExecutor(max_workers=n_codec_instances) + logger.info(f"VideoFrameSamplingDataFlow initialized for short video handling") + + def _process_custom_data(self, tensor: np.ndarray, data_type: str) -> Union[Queue, List, np.ndarray, EnhancedError]: + """ + Process custom video frame sampling data types. + + Returns: + Union[Queue, List, np.ndarray, EnhancedError]: + - Queue: Results queue for frame sampling (streaming) + - List: Fallback to parent processing + - np.ndarray: Fallback to parent processing + - EnhancedError: If asset not found or processing fails + """ + logger.debug("VideoFrameSamplingDataFlow._process_custom_data: %s", data_type) + if data_type == self._video_tensor_type: + return self._do_video_frame_sampling(tensor) + else: + return super()._process_custom_data(tensor, data_type) + + def _collect_frames_from_queue(self, assets, qs, n_frames, results_queue): + """Collect frames from queues in a separate thread - single batch, no chunking""" + total_frames = [[] for _ in qs] + + # Collect n_frames from each queue + for frame_idx in range(n_frames): + for i, q in enumerate(qs): + try: + frame = q.get(timeout=10.0) + if frame is None: + logger.info("Frame extraction completed for stream %s", i) + break + total_frames[i].append(frame) + except Empty: + logger.warning("Frame queue empty for stream %s", i) + break + + # Put the collected frames to results queue + if any(total_frames): + try: + results_queue.put(total_frames, timeout=10.0) + except Full: + error = ErrorFactory.create( + "ERR_DF_005", + caller=self, + severity=ErrorSeverity.WARNING, + metadata={ + "n_frames": n_frames, + "queue_info": "Results queue is full" + } + ) + error.log(logger, as_json=False) + + # Signal completion and unlock assets + results_queue.put(Stop("end")) + for asset in assets: + asset.unlock() + return + + + def _do_video_frame_sampling(self, assets: np.ndarray): + """Extract frames from videos - single batch, no chunking. Parameters: frames, start, duration""" + qs = [] + asset_list = [] + start_times = [] # Store per-asset start times + n_frames = None + duration = None + + for asset_str in assets: + asset_manager = AssetManager() + asset_id, params = self.parse_asset_string(asset_str) + asset = asset_manager.get_asset(asset_id) + if not asset: + error = ErrorFactory.create( + "ERR_DF_003", + message=f"Asset not found: {asset_id}", + caller=self, + input_data={"asset_id": asset_id, "asset_string": asset_str, "params": params} + ) + error.log(logger, as_json=False) + return error + + asset_list.append(asset) + + # Validate frames parameter (required) + if "frames" not in params: + error = ErrorFactory.create( + "ERR_VIDEO_001", + message="Missing required parameter 'frames' for video frame sampling", + caller=self, + severity=ErrorSeverity.CRITICAL, + input_data={"asset_id": asset_id, "params": params}, + expected_data={"required_params": ["frames"]} + ) + error.log(logger, as_json=False) + return error + + try: + n = int(params["frames"]) + if n <= 0: + raise ValueError("frames must be positive") + except (TypeError, ValueError) as e: + error = ErrorFactory.create( + "ERR_VIDEO_001", + message=f"Invalid 'frames' value: {params['frames']} - {e}", + caller=self, + severity=ErrorSeverity.CRITICAL, + input_data={"asset_id": asset_id, "frames": params["frames"]} + ) + error.log(logger, as_json=False) + return error + + if n_frames is None: + n_frames = n + elif n_frames != n: + error = ErrorFactory.create( + "ERR_VIDEO_001", + message=f"frames mismatch on multiple assets: {n_frames} != {n}", + caller=self, + severity=ErrorSeverity.WARNING + ) + error.log(logger, as_json=False) + return error + + # Validate duration parameter (optional, default asset duration) + n = int(params.get("duration", asset.duration)) + if duration is None: + duration = n + elif duration != n: + error = ErrorFactory.create( + "ERR_VIDEO_001", + message=f"duration mismatch on multiple assets: {duration} != {n}", + caller=self, + severity=ErrorSeverity.WARNING + ) + error.log(logger, as_json=False) + return error + + # Extract start parameter (optional, default 0) - can vary per asset + start = int(params.get("start", 0)) + start_times.append(start) + + # Calculate frame sampling interval + interval = duration / n_frames if n_frames > 0 else 0 + + # Setup MediaExtractor queues for each asset + results_queue = Queue() + for asset, start_time in zip(asset_list, start_times): + asset.lock() + chunk = MediaChunk( + asset.path, + start_pts=start_time, + duration=duration if duration else -1, + interval=interval if interval else 0 + ) + qs.append(self._media_extractor.append(chunk)) + + # Submit frame collection task to thread pool + self._frame_collector.submit( + self._collect_frames_from_queue, asset_list, qs, n_frames, results_queue + ) + return results_queue + + def _is_collected_valid(self, collected: Dict): + result = super()._is_collected_valid(collected) + if not result: + return False + return all([n in collected for n in self._video_tensor_names]) + + def stop(self): + super().stop() + if self._media_extractor: + # explicitly call __exit__ due to MediaExtractor internal threads holding the reference + self._media_extractor.__exit__(None, None, None) + self._media_extractor = None + if self._frame_collector is not None: + logger.info(f"VideoFrameSamplingDataFlow: shutting down frame collector") + self._frame_collector.shutdown(wait=True) + logger.info(f"VideoFrameSamplingDataFlow: frame collector shut down") + self._frame_collector = None + +class ImageInputDataFlow(DataFlow): + """A data flow for image data""" + def __init__(self, configs: List[Dict], tensor_names: List[Tuple[str, str]], key_tensor_type: str, timeout=1.0): + super().__init__(configs, tensor_names, True, False, timeout) + self._image_decoder = ImageDecoder(["JPEG", "PNG"]) + self._image_tensor_names = [] + for tensor_name in tensor_names: + config = next((c for c in configs if c["name"] == tensor_name[0]), None) + if config and config["data_type"] == key_tensor_type: + self._image_tensor_names.append(tensor_name[1]) + if not self._image_tensor_names: + raise Exception("Image tensor name not found in the ImageInputDataFlow") + + def _process_custom_data(self, images: np.ndarray, data_type: str) -> Union[List, np.ndarray, EnhancedError]: + """ + Process custom image data types. + + Args: + images: Input image data (base64 or asset references) + data_type: The custom data type string + + Returns: + Union[List, np.ndarray, EnhancedError]: + - List: Decoded images (list of tensors) + - np.ndarray: Fallback to parent processing + - EnhancedError: If decoder not initialized or processing fails + + Note: + Returns EnhancedError (not raises) when decoder is not initialized, + allowing the error to be propagated through the pipeline. + """ + logger.debug("ImageInputDataFlow._process_custom_data: %s", data_type) + if self._image_decoder is None: + return ErrorFactory.create( + "ERR_IMG_003", + caller=self, + severity=ErrorSeverity.CRITICAL + ) + if data_type == "TYPE_CUSTOM_IMAGE_BASE64": + return self._process_base64_image(images) + elif data_type == "TYPE_CUSTOM_IMAGE_ASSETS": + return self._process_image_assets(images) + else: + return super()._process_custom_data(images, data_type) + + def _is_collected_valid(self, collected: Dict): + result = super()._is_collected_valid(collected) + if not result: + return False + return all([n in collected for n in self._image_tensor_names]) + + def _process_base64_image(self, images: np.ndarray): + result = [] + for image in images: + if not isinstance(image, np.str_): + error = ErrorFactory.create( + "ERR_IMG_002", + message=f"base64 image must be bytes or string, got {type(image).__name__}", + caller=self, + input_data={"image_type": type(image).__name__}, + expected_data={"valid_types": ["bytes", "np.bytes_", "str", "np.str_"]} + ) + error.log(logger, as_json=False) + return error + try: + data_prefix, data_payload = image.split(",") + mime_type = data_prefix.split(";")[0].split(":")[1] + format = None + if mime_type == "image/jpeg" or mime_type == "image/jpg": + format = "JPEG" + elif mime_type == "image/png": + format = "PNG" + else: + error = ErrorFactory.create( + "ERR_IMG_001", + message=f"Unsupported image format: {mime_type}", + caller=self, + input_data={"mime_type": mime_type, "data_prefix": data_prefix}, + expected_data={"supported_formats": ["image/jpeg", "image/jpg", "image/png"]} + ) + error.log(logger, as_json=False) + return error + data_payload = base64.b64decode(data_payload) + except Exception: + error = ErrorFactory.create( + "ERR_DF_002", + message=f"Failed to decode base64 string for image data: {image}", + caller=self, + input_data={"input": image}, + expected_data={"valid_encoding": "base64"} + ) + error.log(logger, as_json=False) + return error + tensor = as_tensor(np.frombuffer(data_payload, dtype=np.uint8).copy(), format) + result.append(self._image_decoder.decode(tensor, format)) + logger.debug("ImageInputDataFlow._process_base64_image generates %s tensors", len(result)) + return result + + def _process_image_assets(self, assets: np.ndarray): + result = [] + for asset in assets: + asset_manager = AssetManager() + asset = asset_manager.get_asset(asset) + if not asset: + error = ErrorFactory.create( + "ERR_DF_003", + message=f"Asset not found: {asset}", + caller=self, + input_data={"asset_id": asset} + ) + error.log(logger, as_json=False) + return error + asset.lock() + format = None + if asset.mime_type == "image/jpeg" or asset.mime_type == "image/jpg": + format = "JPEG" + elif asset.mime_type == "image/png": + format = "PNG" + else: + error = ErrorFactory.create( + "ERR_IMG_001", + message=f"Unsupported image format: {asset.mime_type}", + caller=self, + input_data={"mime_type": asset.mime_type, "asset_path": asset.path}, + expected_data={"supported_formats": ["image/jpeg", "image/jpg", "image/png"]} + ) + error.log(logger, as_json=False) + asset.unlock() + return error + with open(asset.path, "rb") as f: + data = f.read() + tensor = as_tensor(np.frombuffer(data, dtype=np.uint8).copy(), format) + result.append(self._image_decoder.decode(tensor, format)) + asset.unlock() + return result + +inbound_dataflow_mapping = { + "TYPE_CUSTOM_IMAGE_BASE64": ImageInputDataFlow, + "TYPE_CUSTOM_IMAGE_ASSETS": ImageInputDataFlow, + "TYPE_CUSTOM_LONG_VIDEO_ASSETS": VideoInputDataFlow, + "TYPE_CUSTOM_VIDEO_CHUNK_ASSETS": VideoFrameSamplingDataFlow +} +class ModelBackend(ABC): + """Interface for standardizing the model backend """ + def __init__(self, model_config: Dict, model_home: str, device_id=0): + self._model_config = model_config + self._model_home = model_home + self._device_id = device_id + + @property + def device_id(self): + return self._device_id + + @property + def model_home(self): + return self._model_home + + @abstractmethod + def __call__(self, *args, **kwargs): + raise Exception("Not Implemented") + + def stop(self): + logger.info(f'Backend for {self._model_config["name"]} stopped') + +class Processor(ABC): + def __init__(self, config: Dict, model_home: str): + self._name = config['name'] + self._kind = config['kind'] if 'kind' in config else 'auto' + self._input = config['input'] if 'input' in config else [] + self._output = config['output'] if 'output' in config else [] + self._config = { 'device_id': 0, 'model_home': model_home } + if 'config' in config: + self._config.update(config['config']) + self._name = config['name'] + self._processor = None + + @property + def name(self): + return self._name + + @property + def input(self): + return self._input + + @property + def output(self): + return self._output + + @property + def kind(self): + return self._kind + + @property + def config(self): + return self._config + + @abstractmethod + def __call__(self, *args, **kwargs): + pass + +class AutoProcessor(Processor): + """AutoPrrocessor loads the preprocessor from pretrained""" + def __init__(self, config: Dict, model_home: str): + super().__init__(config, model_home) + import transformers + self._processor = transformers.AutoProcessor.from_pretrained(model_home) + if self._processor is None: + error = ErrorFactory.create( + "ERR_PROC_002", + message=f"Failed to load AutoProcessor from {model_home}", + caller=self, + severity=ErrorSeverity.CRITICAL, + related_config={"model_home": model_home, "config": config} + ) + error.log(logger, as_json=False) + raise RuntimeError(str(error)) + + def __call__(self, *args): + # TODO value parser should be configurable + return self._processor(*args) + +class CustomProcessor(Processor): + """CustomProcessor loads the processor from custom module""" + def __init__(self, config: Dict, model_home: str): + super().__init__(config, model_home) + if not hasattr(custom, "create_instance"): + error = ErrorFactory.create( + "ERR_PROC_003", + caller=self, + severity=ErrorSeverity.CRITICAL, + related_config={"config": config, "model_home": model_home} + ) + error.log(logger, as_json=False) + raise RuntimeError(str(error)) + self._processor = custom.create_instance(self.name, self.config) + if self._processor is not None: + logger.info(f"Custom processor {self._processor.name} created") + else: + error = ErrorFactory.create( + "ERR_PROC_004", + message=f"Failed to create processor {self.name}", + caller=self, + severity=ErrorSeverity.CRITICAL, + related_config={"processor_name": self.name, "config": config} + ) + error.log(logger, as_json=False) + raise RuntimeError(str(error)) + + def __call__(self, *args): + ret = self._processor(*args) + if not isinstance(ret, tuple): + ret = ret, + return ret + +class Collector(ABC): + """Collector is an interface for collecting data from the data flow""" + def collect(self): + raise NotImplementedError("Not implemented") + + def stop(self): + raise NotImplementedError("Not implemented") + +class SingleFlowCollector(Collector): + """SingleFlowCollector is a collector for a single data flow""" + def __init__(self, data_flow: DataFlow): + self._data_flow = data_flow + + def collect(self): + return self._data_flow.get() + + def stop(self): + return self._data_flow.stop() + +class AggregationFlowCollector(Collector): + """AggregationFlowCollector is a collector aggregating multiple data flows""" + def __init__(self, data_flows: List[DataFlow], timeout=None): + self._data_flows = data_flows + self._executor = ThreadPoolExecutor(max_workers=1) + self._queue = Queue() + self._stop_event = threading.Event() + self._timeout = timeout + self._futures = self._executor.submit(self._collect) + + def collect(self): + return self._queue.get(timeout=self._timeout) + + def _collect(self): + logger.info(f"AggregationFlowCollector starts collecting data") + while not self._stop_event.is_set(): + result = {} + completed = [] + for data_flow in self._data_flows: + # continue reading from each data flow until we get some data + while not self._stop_event.is_set(): + try: + data = data_flow.get() + if isinstance(data, Error) or isinstance(data, Stop): + completed.append(data) + else: + result.update(data) + break + except Empty: + continue + if result: + self._queue.put(result) + if completed and all([isinstance(c, Stop) for c in completed]): + self._queue.put(Stop("All data flows completed")) + elif any([isinstance(c, Error) for c in completed]): + self._queue.put(Error("One or more data flows ended in error")) + + def stop(self): + logger.info(f"AggregationFlowCollector destructing...") + self._stop_event.set() + for data_flow in self._data_flows: + data_flow.stop() + self._executor.shutdown(wait=True) + self._queue.put(Stop("Shutdown")) + logger.info(f"AggregationFlowCollector destructed") + +class MultiFlowCollector(Collector): + """MultiFlowCollector is a collector for multiple data flows""" + def __init__(self, data_flows: List[DataFlow], timeout=None): + self._data_flows = data_flows + self._executor = ThreadPoolExecutor(max_workers=len(data_flows)) + self._queue = Queue() + self._stop_event = threading.Event() + self._condition = threading.Condition() + self._active_flow = -1 + self._timeout = timeout + self._futures = [self._executor.submit(self._poll, i) for i in range(len(self._data_flows))] + + def collect(self): + data = self._queue.get(timeout=self._timeout) + return data + + def _poll(self, index: int): + logger.info("Start polling data flow %s", self._data_flows[index].in_names) + data_flow = self._data_flows[index] + n_data = 0 + while not self._stop_event.is_set(): + try: + data = data_flow.get() + is_stop = isinstance(data, Stop) + if is_stop and n_data == 0: + # empty data flow, skip it + logger.info(f"Empty data flow {data_flow.in_names}, skip it") + continue + elif not is_stop: + n_data += 1 + # try grabbing the queue + with self._condition: + while self._active_flow != index and self._active_flow != -1: + self._condition.wait() + if self._active_flow == -1: + self._active_flow = index + self._queue.put(data) + with self._condition: + if isinstance(data, Stop): + logger.info("Data flow %s ended in: %s", data_flow.in_names, data) + self._active_flow = -1 + n_data = 0 + self._condition.notify_all() + except Empty: + continue + + + def stop(self): + logger.info(f"MultiFlowCollector destructing...") + self._stop_event.set() + for data_flow in self._data_flows: + data_flow.stop() + logger.info(f"MultiFlowCollector data flows stopped") + self._executor.shutdown(wait=True) + self._queue.put(Stop("Shutdown")) + logger.info(f"MultiFlowCollector destructed") + + +class AsyncDispatcher: + """AsyncDispatcher is a dispatcher of synchronous data flows to asynchronous queues""" + def __init__(self, collector: Collector, loop: asyncio.AbstractEventLoop, n_max_async_queues: int = 100): + self._collector = collector + self._loop = loop + self._consumer_queues = Queue(maxsize=n_max_async_queues) + self._stop_event = threading.Event() + self._dispatcher_thread = threading.Thread(target=self.run, daemon=True) + self._dispatcher_thread.start() + + def append_async_queue(self, async_queue: asyncio.Queue): + try: + self._consumer_queues.put_nowait(async_queue) + except Full: + error = ErrorFactory.create( + "ERR_ASYNC_003", + caller=self, + severity=ErrorSeverity.CRITICAL, + metadata={"queue_maxsize": self._consumer_queues.maxsize} + ) + error.log(logger, as_json=False) + raise RuntimeError(str(error)) + + def run(self): + if self._collector is None: + error = ErrorFactory.create( + "ERR_ASYNC_001", + caller=self, + severity=ErrorSeverity.CRITICAL + ) + error.log(logger, as_json=False) + raise RuntimeError(str(error)) + if self._loop is None: + error = ErrorFactory.create( + "ERR_ASYNC_002", + caller=self, + severity=ErrorSeverity.CRITICAL + ) + error.log(logger, as_json=False) + raise RuntimeError(str(error)) + logger.info("AsyncDispatcher started") + while not self._stop_event.is_set(): + try: + async_queue = self._consumer_queues.get(timeout=1.0) + except Empty: + continue + except Exception as e: + logger.exception(e) + continue + + # For this consumer queue, keep collecting until Stop/Error is received + logger.debug("[AsyncDispatcher] Starting collection loop for new async_queue") + collect_attempts = 0 + while not self._stop_event.is_set(): + try: + logger.debug("[AsyncDispatcher] Attempting to collect (attempt #%s)", collect_attempts) + data = self._collector.collect() + logger.debug("[AsyncDispatcher] Collected data: %s", type(data).__name__) + except Empty: + collect_attempts += 1 + if collect_attempts % 100 == 0: + logger.debug("[AsyncDispatcher] Still waiting for data after %s attempts", collect_attempts) + continue + except Exception as e: + logger.exception(e) + data = Error(str(e)) + try: + logger.debug("[AsyncDispatcher] Putting data into async_queue") + async def _async_put(q, item): + await q.put(item) + asyncio.run_coroutine_threadsafe(_async_put(async_queue, data), self._loop) + logger.debug("[AsyncDispatcher] Successfully put data into async_queue") + except Exception as e: + logger.exception(e) + continue + + if isinstance(data, Stop): + logger.info("AsyncDispatcher delivered Stop message: %s", data) + break + + logger.info("AsyncDispatcher stopped") + + def stop(self): + self._stop_event.set() + logger.info("AsyncDispatcher stopping") + +class ModelOperator: + """An model operator runs a single model""" + def __init__(self, model_config:Dict, model_repo: str): + self._model_name = model_config["name"] + self._model_home = os.path.join(model_repo, self._model_name) + self._in: List[DataFlow] = [] + self._out: List[DataFlow] = [] + self._running = False + self._preprocessors = [] + self._postprocessors = [] + self._model_config = model_config + self._backend = None + self._stop_event = threading.Event() + self._collector = None + self._future_consumer = None + + + @property + def model_name(self): + return self._model_name + + @property + def model_config(self): + return self._model_config.copy() + + @property + def inputs(self): + return self._in.copy() + + @property + def outputs(self): + return self._out.copy() + + def bind_input(self, configs: List[Dict], targets: List[str]=[]): + if not targets: + targets = [i['name'] for i in configs] + flow = None + tensor_names = [(i['name'], o) for i, o in zip(configs, targets)] + tensor_types = [i['data_type'] for i in configs] + image_tensor_type = None + for tensor_type in tensor_types: + if tensor_type in inbound_dataflow_mapping: + image_tensor_type = tensor_type + break + if image_tensor_type is None: + flow = DataFlow(configs, tensor_names, inbound=True) + else: + # customized inbound data flow + flow = inbound_dataflow_mapping[image_tensor_type](configs, tensor_names, image_tensor_type) + self._in.append(flow) + logger.info("Data flow < %s -> %s > connected to model %s", flow.in_names, flow.o_names, self._model_name) + return flow + + def bind_output(self, configs: List[Dict], sources: List[str]=[]): + if not sources: + sources = [i['name'] for i in configs] + tensor_names = [(i, o['name']) for i, o in zip(sources, configs)] + flow = DataFlow(configs, tensor_names, outbound=True) + self._out.append(flow) + logger.info(f"model {self._model_name} connected to Data flow < {flow.in_names} -> {flow.o_names} >") + return flow + + def import_input(self, input: DataFlow): + self._in.append(input) + + def import_output(self, output: DataFlow): + self._out.append(output) + + def set_backend(self, backend: ModelBackend): + self._backend = backend + + def _pass_error_or_stop_downstream(self, data: Union[Error, Stop]): + """ + Pass Error or Stop message to downstream dataflows. + + Args: + data: Error or Stop object to pass downstream + + Note: + If _future_consumer exists, the error/stop is wrapped in a Future + and sent through the consumer. Otherwise, it's sent directly to + output dataflows. + """ + if self._future_consumer is None: + # pass the error or stop message downstream + for out in self._out: + logger.debug("Passing error or stop message to %s", out.in_names) + out.put(data) + else: + f = Future() + f.set_result(data) + self._future_consumer.append_future(f) + + def run(self): + logger.debug("Model operator for %s started", self._model_name) + + # create preprocessors + for kind, processors in [("preprocessors", self._preprocessors), ("postprocessors", self._postprocessors)]: + if kind in self._model_config: + for config in self._model_config[kind]: + ProcessorClass = None + if config["kind"] == "auto": + ProcessorClass = AutoProcessor + elif config["kind"] == "custom": + ProcessorClass = CustomProcessor + if ProcessorClass is not None: + processors.append( + ProcessorClass( + config=config, + model_home=self._model_home + ) + ) + else: + raise Exception("Invalid Processor") + # backend loop + self._collector = self._create_collector() + if not self._out: + error = ErrorFactory.create( + "ERR_MO_003", + caller=self, + model_name=self._model_name, + severity=ErrorSeverity.CRITICAL, + related_config={"model_config": self._model_config} + ) + error.log(logger, as_json=False) + raise RuntimeError(str(error)) + while not self._stop_event.is_set(): + try: + # collect input data until Stop is received + data = self._collector.collect() + if isinstance(data, Stop) or isinstance(data, Error): + self._pass_error_or_stop_downstream(data) + continue + logger.info("Input collected from %s: %s", self._collector.__class__.__name__, data) + + # convert data to args and kwargs based on if explicit batching is required + args = [] + kwargs = data + values = [v for v in data.values()] + lengths = [ + len(v) if isinstance(v, list) or v.ndim > 0 else 0 + for v in values + ] + if any(isinstance(v, list) for v in values) and \ + all(length == lengths[0] for length in lengths): + logger.info( + "Explicit batching detected, " + "splitting the data into multiple inference requests" + ) + # construct multiple inference requests + args = split_tensor_in_dict(kwargs) + kwargs = {} + # call preprocess() before passing args to the backend + processed, passthrough_tensors = self._preprocess(args if args else [kwargs]) + in_names = [i["name"] for i in self._model_config["input"]] + if not processed: + error = ErrorFactory.create( + "ERR_MO_001", + caller=self, + model_name=self._model_name, + input_data={ + "args": str(args)[:500] if args else "", + "kwargs_keys": list(kwargs.keys()) if kwargs else [] + }, + related_config={ + "preprocessors": [p.name for p in self._preprocessors], + "model_input": in_names + } + ) + error.log(logger, as_json=False) + self._pass_error_or_stop_downstream(error) + continue + if args: + # explicitly batched data + args = processed + elif kwargs: + # implicitly batched data + kwargs = processed[0] + else: + error = ErrorFactory.create( + "ERR_MO_004", + message="Invalid result from preprocess: both args and kwargs are empty", + caller=self, + model_name=self._model_name, + input_data={ + "processed": str(processed)[:500] if processed else "", + "passthrough_tensors_keys": list(passthrough_tensors[0].keys()) if passthrough_tensors else [] + } + ) + error.log(logger, as_json=False) + self._pass_error_or_stop_downstream(error) + continue + # execute inference backend and collect result + logger.debug("Model %s invokes backend %s with %s", self._model_name, self._backend.__class__.__name__, args if args else kwargs) + rs = self._backend(*args, **kwargs) + if isinstance(rs, Future): + def on_future_result(result: Dict|Error|Stop, user_data: List): + if isinstance(result, Error) or isinstance(result, Stop): + # Pass error/stop directly - already in FutureConsumer callback context + for out in self._out: + out.put(result) + return + self._on_inference_result(result, user_data[0]) + if self._future_consumer is None: + self._future_consumer = FutureConsumer( + name=self._model_name, + stop_event=self._stop_event, + result_callback=on_future_result + ) + self._future_consumer.append_future(rs, passthrough_tensors) + elif inspect.isgenerator(rs): + self._on_inference_result(rs, passthrough_tensors) + elif isinstance(rs, Error) or isinstance(rs, Stop): + self._pass_error_or_stop_downstream(rs) + continue + else: + error_msg = ( + f"Invalid result from backend {self._backend.__class__.__name__}: {rs}" + " Expected a Future or a generator" + ) + raise ValueError(error_msg) + except Empty: + continue + except Exception as e: + error = ErrorFactory.from_exception( + e, + caller=self, + category=ErrorCategory.BACKEND if "backend" in str(e).lower() else ErrorCategory.INTERNAL, + model_name=self._model_name, + dataflow_names=[name for o in self._out for name in o.in_names], + input_data={ + "data_keys": list(data.keys()) if 'data' in locals() and isinstance(data, dict) else [] + }, + related_config={ + "model_config": { + "name": self._model_config.get("name"), + "input": [i["name"] for i in self._model_config.get("input", [])], + "output": [o["name"] for o in self._model_config.get("output", [])] + }, + "backend": self._backend.__class__.__name__ if self._backend else None, + "n_preprocessors": len(self._preprocessors), + "n_postprocessors": len(self._postprocessors) + } + ) + error.log(logger, as_json=False) + + # Pass error downstream + self._pass_error_or_stop_downstream(error) + + logger.info(f"Model operator {self._model_name} stopped") + + def stop(self): + logger.info(f"Model operator {self._model_name} is stopping") + self._stop_event.set() + if self._collector is not None: + self._collector.stop() + if self._backend is not None: + self._backend.stop() + self._in.clear() + self._out.clear() + self._preprocessors.clear() + self._postprocessors.clear() + + def _on_inference_result(self, results: Generator, passthrough_tensors: Dict): + for r in results: + logger.debug( + "Model %s generated result from backend %s: %s", + self._model_name, self._backend.__class__.__name__, r + ) + # iterate the result list and postprocess each of them + for out in self._out: + output_data = {n : [] for n in out.in_names} + if isinstance(r, list): + # we get a batch + if len(passthrough_tensors) == 1: + passthrough_tensors = passthrough_tensors*len(r) + for i, result in enumerate(r): + if passthrough_tensors: + result.update(passthrough_tensors[i]) + result = self._postprocess(result) + if not all([n in result for n in out.in_names]): + error = ErrorFactory.create( + "ERR_MO_002", + message=f"Model output incomplete (batch item {i})", + caller=self, + model_name=self._model_name, + dataflow_names=out.in_names, + expected_data={"required_outputs": out.in_names}, + actual_data={"provided_outputs": list(result.keys())}, + related_config={"postprocessors": [p.name for p in self._postprocessors]} + ) + error.log(logger, as_json=False) + continue + # collect the result + for n, v in output_data.items(): + if n in result: + v.append(result[n]) + else: + v.append(None) + else: + # implicit batching + if passthrough_tensors: + r.update(passthrough_tensors[0]) + output_data = self._postprocess(r) + if not all([n in output_data for n in out.in_names]): + error = ErrorFactory.create( + "ERR_MO_002", + message="Model output incomplete", + caller=self, + model_name=self._model_name, + dataflow_names=out.in_names, + expected_data={"required_outputs": out.in_names}, + actual_data={"provided_outputs": list(output_data.keys())}, + related_config={"postprocessors": [p.name for p in self._postprocessors]} + ) + error.log(logger, as_json=False) + continue + logger.debug("ModelOperator of %s deposits result: %s", self._model_name, output_data) + out.put(output_data) + + def _preprocess(self, args: List): + # go through the preprocess chain + outcome = args + for preprocessor in self._preprocessors: + result = [] + for data in outcome: + # initialize the processed as the original values + processed = {k: v for k, v in data.items()} + # trigger the preprocessor if all the input tensors are present + if all([i in data for i in preprocessor.input]): + input = [processed.pop(i) for i in preprocessor.input] + logger.debug("%s invokes preprocessor %s with given input %s", self._model_name, preprocessor.name, input) + output = preprocessor(*input) + logger.debug("%s preprocessor %s generated output %s", self._model_name, preprocessor.name, output) + if len(output) != len(preprocessor.output): + error = ErrorFactory.create( + "ERR_PROC_001", + message=f"Number of preprocessing output doesn't match the configuration, expecting {len(preprocessor.output)}, while getting {len(output)}", + caller=self, + severity=ErrorSeverity.WARNING, + model_name=self._model_name, + expected_data={"output_count": len(preprocessor.output), "output_names": preprocessor.output}, + actual_data={"output_count": len(output)}, + related_config={"preprocessor": preprocessor.name} + ) + error.log(logger, as_json=False) + continue + # update as processed + for key, value in zip(preprocessor.output, output): + if value is not None: + processed[key] = value + else: + logger.info( + "Pre-processor %s skipped on mismatching input", + preprocessor.name + ) + result.append(processed) + # update outcome + outcome = result + # correct the data type and extract the passthrough tensors + passthrough_tensors = [] + for data in outcome: + passthrough_tensor = {} + for key in data: + value = data[key] + i_config = next((i for i in self._model_config['input'] if i["name"] == key), None) + if i_config is None: + logger.info("%s from preprocessed is not found in the model input config, adding it as a passthrough tensor", key) + passthrough_tensor[key] = value + else: + data_type = i_config["data_type"] + if isinstance(value, np.ndarray): + data_type = np_datatype_mapping[data_type] + if value.dtype != data_type: + data[key] = value.astype(data_type) + elif isinstance(value, torch.Tensor): + data_type = torch_datatype_mapping[data_type] + if value.dtype != data_type: + data[key] = value.to(data_type) + for key in passthrough_tensor: + data.pop(key) + passthrough_tensors.append(passthrough_tensor) + return outcome, passthrough_tensors + + def _postprocess(self, data: Dict): + processed = {k: v for k, v in data.items()} + for processor in self._postprocessors: + if not all([i in processed for i in processor.input]): + logger.info( + "Post-processor %s skipped on mismatching input", + processor.name + ) + continue + input = [processed.pop(i) for i in processor.input] + logger.debug("Post-processor %s invoked with given input %s", processor.name, input) + output = processor(*input) + logger.debug("Post-processor generated output %s", output) + if len(output) != len(processor.output): + error = ErrorFactory.create( + "ERR_PROC_001", + message=f"Number of postprocessing output doesn't match the configuration, expecting {len(processor.output)}, while getting {len(output)}", + caller=self, + severity=ErrorSeverity.WARNING, + model_name=self._model_name, + expected_data={"output_count": len(processor.output), "output_names": processor.output}, + actual_data={"output_count": len(output)}, + related_config={"postprocessor": processor.name} + ) + error.log(logger, as_json=False) + continue + # update as processed + for key, value in zip(processor.output, output): + processed[key] = value + return processed + + def _create_collector(self): + if len(self._in) == 1: + logger.info("Single data flow input detected, using single flow collector on model %s", self._model_name) + return SingleFlowCollector(self._in[0]) + else: + outputs = [set(d.o_names) for d in self._in] + intersection = set.intersection(*outputs) + if len(intersection) == 0 and not any([d.optional for d in self._in]): + logger.info("Aggregation data flow input detected, using aggregation flow collector on model %s", self._model_name) + return AggregationFlowCollector(self._in, timeout=1.0) + else: + logger.info("Multi data flow input detected, using multi flow collector on model %s", self._model_name) + return MultiFlowCollector(self._in, timeout=1.0) + +class InferenceBase: + """The base model that drives the inference flow""" + def initialize(self): + # Enable global error collection FIRST - before any error can occur + enable_global_error_collection(max_errors=5000) + atexit.register(InferenceBase._export_errors) + logger.info("Enhanced error collection enabled (with atexit export)") + + def parse_route(i1, i2) -> Route: + m1 = '' + m2 = '' + d1 = [] + d2 = [] + s = i1.split(':') + t = i2.split(':') + if len(s) > 2 or len(t) > 2: + error = ErrorFactory.create( + "ERR_ROUTE_001", + message=f"Invalid route format: {i1} -> {i2}", + component="InferenceBase", + operation="initialize", + severity=ErrorSeverity.CRITICAL, + input_data={"source": i1, "target": i2} + ) + error.log(logger, as_json=False) + raise RuntimeError(str(error)) + else: + m1 = s[0] + m2 = t[0] + if len(s) == 2 and s[1]: + data = json.loads(s[1]) + if isinstance(data, list): + d1 = data + else: + error = ErrorFactory.create( + "ERR_ROUTE_001", + message="Invalid route: source data must be a list", + component="InferenceBase", + operation="initialize.parse_route", + severity=ErrorSeverity.CRITICAL, + input_data={"source_data": data} + ) + error.log(logger, as_json=False) + raise RuntimeError(str(error)) + if len(t) == 2 and t[1]: + data = json.loads(t[1]) + if isinstance(data, list): + d2 = data + else: + error = ErrorFactory.create( + "ERR_ROUTE_001", + message="Invalid route: target data must be a list", + component="InferenceBase", + operation="initialize.parse_route", + severity=ErrorSeverity.CRITICAL, + input_data={"target_data": data} + ) + error.log(logger, as_json=False) + raise RuntimeError(str(error)) + + return Route(Path(m1, m2), Path(d1, d2)) + + + self._operators: ModelOperator = [] + self._inputs: List[DataFlow] = [] + self._outputs: List[DataFlow] = [] + self._input_config = OmegaConf.to_container(global_config.input) + self._output_config = OmegaConf.to_container(global_config.output) + self._model_repo = global_config.model_repo + self._ready = False + + if not os.path.exists(self._model_repo): + error = ErrorFactory.create( + "ERR_SYS_001", + message=f"Model repository {self._model_repo} does not exist", + caller=self, + severity=ErrorSeverity.CRITICAL, + related_config={"model_repo": self._model_repo} + ) + error.log(logger, as_json=False) + raise RuntimeError(str(error)) + + # set up the inference flow + for model_config in global_config.models: + self._operators.append(ModelOperator(OmegaConf.to_container(model_config), self._model_repo)) + + # initialize the operators + for operator in self._operators: + model_config = next((m for m in global_config.models if m.name == operator.model_name), None) + model_home = os.path.join(self._model_repo, operator.model_name) + backend_spec = model_config.backend.split('/') + backend_instance = self._create_backend(backend_spec, model_config, model_home) + if backend_instance is None: + raise RuntimeError(f"Unable to create backend {model_config.backend}") + operator.set_backend(backend_instance) + + if hasattr(global_config, "routes"): + # go through the routing table + for k, v in global_config.routes.items(): + route = parse_route(k, v) + logger.debug("Adding route %s", route) + # neither source nor target model is specified. + if not route.model.source and not route.model.target: + # this is a direct passthrough in the top level, we can use a standalone dataflow + if not route.data.source and not route.data.target: + logger.error(f"Invalid route: {route}, source or target is required for a direct pass") + continue + dataflow = None + if route.data.source: + s_configs = [OmegaConf.to_container(c) for c in global_config.input if c.name in route.data.source] + if len(s_configs) != len(route.data.source): + logger.error(f"Not all the sources are found in the input configs, unable to create passthrough dataflow") + continue + o_tensor_names = route.data.target if route.data.target else route.data.source + if any(n not in [c.name for c in global_config.output] for n in o_tensor_names): + logger.warning(f"Not all the output tensors from {o_tensor_names} are found in the output configs, consider adding a postprocessor to generate the missing tensors") + tensor_names = [(i['name'], o) for i, o in zip(s_configs, o_tensor_names)] + dataflow = DataFlow(configs=s_configs, tensor_names=tensor_names, inbound=True) + elif route.data.target: + t_configs = [OmegaConf.to_container(c) for c in global_config.output if c.name in route.data.target] + if len(t_configs) != len(route.data.target): + logger.error(f"Not all the targets are found in the output configs, unable to create passthrough dataflow") + continue + if any(n not in [c.name for c in global_config.input] for n in route.data.target): + logger.error(f"Not all the targets are found in the input configs, unable to create passthrough dataflow") + continue + tensor_names = [(i, i) for i in route.data.target] + dataflow = DataFlow(configs=t_configs, tensor_names=tensor_names, outbound=True) + else: + logger.error(f"Invalid route: {route}, source or target is required for a direct pass") + continue + if dataflow is not None: + self._inputs.append(dataflow) + self._outputs.append(dataflow) + elif route.model.target: + operator1 = next((o for o in self._operators if o.model_name == route.model.target), None) + if operator1 is None: + error = ErrorFactory.create( + "ERR_ROUTE_002", + message=f"Model {route.model.target} in the routes not found", + caller=self, + severity=ErrorSeverity.CRITICAL, + input_data={"model_name": route.model.target}, + related_config={"available_models": [o.model_name for o in self._operators]} + ) + error.log(logger, as_json=False) + raise RuntimeError(str(error)) + # both source and target model are specified. + if route.model.source: + # this is the model provides data + operator2 = next((o for o in self._operators if o.model_name == route.model.source), None) + if operator2 is None: + error = ErrorFactory.create( + "ERR_ROUTE_002", + message=f"Model {route.model.source} in the routes not found", + caller=self, + severity=ErrorSeverity.CRITICAL, + input_data={"model_name": route.model.source}, + related_config={"available_models": [o.model_name for o in self._operators]} + ) + error.log(logger, as_json=False) + raise RuntimeError(str(error)) + flow = None + if route.data.source and route.data.target: + tensor_names = [(i, o) for i, o in zip(route.data.source, route.data.target)] + flow = DataFlow(configs=None, tensor_names=tensor_names) + elif route.data.source: + tensor_names = [(i, i) for i in route.data.source] + flow = DataFlow(configs=None, tensor_names=tensor_names) + elif route.data.target: + tensor_names = [(i, i) for i in route.data.target] + flow = DataFlow(configs=None, tensor_names=tensor_names) + else: + logger.error(f"Invalid route: {route}, source or target is required for connecting two models, {operator2.model_name} and {operator1.model_name}") + continue + operator2.import_output(flow) + operator1.import_input(flow) + else: + # this is the top level input + if route.data.source: + s_configs = [] + for s in route.data.source: + c = next((c for c in global_config.input if c.name == s), None) + if c is None: + logger.error(f"Input {s} not found in the input configs, unable to create dataflow") + continue + s_configs.append(OmegaConf.to_container(c)) + else: + s_configs = [OmegaConf.to_container(c) for c in global_config.input] + self._inputs.append(operator1.bind_input(s_configs, route.data.target)) + # only source model is specified. + elif route.model.source: + # this is the top level output + operator = next((o for o in self._operators if o.model_name == route.model.source), None) + if operator is None: + error = ErrorFactory.create( + "ERR_ROUTE_002", + message=f"Model {route.model.source} in the routes not found", + caller=self, + severity=ErrorSeverity.CRITICAL, + input_data={"model_name": route.model.source}, + related_config={"available_models": [o.model_name for o in self._operators]} + ) + error.log(logger, as_json=False) + raise RuntimeError(str(error)) + if route.data.target: + configs = [OmegaConf.to_container(c) for c in global_config.output if c.name in route.data.target] + self._outputs.append(operator.bind_output(configs, route.data.source)) + else: + configs = [OmegaConf.to_container(c) for c in global_config.output if c.name in route.data.source] + if route.data.source == [c['name'] for c in configs]: + self._outputs.append(operator.bind_output(configs)) + else: + logger.warning(f"Output of {operator.model_name} is not compatible with top level output, be sure to add a top level postprocessor") + dataflow = DataFlow(configs=None, tensor_names=[(i, i) for i in route.data.source]) + operator.import_output(dataflow) + self._outputs.append(dataflow) + else: + logger.warning("Empty route entry") + elif len(self._operators) == 1: + # default flow for single model use case without an explicit route + operator = self._operators[0] + # direct connection from top level input to the model + self._inputs.append(operator.bind_input(OmegaConf.to_container(global_config.input))) + # direct connection from the model to top level output + self._outputs.append(operator.bind_output(OmegaConf.to_container(global_config.output))) + else: + logger.error("Unable to set up inference routes") + if len(self._inputs) == 0 or len(self._outputs) == 0: + error = ErrorFactory.create( + "ERR_SYS_002", + message="Either input or output is empty, inference pipeline is not complete", + caller=self, + severity=ErrorSeverity.CRITICAL, + actual_data={ + "n_inputs": len(self._inputs), + "n_outputs": len(self._outputs) + }, + related_config={ + "models": [o.model_name for o in self._operators] + } + ) + error.log(logger, as_json=False) + raise RuntimeError(str(error)) + self._executor = ThreadPoolExecutor(max_workers=len(self._operators)) + self._future = None + + # sanity check on vision pipeline + try: + Pipeline("vision") + except Exception as e: + logger.exception(e) + + # post processing: + self._processors = [] + if hasattr(global_config, "postprocessors"): + configs = OmegaConf.to_container(global_config.postprocessors) + for config in configs: + if config["kind"] == "custom": + self._processors.append( + CustomProcessor(config, self._model_repo) + ) + + # data collector + self._collector = ( + AggregationFlowCollector(self._outputs) + if len(self._outputs) > 1 + else SingleFlowCollector(self._outputs[0]) + ) + # async dispatcher + self._async_dispatcher = None + # stop event for request lifecycle + self._stop_event = threading.Event() + + + # start the inference flow + for operator in self._operators: + self._submit(operator) + + self._ready = True + logger.info("Inference Engine initialized for %s", global_config.name) + logger.info("Inputs: %s", [f.o_names for f in self._inputs]) + logger.info("Outputs: %s", [f.o_names for f in self._outputs]) + + def is_healthy(self): + return self._ready + + def finalize(self): + if self._stop_event is not None: + self._stop_event.set() + if self._async_dispatcher is not None: + self._async_dispatcher.stop() + self._async_dispatcher = None + if self._collector is not None: + self._collector.stop() + self._collector = None + for operator in self._operators: + operator.stop() + self._executor.shutdown() + + logger.info("Inference pipeline is finalized") + + @staticmethod + def _export_errors(): + """Export collected errors to JSON file for test validation. + + Uses ERROR_EXPORT_PATH env var to determine the full file path + (defaults to /tmp/inference_errors.json). + """ + error_export_path = os.environ.get("ERROR_EXPORT_PATH", "/tmp/inference_errors.json") + try: + collector = get_global_error_collector() + if collector: + stats = collector.get_stats(include_recent=100) + collector.export_to_json(error_export_path, include_stack_traces=False) + logger.info("Exported %s errors to %s", stats['total_errors'], error_export_path) + except Exception as e: + logger.warning("Failed to export errors: %s", e) + + def _create_backend(self, backend_spec: List[str], model_config: Dict, model_home: str) -> ModelBackend | None: + raise NotImplementedError("Subclass must implement this method") + + def _on_operator_done(self, future: Future, operator: ModelOperator): + """Callback when an operator thread completes. Handles exceptions by logging to error JSON.""" + try: + future.result() # This raises if the thread had an exception + except Exception as e: + error = ErrorFactory.from_exception( + e, + caller=self, + category=ErrorCategory.INTERNAL, + model_name=operator.model_name, + related_config={ + "model_config": { + "name": operator.model_config.get("name"), + "preprocessors": operator.model_config.get("preprocessors", []), + "postprocessors": operator.model_config.get("postprocessors", []) + } + } + ) + error.log(logger, as_json=False) + + logger.critical(f"Operator {operator.model_name} thread failed with exception: {e}") + logger.critical("Forcing application exit due to model thread failure") + + # Export errors and force exit - no graceful shutdown for unrecoverable critical exception + InferenceBase._export_errors() + os._exit(1) + + def _submit(self, op: ModelOperator): + future = self._executor.submit(lambda: op.run()) + future.add_done_callback(lambda f: self._on_operator_done(f, op)) \ No newline at end of file diff --git a/tools/inference_builder/lib/responder.py b/tools/inference_builder/lib/responder.py new file mode 100644 index 00000000..2e5c24ca --- /dev/null +++ b/tools/inference_builder/lib/responder.py @@ -0,0 +1,137 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from lib.asset_manager import AssetManager +from lib.utils import create_jinja2_env, stack_tensors_in_dict, convert_list, get_logger +from omegaconf.errors import ConfigKeyError +from omegaconf import OmegaConf +import base64 +from config import global_config +import json +import re +from pydantic import BaseModel +from typing import Dict, Any + +jinja2_env = create_jinja2_env() + +class ResponderBase: + def __init__(self): + self._action_map = {} + self._inference = None + self._request_templates = {} + self._response_templates = {} + self._asset_manager = AssetManager() + self.logger = get_logger(__name__) + + # load the request and response templates + try: + input_config = OmegaConf.to_container(global_config.server.responders) + except ConfigKeyError: + raise ValueError("No responders found in the config") + for rp, value in input_config.items(): + req_tpls = value.get("requests", {}) + for k, tpl in req_tpls.items(): + if rp not in self._request_templates: + self._request_templates[rp] = {} + if isinstance(tpl, list): + self._request_templates[rp][k] = tpl + else: + self._request_templates[rp][k] = base64.b64decode(tpl).decode() + res_tpls = value.get("responses", {}) + for k, tpl in res_tpls.items(): + if rp not in self._response_templates: + self._response_templates[rp] = {} + if isinstance(tpl, list): + self._response_templates[rp][k] = tpl + else: + self._response_templates[rp][k] = base64.b64decode(tpl).decode() + + async def take_action(self, action_name:str, *args): + action = self._action_map.get(action_name, None) + if not action: + raise ValueError(f"Unknown action: {action_name}") + return await action(*args) + + def healthy_ready(self): + return self._inference and self._inference.is_healthy() + + def process_request(self, responder: str, request: BaseModel) -> Dict[str, Any]: + self.logger.debug(f"Processing request {request}") + + result = json.loads(request.model_dump_json()) + + # find the request template for the endpoint + templates = self._request_templates.get(responder, None) + if not templates: + # no template to be applied on the request + return result + + request_class = next(iter(templates.keys())) + template = templates[request_class] + json_string = jinja2_env.from_string(template).render(request=result) + try: + result = json.loads(json_string) + except Exception as e: + self.logger.error(f"Error parsing request template: {e}") + self.logger.error(f"Json string: {json_string}") + raise e + + # template filters on each field + for key, value in templates.items(): + if not key in result: + continue + if isinstance(value, list): + # this is regex filter for fields + text = result.pop(key) + if not isinstance(text, str): + continue + regx = value[0] + keys = value[1:] + matches = re.findall(regx, text) + for match in matches: + if len(keys) != len(match): + continue + for x, y in zip(keys, match): + if y: + result.setdefault(x, []).append(y) + return result + + def process_response(self, responder: str, request, response: Dict[str, Any]) -> str: + self.logger.debug(f"Processing response {response}") + # Load the response template for the endpoint + templates = self._response_templates.get(responder, None) + if not templates: + # no template to be applied on the response + return response + + response_class = next(iter(templates.keys())) + template = templates[response_class] + json_string = jinja2_env.from_string(template).render(request=request, response=response) + return json_string + + def process_streamed_response(self, responder: str, request, response: Dict[str, Any]) -> str: + self.logger.debug(f"Processing streamed response {response}") + # Load the response template for the endpoint + templates = self._response_templates.get(responder, None) + if not templates: + # no template to be applied on the response + return response + keys = list(templates.keys()) + if len(keys) < 2: + return response + template = templates[keys[1]] + json_string = jinja2_env.from_string(template).render(request=request, response=response) + return json_string diff --git a/tools/inference_builder/lib/utils.py b/tools/inference_builder/lib/utils.py new file mode 100644 index 00000000..56bd152c --- /dev/null +++ b/tools/inference_builder/lib/utils.py @@ -0,0 +1,387 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import os +import re +import sys +import logging +import time +from typing import Callable, Optional, List, Dict, Any +import importlib +import numpy as np +import torch +import jinja2 +import json +from queue import Queue, Full, Empty +from concurrent.futures import Future +import threading + +kDebug = int(os.getenv("DEBUG", "0")) +PACKAGE_NAME = "ib" + +class NumpyFlatEncoder(json.JSONEncoder): + def default(self, obj): + if isinstance(obj, np.ndarray): + return obj.flatten().tolist() + return super().default(obj) + + +def concat_tensors_in_dict(list_of_tensor_dicts: List) -> Dict: + result = {} + + # Iterate over each dictionary in the list + for d in list_of_tensor_dicts: + for key, value in d.items(): + if isinstance(value, np.ndarray): + result[key] = np.concatenate(result[key], value) if key in result else value + elif isinstance(value, torch.Tensor): + result[key] = torch.cat((result[key], value)) if key in result else value + else: + result[key] = result[key] + value if key in result else value + return result + +def stack_tensors_in_dict(list_of_tensor_dicts: List) -> Dict: + """ + [{'k1': 'v1', 'k2': 'v2'}, {'k1': 'v3', 'k2': 'v4'}] -> + {'k1': ['v1', 'v3'], 'k2': ['v2', 'v4']} + """ + result = {} + + # Iterate over each dictionary in the list + for d in list_of_tensor_dicts: + for key, value in d.items(): + if key not in result: + result[key] = [] # Create a new list for each new key + result[key].append(value) # Append the value to the list + # Iterate over the combined dictionary + for key in result: + tensor_list = result[key] + if isinstance(tensor_list[0], np.ndarray): + result[key] = np.stack(tensor_list, axis=0) + elif isinstance(tensor_list[0], torch.Tensor): + result[key] = torch.stack(tensor_list, dim=0) + + return result + +def split_tensor_in_dict(dict_of_tensor_list: Dict) -> List: + """ + {'k1': ['v1', 'v2'], 'k2': ['v3', 'v4']} -> + [{'k1': 'v1', 'k2': 'v3'}, {'k1': 'v2', 'k2': 'v4'}] + """ + values = [dict_of_tensor_list[k] for k in dict_of_tensor_list] + result = [] + + def _len(v): + if isinstance(v, list): + return len(v) + if isinstance(v, np.ndarray) and v.ndim > 0: + return len(v) + return 1 + + length = min(_len(v) for v in values) + for i in range(length): + result.append({ + k: v.item() if isinstance(v, np.ndarray) and v.ndim == 0 else v[i] + for k, v in dict_of_tensor_list.items() + }) + + return result + + +def convert_list(i: List, f: Callable): + # If the item is a list, apply the function recursively + if isinstance(i, list): + return [convert_list(item, f) for item in i] + else: + return f(i) + +def import_class(module_name, class_name): + # Import the module using importlib + module = importlib.import_module(module_name) + + # Get the class from the module using getattr + class_ = getattr(module, class_name) + + return class_ + +def create_jinja2_env(): + def start_with(field, s): + return field.startswith(s) + + def replace(value, pattern, text): + return re.sub(pattern, text, value) + + def extract(value, pattern): + match = re.search(pattern, value) + return match.group(1) if match else '' + + def raise_helper(message): + raise Exception(message) + + def tolist(value): + if hasattr(value, 'tolist'): + # Convert NumPy array to list and flatten to 1D + return value.flatten().tolist() + elif isinstance(value, list): + # Already a list, return as is + return value + else: + # Fallback: try to convert to list + return list(value) + + jinja2_env = jinja2.Environment() + jinja2_env.tests["startswith"] = start_with + jinja2_env.filters["replace"] = replace + jinja2_env.filters["extract"] = extract + jinja2_env.filters["tolist"] = tolist + jinja2_env.filters["zip"] = zip + jinja2_env.globals["raise"] = raise_helper + + return jinja2_env + +def get_logger(name: Optional[str] = None) -> logging.Logger: + """ Get component logger + + Parameters: + name: a module name + + Returns: A Logger Instance + """ + + log_level = os.getenv("LOG_LEVEL", "INFO").upper() + if kDebug: + log_level = logging.DEBUG + name = f"{PACKAGE_NAME}.{name}" + log_format = "%(asctime)s [%(levelname)s] [%(name)s]: %(message)s" + # sys.stdout.reconfigure(encoding="utf-8") + sys.stderr.reconfigure(encoding="utf-8") + print(f"logger {str(name)}, log_level: {str(log_level)}") + logging.basicConfig( + level=log_level, + format=log_format, + handlers=[logging.StreamHandler(sys.stderr)], + ) + logger = logging.getLogger(name) + logger.propagate = True + # formatter = logging.Formatter(log_format) + # stream_handler = logging.StreamHandler(sys.stdout) + # stream_handler.setFormatter(formatter) + # stream_handler.setLevel(log_level) + # logger.handlers.clear() + # logger.addHandler(stream_handler) + return logger + +def flush(logger): + for h in logger.handlers: + h.flush() + + +class SimpleLogger: + def __init__(self, name: str = ""): + self.name = name + def log_print(self, level, *args, **kwargs): + asctime = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + prefix = f"{asctime} [{str(level)}] [{self.name}]: " + print(prefix, *args, **kwargs) + + def debug(self, *args, **kwargs): + if kDebug: + self.log_print("DEBUG", *args, **kwargs) + + def info(self, *args, **kwargs): + self.log_print("INFO", *args, **kwargs) + + def warning(self, *args, **kwargs): + self.log_print("WARNING", *args, **kwargs) + + def error(self, *args, **kwargs): + self.log_print("ERROR", *args, **kwargs) + +def tensor_info(tensor): + return f" shape: {tensor.shape}, dtype: {tensor.dtype}, dtype: {tensor.device}" + + +logger = get_logger(__name__) + +class FutureWrapper: + """A future wrapper for collecting results from an asynchronous operation""" + def __init__(self, future: Future, depot: Queue, *user_data): + self._future = future + self._depot = depot + self._user_data = list(user_data) + self._future.add_done_callback(self._on_done) + + + def __repr__(self): + return f"FutureWrapper(done={self._future.done()})" + + def _on_done(self, future: Future): + try: + self._depot.put_nowait(self) + except Full: + # fallback policy: best-effort enqueue with small timeout or log-and-drop + try: + self._depot.put(self, timeout=1) + except Full: + logger.warning( + f"FutureWrapper failed to enqueue to depot, dropped" + "consider increasing the future depot size" + ) + + @property + def user_data(self): + return self._user_data + + def result(self): + return self._future.result() + + def done(self): + return self._future.done() + + def cancel(self): + self._future.cancel() + +class FutureConsumer: + def __init__(self, + name: str, + stop_event: threading.Event, + result_callback: Callable[[Any, Any], None]): + self._name = name + self._stop_event = stop_event + self._future_depot = Queue() + self._future_lock = threading.Lock() + self._future_list = [] + self._result_callback = result_callback + self._future_consumer_thread = threading.Thread( + target=self.run, + daemon=True + ) + self._future_consumer_thread.start() + + def append_future(self, future: Future, *user_data): + future_wrapper = FutureWrapper(future, self._future_depot, *user_data) + with self._future_lock: + self._future_list.append(future_wrapper) + + def run(self): + if self._stop_event is None: + logger.error(f"Future consumer {self._name} failed to start, stopped event is None") + return + if not callable(self._result_callback): + logger.error(f"Future consumer {self._name} failed to start, result callback is not callable") + return + + logger.info(f"Future consumer for {self._name} started") + # Keep consuming completion signals and emit results in FIFO order of submission + while not self._stop_event.is_set(): + try: + # Wait for any future to complete; we only use this as a signal + future_wrapper = self._future_depot.get(timeout=1) + except Empty: + continue + except Exception as e: + logger.exception(e) + continue + + # Flush all completed futures from the head of the list (preserving order) + while True: + with self._future_lock: + if not self._future_list: + logger.info(f"Future consumer {self._name} future list is empty.") + break + if not self._future_list[0].done(): + logger.info(f"Future consumer {self._name} the first future in the list is not actually done. {self._future_list}") + break + future_wrapper = self._future_list.pop(0) + self._result_callback(future_wrapper.result(), future_wrapper.user_data) + + logger.info(f"Future consumer for {self._name} stopped") + +class ResultQueue: + def __init__(self, + queue: Queue, + result_callback: Callable[[Any, Any], None], + user_data: Any, + stop_event: Optional[threading.Event] = None): + self._queue = queue + self._result_callback = result_callback + self._user_data = user_data + self._stop_event = stop_event + + def __repr__(self): + return f"ResultQueue(queue={self._queue}, result_callback={self._result_callback}, user_data={self._user_data})" + + def get_all(self): + while self._stop_event is None or not self._stop_event.is_set(): + try: + item = self._queue.get(timeout=1.0) + except Empty: + # nothing to drain right now + continue + except Exception as e: + logger.exception(e) + break + self._result_callback(item, self._user_data) + if not item: + break + logger.debug(f"ResultQueue drained") + +class QueueConsumer: + """Consume ResultQueues in a single daemon thread and emit results. + + Submit work via append_queue(queue, result_callback, user_data=None). + Items pulled from the provided `queue` will be forwarded to + `result_callback(item, user_data)`. + """ + + def __init__(self, + name: str, + stop_event: threading.Event, + queue_get_timeout: float = 1.0): + self._name = name + self._stop_event = stop_event + self._queue_get_timeout = queue_get_timeout + self._queue_depot = Queue() + self._queue_consumer_thread = threading.Thread(target=self.run, daemon=True) + self._queue_consumer_thread.start() + + def append_queue(self, + queue: Queue, + result_callback: Callable[[Any, Any], None], + user_data: Any = None): + if not callable(result_callback): + logger.error(f"Queue consumer {self._name} append_queue failed: result_callback is not callable") + return + result_queue = ResultQueue(queue, result_callback, user_data, self._stop_event) + self._queue_depot.put(result_queue) + + def run(self): + if self._stop_event is None: + logger.error(f"Queue consumer {self._name} failed to start, stopped event is None") + return + + logger.info(f"Queue consumer for {self._name} started") + while not self._stop_event.is_set(): + try: + q = self._queue_depot.get(timeout=self._queue_get_timeout) + except Empty: + continue + except Exception as e: + logger.exception(e) + continue + q.get_all() + + logger.info(f"Queue consumer for {self._name} stopped") diff --git a/tools/inference_builder/mcp.png b/tools/inference_builder/mcp.png new file mode 100644 index 0000000000000000000000000000000000000000..7d0bb1cb124ef85a5316b5dcdbfb87c28f7e5ccb GIT binary patch literal 150765 zcmc$`cT`l{vo*?5F#v)I0VNm^1QZZS62*ilIZKozIp@%zC?Jv~XHdymKyntL2?8R} zgeHiD7McdhLK9wX@Z8_`{`khYcZ~bSV{km(&F;O|UaMBstXZ?Rzr3vEak6t{BqSuq zr5@f>Bq1T4AR#%}cJwfOLK7xo3IF@YUQzN6Nq#5&47@pHEFmL7LQ)t)zNJSB?~mC$ zd}2>R!u$*Q?;mdiQhE}S-889t63Q+*bK9;lT8@e9yLFvesF_*RoJUrbqr*p8pt2?qIa+ zcd3QPj%tpCI&nqEOre_8s-8#()-JST-+jAIT!N5ecICsmPGYtA18r^X2Qad|pLZtG z!~_4n;jTD3@z38kS9w0W{QLQvKyJprub*GK9C+mK>vt{xjl*xw&d&0z`Aqb6cfWTK zvecy@ssE&)qH?8Y@*LmZd9DUa9h<;nv5}9`<^uj9iQ&)A$(gbdL|!95ETmVS@4bM3 z&d}+J{QLU;16r!TuWyH*Y(Dt+wY=f0#eWa^Zyk=zMsuxA_0ZoD_}K_0@=!F^(vm#? zAjN!Y?S5l+etuNr_(v{e#YsqF&M3phWKFdW?Y~$0=YM}p|HE7O|K)K0yhfKs?&j5h zC;NvX_rEJWI-0M{Zlo*ns%J)IUVh7^tgF>9m}n#?OifMA&czkY?C9*AlatdfW;i4` zSddm&s9;s5x#*(R(b2KiN6VVrl`%A=aVt@_lZ{22Czig)+0)Ungr3q5bz|>h^{Fvu zBNv-8t+<-SZt%UjbFm}#rsVQ;hmwp;5Q>i_)HE*chJo*NM|{R$v2iK@HebS zMfmvmvTAE}8lHZuor#!SCr&N87}EMC$QmnnFa4kAt>E4<^!v$5+_2p`eE4w0qqK-t z!!tn-2sv_OA?Osko6j=ezn4f$OS7vQ;i0&jrTl2oW!Nlb+(C%?=pkh_HDt6LXUGPf z`O@xPI@zRu!-$9O$|?U32@A+YxWmrZEx${0`RQMcmRn&XUf8>v=BJN*7M0izB|k!Y zFV5icJ)<@5CGP81C1qt;wPc(3@()*6SC@#}ifj53j~@p(EsW;XxUXkdR6HTB5qgqD z-Ft_JbLZ!+u7pcfXmP5jspX`nKS;lo=Ba#r#$m|qI;_b3V{LL@z=7@kH1S_yv0<

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z%ZXQ7I8=R~Wi|G_&f2QK^wILlj58g#Hdm|qTw0f2Jy_DO`W=1gvMEZ(F`=|l?|Qxg zO^C`rRU5|q7ZaN))8gG2K-M8**z@Chx2qE2nkA=AY!F|i56;=Fw7lwLPcz@;*iN(R uV|bf~Zy!Ii`tRIzyE0>}0fQm`XxY?v{Zaa0z literal 0 HcmV?d00001 diff --git a/tools/inference_builder/mcp/QUICK_NORMALIZATION_REFERENCE.md b/tools/inference_builder/mcp/QUICK_NORMALIZATION_REFERENCE.md new file mode 100644 index 00000000..7dd0504c --- /dev/null +++ b/tools/inference_builder/mcp/QUICK_NORMALIZATION_REFERENCE.md @@ -0,0 +1,211 @@ +# Quick Normalization Reference + +## The Two Critical Parameters + +When generating DeepStream nvinfer configs, **BOTH** of these parameters must exactly match your model's training preprocessing: + +### 1. `net-scale-factor` +**Purpose**: Scales pixel values (typically from [0-255] range) + +**Common values**: +- `0.00392156862745098` (1/255) - Most common, scales to [0,1] +- `0.5` - Scales by half +- `1.0` - No scaling + +### 2. `offsets` (per-channel mean subtraction) +**Purpose**: Subtracts mean values from each color channel after scaling + +**Format**: `R;G;B` (Red;Green;Blue) + +**Common values**: +- **Not specified** or `0;0;0` - No mean subtraction +- `127.5;127.5;127.5` - For [-1,1] normalization +- `123.675;116.28;103.53` - ImageNet mean values + +--- + +## Quick Decision Tree + +### Step 1: Check your training code + +```python +# Example 1: Simple [0,1] scaling +image = image / 255.0 +``` +**Config**: `net_scale_factor: 0.00392156862745098`, **no offsets** + +--- + +```python +# Example 2: [-1,1] normalization +image = (image / 255.0 - 0.5) * 2 +# or: image = (image - 127.5) / 127.5 +``` +**Config**: `net_scale_factor: 0.00392156862745098`, `offsets: "127.5;127.5;127.5"` + +--- + +```python +# Example 3: ImageNet normalization +transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) +``` +**Config**: `net_scale_factor: 0.00392156862745098`, `offsets: "123.675;116.28;103.53"` +*(mean values × 255)* + +--- + +```python +# Example 4: Custom scaling +image = image * 0.5 +``` +**Config**: `net_scale_factor: 0.5`, **no offsets** + +--- + +```python +# Example 5: Custom scaling with mean subtraction +image = (image * 0.01) - custom_mean +``` +**Config**: `net_scale_factor: 0.01`, `offsets: "{R};{G};{B}"` *(your custom mean values)* + +--- + +```python +# Example 6a: Division by std (uniform std across channels) +image = image / 255.0 +image = (image - mean) / std # If std=0.229 for all channels +``` +**Config**: `net_scale_factor: 0.0171302428` *(1/(255×0.229))*, `offsets: "2.118;1.991;1.773"` *(mean/std)* + +⚠️ **Note**: This only works if std is the same for all channels. For per-channel std (e.g., ImageNet `std=[0.229, 0.224, 0.225]`), you need to bake normalization into the ONNX model or use a custom preprocessing plugin, as DeepStream only supports a single scale factor. + +--- + +```python +# Example 6b: Multiplication by scale factor d +image = image * d / 255.0 # Where d might be 1/std or a custom constant +``` +**Config**: `net_scale_factor: d/255` *(if d=0.5, then 0.5/255 = 0.00196078)* + +**Note**: The scale factor becomes `d/255` instead of `1/255`. This pattern is used when training applies a custom scale factor `d`. + +--- + +## The Formula + +DeepStream applies: +``` +output_pixel = (input_pixel × net_scale_factor) - offset +``` + +### Examples: + +**[0,1] normalization** (no offsets): +``` +output = pixel × 0.00392156862745098 +output = pixel / 255 +Result: [0, 1] range +``` + +**[-1,1] normalization** (with offsets): +``` +output = (pixel × 0.00392156862745098) - 0.5 +output = (pixel / 255) - 0.5 +Result: [-0.5, 0.5] range → typically then multiplied by 2 in the model +``` + +**ImageNet normalization**: +``` +For red channel: +output = (pixel × 0.00392156862745098) - 0.485 +output = (pixel / 255) - 0.485 +``` + +--- + +## Common Mistakes + +### ❌ Mistake 1: Using default values without checking training +```yaml +# Default assumes [0,1] normalization +net-scale-factor: 0.00392156862745098 +# No offsets +``` +**Problem**: If your model was trained with ImageNet normalization or [-1,1] range, this will give wrong results! + +### ❌ Mistake 2: Forgetting per-channel offsets +```python +# Training code used: +image = (image / 255) - 0.5 # [-1,1] normalization +``` +```yaml +# But config only sets scale factor: +net-scale-factor: 0.00392156862745098 +# Missing: offsets: "127.5;127.5;127.5" +``` +**Problem**: Mean subtraction is missing, results will be incorrect! + +### ❌ Mistake 3: Wrong offset values +```yaml +# Model trained with ImageNet mean +net-scale-factor: 0.00392156862745098 +offsets: "127.5;127.5;127.5" # WRONG! Should be ImageNet means +``` +**Problem**: Using wrong per-channel values will shift colors incorrectly! + +--- + +## Verification Checklist + +Before deploying your config: + +- [ ] I checked my training code for the exact preprocessing steps +- [ ] I verified the `net-scale-factor` matches the scaling in training +- [ ] I verified the `offsets` match any mean subtraction in training +- [ ] If training had NO mean subtraction, I did NOT set offsets +- [ ] If training had per-channel means, I set the correct RGB values +- [ ] I tested with a known input and compared results + +--- + +## When in Doubt + +1. **Test with known inputs**: Use images with known pixel values +2. **Compare outputs**: Run the same image through training and inference +3. **Check model documentation**: Look for preprocessing specifications +4. **Ask the model creator**: They should know the exact normalization used + +--- + +## Real-World Examples + +### YOLOv5 +```yaml +net-scale-factor: 0.00392156862745098 # Scales to [0,1] +# No offsets - training used simple /255 normalization +``` + +### ResNet-50 (ImageNet pre-trained) +```yaml +net-scale-factor: 0.00392156862745098 # Scales to [0,1] +offsets: "123.675;116.28;103.53" # ImageNet per-channel means +``` + +### TAO ChangeNet ([-1,1] range) +```yaml +net-scale-factor: 0.00392156862745098 # Scales to [0,1] +offsets: "127.5;127.5;127.5" # Subtracts 0.5 per channel +``` + +--- + +## Remember + +> **Both `net-scale-factor` AND `offsets` are equally critical!** +> +> Using the wrong scale factor OR wrong per-channel offsets will result in poor inference accuracy, regardless of how good your model is. + +For detailed information, see: +- [NORMALIZATION_GUIDE.md](NORMALIZATION_GUIDE.md) - Comprehensive guide +- [EXAMPLE_DEEPSTREAM_CONFIG.md](EXAMPLE_DEEPSTREAM_CONFIG.md) - Usage examples + diff --git a/tools/inference_builder/mcp/README-MCP.md b/tools/inference_builder/mcp/README-MCP.md new file mode 100644 index 00000000..c4f16f15 --- /dev/null +++ b/tools/inference_builder/mcp/README-MCP.md @@ -0,0 +1,217 @@ +# MCP Server Internal Documentation + +This document provides detailed technical documentation for the Inference Builder MCP server. For getting started and basic usage, see the [main README](../README.md#mcp-integration). + +## Tool Reference + +### `generate_inference_pipeline` + +Generates an inference pipeline from a YAML configuration file. + +| Parameter | Required | Description | +|-----------|----------|-------------| +| `config_file` | Yes | Path to the YAML configuration file | +| `server_type` | No | Type of server to generate (`triton`, `fastapi`, `nim`, `serverless`) | +| `output_dir` | No | Output directory for generated code | +| `api_spec` | No | Path to OpenAPI specification file | +| `custom_modules` | No | List of custom Python module files | +| `exclude_lib` | No | Exclude common library from generated code | +| `tar_output` | No | Create a tar.gz archive of the output | +| `validation_dir` | No | Validation directory path to build validator | +| `no_docker` | No | Use local OpenAPI Generator instead of Docker | +| `test_cases_abs_path` | No | Use absolute paths in generated test_cases.yaml | + +### `build_docker_image` + +Builds a Docker image from a generated inference pipeline. + +| Parameter | Required | Description | +|-----------|----------|-------------| +| `image_name` | Yes | Name for the Docker image | +| `dockerfile` | Yes | Path to Dockerfile (must be placed in the output directory) | + +### `prepare_model_repository` + +Prepares a model repository by downloading models from NGC or Hugging Face and copying configuration files. + +| Parameter | Required | Description | +|-----------|----------|-------------| +| `model_configs` | Yes | List of model configurations to prepare | +| `config_dir` | No | Base directory for resolving relative config paths | + +### `docker_run_image` + +Runs a Docker image with optional model repository mounting and environment configuration. + +| Parameter | Required | Default | Description | +|-----------|----------|---------|-------------| +| `image_name` | Yes | - | Name of the Docker image to run | +| `model_repo_host` | No | - | Host path to model repository | +| `model_repo_container` | No | `/models` | Container path for model repository | +| `server_type` | No | `serverless` | Server type hint | +| `env` | No | - | Environment variables to set | +| `cmd` | No | - | Command-line arguments. For serverless flows, supply input values here using `-- ` flags. **Use hyphens, not underscores** in argument names (e.g., `media_url` → `--media-url`). Each flag and value is a separate list item: `["--media-url", "/path/to/video.mp4"]` | +| `gpus` | No | `all` | GPU devices to use | +| `timeout` | No | `300` | Timeout in seconds | + +### `generate_nvinfer_config` + +Generates a DeepStream nvinfer runtime configuration file (nvdsinfer_config.yaml). + +**IMPORTANT**: Before generating a new config file, first call `prepare_model_repository` to download the model and check for existing nvinfer configuration files. Models from NGC typically include a configuration file (YAML or TXT) with all required inference parameters - always prefer using this provided configuration when available. + +| Parameter | Required | Default | Description | +|-----------|----------|---------|-------------| +| `output_path` | Yes | - | Path where the generated config file should be saved | +| `onnx_file` | Yes | - | Name of the ONNX model file | +| `network_type` | Yes | - | Network type (0=detection, 1=classification, 2=segmentation, 3=instance_segmentation, 100=custom) | +| `input_dims` | Yes | - | Input dimensions in format `channel;height;width` (e.g., `3;224;224`) | +| `label_file` | Yes | - | Name of the label file | +| `precision_mode` | No | `2` | Precision mode (0=FP32, 1=INT8, 2=FP16) | +| `custom_lib_path` | No | - | Path to a C++ shared library (.so) for custom output parsing. Required for all models except classic ResNet when network_type is 0-3. For TAO models, build from https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps/tree/master/post_processor | +| `custom_parse_func` | No | - | Symbol name of the custom parsing function. (0) Detection: built-in assumes ResNet; (1) Classification: built-in treats as softmax; (2,3) Segmentation: required; (100) Custom: not required | +| `num_classes` | No | - | Number of detected classes | +| `gie_unique_id` | No | `1` | Unique ID for this GIE | +| `net_scale_factor` | No | `0.00392156862745098` | Scale factor for normalization | +| `offsets` | No | - | Mean subtraction values in format `R;G;B` | +| `classifier_threshold` | No | `0.0` | Confidence threshold for classification | +| `input_tensor_from_meta` | No | `0` | Whether to read input from metadata (0 or 1) | +| `output_tensor_meta` | No | `0` | Whether to output raw tensor format (0 or 1) | + +**Important**: The `net_scale_factor` and `offsets` parameters must match your model's training preprocessing. See: +- [QUICK_NORMALIZATION_REFERENCE.md](QUICK_NORMALIZATION_REFERENCE.md) - Quick reference guide +- [STD_NORMALIZATION_CALCULATOR.md](STD_NORMALIZATION_CALCULATOR.md) - For models with std normalization + +## Resource Navigation + +### Documentation Resources + +| URI | Description | +|-----|-------------| +| `docs://README.md` | Main project README with overview, getting started, and installation | +| `docs://mcp/README-MCP.md` | This document - MCP server internal documentation | + +### Schema Resources + +| URI | Description | +|-----|-------------| +| `schema://config.schema.json` | Main JSON Schema for configuration files | +| `schema://readme` | Comprehensive schema documentation | +| `schema://index.json` | Schema navigation index mapping backend types to parameter schemas | +| `schema://backends/{backend}.schema.json` | Backend-specific schemas (deepstream, triton, vllm, etc.) | +| `schema://backends/parameters/{backend}-parameters.schema.json` | Detailed parameter schemas | + +**Schema Navigation Flow:** +1. Read `schema://config.schema.json` for the main configuration structure +2. When a model specifies a `backend` type (e.g., `vllm`, `triton/python`), read `schema://index.json` to find the corresponding parameter schema path +3. Read the backend-specific parameter schema to get valid parameter options + +### Sample Resources + +Sample resources are dynamically discovered and categorized: + +| URI Pattern | Description | +|-------------|-------------| +| `samples://config/{path}` | Pipeline/application configuration YAML files | +| `samples://runtime_config/{path}` | DeepStream nvinfer runtime configuration files | +| `samples://runtime_preprocess/{path}` | DeepStream nvdspreprocess runtime configuration files | +| `samples://openapi/{path}` | OpenAPI server specification YAML files | +| `samples://dockerfile/{path}` | Sample Dockerfiles | +| `samples://processor/{path}` | Preprocessor/postprocessor Python modules | + +## Usage Examples + +### Generate a DeepStream Pipeline + +``` +Use the generate_inference_pipeline tool to create a DeepStream object detection pipeline using the ds_detect.yaml configuration with serverless output. +``` + +### Explore Available Samples + +``` +List the available MCP resources to see sample configurations, Dockerfiles, and processors. +``` + +### View Sample Configurations + +``` +Read the samples://config/ds_app/detection/ds_detect.yaml resource to see the DeepStream detection configuration. +``` + +### Build Docker Image + +``` +Build a Docker image from the generated deepstream-app pipeline with the name 'my-detector' using the build_docker_image tool. +``` + +## Workflow Integration + +### Typical Development Workflow + +1. **Explore Samples**: Browse `samples://config/*` resources to see available configurations +2. **Examine Configurations**: Read sample resources to understand configuration patterns +3. **Create Your Config**: Modify a sample configuration or create your own +4. **Generate Pipeline**: Use `generate_inference_pipeline` to create your code +5. **Build Container**: Use `build_docker_image` to create a deployable container + +### Integration with Version Control + +The generated code can be: +- Committed to version control +- Used as a starting point for custom modifications +- Shared with team members +- Deployed to production environments + +## Troubleshooting + +### Common Issues + +**MCP Server Not Found** +- Ensure the MCP server is running +- Check that the path in your MCP client configuration is correct +- Verify Python environment has required dependencies + +**Configuration Validation Errors** +- Check that required fields are present in your YAML +- Ensure model definitions are complete +- Verify backend specifications are correct + +**Pipeline Generation Failures** +- Check that the configuration file path is correct +- Ensure all required dependencies are installed +- Verify that the output directory is writable + +### Debug Mode + +To run the MCP server with debug output: + +```bash +python -u mcp_server.py +``` + +## Extending the MCP Server + +To add new tools or resources: + +1. **Add new tools** to the `InferenceBuilderMCPServer` class in `mcp_server.py` +2. **Update tool schemas** in the `list_tools` method +3. **Implement tool logic** in the `call_tool` method +4. **Add tests** for new functionality in `test_mcp_server.py` + +### Server Types + +The `server_type` parameter supports: + +| Type | Description | +|------|-------------| +| `fastapi` | RESTful API server with automatic OpenAPI generation | +| `serverless` | Standalone command-line application | + +### Custom Processors + +Custom preprocessors and postprocessors can be integrated: + +1. Create custom processor classes following the base interface +2. Reference them in your configuration file +3. Use the `custom_modules` parameter when generating pipelines diff --git a/tools/inference_builder/mcp/STD_NORMALIZATION_CALCULATOR.md b/tools/inference_builder/mcp/STD_NORMALIZATION_CALCULATOR.md new file mode 100644 index 00000000..c20130b1 --- /dev/null +++ b/tools/inference_builder/mcp/STD_NORMALIZATION_CALCULATOR.md @@ -0,0 +1,395 @@ +# Standard Deviation Normalization Calculator + +## Understanding Std Normalization + +### Case 1: Division by Std (Most Common) + +When training uses standard deviation normalization by **dividing** by std: +```python +normalized = (pixel/255 - mean) / std +``` + +This can be rewritten as: +```python +normalized = pixel * (1/(255*std)) - (mean/std) +``` + +Which maps to DeepStream parameters: +- `net_scale_factor = 1/(255 * std)` +- `offsets = mean/std` + +**Example**: If `std = 0.229`, then `net_scale_factor = 1/(255 × 0.229) = 0.0171302428` + +--- + +### Case 2: Multiplication by Scale Factor (Alternative) + +If training uses a scaling factor `d` (where `d` might be related to std): +```python +normalized = pixel * d / 255 +``` + +This maps to DeepStream parameters: +- `net_scale_factor = d/255` +- `offsets` depend on any mean subtraction + +**Example**: If `d = 0.5`, then `net_scale_factor = 0.5/255 = 0.00196078431` + +**Note**: This is less common but may be used in custom preprocessing schemes where `d` could be `1/std` or another scaling factor. + +## The Challenge + +**DeepStream limitation**: Only supports a **single** `net_scale_factor` value, but many models use **per-channel** std values. + +**Example**: ImageNet uses `std=[0.229, 0.224, 0.225]` - different for each channel! + +## Solutions + +### Solution 1: Uniform Std (Best for DeepStream) + +If your model uses the **same std for all channels**, you can directly convert: + +#### Example: Uniform std = 0.229 +```python +# Training normalization +mean = [0.485, 0.456, 0.406] +std = [0.229, 0.229, 0.229] # Same for R, G, B +``` + +#### Calculation: +```python +net_scale_factor = 1 / (255 * 0.229) = 0.0171302428 + +offsets_R = 0.485 / 0.229 = 2.118122 +offsets_G = 0.456 / 0.229 = 1.991266 +offsets_B = 0.406 / 0.229 = 1.772926 +``` + +#### DeepStream Config: +```yaml +net-scale-factor: 0.0171302428 +offsets: 2.118122;1.991266;1.772926 +``` + +--- + +### Solution 2: Average Std (Approximation) + +If std values are close but not identical, use the average: + +#### Example: ImageNet std = [0.229, 0.224, 0.225] +```python +# Training normalization +mean = [0.485, 0.456, 0.406] +std = [0.229, 0.224, 0.225] # Different per channel +``` + +#### Calculation: +```python +avg_std = (0.229 + 0.224 + 0.225) / 3 = 0.226 + +net_scale_factor = 1 / (255 * 0.226) = 0.0173510648 + +offsets_R = 0.485 / 0.226 = 2.146018 +offsets_G = 0.456 / 0.226 = 2.017699 +offsets_B = 0.406 / 0.226 = 1.796460 +``` + +#### DeepStream Config: +```yaml +net-scale-factor: 0.0173510648 +offsets: 2.146018;2.017699;1.796460 +``` + +⚠️ **Trade-off**: This is an approximation. Accuracy may be slightly reduced because we're not using the exact per-channel std values. + +--- + +### Solution 3: Ignore Std (Mean-only, Less Accurate) + +Use only mean subtraction, ignore std division: + +#### Calculation: +```python +net_scale_factor = 1 / 255 = 0.00392156862745098 + +offsets_R = 0.485 * 255 = 123.675 +offsets_G = 0.456 * 255 = 116.28 +offsets_B = 0.406 * 255 = 103.53 +``` + +#### DeepStream Config: +```yaml +net-scale-factor: 0.00392156862745098 +offsets: 123.675;116.28;103.53 +``` + +⚠️ **Limitation**: This does NOT include std normalization, only mean subtraction. Results will be less accurate. + +--- + +### Solution 4: Bake into ONNX (Recommended for Per-Channel Std) + +The most accurate solution: Include preprocessing in the ONNX model itself. + +#### PyTorch Example: +```python +import torch +import torch.nn as nn + +class ModelWithPreprocessing(nn.Module): + def __init__(self, base_model, mean, std): + super().__init__() + self.base_model = base_model + # Register mean and std as buffers + self.register_buffer('mean', torch.tensor(mean).view(1, 3, 1, 1)) + self.register_buffer('std', torch.tensor(std).view(1, 3, 1, 1)) + + def forward(self, x): + # x is in [0, 255] range + x = x / 255.0 # Scale to [0, 1] + x = (x - self.mean) / self.std # Per-channel normalization + return self.base_model(x) + +# Wrap your model +mean = [0.485, 0.456, 0.406] +std = [0.229, 0.224, 0.225] +wrapped_model = ModelWithPreprocessing(original_model, mean, std) + +# Export to ONNX +torch.onnx.export(wrapped_model, dummy_input, "model_with_preprocessing.onnx") +``` + +#### DeepStream Config: +```yaml +# No normalization needed - it's in the model! +net-scale-factor: 1.0 +# No offsets +``` + +**Benefits**: +- ✅ Exact per-channel std normalization +- ✅ No accuracy loss +- ✅ Simpler DeepStream config + +--- + +## Quick Calculator + +### Method 1: Division by Std (Standard Approach) + +**Given Training Parameters**: +- **mean**: [mean_r, mean_g, mean_b] +- **std**: [std_r, std_g, std_b] + +**Training formula**: `(pixel/255 - mean) / std` + +#### If std is uniform (std_r = std_g = std_b = d): + +``` +net_scale_factor = 1 / (255 × d) +offset_r = mean_r / d +offset_g = mean_g / d +offset_b = mean_b / d +``` + +**Example**: If `d = 0.229`, `mean = [0.485, 0.456, 0.406]` +``` +net_scale_factor = 1 / (255 × 0.229) = 0.0171302428 +offsets = 2.118;1.991;1.773 +``` + +#### If std varies per channel (approximation): + +``` +avg_std = (std_r + std_g + std_b) / 3 +net_scale_factor = 1 / (255 × avg_std) +offset_r = mean_r / avg_std +offset_g = mean_g / avg_std +offset_b = mean_b / avg_std +``` + +--- + +### Method 2: Multiplication by Scale Factor + +**Given Training Parameters**: +- **scale_factor**: d (a custom scaling constant) + +**Training formula**: `pixel × d / 255` + +#### Calculation: + +``` +net_scale_factor = d / 255 +``` + +**Example**: If `d = 0.5` +``` +net_scale_factor = 0.5 / 255 = 0.00196078431 +``` + +**Example**: If `d = 1/0.229` (inverse of std) +``` +net_scale_factor = (1/0.229) / 255 = 4.366812 / 255 = 0.0171302428 +``` + +**Note**: The scale factor becomes `d/255` instead of `1/255`. + +--- + +## Common Datasets + +### ImageNet +``` +mean = [0.485, 0.456, 0.406] +std = [0.229, 0.224, 0.225] +``` + +**Recommended**: Bake into ONNX or use average std approximation + +### COCO (if normalized) +``` +mean = [0.485, 0.456, 0.406] +std = [0.229, 0.224, 0.225] +``` +Same as ImageNet + +### CIFAR-10 +``` +mean = [0.4914, 0.4822, 0.4465] +std = [0.2023, 0.1994, 0.2010] +``` + +**Average std**: 0.2009 + +--- + +## Decision Tree + +``` +Do you use std normalization in training? +│ +├─ No → Use simple config: +│ net_scale_factor: 1/255 +│ offsets: mean × 255 (if mean subtraction used) +│ +└─ Yes → Is std the same for all channels? + │ + ├─ Yes → Use combined config: + │ net_scale_factor: 1/(255×std) + │ offsets: mean/std + │ + └─ No → Choose approach: + │ + ├─ Approximation (easier): + │ net_scale_factor: 1/(255×avg_std) + │ offsets: mean/avg_std + │ ⚠️ Slight accuracy loss + │ + ├─ Mean-only (less accurate): + │ net_scale_factor: 1/255 + │ offsets: mean × 255 + │ ⚠️ Missing std normalization + │ + └─ Bake into ONNX (best): + Include preprocessing in model + ✅ Full accuracy, per-channel std +``` + +--- + +## Verification + +To verify your config is correct: + +1. **Test with known input**: + ```python + test_pixel = 128 # Middle gray + + # Training preprocessing: + train_value = (test_pixel/255 - mean) / std + + # DeepStream preprocessing: + ds_value = (test_pixel * net_scale_factor) - offset + + # These should match! + ``` + +2. **Compare outputs**: Run inference on the same image in training and DeepStream, compare results + +3. **Check accuracy**: Use validation dataset to measure accuracy drop + +--- + +## Python Helper Script + +```python +def calculate_deepstream_params(mean, std, use_average=False): + """ + Calculate DeepStream normalization parameters. + + Args: + mean: List of per-channel means [R, G, B] (in [0,1] range) + std: List of per-channel stds [R, G, B] + use_average: If True, use average std (approximation) + + Returns: + tuple: (net_scale_factor, offsets_string) + """ + mean_r, mean_g, mean_b = mean + std_r, std_g, std_b = std + + # Check if std is uniform + is_uniform = (std_r == std_g == std_b) + + if is_uniform: + # Exact calculation for uniform std + net_scale_factor = 1.0 / (255 * std_r) + offset_r = mean_r / std_r + offset_g = mean_g / std_g + offset_b = mean_b / std_b + print("✅ Uniform std detected - using exact calculation") + elif use_average: + # Approximation using average std + avg_std = (std_r + std_g + std_b) / 3 + net_scale_factor = 1.0 / (255 * avg_std) + offset_r = mean_r / avg_std + offset_g = mean_g / avg_std + offset_b = mean_b / avg_std + print(f"⚠️ Using average std approximation: {avg_std:.6f}") + else: + print("❌ Per-channel std detected. Consider baking into ONNX!") + print(" Using average std approximation...") + avg_std = (std_r + std_g + std_b) / 3 + net_scale_factor = 1.0 / (255 * avg_std) + offset_r = mean_r / avg_std + offset_g = mean_g / avg_std + offset_b = mean_b / avg_std + + offsets = f"{offset_r:.6f};{offset_g:.6f};{offset_b:.6f}" + + print(f"\nDeepStream Config:") + print(f" net-scale-factor: {net_scale_factor}") + print(f" offsets: {offsets}") + + return net_scale_factor, offsets + +# Example usage: +# ImageNet normalization +mean = [0.485, 0.456, 0.406] +std = [0.229, 0.224, 0.225] +calculate_deepstream_params(mean, std, use_average=True) +``` + +--- + +## Summary + +- **Best accuracy**: Bake std normalization into ONNX model +- **Good approximation**: Use average std (if stds are similar) +- **Uniform std**: Direct conversion works perfectly +- **Mean-only**: Simplest but least accurate (ignores std) + +Always verify your configuration with test data before deploying! + diff --git a/tools/inference_builder/mcp/examples.md b/tools/inference_builder/mcp/examples.md new file mode 100644 index 00000000..8406782a --- /dev/null +++ b/tools/inference_builder/mcp/examples.md @@ -0,0 +1,113 @@ +# Using Inference Builder with AI Agents — Examples and Guidelines + +## General Guidelines + +1. **Start simple, then refine.** Begin with a short, clear prompt that states the core requirements. Use follow-up messages to add details or adjust behavior. + +2. **Ask for self-check and verification.** Tell the agent to validate its work (e.g., run a smoke test or sanity check) so the result is correct before you rely on it. + +3. **Request documentation and diagrams.** Ask the agent to produce a README and architecture diagrams. These make the solution easier to understand and easier to extend later. + +4. **Use helper scripts for operations.** Request small scripts for common tasks (e.g., “create a script to start, stop, rebuild, get status, and fetch logs for the microservice”). These simplify testing and debugging. + +5. **Correct course early.** If the agent goes off track, give a direct hint right away. For example: + - If no free GPU is available, tell it to stop and retry later. + - If it picks the wrong model, ask it to double-check the choice. + +## Sample Prompts + +### 1. Create a very basic Deepstream Application + +***Use deepstream inference builder tool to create an object detection pipeline with PeopleNet Transformer model from NGC and verify it with a smoke test.*** + +The Agent is expected to draft a plan based on the prompt and generates required files accordingly (file names may differ in each run unless explicitly specified): + +- peoplenet_transformer_pipeline.yaml (pipeline configuration yaml) +- Dockerfile +- nvdsinfer_config.yaml (nvinfer configuration yaml) +- peoplenet.tgz (python code for inference) +- models/peoplenet (folder for required model files) + +The Agent is expected to fix all the build and runtime errors while doing the smoke test. + +### 2. Create an advanced Deepstream Application Supporting Tracker and Multi-stream Inputs + +***Use deepstream inference builder tool to create an object detection pipeline with the PeopleNet transformer model from NGC; the pipeline should support 4 video inputs in parallel and track all the objects in the inputs using NVDCF tracker. Do a smoke test once done.*** + +The Agent is expected to draft a plan based on the prompt and generates required files accordingly (file names may differ in each run unless explicitly specified): + +- peoplenet_transformer_pipeline.yaml (pipeline configuration yaml) +- Dockerfile +- nvdsinfer_config.yaml (nvinfer configuration yaml) +- peoplenet.tgz (python code for inference) +- models/peoplenet (folder for required model files) + +The Agent is expected to fix all the build and runtime errors while doing the smoke test. + +### 3. Create a Deepstream Inference Application with Customized Logic + +***Use deepstream inferenc builder tool to create an object detection pipeline with PeopleNet transformer model from NGC; the pipeline accepts video url as input and output detected bounding boxes. Count the number of people in each frame and raise an alarm whenever the number exceeds 10 by writing it down to a file. Do a smoke test once done.*** + +The Agent is expected to draft a plan based on the prompt and generates required files accordingly (file names may differ in each run unless explicitly specified): + +- peoplenet_transformer_pipeline.yaml (pipeline configuration yaml) +- Dockerfile +- nvdsinfer_config.yaml (nvinfer configuration yaml) +- processor.py (Customized processor for counting and raising alarm) +- peoplenet.tgz (python code for inference) +- models/peoplenet (folder for required model files) + +The Agent is expected to fix all the build and runtime errors while doing the smoke test. + + +### 4. Create a Realtime Video Intelligence Microservice (rtvi) with vllm Backend and Deepstream Accelerated Decoder + +#### 4.1. Start with the Master Prompt as below + +***Locate the MCP tool deepstream-inference-builder and complete the task: Start a new project under name rtvi_vlm and create a video summarization/caption microservice there using Qwen3-VL-2B-Instruct model driven by vllm backend and Deepstream accelerated decoder. The microservice supports adding/deleting RTSP streams as assets and generating video caption for added streams. The given RTSP stream will be chunked to a batch of frames with a given interval for summarization. I also want to build a container image for the microservice to run on B200. Do a smoke test on the image once done.*** + +The Agent is expected to understand the prompt and creates a folder named “rtvi_vlm” and generates all the files there: +- config.yaml (pipeline configuration yaml) +- Dockerfile +- openapi.yaml (server specification) +- processors.py (customized code) +- rtvi_vlm.tgz (backbone python code) +- model-repo (folder for required model files) + +The Agent is responsible for the smoke testing and troubleshooting of any issues encountered with the generated container image. + +#### 4.2. Add Helper Scripts for Manual Testing with Follow-up Prompts + +***Generate a service management script to start, restart, stop, rebuild the service and check the service status, also fetch the service logs. Generate a client script to add/delete streams and trigger a summarization request on streams.*** + +With the generated server and client script, we can ask Cursor to intensively test stream summarization, and during this process Cursor will fix the generated processor code based on the container log. +We'll need to go through this a few times because Cursor has to figure out the best model setup by running it. + +You can ask Cursor to use your live stream: `rtsp://:/`. + +#### 4.3. New feature request + +New features can always be added through follow-up prompts: + +***Now let's add a new feature to the project. After the model generates summarizations, let's postprocess it for each chunk and send them to a Kafka server. Create an environment variable to toggle the feature.*** + +#### 4.4. Document the project for future development + +***Write a detailed documentation for the project to ensure seamless and consistent future work.*** + +Subsequently, the project remains open, allowing for continued adjustment, addition, or removal of features using the same methods. + +#### 4.5. YOLO26s Detection Deepstream Application + +***Download the YOLO26s detection model using the ultralytics library, then convert the model to ONNX model that supports dynamic batch, in Python virtual environment. Write a DeepStream custom parser for the model. Use deepstream inference builder tool to create an object detection pipeline with the model and custom parser. Do a smoke test once done.*** + +The Agent is expected to draft a plan based on the prompt and generates required files accordingly (file names may differ in each run unless explicitly specified): + +- yolo26s_pipeline.yaml (pipeline configuration yaml) +- Dockerfile +- nvdsinfer_config.yaml (nvinfer configuration yaml) +- yolo26s-detection.tgz (python code for inference) +- models/yolo26s (folder for required model files) +- custom_parser (DeepStream custom parser for the model) + +The Agent is expected to fix all the build and runtime errors while doing the smoke test. diff --git a/tools/inference_builder/mcp/mcp_server.py b/tools/inference_builder/mcp/mcp_server.py new file mode 100644 index 00000000..21a219ff --- /dev/null +++ b/tools/inference_builder/mcp/mcp_server.py @@ -0,0 +1,1994 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. +# All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +MCP (Model Context Protocol) Server for Inference Builder + +This module provides an MCP server that exposes Inference Builder functionality +to MCP-compatible clients like Cursor. +""" + +import asyncio +import os +import sys +from pathlib import Path +from typing import Any, Dict +from mcp.server import Server +from mcp.server.stdio import stdio_server +from mcp.types import ( + CallToolRequest, + CallToolResult, + ListToolsRequest, + ListToolsResult, + Tool, + TextContent, + Resource, +) +import subprocess +import logging +import time +import shutil +import uuid + +# Add the builder directory to Python path +sys.path.append(str(Path(__file__).parent.parent / "builder")) + + +def load_allowed_servers() -> list: + """Load allowed server types from the builder's allowed_servers.txt file. + + Returns: + List of allowed server type strings. + + Raises: + FileNotFoundError: If the configuration file is not found. + ValueError: If the file is empty or contains invalid entries. + """ + file_path = Path(__file__).parent.parent / "builder" / "allowed_servers.txt" + + if not file_path.exists(): + raise FileNotFoundError( + f"Allowed servers configuration file not found: {file_path}" + ) + + allowed_servers = [] + with open(file_path, 'r', encoding='utf-8') as f: + for line in f: + line = line.strip() + if line and not line.startswith('#'): + allowed_servers.append(line) + + if not allowed_servers: + raise ValueError(f"No valid server types found in {file_path}") + + return allowed_servers + + +# Load allowed server types from configuration file +ALLOWED_SERVERS = load_allowed_servers() + + +class InferenceBuilderMCPServer: + """MCP Server that exposes Inference Builder functionality""" + + def __init__(self): + self.server = Server("deepstream-inference-builder") + self.logger = logging.getLogger("deepstream-inference-builder") + + # Register handlers using decorators + self._setup_handlers() + + def _setup_handlers(self): + """Setup MCP server handlers using decorators""" + + @self.server.list_tools() + async def handle_list_tools(): + # The low-level server expects a list[Tool], not ListToolsResult + result = await self.list_tools(None) + return result.tools + + @self.server.call_tool() + async def handle_call_tool(name: str, arguments: Dict[str, Any]): + # Bridge to existing implementation which returns CallToolResult + from mcp.types import CallToolRequestParams + params = CallToolRequestParams(name=name, arguments=arguments) + request = CallToolRequest(params=params) + result = await self.call_tool(request) + if getattr(result, "isError", False): + # Let the low-level server wrap this as an error result + message = "" + for block in getattr(result, "content", []) or []: + if getattr(block, "type", "") == "text": + message = getattr(block, "text", "") + break + raise RuntimeError(message or "Tool execution error") + # Return unstructured content so low-level server wraps it properly + return getattr(result, "content", []) + + @self.server.list_resources() + async def handle_list_resources(): + """List available schema and sample resources""" + self.logger.info("list_resources handler called") + resources = [] + + # Add base schema resources + try: + self.logger.info("Creating base schema resources...") + base_resources = [ + # Schema resources + Resource( + uri="schema://config.schema.json", + name="Configuration Schema", + description=( + "JSON Schema for inference builder configuration files. " + "Defines required fields (name, model_repo, models), " + "tensor specifications, backend types, and server configuration." + ), + mimeType="application/json" + ), + Resource( + uri="schema://readme", + name="Schema Documentation", + description=( + "Comprehensive documentation for configuration schemas including " + "examples, backend-specific parameters, data types, " + "preprocessors/postprocessors, and server responder configuration." + ), + mimeType="text/markdown" + ), + Resource( + uri="schema://index.json", + name="Schema Index", + description=( + "Index mapping backend types to their parameter schemas. " + "Use this to find the correct parameter schema for a given backend " + "(e.g., 'vllm' -> 'backends/parameters/vllm-parameters.schema.json'). " + "Read this first to understand schema navigation." + ), + mimeType="application/json" + ), + # Documentation resources + Resource( + uri="docs://README.md", + name="Project README", + description=( + "Main project documentation for Inference Builder. " + "Includes overview, getting started guide, installation instructions, " + "and links to examples and detailed documentation." + ), + mimeType="text/markdown" + ), + Resource( + uri="docs://mcp/README-MCP.md", + name="MCP Integration Documentation", + description=( + "Detailed documentation for MCP server integration. " + "Includes tool reference with parameters, resource navigation guide, " + "usage examples, workflow integration, and troubleshooting." + ), + mimeType="text/markdown" + ), + Resource( + uri="docs://usage.md", + name="Usage Documentation", + description=( + "Comprehensive usage guide for Inference Builder. " + "Covers command line arguments, configuration file format, " + "model definitions, preprocessors/postprocessors, server configuration, " + "routing, and runtime environment variables." + ), + mimeType="text/markdown" + ), + Resource( + uri="docs://platform-guide.md", + name="Platform Guide", + description=( + "Hardware platform selection guide. " + "Maps target hardware (x86_64 datacenter, Jetson/Tegra, arm-sbsa servers " + "such as GB10/GB300/DGX Spark) to the correct Dockerfile template, " + "DeepStream base image, GPU architecture flags, PyTorch install method, " + "and CUDA version. **Read this first** when building a Docker image to " + "avoid runtime failures caused by mismatched base images or missing " + "platform libraries (e.g. libnvbufsurface on Tegra)." + ), + mimeType="text/markdown" + ), + ] + self.logger.info("Created %d base_resources objects", len(base_resources)) + for i, res in enumerate(base_resources): + self.logger.debug(" base_resource[%d]: uri=%s, name=%s", i, res.uri, res.name) + resources.extend(base_resources) + self.logger.info("After extend, resources has %d items", len(resources)) + except Exception as exc: + self.logger.exception("Error creating base resources: %s", exc) + + # Dynamically discover backend schema resources + try: + schema_dir = Path(__file__).parent.parent / "schemas" + backends_dir = schema_dir / "backends" + self.logger.debug("Schema dir: %s (exists: %s)", schema_dir, schema_dir.exists()) + self.logger.debug("Backends dir: %s (exists: %s)", backends_dir, backends_dir.exists()) + if backends_dir.exists(): + # Backend schemas (e.g., deepstream.schema.json, triton.schema.json) + for schema_file in sorted(backends_dir.glob("*.schema.json")): + backend_name = schema_file.stem.replace(".schema", "") + resources.append(Resource( + uri=f"schema://backends/{schema_file.name}", + name=f"Backend Schema: {backend_name}", + description=( + f"JSON Schema for {backend_name} backend configuration. " + f"Defines backend-specific parameters and settings." + ), + mimeType="application/json" + )) + + # Backend parameter schemas (e.g., deepstream-parameters.schema.json) + params_dir = backends_dir / "parameters" + if params_dir.exists(): + for param_file in sorted(params_dir.glob("*-parameters.schema.json")): + backend_name = param_file.stem.replace("-parameters.schema", "") + resources.append(Resource( + uri=f"schema://backends/parameters/{param_file.name}", + name=f"Backend Parameters: {backend_name}", + description=( + f"JSON Schema for {backend_name} backend parameters. " + f"Defines detailed parameter options for model configuration." + ), + mimeType="application/json" + )) + self.logger.info("After backend discovery, resources has %d items", len(resources)) + except Exception as exc: + self.logger.exception("Error discovering backend schemas: %s", exc) + + # Dynamically discover sample resources categorized by type + try: + samples_dir = Path(__file__).parent.parent / "builder" / "samples" + self.logger.debug("Samples dir: %s (exists: %s)", samples_dir, samples_dir.exists()) + if not samples_dir.exists(): + self.logger.warning("Samples directory not found: %s", samples_dir) + else: + for sample_dir in sorted(samples_dir.iterdir()): + if sample_dir.is_dir(): + sample_name = sample_dir.name + description = self._get_sample_description(sample_name) + + # Collect all files recursively from the sample directory + # This ensures deeply nested configs like nvdsinfer_config.yaml + # under ds_app/*/* are exposed as resources. + all_files = [ + f for f in sample_dir.rglob("*") if f.is_file() + ] + + # Category 1a: DeepStream nvinfer runtime configs (e.g., nvdsinfer_config.yaml) + # These are NOT pipeline definitions; they are runtime nvinfer config + # files that belong in the model repository when DeepStream backend is used. + runtime_yaml_files = [ + f + for f in all_files + if f.is_file() + and f.suffix == ".yaml" + and f.name == "nvdsinfer_config.yaml" + ] + for runtime_yaml in sorted(runtime_yaml_files): + rel_path = runtime_yaml.relative_to(samples_dir) + resources.append(Resource( + uri=f"samples://runtime_config/{rel_path}", + name=f"DeepStream Runtime Config for Inference: {rel_path}", + description=( + "DeepStream nvinfer runtime configuration file " + "(nvdsinfer_config.yaml). " + "This file should live in the model repository when Deepstream backend is used " + "and be referenced via 'infer_config_path' in the " + "DeepStream backend parameters, not used as a pipeline " + "configuration YAML." + ), + mimeType="application/x-yaml" + )) + + # Category 1b: DeepStream preprocess runtime configs (e.g., nvdspreprocess_config*.yaml) + # These are runtime configuration files for the nvdspreprocess plugin and + # should also live in the model repository when DeepStream backend is used. + preprocess_runtime_yaml_files = [ + f + for f in all_files + if f.is_file() + and f.suffix == ".yaml" + and f.name.startswith("nvdspreprocess_config") + ] + for preprocess_yaml in sorted(preprocess_runtime_yaml_files): + rel_path = preprocess_yaml.relative_to(samples_dir) + resources.append(Resource( + uri=f"samples://runtime_preprocess/{rel_path}", + name=f"DeepStream Runtime Config for Preprocessing: {rel_path}", + description=( + "DeepStream nvdspreprocess runtime configuration file " + "(nvdspreprocess_config*.yaml). " + "This file should live in the model repository when DeepStream " + "backend is used and be referenced via 'preprocess_config_path' " + "in the DeepStream backend parameters, not used as a pipeline " + "configuration YAML." + ), + mimeType="application/x-yaml" + )) + + # Category 1c: OpenAPI / server specification YAML files + # These define the HTTP API contract (e.g., for FastAPI, Triton, NIM) + # and should be treated as server configuration, not pipeline configs. + openapi_yaml_files = [ + f + for f in all_files + if f.is_file() + and f.suffix == ".yaml" + and ( + f.name in ("openapi.yaml", "openapi.yml") + or f.name.endswith("_openapi.yaml") + or f.name.endswith("_openapi.yml") + ) + ] + for openapi_yaml in sorted(openapi_yaml_files): + rel_path = openapi_yaml.relative_to(samples_dir) + resources.append(Resource( + uri=f"samples://openapi/{rel_path}", + name=f"OpenAPI Server Spec: {rel_path}", + description=( + "OpenAPI specification YAML describing the HTTP API for an " + "inference server (e.g., FastAPI, Triton, NIM). " + "Treat this as server configuration: it defines request/response " + "schemas and endpoints and is referenced via 'api_spec' when " + "generating a FastAPI/NIM/TRITON server, not as a model or " + "pipeline configuration." + ), + mimeType="application/x-yaml" + )) + + # Category 1d: Pipeline / application configuration YAML files + yaml_files = [ + f + for f in all_files + if f.is_file() + and f.suffix == ".yaml" + and f.name != "nvdsinfer_config.yaml" + and not f.name.startswith("nvdspreprocess_config") + and not ( + f.name in ("openapi.yaml", "openapi.yml") + or f.name.endswith("_openapi.yaml") + or f.name.endswith("_openapi.yml") + ) + ] + for yaml_file in sorted(yaml_files): + rel_path = yaml_file.relative_to(samples_dir) + resources.append(Resource( + uri=f"samples://config/{rel_path}", + name=f"Sample Config: {rel_path}", + description=( + f"Sample pipeline/application configuration YAML for " + f"{sample_name}. {description}" + ) if description else f"Sample configuration: {rel_path}", + mimeType="application/x-yaml" + )) + + # Category 2: Dockerfiles + dockerfiles = [f for f in all_files if f.is_file() and f.name.startswith("Dockerfile")] + for dockerfile in sorted(dockerfiles): + rel_path = dockerfile.relative_to(samples_dir) + resources.append(Resource( + uri=f"samples://dockerfile/{rel_path}", + name=f"Sample Dockerfile: {rel_path}", + description=( + f"Dockerfile for {sample_name}. " + f"Use as reference for container image builds." + ), + mimeType="text/plain" + )) + + # Category 3: Processor Python files (files with "processor" in name) + processor_files = [ + f for f in all_files + if f.is_file() and f.suffix == ".py" and "processor" in f.stem.lower() + ] + for processor in sorted(processor_files): + rel_path = processor.relative_to(samples_dir) + resources.append(Resource( + uri=f"samples://processor/{rel_path}", + name=f"Sample Processor: {rel_path}", + description=( + f"Sample preprocessor/postprocessor for {sample_name}. " + f"Python module with callable classes for pipeline processing." + ), + mimeType="text/x-python" + )) + + self.logger.info("After sample discovery, resources has %d items", len(resources)) + except Exception as exc: + self.logger.exception("Error discovering sample resources: %s", exc) + + self.logger.info("Listed %d MCP resources", len(resources)) + if len(resources) > 0: + self.logger.info("First resource URI: %s", resources[0].uri) + self.logger.info("Last resource URI: %s", resources[-1].uri) + else: + self.logger.warning("Resources list is EMPTY - this is the problem!") + + return resources + + @self.server.read_resource() + async def handle_read_resource(uri): + """Read a schema or sample resource by URI""" + # Convert AnyUrl to string for string operations + uri = str(uri) + schema_dir = Path(__file__).parent.parent / "schemas" + samples_dir = Path(__file__).parent.parent / "builder" / "samples" + + # Handle docs:// URIs for documentation files + if uri.startswith("docs://"): + project_root = Path(__file__).parent.parent + docs_mappings = { + "docs://README.md": project_root / "README.md", + "docs://mcp/README-MCP.md": project_root / "mcp" / "README-MCP.md", + "docs://usage.md": project_root / "doc" / "usage.md", + "docs://platform-guide.md": project_root / "doc" / "platform-guide.md", + } + if uri in docs_mappings: + file_path = docs_mappings[uri] + else: + raise ValueError( + f"Unknown docs resource URI: {uri}. " + f"Available: docs://README.md, docs://mcp/README-MCP.md, docs://usage.md" + ) + + # Handle schema:// URIs + elif uri.startswith("schema://"): + # Static schema mappings + uri_to_file = { + "schema://config.schema.json": schema_dir / "config.schema.json", + "schema://readme": schema_dir / "README.md", + "schema://index.json": schema_dir / "index.json", + } + + if uri in uri_to_file: + file_path = uri_to_file[uri] + elif uri.startswith("schema://backends/"): + # Handle dynamic backend schema URIs + rel_path = uri.replace("schema://backends/", "") + file_path = schema_dir / "backends" / rel_path + + # Security check: ensure path doesn't escape backends directory + backends_dir = schema_dir / "backends" + try: + file_path.resolve().relative_to(backends_dir.resolve()) + except ValueError as exc: + raise ValueError(f"Invalid backend schema path: {rel_path}") from exc + else: + raise ValueError( + f"Unknown schema resource URI: {uri}. " + f"Use schema://config.schema.json, schema://readme, " + f"schema://index.json, or schema://backends/*" + ) + + # Handle samples:// URIs (with category prefix: config/, dockerfile/, processor/, " + # runtime_config/, runtime_preprocess/, openapi/) + elif uri.startswith("samples://"): + rel_path = uri.replace("samples://", "") + + # If the URI is only a category prefix (e.g., samples://runtime_config), + # return a helpful error instead of trying to map it to a file. + category_labels = { + "config": "pipeline/application configuration YAML files", + "dockerfile": "sample Dockerfiles", + "processor": "sample pre/postprocessor Python modules", + "runtime_config": "DeepStream nvinfer runtime configuration files (nvdsinfer_config.yaml)", + "runtime_preprocess": "DeepStream nvdspreprocess runtime configuration files (nvdspreprocess_config*.yaml)", + "openapi": "OpenAPI server specification YAML files", + } + rel_path_stripped = rel_path.rstrip("/") + if rel_path_stripped in category_labels: + label = category_labels[rel_path_stripped] + raise ValueError( + f"'{uri}' is a category prefix for {label}, not a specific resource. " + f"Call list_resources to see the available 'samples://{rel_path_stripped}/...'" + " URIs, then use read_resource on one of those full URIs." + ) + + # Strip category prefix if present + for category in ( + "config/", + "dockerfile/", + "processor/", + "runtime_config/", + "runtime_preprocess/", + "openapi/", + ): + if rel_path.startswith(category): + rel_path = rel_path[len(category):] + break + file_path = samples_dir / rel_path + + # Security check: ensure path doesn't escape samples directory + try: + file_path.resolve().relative_to(samples_dir.resolve()) + except ValueError as exc: + raise ValueError(f"Invalid sample path: {rel_path}") from exc + + else: + raise ValueError( + f"Unknown resource URI scheme: {uri}. " + f"Supported schemes: docs://, schema://, samples://" + ) + + if not file_path.exists(): + raise FileNotFoundError(f"Resource file not found: {file_path}") + + # Check if the path is a directory + if file_path.is_dir(): + # List files in the directory to help the user + try: + files = [f.name for f in file_path.iterdir() if f.is_file()] + files_list = "\n - ".join(sorted(files)) if files else "(no files found)" + raise ValueError( + f"'{uri}' points to a directory, not a file. " + f"The MCP resource reader can only read individual files. " + f"Files in this directory:\n - {files_list}\n\n" + f"To read a specific file, use a URI like:\n" + f" {uri}/{{filename}}" + ) + except Exception as exc: + raise ValueError( + f"'{uri}' points to a directory, not a file. " + f"The MCP resource reader can only read individual files." + ) from exc + + with open(file_path, 'r', encoding='utf-8') as f: + content = f.read() + + return content + + def _sanitize_arguments(self, arguments: Dict[str, Any]) -> Dict[str, Any]: + """Redact sensitive values in tool arguments for logging.""" + if not isinstance(arguments, dict): + return {} + sensitive_keys = { + "api_key", "apikey", "token", "password", "secret", + "authorization", "auth", "bearer", "access_token", + } + redacted: Dict[str, Any] = {} + for key, value in arguments.items(): + if isinstance(key, str) and key.lower() in sensitive_keys: + redacted[key] = "" + else: + redacted[key] = value + return redacted + + async def list_tools(self, _request: ListToolsRequest) -> ListToolsResult: + """List available tools""" + return ListToolsResult( + tools=[ + Tool( + name="generate_inference_pipeline", + description=( + "Generate a deployable inference service from a YAML configuration file. " + "Outputs a complete project with model serving code, API endpoints, and deployment files. " + "Read 'samples://*' resources for example configurations, " + "or 'schema://config.schema.json' resource for the full configuration schema." + ), + inputSchema={ + "type": "object", + "properties": { + "config_file": { + "type": "string", + "description": ( + "Path to the YAML configuration file defining the pipeline. " + "If the user has not provided a config file, generate one based on " + "their requirements: read 'schema://config.schema.json' for the full schema, " + "'schema://index.json' to find backend-specific parameter schemas " + "(maps backend type to 'schema://backends/parameters/*'), " + "'schema://readme' for documentation, and 'samples://*' resources for " + "reference examples. Create the YAML file at a suitable path, then provide " + "that path here." + ) + }, + "server_type": { + "type": "string", + "enum": ALLOWED_SERVERS, + "default": "serverless", + "description": ( + "Type of server to generate. Use 'serverless' for standalone applications " + "designed for batch inference " + "(no API spec needed and no server section should appear in the config_file). " + "Use 'fastapi' for microservices that serve inference requests via HTTP endpoints " + " (requires api_spec and 'server' section in config_file)." + " If the user's intent is unclear, ask them to explicitly choose " + "between 'serverless' and 'fastapi' before proceeding." + ) + }, + "output_dir": { + "type": "string", + "description": ( + "The user's project directory where generated artifacts will be placed. " + "This directory will contain configurations, custom processors, Dockerfiles, " + "and other files for the inference project. A subdirectory named after the " + "pipeline (from config YAML) will be created here, or a tar.gz archive if " + "tar_output is enabled. If the project location is unclear, ask the user to " + "specify it, or prompt them to confirm after creating a new project directory." + ), + "default": "." + }, + "api_spec": { + "type": "string", + "description": ( + "Path to OpenAPI specification file (YAML/JSON). " + "Required for 'fastapi', 'triton', and 'nim' server types. " + "Not needed for 'serverless' type. If the user has not provided an " + "OpenAPI spec, attempt to generate one based on their requirements. " + "If the given information is insufficient to generate a valid spec " + "(e.g., missing endpoint definitions, request/response schemas), " + "ask the user to provide an OpenAPI specification file." + ) + }, + "custom_modules": { + "type": "array", + "items": {"type": "string"}, + "description": ( + "List of paths to custom Python module files for preprocessors and " + "postprocessors. Each module must define classes with a 'name' attribute " + "and '__call__' method to be registered in the pipeline. Determine if " + "processors are needed based on the user's requirements and pipeline " + "structure (e.g., input transformations, output formatting). If processors " + "are required, read 'samples://processor/*' resources for reference examples, " + "then generate the Python files accordingly, save them in the project " + "directory, and include their paths here." + ) + }, + "exclude_lib": { + "type": "boolean", + "default": False, + "description": ( + "If true, exclude the common utility library from generated code. " + "Use when deploying to an environment where the library is already installed." + ) + }, + "tar_output": { + "type": "boolean", + "default": False, + "description": ( + "If true, output a tar.gz archive instead of a directory. " + "This affects how the generated pipeline is copied into the container " + "image in the Dockerfile: use COPY with directory when false, or ADD " + "with tar.gz extraction when true. Set to true when transferring the " + "pipeline to a remote build system." + ) + }, + "validation_dir": { + "type": "string", + "description": ( + "Path to output directory for API validation/test artifacts. " + "When provided, generates OpenAPI client code and test cases " + "for validating the inference endpoints." + ) + }, + "no_docker": { + "type": "boolean", + "default": False, + "description": ( + "If true, use locally installed OpenAPI Generator CLI instead of Docker. " + "Requires 'openapi-generator-cli' to be installed and in PATH." + ) + }, + "test_cases_abs_path": { + "type": "boolean", + "default": False, + "description": ( + "If true, use absolute paths in generated test_cases.yaml. " + "Use when test files are located outside the validation directory." + ) + } + }, + "required": ["config_file"] + } + ), + Tool( + name="build_docker_image", + description=( + "Build a Docker image from a generated inference " + "pipeline. The Dockerfile is expected to consume the " + "tar.gz archive produced by 'generate_inference_pipeline' " + "when 'tar_output' is true (for example, using " + "'ADD .tar.gz /app') rather than copying a " + "loose directory of files. Agents should prefer " + "tar_output=true for Docker-based deployments." + ), + inputSchema={ + "type": "object", + "properties": { + "image_name": { + "type": "string", + "description": "Name for the Docker image" + }, + "dockerfile": { + "type": "string", + "description": ( + "Path to Dockerfile. Always generate the Dockerfile based on user " + "requirements and 'samples://dockerfile/*' resources. " + "**Before choosing a Dockerfile template, read 'docs://platform-guide.md' " + "to identify the correct base image and template for the target hardware** " + "(x86_64 datacenter, Jetson/Tegra, or arm-sbsa servers like GB10/GB300/DGX Spark). " + "Consider model backend (triton, vllm, tensorrt-llm, deepstream), server type " + "(serverless, fastapi), and hardware platform. " + "The Dockerfile MUST be placed in the output_dir where the generated " + "pipeline resides, as its parent directory becomes the Docker build " + "context for properly transferring the pipeline code into the container." + ) + } + }, + "required": ["image_name", "dockerfile"] + } + ), + Tool( + name="docker_run_image", + description=( + "Optionally run a built Docker image to help with testing and " + "troubleshooting. This is a lightweight helper around " + "'docker run' that can mount a prepared model repository " + "from the host into the container, set environment " + "variables, and pass command-line arguments. " + "The container is started with --ipc=host so that " + "PyTorch / TensorRT-LLM multiprocessing can share " + "tensors via /dev/shm without hitting the default " + "64 MB Docker limit. " + "Use this after 'prepare_model_repository' and " + "'build_docker_image' have completed." + ), + inputSchema={ + "type": "object", + "properties": { + "image_name": { + "type": "string", + "description": ( + "Name of the Docker image to run " + "(for example, 'my-inference-service:latest')." + ) + }, + "model_repo_host": { + "type": "string", + "description": ( + "Optional host path to a prepared model repository " + "directory (for example, produced by " + "'prepare_model_repository'). If provided, it will " + "be mounted into the container at " + "model_repo_container via a Docker volume." + ) + }, + "model_repo_container": { + "type": "string", + "description": ( + "Container path where the model repository should " + "be mounted (for example, '/models'). Used only " + "when model_repo_host is provided." + ), + "default": "/models" + }, + "server_type": { + "type": "string", + "description": ( + "High-level server type hint to choose sensible " + "defaults (for example, 'serverless' or 'fastapi'). " + "For non-serverless servers, the container will be " + "run on the host network to make HTTP endpoints " + "reachable from the host." + ), + "default": "serverless" + }, + "env": { + "type": "object", + "description": ( + "Optional environment variables to set inside the " + "container. Keys and values are serialized as " + "`-e KEY=VALUE`." + ), + "additionalProperties": { + "type": "string" + } + }, + "cmd": { + "type": "array", + "description": ( + "Optional list of command-line arguments or an " + "alternative command to pass to 'docker run' after " + "the image name. For serverless flows this is " + "typically the inference entrypoint arguments. " + "IMPORTANT: when server_type is 'serverless', the " + "user's input must be supplied via CLI arguments in " + "this list. Argument names use hyphens, not " + "underscores (e.g., the input named 'media_url' " + "becomes '--media-url '). Each flag and its " + "value should be separate items, for example: " + "[\"--media-url\", \"/path/to/video.mp4\"]." + ), + "items": { + "type": "string" + } + }, + "gpus": { + "type": "string", + "description": ( + "GPU devices to use for the container, passed as " + "`--gpus` (for example, 'all', 'device=0', " + "'device=0,1')." + ), + "default": "all" + }, + "timeout": { + "type": "integer", + "description": ( + "Maximum time in seconds to wait for the container " + "process to complete before timing out. For " + "non-serverless servers this is the time to wait " + "before collecting logs and stopping the container." + ), + "default": 300 + } + }, + "required": ["image_name"] + } + ), + Tool( + name="prepare_model_repository", + description=( + "Optionally prepare a model repository directory for deployment. " + "Given a model configuration list, this tool can download model artifacts " + "(for example from NGC or Hugging Face) and copy associated runtime " + "configuration files (such as DeepStream nvdsinfer_config.yaml and " + "nvdspreprocess_config*.yaml) into a model repository layout that " + "matches the generated pipeline and Dockerfile. Use this when the " + "user does not already have a model repository prepared." + "It is recommended to mount the model repository as a volume when running the Docker image." + ), + inputSchema={ + "type": "object", + "properties": { + "model_configs": { + "type": "array", + "description": ( + "List of model configurations to prepare, following the same " + "structure as the 'models' section in builder/tests/" + "test_docker_builds.py. Each item is an object with fields: " + "'name' (required, model name), 'source' (optional, 'NGC' or " + "'HF', default 'NGC'), 'target' (required, directory where the " + "prepared model repository should be created), 'path' (for NGC: " + "registry model path; for HF: repo id like " + "'Qwen/Qwen2.5-VL-3B-Instruct'), 'version' (for NGC), " + "'configs' (optional, path relative to config_dir of a directory " + "of runtime config files to copy into the final model repository), and " + "'post_script' (optional, shell command to execute after model download " + "for post-processing steps)." + ), + "items": { + "type": "object", + "properties": { + "name": { + "type": "string", + "description": "Model name (used as the final directory name).", + }, + "source": { + "type": "string", + "enum": ["NGC", "HF"], + "description": ( + "Model source: 'NGC' for NVIDIA NGC registry models, " + "or 'HF' for Hugging Face models." + ), + }, + "target": { + "type": "string", + "description": ( + "Target directory on the local filesystem where the prepared " + "model repository should be created." + ), + }, + "path": { + "type": "string", + "description": ( + "For NGC: registry model path (for example, " + "'nvidia/tao/grounding_dino'). For HF: model repo id " + "(for example, 'Qwen/Qwen2.5-VL-3B-Instruct')." + ), + }, + "version": { + "type": "string", + "description": ( + "For NGC models, the version/tag to download (for example, " + "'grounding_dino_swin_tiny_commercial_deployable_v1.0')." + ), + }, + "configs": { + "type": "string", + "description": ( + "Optional path (relative to config_dir) to a directory containing " + "runtime configuration files (for example, DeepStream " + "nvdsinfer_config.yaml, nvdspreprocess_config*.yaml, label files) " + "that should be copied into the final model repository." + ), + }, + "post_script": { + "type": "string", + "description": ( + "Optional shell command or script to execute after the model is downloaded. " + "This can be used for post-processing steps like installing additional " + "dependencies, converting model formats, or applying patches. " + "The script is executed with the model's target directory as the current " + "working directory." + ), + }, + }, + "required": ["name", "target"], + }, + }, + "config_dir": { + "type": "string", + "description": ( + "Base directory used to resolve relative 'configs' " + "paths in model_configs. Typically this is the " + "directory containing your pipeline/test " + "configuration files." + ), + "default": "." + } + }, + "required": ["model_configs"] + } + ), + Tool( + name="generate_nvinfer_config", + description=( + "Generate a DeepStream nvinfer runtime configuration file (nvdsinfer_config.yaml). " + "This configuration file is required by the DeepStream backend and must be placed " + "in the model repository. It defines inference parameters such as model file paths, " + "precision mode, network type, input dimensions, and custom parsers. " + "The generated file should be referenced via 'infer_config_path' in the DeepStream " + "backend parameters. See 'samples://runtime_config/*' resources for examples." + "**IMPORTANT: Before generating a new config file, first call 'prepare_model_repository' to download the model and check for existing nvinfer configuration files.** " + "Models from NGC typically include a configuration file (YAML or TXT) with all required inference parameters - always prefer using this provided configuration when available. "), + inputSchema={ + "type": "object", + "properties": { + "output_path": { + "type": "string", + "description": ( + "Path where the generated nvdsinfer_config.yaml file should be saved. " + "This should typically be in your model repository directory." + ) + }, + "onnx_file": { + "type": "string", + "description": ( + "Name of the ONNX model file (e.g., 'model.onnx'). " + "This file should exist in the same directory as the config file." + ) + }, + "precision_mode": { + "type": "integer", + "enum": [0, 1, 2], + "default": 2, + "description": ( + "Precision mode for inference: 0=FP32, 1=INT8, 2=FP16. " + "Default is FP16 (2) for optimal performance on modern GPUs." + ) + }, + "network_type": { + "type": "integer", + "enum": [0, 1, 2, 3, 100], + "description": ( + "Network type: 0=detection, 1=classification, 2=segmentation, " + "3=instance_segmentation. This determines how the model output is interpreted." + "100=custom is used for models that need to output raw tensors for custom downstream processing along with output-tensor-meta: 1." + ) + }, + "input_dims": { + "type": "string", + "description": ( + "Model input dimensions in format 'channel;height;width' " + "(e.g., '3;224;224' for a 224x224 RGB image). " + "Note: the format is C;H;W, not C;W;H." + ) + }, + "label_file": { + "type": "string", + "description": ( + "Name of the label file (e.g., 'labels.txt'). " + "This file should contain class names, one per line, and be placed " + "in the same directory as the config file." + ) + }, + "custom_lib_path": { + "type": "string", + "description": ( + "Path to a C++ shared library (.so) that provides custom output parsing functions. " + "Required for all models except classic ResNet when network_type is 0, 1, 2, or 3 (not required for network_type 100 which outputs raw tensors). " + "For TAO-trained models, build the library from https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps/tree/master/post_processor. " + "For other models, provide the path to your own custom parser library." + ) + }, + "custom_parse_func": { + "type": "string", + "description": ( + "Symbol name of the custom parsing function exported by custom_lib_path. " + "Behavior varies by network_type: " + "(0) Detection - if omitted, the built-in parser assumes a ResNet model with 'bbox' and 'cov' output layers. " + "(1) Classification - if omitted, the built-in parser treats model output as a softmax layer. " + "(2) Segmentation, (3) Instance segmentation - no built-in parser available, custom_parse_func is required. " + "(100) Custom - not required, raw tensors are output directly for downstream processing." + ) + }, + "num_classes": { + "type": "integer", + "description": ( + "Number of detected classes for the model. Required for detection and classification networks and can be deducted from the label file." + ) + }, + "gie_unique_id": { + "type": "integer", + "default": 1, + "description": ( + "Unique ID for this GIE (inference engine) in the pipeline. " + "Use different IDs if you have multiple inference engines in the same pipeline." + ) + }, + "net_scale_factor": { + "type": "number", + "default": 0.00392156862745098, + "description": ( + "Scale factor for input normalization. Default is 1/255 = 0.00392156862745098 " + "for models expecting [0,1] normalized inputs. IMPORTANT: This must match the " + "scaling factor used during model training. Common patterns: " + "(1) Simple scaling: 1/255; " + "(2) With uniform std: 1/(255*std) - dividing by std; " + "(3) With scale factor d: d/255 - multiplying by d; " + "(4) Custom: any value matching training. " + "For per-channel std normalization, consider baking into the ONNX model or using " + "a custom preprocessing plugin, as DeepStream only supports a single scale factor." + ) + }, + "offsets": { + "type": "string", + "description": ( + "Per-channel mean subtraction values in format 'R;G;B' " + "(e.g., '127.5;127.5;127.5' for [-1,1] normalization, " + "'123.675;116.28;103.53' for ImageNet models). " + "IMPORTANT: This must match the per-channel mean subtraction used during " + "model training. If training used mean subtraction, you MUST specify this " + "parameter. If no mean subtraction was used during training, omit this parameter " + "or use '0;0;0'. Incorrect offsets will result in poor inference accuracy." + ) + }, + "classifier_threshold": { + "type": "number", + "default": 0.0, + "description": ( + "Confidence threshold for classification results. " + "Only classifications above this threshold will be reported." + ) + }, + "input_tensor_from_meta": { + "type": "integer", + "enum": [0, 1], + "default": 0, + "description": ( + "Whether to read input tensor from metadata (1) or from frame buffer (0). " + "Set to 1 when using nvdspreprocess for custom preprocessing." + ) + }, + "output_tensor_meta": { + "type": "integer", + "enum": [0, 1], + "default": 0, + "description": ( + "Whether to output in DeepStream metadata format (0) or raw tensor format (1). " + "Set to 1 if raw tensors are expected as inference output, which allows downstream " + "components to access raw tensor data as a Dictionary." + "Set to 0 for TYPE_CUSTOM_DS_METADATA output type, where the custom parser library processes the raw tensors into detected objects." + ) + } + }, + "required": ["output_path", "onnx_file", "network_type", "input_dims", "label_file"] + } + ), + ] + ) + + async def call_tool(self, request: CallToolRequest) -> CallToolResult: + """Execute a tool""" + name = request.params.name + arguments = request.params.arguments + + start_time = time.perf_counter() + self.logger.info( + "tool_call_started name=%s args=%s", + name, + self._sanitize_arguments(arguments), + ) + + try: + if name == "generate_inference_pipeline": + result = await self._generate_pipeline(arguments) + elif name == "build_docker_image": + result = await self._build_docker_image(arguments) + elif name == "docker_run_image": + result = await self._docker_run_image(arguments) + elif name == "prepare_model_repository": + result = await self._prepare_model_repository(arguments) + elif name == "generate_nvinfer_config": + result = await self._generate_deepstream_nvinfer_config(arguments) + else: + raise ValueError(f"Unknown tool: {name}") + + duration_ms = (time.perf_counter() - start_time) * 1000.0 + self.logger.info( + "tool_call_completed name=%s error=%s duration_ms=%.2f", + name, + bool(getattr(result, "isError", False)), + duration_ms, + ) + return result + except Exception as e: + duration_ms = (time.perf_counter() - start_time) * 1000.0 + self.logger.exception( + "tool_call_failed name=%s duration_ms=%.2f error=%s", + name, + duration_ms, + str(e), + ) + return CallToolResult( + content=[TextContent(type="text", text=f"Error: {str(e)}")], + isError=True + ) + + async def _generate_pipeline( + self, arguments: Dict[str, Any] + ) -> CallToolResult: + """Generate inference pipeline""" + config_file = arguments["config_file"] + server_type = arguments.get("server_type", "serverless") + output_dir = arguments.get("output_dir", ".") + + # Build command - use the same Python executable as the parent process + cmd = [ + sys.executable, "builder/main.py", + "--server-type", server_type, + "-o", output_dir, + config_file + ] + + # Add optional arguments + if "api_spec" in arguments: + cmd.extend(["-a", arguments["api_spec"]]) + + if "custom_modules" in arguments: + for module in arguments["custom_modules"]: + cmd.extend(["-c", module]) + + if arguments.get("exclude_lib", False): + cmd.append("-x") + + if arguments.get("tar_output", False): + cmd.append("-t") + + if "validation_dir" in arguments: + cmd.extend(["--validation-dir", arguments["validation_dir"]]) + + if arguments.get("no_docker", False): + cmd.append("--no-docker") + + if arguments.get("test_cases_abs_path", False): + cmd.append("--test-cases-abs-path") + + # Execute the command - inherit the current process environment + + result = subprocess.run( + cmd, + capture_output=True, + text=True, + cwd=Path(__file__).parent.parent, + env=os.environ.copy(), # Inherit the current environment + check=False, + ) + + if result.returncode == 0: + return CallToolResult( + content=[ + TextContent( + type="text", + text=f"Successfully generated inference pipeline!\n\n" + f"Output directory: {output_dir}\n\n" + f"Command executed: {' '.join(cmd)}\n\n" + f"Output:\n{result.stdout}" + ) + ] + ) + else: + return CallToolResult( + content=[ + TextContent( + type="text", + text=( + "Failed to generate pipeline:\n\n" + f"Error:\n{result.stderr}\n\n" + f"Command: {' '.join(cmd)}" + ) + ) + ], + isError=True + ) + + def _get_sample_description(self, sample_name: str) -> str: + """Get description for a sample""" + descriptions = { + "ds_app": ( + "DeepStream applications for object detection and " + "segmentation" + ), + "qwen": "Qwen language models with various backends", + "changenet": "Change detection models", + "nvclip": "NVIDIA CLIP models for vision-language tasks", + "tao": "NVIDIA TAO toolkit models", + "dummy": "Dummy models for testing and development" + } + return descriptions.get(sample_name, "") + + async def _docker_run_image( + self, arguments: Dict[str, Any] + ) -> CallToolResult: + """Run a Docker image for testing/troubleshooting.""" + image_name = arguments["image_name"] + model_repo_host = arguments.get("model_repo_host") + model_repo_container = arguments.get("model_repo_container", "/models") + server_type = arguments.get("server_type", "serverless") + env_vars = arguments.get("env") or {} + cmd_args = arguments.get("cmd") or [] + gpus = arguments.get("gpus", "all") + timeout = int(arguments.get("timeout", 300)) + + # Base docker run command (no shell, argument list only) + # Use --ipc=host so the container shares the host's /dev/shm. + # Docker's default 64 MB /dev/shm is insufficient for + # PyTorch / TensorRT-LLM multiprocessing which shares tensors + # via shared memory, causing SIGBUS when /dev/shm is exhausted. + # Assign a deterministic name so we can force-remove the container + # on timeout — subprocess.TimeoutExpired kills the `docker run` + # client process but leaves the container running with GPUs held. + container_name = f"ib-run-{uuid.uuid4().hex[:12]}" + cmd: list[str] = [ + "docker", "run", "--rm", "--ipc=host", + "--name", container_name, + ] + + # GPU configuration + if gpus: + cmd.extend(["--gpus", str(gpus)]) + + # Network: for serverless we still use host network so local clients can connect + cmd.append("--network=host") + + # Volume for model repository if provided + if model_repo_host: + host_path = str(Path(model_repo_host).expanduser().resolve()) + volume_arg = f"{host_path}:{model_repo_container}" + cmd.extend(["-v", volume_arg]) + + # Environment variables + if isinstance(env_vars, dict): + for key, value in env_vars.items(): + cmd.extend(["-e", f"{key}={value}"]) + + # Image name and optional command/args + cmd.append(image_name) + if isinstance(cmd_args, list): + cmd.extend([str(a) for a in cmd_args]) + + self.logger.info( + "docker_run_invoked image=%s model_repo_host=%s cmd=%s", + image_name, + model_repo_host, + " ".join(cmd), + ) + + try: + result = subprocess.run( + cmd, + capture_output=True, + text=True, + timeout=timeout, + check=False, + ) + except FileNotFoundError: + return CallToolResult( + content=[ + TextContent( + type="text", + text=( + "Docker not found. Please ensure Docker is installed " + "and available in your PATH before using docker_run_image." + ), + ) + ], + isError=True, + ) + except subprocess.TimeoutExpired: + is_serverless = server_type == "serverless" + if is_serverless: + # Serverless containers should finish within the timeout. + # The `docker run` client is dead but the container keeps + # running (and holding GPU memory). Force-remove it. + cleanup_msg = "" + try: + subprocess.run( + ["docker", "rm", "-f", container_name], + capture_output=True, + timeout=30, + check=False, + ) + cleanup_msg = ( + f" The orphaned container '{container_name}' has " + "been force-removed." + ) + except Exception as cleanup_exc: + cleanup_msg = ( + f" WARNING: failed to remove orphaned container " + f"'{container_name}': {cleanup_exc}. " + "Please remove it manually with: " + f"docker rm -f {container_name}" + ) + self.logger.warning( + "Failed to remove timed-out container %s: %s", + container_name, + cleanup_exc, + ) + return CallToolResult( + content=[ + TextContent( + type="text", + text=( + f"Docker run timed out after {timeout} seconds " + f"for image '{image_name}'.{cleanup_msg} You may " + "want to increase the timeout or inspect the " + "image logs manually." + ), + ) + ], + isError=True, + ) + else: + # Non-serverless (persistent) servers are expected to keep + # running past the timeout — that means the server started + # successfully. Leave the container running. + return CallToolResult( + content=[ + TextContent( + type="text", + text=( + f"Container '{container_name}' is running " + f"(image '{image_name}'). The server appears to " + f"have started successfully (still alive after " + f"{timeout}s). To stop it later: " + f"docker rm -f {container_name}" + ), + ) + ], + isError=False, + ) + except Exception as exc: + return CallToolResult( + content=[ + TextContent( + type="text", + text=f"Exception while running Docker image '{image_name}': {exc}", + ) + ], + isError=True, + ) + + text = ( + f"docker run command: {' '.join(cmd)}\n\n" + f"exit code: {result.returncode}\n\n" + f"stdout:\n{result.stdout}\n\n" + f"stderr:\n{result.stderr}" + ) + + return CallToolResult( + content=[TextContent(type="text", text=text)], + isError=result.returncode != 0, + ) + + async def _prepare_model_repository( + self, arguments: Dict[str, Any] + ) -> CallToolResult: + """Prepare model repositories by downloading models and copying configs. + + This mirrors the 'models' handling in builder/tests/test_docker_builds.py, + but is exposed as an optional MCP tool so agents can help set up + model repositories before Docker image builds. + """ + # New interface: model_configs is a list of dicts (each includes 'name') + # Backward-compat: accept legacy 'models_config' dict keyed by model name. + model_configs = arguments.get("model_configs") + legacy_models_config = arguments.get("models_config") + config_dir_arg = arguments.get("config_dir", ".") + + if model_configs is None and legacy_models_config is not None: + # Convert dict[name -> config] into list[{name, ...config}] + if isinstance(legacy_models_config, dict): + converted: list[dict[str, Any]] = [] + for legacy_name, legacy_info in legacy_models_config.items(): + if isinstance(legacy_info, dict): + merged = dict(legacy_info) + merged["name"] = legacy_name + converted.append(merged) + model_configs = converted + else: + model_configs = [] + + if not isinstance(model_configs, list) or not model_configs: + return CallToolResult( + content=[ + TextContent( + type="text", + text=( + "No models specified in model_configs. " + "Nothing to prepare for model repositories." + ), + ) + ] + ) + + # Resolve config_dir relative to project root (repo root) + project_root = Path(__file__).resolve().parents[1] + config_dir = Path(config_dir_arg) + if not config_dir.is_absolute(): + config_dir = (project_root / config_dir).resolve() + + messages: list[str] = [] + + for model_info in model_configs: + try: + if not isinstance(model_info, dict): + messages.append( + "❌ Skipping model entry: config must be an object" + ) + continue + + model_name = model_info.get("name") + if not model_name or not isinstance(model_name, str): + messages.append( + "❌ Skipping model entry: missing required 'name'" + ) + continue + + source = str(model_info.get("source", "NGC")).upper() + target = model_info.get("target") + if not target: + messages.append( + f"❌ Skipping model '{model_name}': missing required 'target'" + ) + continue + + target_dir = Path(target).expanduser() + final_model_dir = target_dir / model_name + + # Create target parent directory + target_dir.mkdir(parents=True, exist_ok=True) + + if final_model_dir.exists(): + messages.append( + f"✅ Model '{model_name}' already exists at {final_model_dir}, " + f"skipping download" + ) + else: + if source == "HF": + # Hugging Face model download, based on DockerBuildTester.download_models + hf_repo = model_info.get("path") + if not hf_repo: + messages.append( + f"❌ Skipping HF model '{model_name}': missing 'path'" + ) + continue + + messages.append( + f"📥 Downloading Hugging Face model '{model_name}' " + f"({hf_repo}) to {final_model_dir}" + ) + + # Use huggingface_hub.snapshot_download instead of git clone + # so that actual binary weights are fetched via the HF CDN + # (git clone only retrieves LFS pointer stubs unless a full + # LFS pull is performed, which fails for gated repos without + # proper credential configuration). + try: + from huggingface_hub import snapshot_download + snapshot_download( + repo_id=hf_repo, + local_dir=str(final_model_dir), + ignore_patterns=["*.pt", "*.bin", "original/*"], + ) + except Exception as e: + messages.append( + f"❌ Failed to download HF model '{model_name}': {e}" + ) + continue + else: + # Default to NGC + ngc_path = model_info.get("path") + version = model_info.get("version") + if not ngc_path or not version: + messages.append( + f"❌ Skipping NGC model '{model_name}': " + f"both 'path' and 'version' are required" + ) + continue + + full_ngc_path = f"{ngc_path}:{version}" + messages.append( + f"📥 Downloading NGC model '{model_name}' " + f"({full_ngc_path}) to {final_model_dir}" + ) + + # Run NGC CLI to download the model version + download_cmd = [ + "ngc", + "registry", + "model", + "download-version", + full_ngc_path, + ] + result = subprocess.run( + download_cmd, + capture_output=True, + text=True, + timeout=900, + check=False, + ) + if result.returncode != 0: + messages.append( + f"❌ Failed to download NGC model '{model_name}': " + f"{result.stderr.strip()}" + ) + continue + + # Infer downloaded folder name as in test_docker_builds.py + model_base_name = ngc_path.split("/")[-1] + downloaded_folder = Path( + f"{model_base_name}_v{version}" + ) + if not downloaded_folder.exists(): + messages.append( + f"❌ Downloaded folder not found for model " + f"'{model_name}': {downloaded_folder}" + ) + continue + + try: + shutil.move(str(downloaded_folder), str(final_model_dir)) + except Exception as exc: + messages.append( + f"❌ Failed to move downloaded model '{model_name}' " + f"into {final_model_dir}: {exc}" + ) + continue + + # Copy configs into final_model_dir if specified + configs_rel = model_info.get("configs") + if configs_rel: + source_configs = (config_dir / configs_rel).resolve() + if source_configs.exists(): + messages.append( + f"📄 Copying runtime configs from {source_configs} " + f"to {final_model_dir}" + ) + try: + for item in source_configs.iterdir(): + dest = final_model_dir / item.name + if item.is_file(): + shutil.copy2(str(item), str(dest)) + elif item.is_dir(): + shutil.copytree( + str(item), str(dest), dirs_exist_ok=True + ) + except Exception as exc: + messages.append( + f"⚠️ Failed to copy configs for '{model_name}': {exc}" + ) + else: + messages.append( + f"⚠️ Config path not found for model '{model_name}': " + f"{source_configs}" + ) + + # Execute post-script if specified + post_script = model_info.get("post_script") + if post_script: + messages.append( + f"🔧 Executing post-script for '{model_name}': {post_script}" + ) + try: + script_result = subprocess.run( + post_script, + shell=True, + capture_output=True, + text=True, + cwd=str(final_model_dir), + timeout=600, + check=False, + ) + if script_result.returncode == 0: + messages.append( + f"✅ Post-script executed successfully for '{model_name}'" + ) + if script_result.stdout.strip(): + messages.append( + f" Output: {script_result.stdout.strip()}" + ) + else: + messages.append( + f"❌ Post-script failed for '{model_name}' " + f"(exit code {script_result.returncode}): " + f"{script_result.stderr.strip()}" + ) + except subprocess.TimeoutExpired: + messages.append( + f"❌ Post-script timed out for '{model_name}' after 600 seconds" + ) + except Exception as exc: + messages.append( + f"❌ Failed to execute post-script for '{model_name}': {exc}" + ) + + messages.append( + f"✅ Prepared model repository for '{model_name}' at " + f"{final_model_dir}" + ) + + except Exception as exc: # Catch-all per model + messages.append( + f"❌ Exception while preparing model '{model_name}': {exc}" + ) + + summary = "\n".join(messages) + return CallToolResult( + content=[ + TextContent( + type="text", + text=( + "Model repository preparation completed.\n\n" + f"{summary}" + ), + ) + ] + ) + + async def _build_docker_image( + self, arguments: Dict[str, Any] + ) -> CallToolResult: + """Build Docker image from generated pipeline""" + image_name = arguments["image_name"] + dockerfile = arguments["dockerfile"] + + # Validate Dockerfile exists + dockerfile_path = Path(dockerfile) + if not dockerfile_path.exists(): + return CallToolResult( + content=[ + TextContent( + type="text", + text=f"Dockerfile not found: {dockerfile}. Please " + f"provide a valid Dockerfile path." + ) + ], + isError=True + ) + + # Use Dockerfile's parent directory as build context + build_context = str(dockerfile_path.parent) + + # Build Docker image + cmd = [ + "docker", "build", + "-f", dockerfile, + "-t", image_name, + build_context + ] + self.logger.info( + "docker_build_invoked image=%s dockerfile=%s context=%s", + image_name, + dockerfile, + build_context, + ) + + try: + result = subprocess.run( + cmd, + capture_output=True, + text=True, + cwd=Path(__file__).parent.parent, + check=False, + ) + + if result.returncode == 0: + return CallToolResult( + content=[ + TextContent( + type="text", + text=( + f"Successfully built Docker image " + f"'{image_name}'!\n\n" + f"Command executed: {' '.join(cmd)}\n\n" + f"Output:\n{result.stdout}" + ) + ) + ] + ) + else: + return CallToolResult( + content=[ + TextContent( + type="text", + text=f"Failed to build Docker image:\n\n" + f"Error:\n{result.stderr}\n\n" + f"Command: {' '.join(cmd)}" + ) + ], + isError=True + ) + except FileNotFoundError: + return CallToolResult( + content=[ + TextContent( + type="text", + text=( + "Docker not found. Please ensure Docker is " + "installed and available in your PATH." + ) + ) + ], + isError=True + ) + + async def _generate_deepstream_nvinfer_config( + self, arguments: Dict[str, Any] + ) -> CallToolResult: + """Generate DeepStream nvinfer runtime configuration file""" + import yaml + + # Extract required parameters + output_path = arguments["output_path"] + onnx_file = arguments["onnx_file"] + network_type = arguments["network_type"] + input_dims = arguments["input_dims"] + label_file = arguments["label_file"] + + # Extract optional parameters with defaults + precision_mode = arguments.get("precision_mode", 2) # Default FP16 + custom_lib_path = arguments.get("custom_lib_path", "") + custom_parse_func = arguments.get("custom_parse_func", "") + num_classes = arguments.get("num_classes") + gie_unique_id = arguments.get("gie_unique_id", 1) + net_scale_factor = arguments.get("net_scale_factor", 0.00392156862745098) + offsets = arguments.get("offsets") + classifier_threshold = arguments.get("classifier_threshold", 0.0) + input_tensor_from_meta = arguments.get("input_tensor_from_meta", 0) + output_tensor_meta = arguments.get("output_tensor_meta", 0) + + # Validate network_type + network_type_names = { + 0: "detection", + 1: "classification", + 2: "segmentation", + 3: "instance_segmentation", + 100: "custom" + } + if network_type not in network_type_names: + return CallToolResult( + content=[ + TextContent( + type="text", + text=f"Invalid network_type: {network_type}. Must be 0 (detection), " + f"1 (classification), 2 (segmentation), 3 (instance_segmentation), " + f"or 100 (custom)." + ) + ], + isError=True + ) + + # Validate precision_mode + precision_names = {0: "FP32", 1: "INT8", 2: "FP16"} + if precision_mode not in precision_names: + return CallToolResult( + content=[ + TextContent( + type="text", + text=f"Invalid precision_mode: {precision_mode}. Must be 0 (FP32), 1 (INT8), or 2 (FP16)." + ) + ], + isError=True + ) + + # Validate input_dims format (should be channel;height;width) + dims_parts = input_dims.split(';') + if len(dims_parts) != 3: + return CallToolResult( + content=[ + TextContent( + type="text", + text=f"Invalid input_dims format: '{input_dims}'. " + f"Expected format: 'channel;height;width' (e.g., '3;224;224')" + ) + ], + isError=True + ) + + try: + # Validate dimensions are integers + for dim in dims_parts: + int(dim) + except ValueError: + return CallToolResult( + content=[ + TextContent( + type="text", + text=f"Invalid input_dims: '{input_dims}'. All dimensions must be integers." + ) + ], + isError=True + ) + + # Build the configuration + config = { + "property": { + "gie-unique-id": gie_unique_id, + "net-scale-factor": net_scale_factor, + "onnx-file": onnx_file, + "network-mode": precision_mode, + "network-type": network_type, + "infer-dims": input_dims, + "labelfile-path": label_file, + } + } + + # Add optional fields + if offsets: + config["property"]["offsets"] = offsets + + if num_classes is not None: + config["property"]["num-detected-classes"] = num_classes + + if input_tensor_from_meta: + config["property"]["input-tensor-from-meta"] = input_tensor_from_meta + + if output_tensor_meta: + config["property"]["output-tensor-meta"] = output_tensor_meta + + if network_type == 1: # classification + config["property"]["classifier-threshold"] = classifier_threshold + + if custom_lib_path: + config["property"]["custom-lib-path"] = custom_lib_path + + # Add custom parse function name based on network type + if custom_parse_func: + if network_type == 0: # detection + config["property"]["parse-bbox-func-name"] = custom_parse_func + elif network_type == 1: # classification + config["property"]["parse-classifier-func-name"] = custom_parse_func + elif network_type == 2: # segmentation + config["property"]["parse-segmentation-func-name"] = custom_parse_func + elif network_type == 3: # instance_segmentation + config["property"]["parse-bbox-instance-mask-func-name"] = custom_parse_func + # network_type 100 (custom) doesn't need a parse function name + + # Generate YAML content with header + header = """# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# IMPORTANT NORMALIZATION PARAMETERS: +# - net-scale-factor: Must match the scaling factor used during training +# - offsets: Must match any per-channel mean subtraction used during training +# Incorrect normalization will result in poor inference accuracy! + +""" + + yaml_content = yaml.dump(config, default_flow_style=False, sort_keys=False) + full_content = header + yaml_content + + # Write to file + try: + output_file = Path(output_path) + output_file.parent.mkdir(parents=True, exist_ok=True) + + with open(output_file, 'w', encoding='utf-8') as f: + f.write(full_content) + + return CallToolResult( + content=[ + TextContent( + type="text", + text=( + f"Successfully generated DeepStream nvinfer configuration!\n\n" + f"Output file: {output_path}\n\n" + f"Configuration summary:\n" + f" - Network type: {network_type_names[network_type]} ({network_type})\n" + f" - Precision mode: {precision_names[precision_mode]} ({precision_mode})\n" + f" - ONNX file: {onnx_file}\n" + f" - Input dimensions: {input_dims}\n" + f" - Label file: {label_file}\n" + f" - Scale factor: {net_scale_factor}\n" + + (f" - Per-channel offsets: {offsets}\n" if offsets else " - Per-channel offsets: NOT SET (no mean subtraction)\n") + + (f" - Number of classes: {num_classes}\n" if num_classes else "") + + (f" - Input from metadata: {bool(input_tensor_from_meta)}\n" if input_tensor_from_meta else "") + + (f" - Output as metadata: {bool(output_tensor_meta)} (raw tensors in DS META)\n" if output_tensor_meta else "") + + (f" - Custom library: {custom_lib_path}\n" if custom_lib_path else "") + + (f" - Custom parse function: {custom_parse_func}\n" if custom_parse_func else "") + + "\n⚠️ IMPORTANT: Verify that net-scale-factor and per-channel offsets match your model's training normalization!\n" + + " - net-scale-factor must match the scaling applied during training\n" + + " - offsets must match any per-channel mean subtraction used during training\n" + + " - If training used no mean subtraction, offsets should not be set (or set to 0;0;0)\n" + + f"\nGenerated content:\n\n{full_content}" + ) + ) + ] + ) + except Exception as e: + return CallToolResult( + content=[ + TextContent( + type="text", + text=f"Failed to write configuration file:\n\n{str(e)}" + ) + ], + isError=True + ) + + +async def main(): + """Main entry point for the MCP server""" + logging.basicConfig( + level=logging.DEBUG, + format=("%(asctime)s %(levelname)s %(name)s - %(message)s"), + ) + logger = logging.getLogger("deepstream-inference-builder") + logger.info("starting_mcp_server") + server = InferenceBuilderMCPServer() + logger.info("server_created") + + async with stdio_server() as (read_stream, write_stream): + logger.info("stdio_server_started") + init_options = server.server.create_initialization_options() + logger.info("initialization_options_created") + await server.server.run( + read_stream, + write_stream, + init_options, + ) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/tools/inference_builder/mcp/setup_mcp.py b/tools/inference_builder/mcp/setup_mcp.py new file mode 100644 index 00000000..27bfb593 --- /dev/null +++ b/tools/inference_builder/mcp/setup_mcp.py @@ -0,0 +1,207 @@ +#!/usr/bin/env python3 +""" +Setup script for Inference Builder MCP Integration + +This script helps install dependencies and configure the MCP integration +for use with MCP-compatible clients such as Cursor and Claude Code. + +Usage: + python setup_mcp.py [CONFIG_PATH] + +Arguments: + CONFIG_PATH Path to the MCP config file (default: ~/.cursor/mcp.json) + +Examples: + Cursor (global): python setup_mcp.py ~/.cursor/mcp.json + Cursor (project): python setup_mcp.py /path/to/project/.cursor/mcp.json + Claude Code: python setup_mcp.py ~/.claude/.mcp.json +""" + +import argparse +import subprocess +import sys +from pathlib import Path + +def run_command(cmd, description): + """Run a command and handle errors""" + print(f"Running: {description}") + print(f"Command: {' '.join(cmd)}") + + try: + result = subprocess.run(cmd, capture_output=True, text=True, check=True) + print(f"✓ {description} completed successfully") + if result.stdout: + print(f"Output: {result.stdout.strip()}") + return True + except subprocess.CalledProcessError as e: + print(f"✗ {description} failed") + print(f"Error: {e.stderr.strip()}") + return False + except FileNotFoundError: + print(f"✗ Command not found: {cmd[0]}") + return False + +def check_python_version(): + """Check if Python version is compatible""" + version = sys.version_info + if version.major < 3 or (version.major == 3 and version.minor < 8): + print(f"✗ Python 3.8+ required, found {version.major}.{version.minor}") + return False + print(f"✓ Python {version.major}.{version.minor}.{version.micro} is compatible") + return True + +def test_integration(): + """Test the MCP integration""" + print("\nTesting MCP integration...") + + if not run_command([sys.executable, "mcp/test_mcp_server.py"], "Running integration tests"): + print("Integration tests failed. Please check the errors above.") + return False + + return True + +def create_mcp_config(config_path=None): + """Create or update MCP configuration file + + Args: + config_path: Optional path to the MCP config file. + Defaults to ~/.cursor/mcp.json if not specified. + """ + import json + + print("\nSetting up MCP configuration...") + + if config_path: + config_file = Path(config_path).expanduser().resolve() + config_dir = config_file.parent + if not config_dir.exists(): + config_dir.mkdir(parents=True, exist_ok=True) + print(f"✓ Created directory: {config_dir}") + else: + default_dir = Path.home() / ".cursor" + if not default_dir.exists(): + default_dir.mkdir(parents=True, exist_ok=True) + print(f"✓ Created directory: {default_dir}") + config_file = default_dir / "mcp.json" + + # Define the server configuration for this project + server_name = "deepstream-inference-builder" + server_config = { + "command": str(Path(sys.executable)), + "args": [str(Path.cwd() / "mcp" / "mcp_server.py")], + "cwd": str(Path.cwd()), + "env": { + "PYTHONPATH": str(Path.cwd() / "mcp") + } + } + + # Load existing config or start with empty structure + existing_config = {"mcpServers": {}} + if config_file.exists(): + try: + with open(config_file, 'r') as f: + existing_config = json.load(f) + # Ensure mcpServers key exists + if "mcpServers" not in existing_config: + existing_config["mcpServers"] = {} + print(f"✓ Found existing config with {len(existing_config['mcpServers'])} server(s)") + except json.JSONDecodeError as e: + print(f"⚠️ Existing config is not valid JSON: {e}") + print("Creating new configuration (existing file will be overwritten)...") + existing_config = {"mcpServers": {}} + except Exception as e: + print(f"⚠️ Could not read existing config: {e}") + print("Creating new configuration...") + existing_config = {"mcpServers": {}} + + # Check if the server is already correctly configured + existing_server = existing_config["mcpServers"].get(server_name, {}) + if (existing_server.get("cwd") == server_config["cwd"] and + existing_server.get("command") == server_config["command"]): + print(f"✓ MCP server '{server_name}' already exists and is correct: {config_file}") + return True + + # Update or add the server configuration (preserving other servers) + if server_name in existing_config["mcpServers"]: + print(f"⚠️ Updating existing '{server_name}' server configuration...") + else: + print(f"Adding '{server_name}' server to configuration...") + + existing_config["mcpServers"][server_name] = server_config + + # Write the merged config back + try: + with open(config_file, 'w') as f: + json.dump(existing_config, f, indent=2) + print(f"✓ Updated MCP config: {config_file}") + print(f" Total servers configured: {len(existing_config['mcpServers'])}") + print("Note: You may need to restart your MCP client for the configuration to take effect.") + return True + except Exception as e: + print(f"✗ Failed to update MCP config: {e}") + print("Please manually update the MCP configuration file.") + return False + +def parse_args(): + """Parse command-line arguments""" + parser = argparse.ArgumentParser( + description="Setup script for Inference Builder MCP Integration", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + python setup_mcp.py # Default: ~/.cursor/mcp.json + python setup_mcp.py ~/.cursor/mcp.json # Cursor (global) + python setup_mcp.py .cursor/mcp.json # Cursor (project-specific) + python setup_mcp.py ~/.claude/.mcp.json # Claude Code (global) + python setup_mcp.py .mcp.json # Claude Code (project-specific) + """ + ) + parser.add_argument( + "config_path", + nargs="?", + default=None, + help="Path to the MCP config file (default: ~/.cursor/mcp.json)" + ) + return parser.parse_args() + + +def main(config_path=None): + """Main setup function + + Args: + config_path: Optional path to the MCP config file. + """ + print("Inference Builder MCP Integration Setup") + print("=" * 50) + + # Check Python version + if not check_python_version(): + print("\n❌ Setup failed: Python version incompatible") + return False + + # Test integration + if not test_integration(): + print("\n❌ Setup failed: Integration tests failed") + return False + + # Setup MCP config + create_mcp_config(config_path) + + print("\n" + "=" * 50) + print("🎉 Setup completed successfully!") + print("\nNext steps:") + print("1. Restart your MCP client (Cursor, Claude Code, etc.)") + print("2. Verify the 'deepstream-inference-builder' MCP server is connected") + print("3. Try using the tools:") + print(" - 'Show me what sample configurations are available from the inference builder?'") + print(" - 'Generate a DeepStream object detection pipeline using the inference builder with PeopleNet transformer model from NGC.'") + print("\nFor more information, see mcp/README-MCP.md") + + return True + + +if __name__ == "__main__": + args = parse_args() + success = main(args.config_path) + sys.exit(0 if success else 1) + diff --git a/tools/inference_builder/mcp/test_mcp_client.py b/tools/inference_builder/mcp/test_mcp_client.py new file mode 100644 index 00000000..e19c3ab5 --- /dev/null +++ b/tools/inference_builder/mcp/test_mcp_client.py @@ -0,0 +1,171 @@ +#!/usr/bin/env python3 +""" +Simple MCP client to test direct communication with the inference-builder MCP server +""" + +import asyncio +import json +import subprocess +import sys +from pathlib import Path + + +class SimpleMCPClient: + def __init__(self, server_command, server_args, cwd=None): + self.server_command = server_command + self.server_args = server_args + self.cwd = cwd or Path.cwd() + self.process = None + + async def start_server(self): + """Start the MCP server process""" + cmd = [self.server_command] + self.server_args + print(f"🚀 Starting MCP server: {' '.join(cmd)}") + print(f"📁 Working directory: {self.cwd}") + + self.process = await asyncio.create_subprocess_exec( + *cmd, + cwd=self.cwd, + stdin=asyncio.subprocess.PIPE, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE + ) + print("✅ MCP server started") + + async def send_request(self, request): + """Send a JSON-RPC request to the server""" + if not self.process: + raise RuntimeError("Server not started") + + request_str = json.dumps(request) + '\n' + print(f"📤 Sending request: {request}") + + self.process.stdin.write(request_str.encode()) + await self.process.stdin.drain() + + # Read response + response_line = await self.process.stdout.readline() + if not response_line: + stderr_output = await self.process.stderr.read() + raise RuntimeError(f"No response from server. stderr: {stderr_output.decode()}") + + response = json.loads(response_line.decode().strip()) + print(f"📥 Received response: {json.dumps(response, indent=2)}") + return response + + async def initialize(self): + """Initialize the MCP connection""" + init_request = { + "jsonrpc": "2.0", + "id": 1, + "method": "initialize", + "params": { + "protocolVersion": "2024-11-05", + "capabilities": { + "tools": {} + }, + "clientInfo": { + "name": "test-client", + "version": "1.0.0" + } + } + } + response = await self.send_request(init_request) + + # Send initialized notification + initialized_notification = { + "jsonrpc": "2.0", + "method": "notifications/initialized", + "params": {} + } + print(f"📤 Sending initialized notification: {initialized_notification}") + request_str = json.dumps(initialized_notification) + '\n' + self.process.stdin.write(request_str.encode()) + await self.process.stdin.drain() + + return response + + async def list_tools(self): + """List available tools""" + request = { + "jsonrpc": "2.0", + "id": 2, + "method": "tools/list", + "params": {} + } + return await self.send_request(request) + + async def call_tool(self, tool_name, arguments=None): + """Call a specific tool""" + request = { + "jsonrpc": "2.0", + "id": 3, + "method": "tools/call", + "params": { + "name": tool_name, + "arguments": arguments or {} + } + } + return await self.send_request(request) + + async def cleanup(self): + """Clean up the server process""" + if self.process: + self.process.terminate() + await self.process.wait() + print("🛑 MCP server stopped") + + +async def main(): + """Test the MCP server with a direct client""" + print("🧪 Testing MCP Server with Direct Client") + print("=" * 50) + + # Configuration matching the Cursor MCP setup + script_dir = Path(__file__).parent + server_command = str(Path.cwd() / ".venv" / "bin" / "python") + server_args = [str(script_dir / "mcp_server.py")] + cwd = Path.cwd() + + client = SimpleMCPClient(server_command, server_args, cwd) + + try: + # Start server + await client.start_server() + + # Initialize connection + print("\n📋 Step 1: Initialize MCP connection") + init_response = await client.initialize() + + # List tools + print("\n📋 Step 2: List available tools") + tools_response = await client.list_tools() + + if 'result' in tools_response: + tools = tools_response['result']['tools'] + print(f"✅ Found {len(tools)} tools:") + for tool in tools: + print(f" • {tool['name']}: {tool['description']}") + + # Test generate_inference_pipeline with missing config to verify error handling + print("\n📋 Step 3: Test generate_inference_pipeline error handling") + gen_response = await client.call_tool("generate_inference_pipeline", { + "config_file": "/nonexistent/path/config.yaml" + }) + + if 'error' in gen_response or ('result' in gen_response and gen_response['result'].get('isError')): + print("✅ generate_inference_pipeline correctly returned error for missing config") + else: + print(f"📋 generate_inference_pipeline response: {gen_response}") + + except Exception as e: + print(f"❌ Error: {e}") + import traceback + traceback.print_exc() + + finally: + await client.cleanup() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/tools/inference_builder/mcp/test_mcp_server.py b/tools/inference_builder/mcp/test_mcp_server.py new file mode 100644 index 00000000..9847bb7b --- /dev/null +++ b/tools/inference_builder/mcp/test_mcp_server.py @@ -0,0 +1,144 @@ +import unittest +import tempfile +import os +from pathlib import Path +from types import SimpleNamespace + +from mcp_server import InferenceBuilderMCPServer +from mcp.types import CallToolRequest, CallToolRequestParams + + +class MCPServerTests(unittest.IsolatedAsyncioTestCase): + async def asyncSetUp(self): + self.server = InferenceBuilderMCPServer() + + async def test_list_tools_contains_expected(self): + # Our server ignores the request payload; pass None + result = await self.server.list_tools(None) + tool_names = {t.name for t in result.tools} + self.assertIn("generate_inference_pipeline", tool_names) + self.assertIn("build_docker_image", tool_names) + self.assertIn("generate_nvinfer_config", tool_names) + + async def test_call_unknown_tool(self): + params = CallToolRequestParams(name="does_not_exist", arguments={}) + req = CallToolRequest(params=params) + res = await self.server.call_tool(req) + self.assertTrue(res.isError) + self.assertIn("Unknown tool", res.content[0].text) + + async def test_generate_deepstream_nvinfer_config(self): + """Test generating a DeepStream nvinfer config file""" + with tempfile.TemporaryDirectory() as tmpdir: + output_path = os.path.join(tmpdir, "nvdsinfer_config.yaml") + + params = CallToolRequestParams( + name="generate_nvinfer_config", + arguments={ + "output_path": output_path, + "onnx_file": "test_model.onnx", + "network_type": 0, # detection + "input_dims": "3;640;640", + "label_file": "labels.txt", + "precision_mode": 2, # FP16 + "num_classes": 80, + "custom_lib_path": "/opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so" + } + ) + req = CallToolRequest(params=params) + res = await self.server.call_tool(req) + + # Check that the call was successful + self.assertFalse(res.isError, f"Tool call failed: {res.content[0].text if res.content else 'No content'}") + + # Check that the file was created + self.assertTrue(os.path.exists(output_path), f"Config file was not created at {output_path}") + + # Check file contents + with open(output_path, 'r') as f: + content = f.read() + self.assertIn("onnx-file: test_model.onnx", content) + self.assertIn("network-type: 0", content) + self.assertIn("network-mode: 2", content) + self.assertIn("infer-dims: 3;640;640", content) + self.assertIn("num-detected-classes: 80", content) + + async def test_generate_deepstream_nvinfer_config_invalid_network_type(self): + """Test that invalid network type is rejected""" + with tempfile.TemporaryDirectory() as tmpdir: + output_path = os.path.join(tmpdir, "nvdsinfer_config.yaml") + + params = CallToolRequestParams( + name="generate_nvinfer_config", + arguments={ + "output_path": output_path, + "onnx_file": "test_model.onnx", + "network_type": 99, # invalid + "input_dims": "3;640;640", + "label_file": "labels.txt" + } + ) + req = CallToolRequest(params=params) + res = await self.server.call_tool(req) + + # Check that the call failed + self.assertTrue(res.isError) + self.assertIn("Invalid network_type", res.content[0].text) + + async def test_generate_deepstream_nvinfer_config_invalid_dims(self): + """Test that invalid input dimensions are rejected""" + with tempfile.TemporaryDirectory() as tmpdir: + output_path = os.path.join(tmpdir, "nvdsinfer_config.yaml") + + params = CallToolRequestParams( + name="generate_nvinfer_config", + arguments={ + "output_path": output_path, + "onnx_file": "test_model.onnx", + "network_type": 0, + "input_dims": "3;640", # invalid - missing dimension + "label_file": "labels.txt" + } + ) + req = CallToolRequest(params=params) + res = await self.server.call_tool(req) + + # Check that the call failed + self.assertTrue(res.isError) + self.assertIn("Invalid input_dims format", res.content[0].text) + + async def test_generate_deepstream_nvinfer_config_with_output_tensor_meta(self): + """Test generating config with output-tensor-meta parameter""" + with tempfile.TemporaryDirectory() as tmpdir: + output_path = os.path.join(tmpdir, "nvdsinfer_config.yaml") + + params = CallToolRequestParams( + name="generate_nvinfer_config", + arguments={ + "output_path": output_path, + "onnx_file": "custom_model.onnx", + "network_type": 0, # detection + "input_dims": "3;640;640", + "label_file": "labels.txt", + "precision_mode": 2, + "num_classes": 80, + "output_tensor_meta": 1 # Output raw tensors in DS META format + } + ) + req = CallToolRequest(params=params) + res = await self.server.call_tool(req) + + # Check that the call was successful + self.assertFalse(res.isError, f"Tool call failed: {res.content[0].text if res.content else 'No content'}") + + # Check that the file was created + self.assertTrue(os.path.exists(output_path), f"Config file was not created at {output_path}") + + # Check file contents + with open(output_path, 'r') as f: + content = f.read() + self.assertIn("output-tensor-meta: 1", content) + self.assertIn("onnx-file: custom_model.onnx", content) + +if __name__ == "__main__": + unittest.main() diff --git a/tools/inference_builder/overview.png b/tools/inference_builder/overview.png new file mode 100644 index 0000000000000000000000000000000000000000..608fd1fa0c119f7cb13d2a13182cc86b997d1c7c GIT binary patch literal 81445 zcmeFZWmuHm_clx;pnxFKB_#|kAq@f&QqmwGsWj5kEg?!tHxg1qNH@~mEuh5EAl>ln zv3~dckLP$lykFkqct033FxRYW@3q%D*E-L21uH2?-NGcnL_k2eB_l1NjDUcmfq;PY z@&+1sNBN-69Q=c5uPpTl0oqTt240}N6q6T2Kqv{rx-dWmuQ6UpYuO_p(5}M&Ao?1i zk|7{mXURy2slL|ROh%8p(>5JYgyGXy%gl!rd7D5&Rf6vReVM1dA($CKAt;Dqh+=n? 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diff --git a/tools/inference_builder/schemas/ARCHITECTURE.md b/tools/inference_builder/schemas/ARCHITECTURE.md new file mode 100644 index 00000000..e823c64b --- /dev/null +++ b/tools/inference_builder/schemas/ARCHITECTURE.md @@ -0,0 +1,244 @@ +# Schema Architecture + +This document describes the unified schema architecture for the Inference Builder. + +## Design Principles + +1. **DRY (Don't Repeat Yourself)**: Common structures are defined once in base schemas +2. **Composition over Inheritance**: Use JSON Schema's `allOf` to compose schemas +3. **Separation of Concerns**: Backend logic is separate from parameter validation +4. **Extensibility**: Easy to add new backends without duplicating code + +## Schema Hierarchy + +``` +┌─────────────────────────────────────────────────────────────┐ +│ config.schema.json │ +│ (Top-level configuration) │ +│ ┌───────────────────────────────────────────────────────┐ │ +│ │ - name, model_repo │ │ +│ │ - input[], output[] │ │ +│ │ - server (responders) │ │ +│ │ - models[] ←──────────────────────────────────┐ │ │ +│ │ - routes │ │ │ +│ └────────────────────────────────────────────────│─────┘ │ +└─────────────────────────────────────────────────────────────┘ + │ + ▼ + ┌────────────────────────────────────────┐ + │ common/base-model.schema.json │ + │ (Base Model Structure) │ + │ ┌──────────────────────────────────┐ │ + │ │ - name │ │ + │ │ - backend │ │ + │ │ - max_batch_size │ │ + │ │ - input[] │ │ + │ │ - output[] │ │ + │ │ - parameters (generic) │ │ + │ │ - preprocessors[] │ │ + │ │ - postprocessors[] │ │ + │ └──────────────────────────────────┘ │ + └────────────────────────────────────────┘ + │ + ┌───────────────┴───────────────┐ + │ allOf Composition │ + │ │ + ┌───────────▼──────────┐ ┌────────────▼───────────┐ + │ Backend Schemas │ │ Parameter Schemas │ + │ (backends/*.json) │ │ (parameters/*.json) │ + │ │ │ │ + │ ┌──────────────────┐ │ │ ┌────────────────────┐ │ + │ │ deepstream.json ├─┼──────┼→│ deepstream-params │ │ + │ └──────────────────┘ │ │ └────────────────────┘ │ + │ │ │ │ + │ ┌──────────────────┐ │ │ ┌────────────────────┐ │ + │ │ triton.json ├─┼──────┼→│ triton-params │ │ + │ └──────────────────┘ │ │ └────────────────────┘ │ + │ │ │ │ + │ ┌──────────────────┐ │ │ ┌────────────────────┐ │ + │ │ vllm.json ├─┼──────┼→│ vllm-params │ │ + │ └──────────────────┘ │ │ └────────────────────┘ │ + │ │ │ │ + │ ┌──────────────────┐ │ │ ┌────────────────────┐ │ + │ │ tensorrtllm.json ├─┼──────┼→│ tensorrtllm-params │ │ + │ └──────────────────┘ │ │ └────────────────────┘ │ + │ │ │ │ + │ ┌──────────────────┐ │ │ ┌────────────────────┐ │ + │ │ polygraphy.json ├─┼──────┼→│ polygraphy-params │ │ + │ └──────────────────┘ │ │ └────────────────────┘ │ + │ │ │ │ + │ ┌──────────────────┐ │ │ ┌────────────────────┐ │ + │ │ dummy.json ├─┼──────┼→│ dummy-params │ │ + │ └──────────────────┘ │ │ └────────────────────┘ │ + └──────────────────────┘ └────────────────────────┘ +``` + +## Schema Composition Example + +### DeepStream Backend + +The DeepStream backend schema is composed as follows: + +```json +{ + "allOf": [ + { + "$ref": "../common/base-model.schema.json" // Inherit common structure + }, + { + "properties": { + "backend": { + "enum": ["deepstream/nvinfer"] // Restrict backend type + }, + "parameters": { + "$ref": "parameters/deepstream-parameters.schema.json" // Specific params + } + } + } + ] +} +``` + +This composition means: +- ✅ The model **must have** all fields from `base-model.schema.json` (name, backend, input, output, etc.) +- ✅ The `backend` field **must be** `"deepstream/nvinfer"` +- ✅ The `parameters` field **must validate** against `deepstream-parameters.schema.json` + +## Benefits of This Architecture + +### 1. Maintainability +- **Single source of truth**: Common fields defined once in `base-model.schema.json` +- **Easy updates**: Change base schema → all backends updated +- **Clear ownership**: Parameters are isolated in their own files + +### 2. Consistency +- **Guaranteed structure**: All backends share the same base structure +- **Type safety**: Common types defined in `definitions.schema.json` +- **Validation rules**: Consistent validation across all backends + +### 3. Extensibility +- **Add new backend**: Create 2 files (backend schema + parameters) +- **No duplication**: Reuse base model schema via `allOf` +- **Clear pattern**: Follow existing backend schemas + +### 4. Developer Experience +- **Better IDE support**: $ref resolution provides autocomplete +- **Clear documentation**: Each parameter schema documents its options +- **Validation messages**: Precise error messages point to exact issues + +## Adding a New Backend + +To add a new backend (e.g., `onnxruntime`): + +1. **Create parameter schema**: `backends/parameters/onnxruntime-parameters.schema.json` + ```json + { + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/parameters/onnxruntime-parameters.schema.json", + "title": "ONNX Runtime Backend Parameters", + "type": "object", + "properties": { + "execution_providers": { + "type": "array", + "items": {"type": "string"} + }, + "graph_optimization_level": { + "type": "string", + "enum": ["basic", "extended", "all"] + } + } + } + ``` + +2. **Create backend schema**: `backends/onnxruntime.schema.json` + ```json + { + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/onnxruntime.schema.json", + "title": "ONNX Runtime Backend Configuration", + "allOf": [ + {"$ref": "../common/base-model.schema.json"}, + { + "properties": { + "backend": { + "const": "onnxruntime" + }, + "parameters": { + "$ref": "parameters/onnxruntime-parameters.schema.json" + } + } + } + ] + } + ``` + +3. **Update definitions**: Add `"onnxruntime"` to `common/definitions.schema.json#/definitions/backendTypes` + +4. **Update index**: Add entry to `index.json` + +That's it! No need to duplicate input/output/preprocessor definitions. + +## Schema References + +### Internal References (within a schema) +```json +"$ref": "#/definitions/tensorSpec" +``` + +### External References (to other schemas) +```json +"$ref": "../common/base-model.schema.json" +"$ref": "parameters/deepstream-parameters.schema.json" +"$ref": "definitions.schema.json#/definitions/dataTypes" +``` + +### GitHub URL References (for public access) +```json +"$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/config.schema.json" +``` + +## Validation Flow + +``` +User YAML Config + ↓ +config.schema.json (validates top-level) + ↓ +models[] → base-model.schema.json (validates common fields) + ↓ +backend type check → specific backend schema + ↓ +parameters validation → backend-specific parameter schema + ↓ +✓ Valid Configuration +``` + +## Testing Schemas + +### Using the validation script: +```bash +python schemas/validate_config.py builder/samples/vllm/vllm_cosmos.yaml +``` + +### Using Python directly: +```python +import json +import yaml +from jsonschema import validate + +with open('schemas/config.schema.json') as f: + schema = json.load(f) + +with open('builder/samples/vllm/vllm_cosmos.yaml') as f: + config = yaml.safe_load(f) + +validate(instance=config, schema=schema) +``` + +## Future Enhancements + +1. **Conditional validation**: Use `if/then/else` to validate parameters based on backend type +2. **oneOf for backend selection**: Automatically select parameter schema based on backend +3. **Custom formats**: Define custom string formats for paths, URIs, etc. +4. **Schema versioning**: Support multiple schema versions for backward compatibility + diff --git a/tools/inference_builder/schemas/README.md b/tools/inference_builder/schemas/README.md new file mode 100644 index 00000000..c482ac1a --- /dev/null +++ b/tools/inference_builder/schemas/README.md @@ -0,0 +1,686 @@ +# Inference Builder JSON Schemas + +This directory contains JSON Schema definitions for NVIDIA Inference Builder YAML configurations. These schemas provide validation, autocompletion, and documentation for creating inference pipeline configurations. + +## Overview + +The Inference Builder allows you to create inference pipelines with various backends such as DeepStream, TensorRT, Triton, TensorRT-LLM, vLLM, etc. These schemas help validate your configuration files and provide IDE support. + +## Schema Organization + +``` +schemas/ +├── config.schema.json # Main configuration schema +├── backends/ # Backend-specific schemas +│ ├── deepstream.schema.json # DeepStream backend +│ ├── triton.schema.json # Triton backend +│ ├── vllm.schema.json # vLLM backend +│ ├── tensorrtllm.schema.json # TensorRT-LLM backend +│ ├── polygraphy.schema.json # Polygraphy backend +│ ├── dummy.schema.json # Dummy backend +│ └── parameters/ # Backend-specific parameters +│ ├── deepstream-parameters.schema.json +│ ├── triton-parameters.schema.json +│ ├── vllm-parameters.schema.json +│ ├── tensorrtllm-parameters.schema.json +│ ├── polygraphy-parameters.schema.json +│ └── dummy-parameters.schema.json +├── common/ # Common definitions +│ ├── base-model.schema.json # Base model schema (shared structure) +│ ├── definitions.schema.json # Shared type definitions +│ ├── preprocessors.schema.json # Preprocessor schemas +│ └── postprocessors.schema.json # Postprocessor schemas +└── README.md # This file +``` + +### Schema Composition + +The schemas use JSON Schema's composition features for better maintainability: + +- **Base Model Schema** (`common/base-model.schema.json`): Defines the common structure shared by all backends (name, backend, input, output, max_batch_size, preprocessors, postprocessors) +- **Backend Schemas** (`backends/*.schema.json`): Extend the base model using `allOf` and specify which backend types are valid +- **Parameter Schemas** (`backends/parameters/*.schema.json`): Define backend-specific parameters with validation rules + +This approach: +- ✅ Eliminates duplication across backend schemas +- ✅ Ensures consistency in common fields +- ✅ Makes it easy to add new backends +- ✅ Provides clear validation for backend-specific options + +## Main Configuration Schema + +The main schema (`config.schema.json`) defines the structure of inference builder configuration files: + +### Required Fields + +- `name`: String identifier for the microservice +- `model_repo`: Path to the directory containing model files +- `models`: Array of model specifications (at least one required) + +### Optional Fields + +- `input`: Array of input tensor specifications (required when pipeline includes multiple models or when inputs use custom types) +- `output`: Array of output tensor specifications (required when pipeline includes multiple models or when outputs use custom types) +- `server`: Server configuration including responders (not required for serverless) +- `routes`: Route map for tensor flow between models (required for multi-model pipelines) +- `postprocessors`: Top-level postprocessors (for consolidating outputs from multiple models) + +### Example Configuration + +```yaml +name: "my_service" +model_repo: "/workspace/model-repo" + +input: + - name: "text" + data_type: TYPE_STRING + dims: [-1] + - name: "images" + data_type: TYPE_CUSTOM_IMAGE_BASE64 + dims: [-1] + optional: true + +output: + - name: "output" + data_type: TYPE_FP32 + dims: [-1, -1, -1] + +models: + - name: "my_model" + backend: "triton/tensorrt" + max_batch_size: 1 + input: + - name: "input0" + data_type: TYPE_FP32 + dims: [3, 768, 768] + output: + - name: "output0" + data_type: TYPE_FP32 + dims: [10, 768, 768] +``` + +## Backend-Specific Schemas + +### DeepStream Backend + +**Files**: +- Schema: `backends/deepstream.schema.json` +- Parameters: `backends/parameters/deepstream-parameters.schema.json` + +Used for DeepStream-based inference pipelines. + +**Backend Type**: `deepstream/nvinfer` + +**Parameters** (defined in `deepstream-parameters.schema.json`): +- `infer_config_path`: Array of paths to DeepStream nvinfer runtime configuration files (required). The configuration file typically accompanies the ONNX model from NGC as a YAML or TXT file - always prefer using this provided configuration when available. If no configuration file is provided with the model, one must be created based on the model architecture, inference implementation details, and post-processing approaches. +- `preprocess_config_path`: Array of paths to nvdspreprocess configuration files (optional) +- `batch_timeout`: Timeout in microseconds for batching multiple inputs +- `inference_timeout`: Inference timeout in seconds +- `tracker_config`: Configuration for object tracking +- `msgbroker_config`: Configuration for message broker integration +- `render_config`: Configuration for rendering and display +- `perf_config`: Performance monitoring configuration +- `kitti_output_path`: Paths for KITTI format output dumps + +**Example**: +```yaml +models: + - name: tao + backend: deepstream/nvinfer + max_batch_size: 1 + parameters: + infer_config_path: + - nvdsinfer_config.yaml + preprocess_config_path: + - nvdspreprocess_config.yaml + batch_timeout: -1 + inference_timeout: 5 +``` + +### Triton Backend + +**Files**: +- Schema: `backends/triton.schema.json` +- Parameters: `backends/parameters/triton-parameters.schema.json` + +Used for Triton Inference Server with various frameworks. + +**Backend Types**: +- `triton/python` +- `triton/tensorrt` +- `triton/onnx` +- `triton/pytorch` + +**Parameters**: +Triton backend parameters are intentionally open and backend-specific. All parameters are converted to `string_value` in the generated `config.pbtxt` file as defined in Triton's ModelConfig protobuf schema ([model_config.proto](https://github.com/triton-inference-server/common/blob/main/protobuf/model_config.proto)). + +Common parameters include: +- `FORCE_CPU_ONLY_INPUT_TENSORS`: Controls whether input tensors are kept on GPU or copied to CPU (Triton Python backend). Set to `"no"` to keep tensors on GPU for better performance. +- Backend-specific parameters (e.g., TensorRT engine paths, ONNX model paths, etc.) + +For complete parameter documentation, refer to the [Triton Model Configuration documentation](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/model_configuration.html). + +**Example**: +```yaml +models: + - name: visual_changenet + backend: "triton/tensorrt" + max_batch_size: 1 + parameters: + FORCE_CPU_ONLY_INPUT_TENSORS: "no" +``` + +### vLLM Backend + +**File**: `backends/vllm.schema.json` + +Used for vLLM-based large language model inference. + +**Backend Type**: `vllm` + +**Key Parameters**: +- `max_num_tokens`: Maximum number of tokens +- `async_mode`: Enable asynchronous mode +- `gpu_memory_utilization`: GPU memory utilization (0.0 to 1.0) +- `tensor_parallel_size`: Number of GPUs for tensor parallelism + +**Example**: +```yaml +models: + - name: "Cosmos" + backend: "vllm" + parameters: + max_num_tokens: 19200 + async_mode: true +``` + +### TensorRT-LLM Backend + +**File**: `backends/tensorrtllm.schema.json` + +Used for TensorRT-LLM inference with optional PyTorch fallback. + +**Backend Types**: +- `tensorrtllm` +- `tensorrtllm/pytorch` + +**Key Parameters**: +- `max_num_tokens`: Maximum number of tokens +- `kv_cache_config`: KV cache configuration + - `enable_block_reuse`: Enable KV cache block reuse +- `decoding_config`: Decoding configuration + +**Example**: +```yaml +models: + - name: "Qwen2.5-VL" + backend: "tensorrtllm/pytorch" + parameters: + max_num_tokens: 19200 + kv_cache_config: + enable_block_reuse: false +``` + +### Polygraphy Backend + +**File**: `backends/polygraphy.schema.json` + +Used for TensorRT inference via Polygraphy. + +**Backend Type**: `polygraphy` + +**Key Parameters**: +- `tensorrt_engine`: Path to TensorRT engine file (required) +- `precision`: Inference precision (fp32, fp16, int8) + +**Example**: +```yaml +models: + - name: nvclip_text + backend: "polygraphy" + max_batch_size: 64 + parameters: + tensorrt_engine: "model.plan" +``` + +## Data Types + +The following data types are supported: + +### Numeric Types +- `TYPE_BOOL`, `TYPE_UINT8`, `TYPE_UINT16`, `TYPE_UINT32`, `TYPE_UINT64` +- `TYPE_INT8`, `TYPE_INT16`, `TYPE_INT32`, `TYPE_INT64` +- `TYPE_FP16`, `TYPE_FP32`, `TYPE_FP64` + +### Basic Types +- `TYPE_STRING`: String data + +### Custom Types +- `TYPE_CUSTOM_IMAGE_BASE64`: Base64-encoded JPEG/PNG image data as a data URI string. Each element must follow the format `data:image/{format};base64,{payload}` where `{format}` is `jpeg`, `jpg`, or `png`, and `{payload}` is the base64-encoded image bytes. This type is mapped to `TYPE_STRING` in Triton and arrives as a list of strings. When used as a top-level input, it creates an `ImageInputDataFlow` that parses the data URI prefix to determine the image format, decodes the base64 payload into raw bytes, and decodes the image using a hardware-accelerated DeepStream pipeline (`nvjpegdec`/`pngdec`) to produce RGB `uint8` image tensors in HWC layout `[H, W, 3]` on GPU at the original image resolution. These decoded tensors are then passed to downstream models. When used for an output, image tensors are encoded into a base64 string. +- `TYPE_CUSTOM_IMAGE_ASSETS`: Image asset identifiers (file paths, URLs, or asset IDs) referring to JPEG/PNG images. When used as a top-level input, it creates an `ImageInputDataFlow` that loads and decodes the referenced images into image tensors before they are passed to downstream models. +- `TYPE_CUSTOM_LONG_VIDEO_ASSETS`: Video asset identifiers for **long videos and live streams requiring temporal chunking**. Particularly suitable for: (1) **live streams** such as RTSP feeds that need continuous processing in chunks, (2) long video files that should be split into temporal segments, (3) scenarios where frames need to be delivered in multiple batches over time. Supports file paths, URLs, RTSP streams, or asset IDs, with query parameters such as `?frames=N&interval=INTERVAL_NSEC&chunks=M&scale=WIDTHxHEIGHT`. When used as a top-level input, it creates a `VideoInputDataFlow` that asynchronously extracts frames in chunks, with each chunk containing N frames sampled at the specified interval. Results are delivered via a queue as they become available, enabling streaming processing. Processing always starts from the beginning of the video or live stream. Use `TYPE_CUSTOM_VIDEO_CHUNK_ASSETS` instead for short videos that fit in a single batch without temporal chunking, or when you need to specify a start position. Supported query parameters: + - `frames`: (Required) Number of frames to sample per chunk. + - `interval`: (Required) Time interval between frames in nanoseconds (e.g., `100*1e6` for 100ms between frames). + - `chunks`: (Optional) Maximum number of chunks to yield before stopping. Useful for RTSP streams that don't have a natural end-of-stream. + - `scale`: (Optional) Target resolution for frame scaling in format `WIDTHxHEIGHT` (e.g., `scale=640x360`). When specified, all extracted frames are resized to the target resolution. This is useful for reducing memory usage and inference time when processing high-resolution video streams. All assets in a batch must use the same scale value. +- `TYPE_CUSTOM_VIDEO_CHUNK_ASSETS`: Video asset identifiers for **short videos requiring simple frame sampling without temporal chunking**. Use this type when: + 1. Processing short video clips that fit in memory as a single batch + 2. Extracting a fixed number of frames from a specific video segment + 3. Simple uniform frame sampling from a video duration is needed + + Supports file paths, URLs, or asset IDs, with query parameters such as `?frames=N&start=START_NSEC&duration=DURATION_NSEC`. When used as a top-level input, it creates a `VideoFrameSamplingDataFlow` that asynchronously extracts exactly N frames evenly distributed over the specified duration (interval is automatically calculated as duration/frames), decodes them, and produces a single frame batch delivered via queue. The result is one batch containing all requested frames. For long videos or live streams requiring temporal chunking and streaming delivery of multiple batches over time, use `TYPE_CUSTOM_LONG_VIDEO_ASSETS` instead. Supported query parameters: + - `frames`: (Required) Number of frames to sample. + - `start`: (Optional) Start timestamp in nanoseconds (default 0). + - `duration`: (Optional) Duration to sample from in nanoseconds (defaults to entire video). +- `TYPE_CUSTOM_BINARY_BASE64`: Base64-encoded binary data. For inputs, the strings are automatically decoded into uint8 tensors before being passed downstream. For outputs, binary tensors are base64-encoded into strings. +- `TYPE_CUSTOM_LIST`: Converts the input values into a list, which implies explicit batching of the input data. Use this for non-DS inputs that need list conversion (e.g., video URLs for VLM models). +- `TYPE_CUSTOM_DS_MEDIA_URL`: DeepStream media URL input. A string URL (file path or remote URL) pointing to an image or video for DeepStream pipeline processing. Unlike `TYPE_CUSTOM_LIST`, this type follows the implicit batch path and is recognized by the DeepStream backend as a media source. Must be paired with a `TYPE_CUSTOM_DS_MIME` input. +- `TYPE_CUSTOM_VLM_INPUT`: Vision-Language Model input in dictionary form, passed through to downstream models as-is. +- `TYPE_CUSTOM_OBJECT`: Generic custom object type for complex structured data. + +### DeepStream-Specific Types + +#### TYPE_CUSTOM_DS_IMAGE +DeepStream custom type for image buffers, usable as both input and output. + +**As Input:** A 1-D uint8 tensor (or list of tensors) containing encoded JPEG/PNG image bytes that can be passed directly into DeepStream backends without additional decoding in the Python runtime. **This input must be defined in the model configuration to enable the image decoder in the DeepStream pipeline.** + +**As Output:** Extracts the decoded image frames from the DeepStream pipeline and returns them as RGB uint8 image tensors in HWC (Height, Width, Channels) layout. Each tensor has shape `[H, W, 3]` where Channels is 3 (R, G, B), representing the decoded frame at its processed resolution. + +**Example Output Usage:** +```yaml +output: + - name: "decoded_frames" + data_type: TYPE_CUSTOM_DS_IMAGE + dims: [-1, -1, 3] +``` + +#### TYPE_CUSTOM_DS_METADATA +DeepStream custom type for metadata output, containing per-frame detection, classification, and segmentation results. The output is returned as a **dictionary** with the following structure (defined in `definitions/deepstreamMetadata`): + +**Output Structure:** +```python +{ + 'shape': [height, width], # Image dimensions in pixels + 'bboxes': [[left, top, right, bottom], ...], # Bounding boxes for detected objects + 'probs': [0.95, 0.87, ...], # Confidence scores (0.0-1.0) + 'labels': [["car"], ["person"], ...], # Classification labels (multi-label support) + 'seg_maps': [array([[...]]), ...], # Segmentation maps (2D arrays) + 'objects': [1, 2, 3, ...], # Tracking IDs (when tracker enabled) + 'timestamp': 1234567890 # Buffer PTS in nanoseconds +} +``` + +**Field Details:** +- **`shape`**: Array of 2 integers `[height, width]` representing frame dimensions +- **`bboxes`**: Array of bounding boxes, each with 4 integers `[left, top, right, bottom]` in pixel coordinates +- **`probs`**: Array of confidence scores (floats between 0.0 and 1.0) +- **`labels`**: Array of arrays of label strings, enabling multi-label classification (e.g., object type and attributes) +- **`seg_maps`**: Array of 2D segmentation maps for semantic/instance segmentation (numpy arrays or lists) +- **`objects`**: Array of tracking IDs (integers) assigned by DeepStream tracker (populated only when tracker is enabled) +- **`timestamp`**: Buffer presentation timestamp in nanoseconds for temporal synchronization + +**Important**: This type requires a low-level C++ postprocessor library (e.g., `libnvds_infercustomparser_tao.so`) to be available and copied into the container image. The library can be obtained from: +1. DeepStream SDK built-in parsers +2. TAO model parsers from https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git +3. Custom user-implemented parsers + +Without such a library, use regular tensor output types (`TYPE_FP32`, etc.) with Python postprocessors instead. + +**Example Usage in Response Templates:** +```yaml +responses: + InferenceResponse: > + { + "data": [ + {% for item in response.output %} { + "shape": {{item['shape']}}, + "bboxes": {{item['bboxes']}}, + "probs": {{item['probs']}}, + "labels": {{item['labels']|tojson}}, + "masks": {{item['seg_maps']}}, + "timestamp": {{item['timestamp']}} + } {% if not loop.last %}, {% endif %} {% endfor %} + ] + } +``` + +#### TYPE_CUSTOM_DS_MIME +DeepStream custom type for MIME type string (for example, `image/jpeg` or `image/png`) describing the associated DeepStream image or media buffers. Required alongside `TYPE_CUSTOM_DS_IMAGE` or `TYPE_CUSTOM_DS_MEDIA_URL` inputs so the DeepStream pipeline can select the appropriate decoder. + +#### TYPE_CUSTOM_DS_SOURCE_CONFIG +DeepStream custom type for path to a YAML source configuration file. When used as a model input, consider using it when sources such as live streams are dynamically added and removed. + +## Preprocessors and Postprocessors + +Processors transform data at different stages of the inference pipeline. Preprocessors operate before model inference, while postprocessors operate after. + +**Base Schema**: `common/base-processor.schema.json` +**Preprocessor Schema**: `common/preprocessors.schema.json` +**Postprocessor Schema**: `common/postprocessors.schema.json` + +### Data Flow and Processor Interface + +Processors follow a standard interface pattern where data flows through dictionaries: + +**Input Flow**: +1. Upstream data (from model outputs, top-level inputs, or previous processors) is provided as a dictionary +2. The processor's `input` field specifies keys to extract from this dictionary, in order +3. Extracted values are passed as positional arguments to the processor's `__call__` method + +**Output Flow**: +1. The processor's `__call__` method returns a tuple +2. The tuple length must match the length of the `output` field +3. Returned values are mapped to output names to form a dictionary for downstream consumption + +**Example Flow**: +```python +# Upstream data dictionary +upstream = { + 'image': , + 'text': , + 'metadata': +} + +# Processor configuration +processor = { + 'input': ['text', 'image'], # Extract in this order + 'output': ['tokens', 'pixels'] # Map results to these names +} + +# Processor invocation +result = processor.__call__(upstream['text'], upstream['image']) +# result = (, ) + +# Downstream receives +downstream = { + 'tokens': , + 'pixels': +} +``` + +### Preprocessors + +**File**: `common/preprocessors.schema.json` + +Preprocessors transform input data before model inference. They typically handle tasks like: +- Image normalization and resizing +- Text tokenization +- Data format conversions +- Feature extraction + +**Required Fields**: +- `kind`: Processor type (`"custom"` or `"builtin"`) +- `name`: Unique identifier for the processor; should match the `name` attribute defined within the processor class +- `input`: Array of input tensor names (extracted from upstream data) +- `output`: Array of output tensor names (mapped to returned tuple) +- `config`: Optional processor-specific configuration + +**Example**: +```yaml +preprocessors: + - kind: "custom" + name: "image-normalizer" + input: ["raw_image"] # Extract 'raw_image' from upstream + output: ["normalized_image"] # Return maps to 'normalized_image' + config: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] +``` + +**Corresponding Python Implementation**: +```python +class ImageNormalizer: + name = "image-normalizer" + + def __init__(self, config): + self.mean = config['mean'] + self.std = config['std'] + + def __call__(self, raw_image): + # Process the input + normalized = (raw_image - self.mean) / self.std + # Return tuple matching output length + return (normalized,) +``` + +### Postprocessors + +**File**: `common/postprocessors.schema.json` + +Postprocessors transform model outputs after inference. They typically handle tasks like: +- Output decoding +- Masking and filtering +- Embedding normalization +- Result formatting + +**Required Fields**: Same as preprocessors + +**Example**: +```yaml +postprocessors: + - kind: "custom" + name: "detection-decoder" + input: ["boxes", "scores", "classes"] # Extract from model outputs + output: ["detections"] # Return as single output + config: + confidence_threshold: 0.5 + max_detections: 100 +``` + +**Corresponding Python Implementation**: +```python +class DetectionDecoder: + name = "detection-decoder" + + def __init__(self, config): + self.threshold = config['confidence_threshold'] + self.max_detections = config['max_detections'] + + def __call__(self, boxes, scores, classes): + # Process three inputs + mask = scores > self.threshold + filtered_boxes = boxes[mask][:self.max_detections] + filtered_scores = scores[mask][:self.max_detections] + filtered_classes = classes[mask][:self.max_detections] + + detections = { + 'boxes': filtered_boxes, + 'scores': filtered_scores, + 'classes': filtered_classes + } + # Return tuple with one element + return (detections,) +``` + +### Multi-Input, Multi-Output Example + +Processors can handle multiple inputs and outputs: + +```yaml +preprocessors: + - kind: "custom" + name: "dual-stream-processor" + input: ["reference_image", "test_image", "threshold"] + output: ["ref_features", "test_features"] + config: + feature_dim: 256 +``` + +```python +class DualStreamProcessor: + name = "dual-stream-processor" + + def __init__(self, config): + self.feature_dim = config['feature_dim'] + + def __call__(self, reference_image, test_image, threshold): + # Process inputs (receives 3 arguments in order) + ref_features = self.extract_features(reference_image) + test_features = self.extract_features(test_image) + + # Apply threshold + ref_features = ref_features * (ref_features > threshold) + test_features = test_features * (test_features > threshold) + + # Return tuple matching output length (2 elements) + return (ref_features, test_features) +``` + +## Server Configuration + +The server section defines how the inference service handles requests and responses. + +### Responders + +Responders map API operations to request/response transformations using Jinja2 templates. + +**Available Responder Keys** (must match templates in `templates/responder/`): + +| Responder | Description | Input | Output | +|-----------|-------------|-------|--------| +| `infer` | Primary inference endpoint. Handles model inference requests. Supports streaming responses with `Accept: application/x-ndjson` header. | Top-level pipeline inputs (as defined in config `input` section) | Top-level pipeline outputs (as defined in config `output` section) | +| `add_file` | File upload responder. Adds files to the asset store for later processing. | Multipart file upload (file object with `filename` and `content_type`) | Asset dict: `id`, `file_name`, `content_type`, `created_at` | +| `del_file` | File deletion responder. Removes files from the asset store. | `asset_id` (string, from path parameter) | `status` (boolean, deletion success) | +| `list_files` | File listing responder. Returns a list of files in the asset store. | None | `assets` (array of asset objects with `id`, `file_name`, `content_type`, `created_at`) | +| `add_live_stream` | Live stream addition responder. Registers a new RTSP or video stream for processing. | `url` (string, required), `description` (string, optional), `username` (string, optional), `password` (string, optional) | Asset dict: `id`, `url`, `description`, `created_at` | +| `del_live_stream` | Live stream removal responder. Stops and removes a registered live stream. | `asset_id` (string, from path parameter) | `status` (boolean, deletion success) | +| `list_live_streams` | Live stream listing responder. Returns all registered live streams. | None | `assets` (array of stream asset objects with `id`, `url`, `description`, `created_at`) | +| `healthy_ready` | Health check responder. Reports service health and readiness status. No request/response template mapping needed. | None | HTTP 200 with "Ready" or HTTP 503 with "Service Unavailable" | + +**Request/Response Template Mapping**: + +- **`requests`**: Jinja2 templates to map HTTP request body to responder inputs. Keys are **OpenAPI request schema/class names** (e.g., `InferenceRequest`, `AddLiveStreamRequest`), values are Jinja2 templates that transform the incoming `request` object into a JSON structure matching the responder's expected inputs. + +- **`responses`**: Jinja2 templates to map responder outputs to HTTP response body. Keys are **OpenAPI response schema/class names** (e.g., `InferenceResponse`, `AddFileResponse`), values are Jinja2 templates that transform the `response` object from the responder into the desired HTTP response JSON structure. + +**Common Operations** (values for the `operation` field): +- `inference`: General inference operation +- `create_chat_completion_v1_chat_completions_post`: Chat completion for LLMs +- `create_embedding`: Embedding generation +- `add_media_file`, `delete_media_file`, `list_media_files`: Media file management +- `add_live_stream`, `delete_live_stream`, `list_live_streams`: Live stream management +- `health_ready_v1_health_ready_get`: Health check + +See [RESPONDERS.md](RESPONDERS.md) for complete documentation. + +**Example**: +```yaml +server: + responders: + infer: + operation: inference + requests: + InferenceRequest: > + { + "images": [{{ request.input[0]|tojson }}] + } + responses: + InferenceResponse: > + { + "data": {{ response.output|tojson }}, + "model": "my-model" + } +``` + +## Routes + +Routes define the flow of tensors between models in multi-model pipelines. + +**Format**: `: : :` + +- Source/destination can be model names or `:` for top-level +- Tensor list is optional: `["tensor1", "tensor2"]` + +**Example**: +```yaml +routes: + ':["reference_image", "test_image"]': 'visual_changenet' + 'visual_changenet:["output_final"]': ':["output"]' +``` + +## Using the Schemas + +### In VS Code + +Add to your workspace settings (`.vscode/settings.json`): + +```json +{ + "yaml.schemas": { + "./schemas/config.schema.json": "*.yaml" + } +} +``` + +Or use the GitHub URL for the latest schema: + +```json +{ + "yaml.schemas": { + "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/config.schema.json": "*.yaml" + } +} +``` + +### With YAML Language Server + +Add to your `.yaml-language-server.json`: + +```json +{ + "yaml.schemas": { + "file:///path/to/schemas/config.schema.json": "/*.yaml" + } +} +``` + +### Validation with Python + +```python +import json +import yaml +from jsonschema import validate + +# Load schema +with open('schemas/config.schema.json') as f: + schema = json.load(f) + +# Load and validate config +with open('config.yaml') as f: + config = yaml.safe_load(f) + +validate(instance=config, schema=schema) +``` + +## Examples + +See the `builder/samples/` directory for complete examples of configurations for different backends: + +- `builder/samples/dummy/dummy.yaml` - Dummy backend example +- `builder/samples/vllm/vllm_cosmos.yaml` - vLLM backend example +- `builder/samples/qwen/trtllm_qwen.yaml` - TensorRT-LLM backend example +- `builder/samples/tao/ds_tao.yaml` - DeepStream backend example +- `builder/samples/changenet/trt_changenet.yaml` - Triton/TensorRT backend example +- `builder/samples/nvclip/tensorrt_nvclip.yaml` - Polygraphy backend example + +## References + +- [Inference Builder Documentation](../README.md) +- [JSON Schema Specification](https://json-schema.org/) +- [YAML Language Server](https://github.com/redhat-developer/yaml-language-server) + +## Contributing + +When adding new backends or features: + +1. Update the appropriate schema files +2. Add examples to this README +3. Test with sample configurations +4. Update the `common/definitions.schema.json` if adding new types + +## License + +SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +SPDX-License-Identifier: Apache-2.0 + diff --git a/tools/inference_builder/schemas/RESPONDERS.md b/tools/inference_builder/schemas/RESPONDERS.md new file mode 100644 index 00000000..a8a61b8b --- /dev/null +++ b/tools/inference_builder/schemas/RESPONDERS.md @@ -0,0 +1,230 @@ +# Available Responders + +This document lists the available responder templates that can be used in the `server.responders` section of the configuration. + +## Overview + +Responders map API endpoints to their implementation. Each responder corresponds to a template file in `templates/responder/` and handles a specific operation. + +## Available Responder Keys + +The following keys are valid for use in `server.responders`: + +| Key | Template File | Operation | Description | +|-----|--------------|-----------|-------------| +| `infer` | `infer.jinja.py` | `inference` or chat completion | Main inference endpoint for model predictions | +| `add_file` | `add_file.jinja.py` | `add_media_file` | Upload and register a media file as an asset | +| `del_file` | `del_file.jinja.py` | `delete_media_file` | Delete a registered media file | +| `list_files` | `list_files.jinja.py` | `list_media_files` | List all registered media files | +| `add_live_stream` | `add_live_stream.jinja.py` | `add_live_stream` | Add a live stream source | +| `del_live_stream` | `del_live_stream.jinja.py` | `delete_live_stream` | Delete a live stream source | +| `list_live_streams` | `list_live_streams.jinja.py` | `list_live_streams` | List all registered live streams | +| `healthy_ready` | `healthy_ready.jinja.py` | `health_ready_v1_health_ready_get` | Health check endpoint | + +## Schema Validation + +The schema restricts responder keys to the above list using `propertyNames.enum`. Invalid responder keys will cause validation to fail: + +```yaml +server: + responders: + infer: # ✅ Valid + operation: inference + + custom_op: # ❌ Error: invalid responder key + operation: custom +``` + +**Error message**: `Property name must be one of: infer, add_file, del_file, list_files, add_live_stream, del_live_stream, list_live_streams, healthy_ready` + +## Usage Examples + +### Basic Inference + +```yaml +server: + responders: + infer: + operation: inference + requests: + InferenceRequest: > + { + "images": {{ request.input|tojson }} + } + responses: + InferenceResponse: > + { + "data": {{ response.output|tojson }}, + "model": "my-model" + } +``` + +### LLM Chat Completion + +```yaml +server: + responders: + infer: + operation: create_chat_completion_v1_chat_completions_post + requests: + NIMLLMChatCompletionRequest: > + { + "messages": {{ request.messages|tojson }}, + "max_tokens": {{ request.max_tokens }} + } + responses: + NIMLLMChatCompletionResponse: > + { + "id": "{{ request._id }}", + "choices": [...] + } +``` + +### File Management + +```yaml +server: + responders: + add_file: + operation: add_media_file + responses: + AddFileResponse: > + { + "data": { + "id": {{response.id|tojson}}, + "path": {{response.path|tojson}}, + "contentType": {{response.mime_type|tojson}} + } + } + + list_files: + operation: list_media_files + responses: + ListFilesResponse: > + { + "data": [ + {% for item in response.assets %} + { + "id": {{item.id|tojson}}, + "path": {{item.path|tojson}} + } + {% if not loop.last %}, {% endif %} + {% endfor %} + ] + } +``` + +### Health Check + +```yaml +server: + responders: + healthy_ready: + operation: health_ready_v1_health_ready_get +``` + +## Responder Configuration + +Each responder has the following structure: + +| Field | Required | Type | Description | +|-------|----------|------|-------------| +| `operation` | ✅ Yes | string | Name of the operation (must match OpenAPI spec) | +| `requests` | ❌ No | object | Request transformation templates (Jinja2) | +| `responses` | ❌ No | object | Response transformation templates (Jinja2) | + +### Request Templates + +Transform incoming API requests to the internal inference format: + +```yaml +requests: + RequestTypeName: > + { + "field1": {{ request.some_field|tojson }}, + "field2": "{{ request.another_field }}" + } +``` + +- Keys can be any string (typically match OpenAPI schema names) +- Values are Jinja2 templates +- Access request data via `request.*` variables + +### Response Templates + +Transform internal inference results to API responses: + +```yaml +responses: + ResponseTypeName: > + { + "result": {{ response.output|tojson }}, + "metadata": { + "model": "my-model" + } + } +``` + +- Keys can be any string (typically match OpenAPI schema names) +- Values are Jinja2 templates +- Access response data via `response.*` variables + +## Common Operations + +### Inference Operations + +- `inference` - General inference endpoint +- `create_chat_completion_v1_chat_completions_post` - LLM chat completion + +### Media File Operations + +- `add_media_file` - Upload media file +- `delete_media_file` - Delete media file +- `list_media_files` - List media files + +### Live Stream Operations + +- `add_live_stream` - Add live stream +- `delete_live_stream` - Delete live stream +- `list_live_streams` - List live streams + +### System Operations + +- `health_ready_v1_health_ready_get` - Health check + +## Template Implementation + +Responder templates are located in `templates/responder/`. Each template: + +1. **Receives** the processed request data +2. **Calls** the inference pipeline +3. **Returns** the formatted response + +Templates use Jinja2 syntax and have access to: +- `request` - Incoming request data +- `response` - Inference pipeline output +- Various helper filters (e.g., `tojson`, `tojinja`) + +## Adding Custom Responders + +To add a new responder: + +1. Create template file: `templates/responder/my_responder.jinja.py` +2. Implement the responder logic +3. Add `"my_responder"` to the enum in: + - `schemas/config.schema.json` (propertyNames.enum) + - `schemas/common/definitions.schema.json` (responderNames) +4. Update this documentation + +## Related Documentation + +- [Usage Guide](../../doc/usage.md) - Server configuration details +- [Architecture](../ARCHITECTURE.md) - How responders fit into the system +- [OpenAPI Specification](../../builder/samples/*/openapi.yaml) - API definitions + +## References + +- Templates: `templates/responder/*.jinja.py` +- Schema: `schemas/config.schema.json#/definitions/serverConfig` +- Common Definitions: `schemas/common/definitions.schema.json#/definitions/responderNames` + diff --git a/tools/inference_builder/schemas/backends/deepstream.schema.json b/tools/inference_builder/schemas/backends/deepstream.schema.json new file mode 100644 index 00000000..920b5464 --- /dev/null +++ b/tools/inference_builder/schemas/backends/deepstream.schema.json @@ -0,0 +1,47 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/deepstream.schema.json", + "title": "DeepStream Backend Configuration", + "description": "Schema for DeepStream backend model configuration", + "allOf": [ + { + "$ref": "../common/base-model.schema.json" + }, + { + "properties": { + "backend": { + "type": "string", + "enum": ["deepstream/nvinfer", "deepstream/nvinferserver"], + "description": "DeepStream backend type" + }, + "parameters": { + "$ref": "parameters/deepstream-parameters.schema.json" + } + } + } + ], + "examples": [ + { + "name": "tao", + "backend": "deepstream/nvinfer", + "max_batch_size": 1, + "parameters": { + "infer_config_path": ["nvdsinfer_config.yaml"] + }, + "input": [ + { + "name": "image", + "data_type": "TYPE_CUSTOM_DS_IMAGE", + "dims": [-1, -1, 3] + } + ], + "output": [ + { + "name": "output", + "data_type": "TYPE_CUSTOM_DS_METADATA", + "dims": [-1] + } + ] + } + ] +} diff --git a/tools/inference_builder/schemas/backends/dummy.schema.json b/tools/inference_builder/schemas/backends/dummy.schema.json new file mode 100644 index 00000000..8b35c6df --- /dev/null +++ b/tools/inference_builder/schemas/backends/dummy.schema.json @@ -0,0 +1,45 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/dummy.schema.json", + "title": "Dummy Backend Configuration", + "description": "Schema for dummy backend model configuration (testing)", + "allOf": [ + { + "$ref": "../common/base-model.schema.json" + }, + { + "properties": { + "backend": { + "type": "string", + "const": "dummy", + "description": "Dummy backend for testing purposes" + }, + "parameters": { + "$ref": "parameters/dummy-parameters.schema.json" + } + } + } + ], + "examples": [ + { + "name": "dummy", + "backend": "dummy", + "max_batch_size": 1, + "parameters": {}, + "input": [ + { + "name": "images", + "data_type": "TYPE_FP32", + "dims": [3, 768, 768] + } + ], + "output": [ + { + "name": "output", + "data_type": "TYPE_FP32", + "dims": [10, 10] + } + ] + } + ] +} diff --git a/tools/inference_builder/schemas/backends/parameters/deepstream-parameters.schema.json b/tools/inference_builder/schemas/backends/parameters/deepstream-parameters.schema.json new file mode 100644 index 00000000..07f6a276 --- /dev/null +++ b/tools/inference_builder/schemas/backends/parameters/deepstream-parameters.schema.json @@ -0,0 +1,207 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/parameters/deepstream-parameters.schema.json", + "title": "DeepStream Backend Parameters", + "description": "Parameter schema for DeepStream backend", + "type": "object", + "required": ["infer_config_path"], + "properties": { + "infer_config_path": { + "type": "array", + "description": "Paths (inside the model repository) to DeepStream nvinfer runtime configuration files (for example, nvdsinfer_config.yaml). These files are used by the DeepStream nvinfer plugin at runtime. **Creating or locating config files:** (1) The configuration file typically accompanies the ONNX model from the remote model repository (NGC) as a YAML or TXT file containing all required inference parameters - always prefer using this provided configuration when available. (2) If no configuration file is provided with the model, one must be created based on the model architecture, inference implementation details, and post-processing approaches specific to the model. (3) The config file must be copied into the model repository before deployment. Refer to https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvinfer.html#gst-nvinfer-file-configuration-specifications for the complete configuration specification.", + "items": { + "type": "string" + } + }, + "preprocess_config_path": { + "type": "array", + "description": "Paths (inside the model repository) to DeepStream nvdspreprocess runtime configuration files (for example, nvdspreprocess_config*.yaml). These files define preprocessing operations that run before inference and are required when non-image tensors must be injected into the pipeline. They must already be copied into the model repository before deployment. Refer to https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvdspreprocess.html for more details.", + "items": { + "type": "string" + } + }, + "batch_timeout": { + "type": "integer", + "minimum": -1, + "description": "Timeout in microseconds for batching multiple inputs. Set to -1 to wait infinitely for batch formation. Default is 1000 * max_batch_size microseconds." + }, + "inference_timeout": { + "type": "integer", + "minimum": 0, + "description": "Inference timeout in seconds, absence of this field means no timeout is enforced" + }, + "tracker_config": { + "type": "object", + "description": "Configuration for object tracking, absence of this field means no tracking is required", + "required": ["ll_lib_file", "ll_config_file"], + "properties": { + "ll_lib_file": { + "type": "string", + "description": "Path to the low-level tracker library file" + }, + "ll_config_file": { + "type": "string", + "description": "Path to the low-level tracker configuration file" + }, + "width": { + "type": "integer", + "minimum": 1, + "description": "Width of the tracker processing frame", + "default": 1920 + }, + "height": { + "type": "integer", + "minimum": 1, + "description": "Height of the tracker processing frame", + "default": 1088 + }, + "display_tracking_id": { + "type": "boolean", + "description": "Whether to display tracking IDs in the output", + "default": false + } + }, + "additionalProperties": false + }, + "msgbroker_config": { + "type": "object", + "description": "Configuration for message broker integration, absence of this field means no message broker is required", + "required": ["msgbroker_proto_lib_path", "msgbroker_conn_str", "msgbroker_topic", "msgconv_config_path"], + "properties": { + "msgbroker_proto_lib_path": { + "type": "string", + "description": "Path to the message broker protocol library" + }, + "msgbroker_conn_str": { + "type": "string", + "description": "Connection string for the message broker (e.g., 'localhost;9092' for Kafka)" + }, + "msgbroker_topic": { + "type": "string", + "description": "Topic name for publishing messages" + }, + "msgconv_config_path": { + "type": "string", + "description": "Path to the message conversion configuration file, refer to https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvmsgconv.html for more details" + }, + "msgconv_payload_type": { + "type": "integer", + "enum": [0, 1, 2], + "description": "Payload type for message conversion (0=DeepStream JSON, 1=DeepStream Minimal JSON, 2=DeepStream Protobuf)" + }, + "msgconv_msg2p_new_api": { + "type": "boolean", + "description": "Use new API which supports publishing multiple payloads using NvDsFrameMeta", + "default": false + }, + "msgconv_frame_interval": { + "type": "integer", + "minimum": 1, + "description": "Frame interval at which payload is generated (every Nth frame)", + "default": 1 + }, + "msgconv_msg2p_lib": { + "type": "string", + "description": "Path to the message converter library" + } + }, + "additionalProperties": false + }, + "render_config": { + "type": "object", + "description": "Configuration for rendering and display", + "properties": { + "enable_display": { + "type": "boolean", + "description": "Enable on-screen display", + "default": false + }, + "enable_osd": { + "type": "boolean", + "description": "Enable on-screen display overlays (bounding boxes, labels, etc.)", + "default": false + }, + "enable_stream": { + "type": "boolean", + "description": "Enable RTSP streaming output", + "default": false + }, + "rtsp_mount_point": { + "type": "string", + "description": "RTSP mount point path (e.g., '/ds_app'), not required if enable_stream is false", + "default": "/ds-test" + }, + "rtsp_port": { + "type": "integer", + "minimum": 1, + "maximum": 65535, + "description": "RTSP server port number, not required if enable_stream is false", + "default": 8554 + } + }, + "additionalProperties": false + }, + "perf_config": { + "type": "object", + "description": "Performance monitoring configuration", + "properties": { + "enable_fps_logs": { + "type": "boolean", + "description": "Enable FPS (frames per second) logging", + "default": false + }, + "enable_latency_logs": { + "type": "boolean", + "description": "Enable latency logging", + "default": false + } + }, + "additionalProperties": false + }, + "kitti_output_path": { + "type": "object", + "description": "Paths for KITTI format output dumps", + "properties": { + "infer": { + "type": "string", + "description": "Path for inference KITTI output" + }, + "tracker": { + "type": "string", + "description": "Path for tracker KITTI output" + } + }, + "additionalProperties": false + }, + "analytics_config": { + "type": "object", + "description": "Configuration for nvdsanalytics (ROI, line-crossing, overcrowding, direction). Absence means no analytics. Typically used after tracker. Refer to https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvdsanalytics.html", + "required": ["config_file_path"], + "properties": { + "config_file_path": { + "type": "string", + "description": "Path to the analytics configuration file (YAML or TXT). Path is relative to model repository unless absolute." + }, + "gpu_id": { + "type": "integer", + "minimum": 0, + "description": "GPU device id for nvdsanalytics", + "default": 0 + } + }, + "additionalProperties": false + }, + "resize_video": { + "type": "array", + "description": "Target resolution for input stream resizing in format [height, width]. When specified, the input stream will be resized to these dimensions before processing. When tracker is being enabled, it requires a specific resolution consistent with the tracker config. In such cases, the resize_video configuration is required.", + "items": { + "type": "integer", + "minimum": 1 + }, + "minItems": 2, + "maxItems": 2 + } + }, + "additionalProperties": false +} + diff --git a/tools/inference_builder/schemas/backends/parameters/dummy-parameters.schema.json b/tools/inference_builder/schemas/backends/parameters/dummy-parameters.schema.json new file mode 100644 index 00000000..ebd75184 --- /dev/null +++ b/tools/inference_builder/schemas/backends/parameters/dummy-parameters.schema.json @@ -0,0 +1,10 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/parameters/dummy-parameters.schema.json", + "title": "Dummy Backend Parameters", + "description": "Parameter schema for dummy backend (testing)", + "type": "object", + "properties": {}, + "additionalProperties": true +} + diff --git a/tools/inference_builder/schemas/backends/parameters/polygraphy-parameters.schema.json b/tools/inference_builder/schemas/backends/parameters/polygraphy-parameters.schema.json new file mode 100644 index 00000000..e8e432f7 --- /dev/null +++ b/tools/inference_builder/schemas/backends/parameters/polygraphy-parameters.schema.json @@ -0,0 +1,16 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/parameters/polygraphy-parameters.schema.json", + "title": "Polygraphy Backend Parameters", + "description": "Parameter schema for Polygraphy (TensorRT) backend", + "type": "object", + "required": ["tensorrt_engine"], + "properties": { + "tensorrt_engine": { + "type": "string", + "description": "Path to TensorRT engine file (typically model.plan) relative to the model home directory (typically folder named after model name under model repository)" + } + }, + "additionalProperties": false +} + diff --git a/tools/inference_builder/schemas/backends/parameters/pytorch-parameters.schema.json b/tools/inference_builder/schemas/backends/parameters/pytorch-parameters.schema.json new file mode 100644 index 00000000..1bd61ce1 --- /dev/null +++ b/tools/inference_builder/schemas/backends/parameters/pytorch-parameters.schema.json @@ -0,0 +1,10 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/parameters/pytorch-parameters.schema.json", + "title": "PyTorch Backend Parameters", + "description": "Parameter schema for PyTorch backend (Huggingface Transformers)", + "type": "object", + "properties": {}, + "additionalProperties": true +} + diff --git a/tools/inference_builder/schemas/backends/parameters/tensorrtllm-parameters.schema.json b/tools/inference_builder/schemas/backends/parameters/tensorrtllm-parameters.schema.json new file mode 100644 index 00000000..7718a901 --- /dev/null +++ b/tools/inference_builder/schemas/backends/parameters/tensorrtllm-parameters.schema.json @@ -0,0 +1,10 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/parameters/tensorrtllm-parameters.schema.json", + "title": "TensorRT-LLM Backend Parameters", + "description": "Parameter schema for TensorRT-LLM backend", + "type": "object", + "properties": {}, + "additionalProperties": true +} + diff --git a/tools/inference_builder/schemas/backends/parameters/triton-parameters.schema.json b/tools/inference_builder/schemas/backends/parameters/triton-parameters.schema.json new file mode 100644 index 00000000..e774bd62 --- /dev/null +++ b/tools/inference_builder/schemas/backends/parameters/triton-parameters.schema.json @@ -0,0 +1,10 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/parameters/triton-parameters.schema.json", + "title": "Triton Backend Parameters", + "description": "Parameter schema for Triton Inference Server backend. Parameters are backend-specific and defined as map in model_config.proto (https://github.com/triton-inference-server/common/blob/main/protobuf/model_config.proto). All parameter values are converted to string_value in the generated config.pbtxt. Refer to Triton documentation for backend-specific parameters: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/model_configuration.html", + "type": "object", + "properties": {}, + "additionalProperties": true +} + diff --git a/tools/inference_builder/schemas/backends/parameters/vllm-parameters.schema.json b/tools/inference_builder/schemas/backends/parameters/vllm-parameters.schema.json new file mode 100644 index 00000000..6e5c21b4 --- /dev/null +++ b/tools/inference_builder/schemas/backends/parameters/vllm-parameters.schema.json @@ -0,0 +1,55 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/parameters/vllm-parameters.schema.json", + "title": "vLLM Backend Parameters", + "description": "Parameter schema for vLLM backend", + "type": "object", + "properties": { + "max_num_tokens": { + "type": "integer", + "minimum": 1, + "description": "Maximum number of tokens" + }, + "async_mode": { + "type": "boolean", + "description": "Enable asynchronous mode" + }, + "dtype": { + "type": "string", + "enum": ["auto", "half", "float16", "bfloat16", "float", "float32"], + "description": "Data type for model weights and activations" + }, + "gpu_memory_utilization": { + "type": "number", + "minimum": 0.0, + "maximum": 1.0, + "description": "GPU memory utilization (0.0 to 1.0)" + }, + "max_model_len": { + "type": "integer", + "minimum": 1, + "description": "Maximum model context length" + }, + "tensor_parallel_size": { + "type": "integer", + "minimum": 1, + "description": "Number of GPUs to use for tensor parallelism" + }, + "pipeline_parallel_size": { + "type": "integer", + "minimum": 1, + "description": "Number of pipeline stages" + }, + "trust_remote_code": { + "type": "boolean", + "description": "Trust remote code from model hub" + }, + "max_num_seqs": { + "type": "integer", + "minimum": 1, + "description": "Maximum number of sequences per iteration" + } + }, + "additionalProperties": false +} + diff --git a/tools/inference_builder/schemas/backends/polygraphy.schema.json b/tools/inference_builder/schemas/backends/polygraphy.schema.json new file mode 100644 index 00000000..6f2d3346 --- /dev/null +++ b/tools/inference_builder/schemas/backends/polygraphy.schema.json @@ -0,0 +1,47 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/polygraphy.schema.json", + "title": "Polygraphy Backend Configuration", + "description": "Schema for Polygraphy (TensorRT) backend model configuration", + "allOf": [ + { + "$ref": "../common/base-model.schema.json" + }, + { + "properties": { + "backend": { + "type": "string", + "const": "polygraphy", + "description": "Polygraphy backend for TensorRT inference" + }, + "parameters": { + "$ref": "parameters/polygraphy-parameters.schema.json" + } + } + } + ], + "examples": [ + { + "name": "nvclip_text", + "backend": "polygraphy", + "max_batch_size": 64, + "parameters": { + "tensorrt_engine": "model.plan" + }, + "input": [ + { + "name": "TEXT", + "data_type": "TYPE_INT64", + "dims": [-1, -1] + } + ], + "output": [ + { + "name": "LOGITS_PER_TEXT", + "data_type": "TYPE_FP32", + "dims": [-1, -1] + } + ] + } + ] +} diff --git a/tools/inference_builder/schemas/backends/pytorch.schema.json b/tools/inference_builder/schemas/backends/pytorch.schema.json new file mode 100644 index 00000000..e19995b9 --- /dev/null +++ b/tools/inference_builder/schemas/backends/pytorch.schema.json @@ -0,0 +1,50 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/pytorch.schema.json", + "title": "PyTorch Backend Configuration", + "description": "Schema for PyTorch backend model configuration (Huggingface Transformers)", + "allOf": [ + { + "$ref": "../common/base-model.schema.json" + }, + { + "properties": { + "backend": { + "type": "string", + "const": "pytorch", + "description": "PyTorch backend for Huggingface Transformers models" + }, + "parameters": { + "$ref": "parameters/pytorch-parameters.schema.json" + } + } + } + ], + "examples": [ + { + "name": "llama_model", + "backend": "pytorch", + "parameters": { + "model_name_or_path": "meta-llama/Llama-2-7b-hf", + "device": "cuda", + "torch_dtype": "float16", + "trust_remote_code": true + }, + "input": [ + { + "name": "input_ids", + "data_type": "TYPE_INT64", + "dims": [-1, -1] + } + ], + "output": [ + { + "name": "logits", + "data_type": "TYPE_FP32", + "dims": [-1, -1, -1] + } + ] + } + ] +} + diff --git a/tools/inference_builder/schemas/backends/tensorrtllm.schema.json b/tools/inference_builder/schemas/backends/tensorrtllm.schema.json new file mode 100644 index 00000000..5441c014 --- /dev/null +++ b/tools/inference_builder/schemas/backends/tensorrtllm.schema.json @@ -0,0 +1,49 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/tensorrtllm.schema.json", + "title": "TensorRT-LLM Backend Configuration", + "description": "Schema for TensorRT-LLM backend model configuration", + "allOf": [ + { + "$ref": "../common/base-model.schema.json" + }, + { + "properties": { + "backend": { + "type": "string", + "enum": ["tensorrtllm", "tensorrtllm/pytorch"], + "description": "TensorRT-LLM backend type with optional PyTorch fallback" + }, + "parameters": { + "$ref": "parameters/tensorrtllm-parameters.schema.json" + } + } + } + ], + "examples": [ + { + "name": "Qwen2.5-VL", + "backend": "tensorrtllm/pytorch", + "parameters": { + "max_num_tokens": 19200, + "kv_cache_config": { + "enable_block_reuse": false + } + }, + "input": [ + { + "name": "inputs", + "data_type": "TYPE_CUSTOM_VLM_INPUT", + "dims": [-1] + } + ], + "output": [ + { + "name": "outputs", + "data_type": "TYPE_STRING", + "dims": [-1] + } + ] + } + ] +} diff --git a/tools/inference_builder/schemas/backends/triton.schema.json b/tools/inference_builder/schemas/backends/triton.schema.json new file mode 100644 index 00000000..6825348e --- /dev/null +++ b/tools/inference_builder/schemas/backends/triton.schema.json @@ -0,0 +1,54 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/triton.schema.json", + "title": "Triton Backend Configuration", + "description": "Schema for Triton Inference Server backend model configuration", + "allOf": [ + { + "$ref": "../common/base-model.schema.json" + }, + { + "properties": { + "backend": { + "type": "string", + "enum": [ + "triton/python", + "triton/tensorrt", + "triton/onnx", + "triton/pytorch", + "triton/tensorflow", + "triton/python/tensorrtllm" + ], + "description": "Triton backend type with specific framework" + }, + "parameters": { + "$ref": "parameters/triton-parameters.schema.json" + } + } + } + ], + "examples": [ + { + "name": "visual_changenet", + "backend": "triton/tensorrt", + "max_batch_size": 1, + "parameters": { + "FORCE_CPU_ONLY_INPUT_TENSORS": "no" + }, + "input": [ + { + "name": "input0", + "data_type": "TYPE_FP32", + "dims": [3, 768, 768] + } + ], + "output": [ + { + "name": "output_final", + "data_type": "TYPE_FP32", + "dims": [10, 768, 768] + } + ] + } + ] +} diff --git a/tools/inference_builder/schemas/backends/vllm.schema.json b/tools/inference_builder/schemas/backends/vllm.schema.json new file mode 100644 index 00000000..10219ac8 --- /dev/null +++ b/tools/inference_builder/schemas/backends/vllm.schema.json @@ -0,0 +1,47 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/vllm.schema.json", + "title": "vLLM Backend Configuration", + "description": "Schema for vLLM backend model configuration", + "allOf": [ + { + "$ref": "../common/base-model.schema.json" + }, + { + "properties": { + "backend": { + "type": "string", + "const": "vllm", + "description": "vLLM backend for large language models" + }, + "parameters": { + "$ref": "parameters/vllm-parameters.schema.json" + } + } + } + ], + "examples": [ + { + "name": "Cosmos", + "backend": "vllm", + "parameters": { + "max_num_tokens": 19200, + "async_mode": true + }, + "input": [ + { + "name": "inputs", + "data_type": "TYPE_CUSTOM_VLM_INPUT", + "dims": [-1] + } + ], + "output": [ + { + "name": "outputs", + "data_type": "TYPE_STRING", + "dims": [-1] + } + ] + } + ] +} diff --git a/tools/inference_builder/schemas/common/base-model.schema.json b/tools/inference_builder/schemas/common/base-model.schema.json new file mode 100644 index 00000000..da387ae7 --- /dev/null +++ b/tools/inference_builder/schemas/common/base-model.schema.json @@ -0,0 +1,96 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/common/base-model.schema.json", + "title": "Base Model Configuration", + "description": "Common base schema for all model configurations", + "type": "object", + "required": ["name", "backend"], + "additionalProperties": false, + "properties": { + "name": { + "type": "string", + "description": "Model name identifier" + }, + "backend": { + "$ref": "definitions.schema.json#/definitions/backendTypes" + }, + "max_batch_size": { + "oneOf": [ + { + "type": "integer", + "minimum": 1 + }, + { + "type": "string", + "pattern": "^\\$[A-Z_]+\\|[0-9]+$", + "description": "Environment variable with default value (e.g., $MAX_BATCH_SIZE|1)" + } + ], + "description": "Maximum batch size for the model", + "default": 1 + }, + "input": { + "type": "array", + "description": "Model input specification", + "items": { + "$ref": "#/definitions/tensorSpec" + } + }, + "output": { + "type": "array", + "description": "Model output specification", + "items": { + "$ref": "#/definitions/tensorSpec" + } + }, + "parameters": { + "type": "object", + "description": "Backend-specific parameters (see backend schemas for details)", + "additionalProperties": true + }, + "preprocessors": { + "type": "array", + "description": "Ordered list of preprocessors applied to this model's inputs before inference. Each preprocessor transforms or derives tensors from existing ones (for example, tokenization or image normalization) and is executed in sequence. Typically used to adapt data from top-level inputs or previous models into the format and shape expected by this model's inputs.", + "items": { + "$ref": "preprocessors.schema.json" + } + }, + "postprocessors": { + "type": "array", + "description": "Ordered list of postprocessors applied to this model's outputs after inference. Each postprocessor can transform, filter, or aggregate output tensors (for example, masking, decoding, or embedding processing) and is executed in sequence. Commonly used to convert raw model outputs into the tensors or objects expected by downstream models or top-level outputs.", + "items": { + "$ref": "postprocessors.schema.json" + } + } + }, + "definitions": { + "tensorSpec": { + "type": "object", + "required": ["name", "data_type", "dims"], + "additionalProperties": true, + "properties": { + "name": { + "type": "string", + "description": "Name of the tensor" + }, + "data_type": { + "$ref": "definitions.schema.json#/definitions/dataTypes" + }, + "dims": { + "type": "array", + "description": "Dimensions of the tensor (-1 for dynamic dimensions)", + "items": { + "type": "integer" + }, + "minItems": 1 + }, + "optional": { + "type": "boolean", + "description": "Whether this tensor is optional", + "default": false + } + } + } + } +} + diff --git a/tools/inference_builder/schemas/common/base-processor.schema.json b/tools/inference_builder/schemas/common/base-processor.schema.json new file mode 100644 index 00000000..d1b5901c --- /dev/null +++ b/tools/inference_builder/schemas/common/base-processor.schema.json @@ -0,0 +1,36 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/common/base-processor.schema.json", + "title": "Base Processor Configuration", + "description": "Common base schema for preprocessors and postprocessors", + "type": "object", + "required": ["kind", "name", "input", "output"], + "properties": { + "kind": { + "type": "string", + "enum": ["custom", "builtin"], + "description": "Type of processor: 'custom' for user-defined processors, 'builtin' for system-provided processors" + }, + "name": { + "type": "string", + "description": "Processor identifier used to locate the processor implementation" + }, + "input": { + "type": "array", + "description": "Ordered list of input tensor names that the processor's __call__ method receives as positional arguments. These names are used to extract values from the upstream data dictionary. For example, if the upstream provides {'image': img_data, 'text': txt_data, 'metadata': meta} and input is ['text', 'image'], the processor's __call__ method will be invoked as __call__(txt_data, img_data) - extracting values in the order specified by the input list.", + "items": { + "type": "string" + }, + "minItems": 1 + }, + "output": { + "type": "array", + "description": "Ordered list of output tensor names corresponding to the tuple returned by the processor's __call__ method. The processor must return a tuple with length equal to the output array length. The returned values are mapped to output names to form a dictionary for downstream consumption. For example, if output is ['embeddings', 'attention_mask'] and __call__ returns (emb_tensor, mask_tensor), the downstream receives {'embeddings': emb_tensor, 'attention_mask': mask_tensor}.", + "items": { + "type": "string" + }, + "minItems": 1 + } + } +} + diff --git a/tools/inference_builder/schemas/common/definitions.schema.json b/tools/inference_builder/schemas/common/definitions.schema.json new file mode 100644 index 00000000..5f84b8be --- /dev/null +++ b/tools/inference_builder/schemas/common/definitions.schema.json @@ -0,0 +1,111 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/common/definitions.schema.json", + "title": "Common Definitions", + "description": "Common definitions and reusable schemas for Inference Builder", + "definitions": { + "dataTypes": { + "type": "string", + "enum": [ + "TYPE_BOOL", + "TYPE_UINT8", + "TYPE_UINT16", + "TYPE_UINT32", + "TYPE_UINT64", + "TYPE_INT8", + "TYPE_INT16", + "TYPE_INT32", + "TYPE_INT64", + "TYPE_FP16", + "TYPE_FP32", + "TYPE_FP64", + "TYPE_STRING", + "TYPE_CUSTOM_IMAGE_BASE64", + "TYPE_CUSTOM_IMAGE_ASSETS", + "TYPE_CUSTOM_LONG_VIDEO_ASSETS", + "TYPE_CUSTOM_VIDEO_CHUNK_ASSETS", + "TYPE_CUSTOM_BINARY_BASE64", + "TYPE_CUSTOM_LIST", + "TYPE_CUSTOM_DS_MEDIA_URL", + "TYPE_CUSTOM_VLM_INPUT", + "TYPE_CUSTOM_OBJECT", + "TYPE_CUSTOM_DS_IMAGE", + "TYPE_CUSTOM_DS_METADATA", + "TYPE_CUSTOM_DS_MIME", + "TYPE_CUSTOM_DS_SOURCE_CONFIG" + ], + "description": "Supported data types for tensors" + }, + "backendTypes": { + "type": "string", + "enum": [ + "dummy", + "vllm", + "triton/python", + "triton/tensorrt", + "triton/onnx", + "triton/pytorch", + "triton/tensorflow", + "tensorrtllm", + "tensorrtllm/pytorch", + "deepstream/nvinfer", + "deepstream/nvinferserver", + "polygraphy", + "pytorch" + ], + "description": "Supported backend types" + }, + "operationTypes": { + "type": "string", + "enum": [ + "inference", + "create_chat_completion_v1_chat_completions_post", + "create_embedding", + "add_media_file", + "delete_media_file", + "list_media_files", + "add_live_stream", + "delete_live_stream", + "list_live_streams", + "health_ready_v1_health_ready_get" + ], + "description": "Supported operation types for responders" + }, + "processorKinds": { + "type": "string", + "enum": ["custom", "builtin"], + "description": "Types of processors" + }, + "commonProcessorNames": { + "type": "string", + "enum": [ + "dummy-preprocessor", + "dummy-tokenizer", + "qwen-vl-loader", + "qwen-vl-image-loader", + "qwen-vl-video-loader", + "openclip-tokenizer", + "nvclip-vision-preprocessor", + "nvclip-postprocessor", + "changenet-normalizer", + "changenet-masking" + ], + "description": "Common processor names" + }, + "responderNames": { + "type": "string", + "enum": [ + "infer", + "add_file", + "del_file", + "list_files", + "add_live_stream", + "del_live_stream", + "list_live_streams", + "healthy_ready" + ], + "description": "Available responder template names (must match templates/responder/*.jinja.py)" + } + } +} + diff --git a/tools/inference_builder/schemas/common/postprocessors.schema.json b/tools/inference_builder/schemas/common/postprocessors.schema.json new file mode 100644 index 00000000..ed101e8f --- /dev/null +++ b/tools/inference_builder/schemas/common/postprocessors.schema.json @@ -0,0 +1,27 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/common/postprocessors.schema.json", + "title": "Postprocessor Configurations", + "description": "Schema for custom postprocessor configurations", + "type": "object", + "$ref": "#/definitions/customPostprocessor", + "definitions": { + "customPostprocessor": { + "allOf": [ + { + "$ref": "base-processor.schema.json" + }, + { + "properties": { + "config": { + "type": "object", + "description": "Custom configuration for the postprocessor", + "additionalProperties": true + } + } + } + ] + } + } +} + diff --git a/tools/inference_builder/schemas/common/preprocessors.schema.json b/tools/inference_builder/schemas/common/preprocessors.schema.json new file mode 100644 index 00000000..0736d352 --- /dev/null +++ b/tools/inference_builder/schemas/common/preprocessors.schema.json @@ -0,0 +1,27 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/common/preprocessors.schema.json", + "title": "Preprocessor Configurations", + "description": "Schema for custom preprocessor configurations", + "type": "object", + "$ref": "#/definitions/customPreprocessor", + "definitions": { + "customPreprocessor": { + "allOf": [ + { + "$ref": "base-processor.schema.json" + }, + { + "properties": { + "config": { + "type": "object", + "description": "Custom configuration for the preprocessor", + "additionalProperties": true + } + } + } + ] + } + } +} + diff --git a/tools/inference_builder/schemas/config.schema.json b/tools/inference_builder/schemas/config.schema.json new file mode 100644 index 00000000..2653b5dd --- /dev/null +++ b/tools/inference_builder/schemas/config.schema.json @@ -0,0 +1,371 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/config.schema.json", + "title": "Inference Builder Configuration", + "description": "Schema for NVIDIA Inference Builder configuration files", + "type": "object", + "required": ["name", "model_repo", "models"], + "additionalProperties": false, + "properties": { + "name": { + "type": "string", + "description": "Unique project name. Used as the directory name for generated code and as the package name when bundling artifacts.", + "pattern": "^[a-zA-Z][a-zA-Z0-9_-]*$" + }, + "model_repo": { + "type": "string", + "description": "Root path to the directory containing the model files" + }, + "input": { + "type": "array", + "description": "Top level inference input specification. Required when pipeline includes multiple models or when inputs use custom types requiring preprocessing.", + "minItems": 1, + "items": { + "$ref": "#/definitions/tensorSpec" + } + }, + "output": { + "type": "array", + "description": "Top level inference output specification. Required when pipeline includes multiple models or when outputs use custom types requiring postprocessing.", + "minItems": 1, + "items": { + "$ref": "#/definitions/tensorSpec" + } + }, + "server": { + "$ref": "#/definitions/serverConfig", + "description": "Server configuration mapping endpoints to their implementation templates." + }, + "models": { + "type": "array", + "description": "List of model specifications for inference", + "minItems": 1, + "items": { + "$ref": "#/definitions/modelSpec" + } + }, + "routes": { + "type": "object", + "description": "Data-flow map between top-level inputs, models, and top-level outputs.", + "additionalProperties": { + "type": "string" + } + }, + "postprocessors": { + "type": "array", + "description": "Top-level postprocessors. Required when pipeline includes multiple models and outputs need to be consolidated.", + "items": { + "$ref": "#/definitions/processor" + } + } + }, + "definitions": { + "tensorSpec": { + "type": "object", + "required": ["name", "data_type", "dims"], + "additionalProperties": false, + "properties": { + "name": { + "type": "string", + "description": "Name of the tensor" + }, + "data_type": { + "description": "Data type of the tensor", + "oneOf": [ + { "const": "TYPE_BOOL", "description": "Tensor with bit type" }, + { "const": "TYPE_UINT8", "description": "Tensor with unsigned 8-bit integer type" }, + { "const": "TYPE_UINT16", "description": "Tensor with unsigned 16-bit integer type" }, + { "const": "TYPE_UINT32", "description": "Tensor with unsigned 32-bit integer type" }, + { "const": "TYPE_UINT64", "description": "Tensor with unsigned 64-bit integer type" }, + { "const": "TYPE_INT8", "description": "Tensor with signed 8-bit integer type" }, + { "const": "TYPE_INT16", "description": "Tensor with signed 16-bit integer type" }, + { "const": "TYPE_INT32", "description": "Tensor with signed 32-bit integer type" }, + { "const": "TYPE_INT64", "description": "Tensor with signed 64-bit integer type" }, + { "const": "TYPE_FP16", "description": "Tensor with 16-bit floating point (half precision) type" }, + { "const": "TYPE_FP32", "description": "Tensor with 32-bit floating point (single precision) type" }, + { "const": "TYPE_FP64", "description": "Tensor with 64-bit floating point (double precision) type" }, + { "const": "TYPE_STRING", "description": "Tensor with string type" }, + { "const": "TYPE_CUSTOM_IMAGE_BASE64", "description": "Base64-encoded JPEG/PNG image data as a data URI string. Each element must follow the format 'data:image/{format};base64,{payload}' where {format} is 'jpeg', 'jpg', or 'png', and {payload} is the base64-encoded image bytes. This type is mapped to TYPE_STRING in Triton and arrives as a list of strings. When used as a top-level input, an ImageInputDataFlow is created that (1) parses the data URI prefix to determine the image format, (2) decodes the base64 payload into raw bytes, and (3) decodes the image using a hardware-accelerated DeepStream pipeline (nvjpegdec/pngdec) to produce RGB uint8 image tensors in HWC layout [H, W, 3] on GPU at the original image resolution. These decoded tensors are then passed to downstream models." }, + { "const": "TYPE_CUSTOM_IMAGE_ASSETS", "description": "Image asset identifiers (file paths, URLs, or asset IDs) referring to JPEG/PNG images. When used as a top-level input, an ImageInputDataFlow will be created that loads and decodes the referenced images into image tensors before they are passed to the downstream models." }, + { "const": "TYPE_CUSTOM_LONG_VIDEO_ASSETS", "description": "Video asset identifiers for long videos and live streams requiring temporal chunking. Particularly suitable for: (1) live streams such as RTSP feeds that need continuous processing in chunks, (2) long video files that should be split into temporal segments, (3) scenarios where frames need to be delivered in multiple batches over time. Supports file paths, URLs, RTSP streams, or asset IDs, with required query parameters: ?frames=N (number of frames per chunk), &interval=INTERVAL_NSEC (time interval between frames in nanoseconds), and optional &chunks=M (maximum number of chunks to yield before stopping - useful for RTSP streams without natural end-of-stream), &scale=WIDTHxHEIGHT (target resolution for frame scaling, e.g., scale=640x360 - all assets in a batch must use the same scale value). When used as a top-level input, a VideoInputDataFlow will be created that asynchronously extracts frames in chunks, with each chunk containing N frames sampled at the specified interval. Results are delivered via a queue as they become available, enabling streaming processing. Processing always starts from the beginning of the video or live stream. For short videos that fit in a single batch without temporal chunking, or when you need to specify a start position, use TYPE_CUSTOM_VIDEO_CHUNK_ASSETS instead." }, + { "const": "TYPE_CUSTOM_VIDEO_CHUNK_ASSETS", "description": "Video asset identifiers for short videos requiring simple frame sampling without temporal chunking. Use this type when: (1) processing short video clips that fit in memory as a single batch, (2) extracting a fixed number of frames from a specific video segment, (3) simple uniform frame sampling from a video duration is needed. Supports file paths, URLs, or asset IDs, with required query parameter ?frames=N (number of frames to sample), and optional &start=START_NSEC (start time in nanoseconds, default 0), &duration=DURATION_NSEC (duration to sample from in nanoseconds, defaults to entire video). When used as a top-level input, a VideoFrameSamplingDataFlow will be created that asynchronously extracts exactly N frames evenly distributed over the specified duration (interval is automatically calculated as duration/frames), decodes them, and produces a single frame batch delivered via queue. The result is one batch containing all requested frames. For long videos or live streams requiring temporal chunking and streaming delivery of multiple batches over time, use TYPE_CUSTOM_LONG_VIDEO_ASSETS instead." }, + { "const": "TYPE_CUSTOM_BINARY_BASE64", "description": "Base64-encoded binary data. The input strings will be decoded into uint8 tensors before being passed to the downstream models." }, + { "const": "TYPE_CUSTOM_LIST", "description": "Converts the input values into a list, which implies explicit batching of the input data. Use for non-DS inputs that need list conversion (e.g., video URLs for VLM models)." }, + { "const": "TYPE_CUSTOM_DS_MEDIA_URL", "description": "DeepStream media URL input. A string URL (file path or remote URL) pointing to an image or video for DeepStream pipeline processing. Unlike TYPE_CUSTOM_LIST, this type follows the implicit batch path and is recognized by the DeepStream backend as a media source. Must be paired with a TYPE_CUSTOM_DS_MIME input." }, + { "const": "TYPE_CUSTOM_VLM_INPUT", "description": "Vision-Language Model input in dict format and will be passed to the downstream models as is." }, + { "const": "TYPE_CUSTOM_OBJECT", "description": "Custom object type for complex structured data" }, + { "const": "TYPE_CUSTOM_DS_IMAGE", "description": "DeepStream custom type for image buffers, usable as both input and output. As INPUT: a 1-D uint8 tensor (or list of tensors) containing encoded JPEG/PNG image bytes that can be passed directly into DeepStream backends without additional decoding in the Python runtime. This input must be defined in the model configuration to enable the image decoder in the DeepStream pipeline. As OUTPUT: extracts the decoded image frames from the DeepStream pipeline and returns them as RGB uint8 image tensors in HWC (Height, Width, Channels) layout, where Channels is 3 (R, G, B). Each tensor has shape [H, W, 3] representing the decoded frame at its processed resolution. Enable the image output only if raw image data is required for downstream models or postprocessors since it adds additional overhead to the pipeline." }, + { "const": "TYPE_CUSTOM_DS_METADATA", "description": "Deepstream custom type for metadata output. The output structure follows the deepstreamMetadata schema (see definitions/deepstreamMetadata), returned as a dictionary with flexible fields that vary based on the model type and parser implementation. This type requires a low-level C++ postprocessor library (e.g., libnvds_infercustomparser_tao.so) to be available and copied into the container image. The library can be obtained from: (1) TAO model parsers from https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git, or (2) custom user-implemented parsers. Without such a library, use regular tensor output types (TYPE_FP32, etc.) with Python postprocessors instead." }, + { "const": "TYPE_CUSTOM_DS_MIME", "description": "Deepstream custom type for MIME type string (for example, 'image/jpeg' or 'image/png') describing the associated DeepStream image or media buffers. Required alongside TYPE_CUSTOM_DS_IMAGE or TYPE_CUSTOM_DS_MEDIA_URL inputs so the DeepStream pipeline can select the appropriate decoder." }, + { "const": "TYPE_CUSTOM_DS_SOURCE_CONFIG", "description": "DeepStream custom type for path to a YAML source configuration file. Use this type when handling live cameras or video streams with DeepStream inference pipelines. The source config supports two deployment strategies: (1) STATIC CAMERA SETUP: For fixed scenarios with a known number of cameras (e.g., a retail store with 4 fixed cameras), set source-bin to 'nvurisrcbin' in the YAML config. All camera URIs are defined upfront in the config file and remain unchanged during runtime. This is simpler and recommended when camera topology is stable. (2) DYNAMIC CAMERA MANAGEMENT: For scenarios requiring runtime addition/removal of cameras (e.g., smart city deployments, fleet management), set source-bin to 'nvmultiurisrcbin' in the YAML config. This enables a built-in REST API server for dynamic stream management. Add streams via POST to /api/v1/stream/add with JSON payload containing camera_id, camera_url, and change='camera_add'. Remove streams via POST to /api/v1/stream/remove with change='camera_remove'. The REST API server endpoint (ip-address, port) is configured in the source config. Full API specification: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvmultiurisrcbin.html. Choose nvurisrcbin for static setups to reduce complexity; choose nvmultiurisrcbin when dynamic stream management is a requirement." } + ] + }, + "dims": { + "type": "array", + "description": "Dimensions of the tensor (-1 for dynamic dimensions)", + "items": { + "type": "integer" + }, + "minItems": 1 + }, + "optional": { + "type": "boolean", + "description": "Whether this tensor is optional", + "default": false + } + } + }, + "serverConfig": { + "type": "object", + "additionalProperties": true, + "properties": { + "responders": { + "type": "object", + "description": "Map of responder configurations. Keys must match available responder templates.", + "additionalProperties": false, + "properties": { + "infer": { + "description": "Primary inference endpoint responder. Handles model inference requests. Input: mapped from HTTP request body to top-level pipeline inputs (as defined in config 'input' section). Output: top-level pipeline outputs (as defined in config 'output' section). Supports streaming responses with 'Accept: application/x-ndjson' header.", + "$ref": "#/definitions/responder" + }, + "add_file": { + "description": "File upload responder. Adds files to the asset store for later processing. Input: multipart file upload (file object with filename and content_type). Output: asset dict with fields 'id', 'file_name', 'content_type', 'created_at'.", + "$ref": "#/definitions/responder" + }, + "del_file": { + "description": "File deletion responder. Removes files from the asset store. Input: 'asset_id' (string, from path parameter). Output: 'status' (boolean, deletion success).", + "$ref": "#/definitions/responder" + }, + "list_files": { + "description": "File listing responder. Returns a list of files in the asset store. Input: none. Output: 'assets' (array of asset objects with 'id', 'file_name', 'content_type', 'created_at').", + "$ref": "#/definitions/responder" + }, + "add_live_stream": { + "description": "Live stream addition responder. Registers a new RTSP or video stream for processing. Input: 'url' (string, required), 'description' (string, optional), 'username' (string, optional), 'password' (string, optional). Output: asset dict with fields 'id', 'url', 'description', 'created_at'.", + "$ref": "#/definitions/responder" + }, + "del_live_stream": { + "description": "Live stream removal responder. Stops and removes a registered live stream. Input: 'asset_id' (string, from path parameter). Output: 'status' (boolean, deletion success).", + "$ref": "#/definitions/responder" + }, + "list_live_streams": { + "description": "Live stream listing responder. Returns all registered live streams. Input: none. Output: 'assets' (array of stream asset objects with 'id', 'url', 'description', 'created_at').", + "$ref": "#/definitions/responder" + }, + "healthy_ready": { + "description": "Health check responder. Reports service health and readiness status. Input: none. Output: HTTP 200 with 'Ready' or HTTP 503 with 'Service Unavailable'. No request/response template mapping needed.", + "$ref": "#/definitions/responder" + } + } + } + } + }, + "responder": { + "type": "object", + "required": ["operation"], + "additionalProperties": false, + "properties": { + "operation": { + "type": "string", + "description": "Name of the operation this responder handles, which is defined in the openapi specification." + }, + "requests": { + "type": "object", + "description": "Request templates (Jinja2 templates) to map HTTP request body to responder inputs. Keys are OpenAPI request schema/class names (e.g., 'InferenceRequest', 'AddLiveStreamRequest'), values are Jinja2 templates that transform the incoming 'request' object into a JSON structure matching the responder's expected inputs.", + "additionalProperties": { + "type": "string" + } + }, + "responses": { + "type": "object", + "description": "Response templates (Jinja2 templates) to map responder outputs to HTTP response body. Keys are OpenAPI response schema/class names (e.g., 'InferenceResponse', 'AddFileResponse'), values are Jinja2 templates that transform the 'response' object from the responder into the desired HTTP response JSON structure.", + "additionalProperties": { + "type": "string" + } + } + } + }, + "modelSpec": { + "type": "object", + "required": ["name", "backend", "input", "output"], + "additionalProperties": false, + "properties": { + "name": { + "type": "string", + "description": "Model name identifier for locating the model in the model repository. Must be unique within the project." + }, + "backend": { + "type": "string", + "description": "Backend type for the model", + "enum": [ + "dummy", + "vllm", + "triton/python", + "triton/tensorrt", + "triton/onnx", + "triton/pytorch", + "triton/tensorflow", + "tensorrtllm", + "tensorrtllm/pytorch", + "deepstream/nvinfer", + "deepstream/nvinferserver", + "polygraphy", + "pytorch" + ] + }, + "max_batch_size": { + "oneOf": [ + { + "type": "integer", + "minimum": 1 + }, + { + "type": "string", + "pattern": "^\\$[A-Z_]+\\|[0-9]+$", + "description": "Environment variable with default value (e.g., $MAX_BATCH_SIZE|1)" + } + ], + "description": "Maximum batch size for the model", + "default": 1 + }, + "input": { + "type": "array", + "description": "Model input specification. Note on passthrough tensors: any keys present in the request after preprocessing that are NOT listed in this model's input spec are treated as passthrough (they are not sent to the backend). They are merged back into each backend result dict before this model's postprocessors run, so a model postprocessor can consume them by listing those names in its `input`. If you need passthrough values to propagate further downstream/top-level, ensure your postprocessor emits/mappings include them via configured outputs/routes.", + "items": { + "$ref": "#/definitions/tensorSpec" + } + }, + "output": { + "type": "array", + "description": "Model output specification", + "items": { + "$ref": "#/definitions/tensorSpec" + } + }, + "parameters": { + "type": "object", + "description": "Backend-specific parameters. The structure depends on the 'backend' field value. Read 'schema://index.json' to find the mapping from backend type to its parameter schema (e.g., backend 'vllm' -> 'schema://backends/parameters/vllm-parameters.schema.json').", + "additionalProperties": true + }, + "preprocessors": { + "type": "array", + "description": "Ordered list of preprocessors applied to this model's inputs before inference. Each processor transforms or derives tensors from existing ones (for example, tokenization or image normalization) and is executed in sequence. Typically used to adapt data from top-level inputs or previous models into the format and shape expected by this model's inputs.", + "items": { + "$ref": "#/definitions/processor" + } + }, + "postprocessors": { + "type": "array", + "description": "Ordered list of postprocessors applied to this model's outputs after inference, before passing results downstream. Each processor can transform, filter, or aggregate output tensors (for example, masking, decoding, or embedding processing) and is executed in sequence. This is also a primary extension point for adding custom logic, giving you room to expand pipeline functionality without changing the core runtime (e.g., adding new derived tensors, validation, formatting, or routing-friendly outputs).", + "items": { + "$ref": "#/definitions/processor" + } + } + } + }, + "processor": { + "type": "object", + "required": ["kind", "name", "input", "output"], + "additionalProperties": false, + "properties": { + "kind": { + "type": "string", + "description": "Type of processor", + "enum": ["custom", "auto"] + }, + "name": { + "type": "string", + "description": "Name/identifier of the processor" + }, + "input": { + "type": "array", + "description": "Input tensor names", + "items": { + "type": "string" + } + }, + "output": { + "type": "array", + "description": "Output tensor names", + "items": { + "type": "string" + } + }, + "config": { + "type": "object", + "description": "Processor-specific configuration", + "additionalProperties": true + } + } + }, + "deepstreamMetadata": { + "type": "object", + "description": "DeepStream metadata structure returned by TYPE_CUSTOM_DS_METADATA outputs. Contains per-frame detection, classification, and segmentation results extracted from DeepStream's internal metadata by C++ parser libraries. The structure is flexible and fields may be empty depending on the model type and parser implementation.", + "additionalProperties": true, + "properties": { + "shape": { + "type": "array", + "description": "Image or frame dimensions as [height, width] in pixels. Represents the spatial dimensions of the processed frame.", + "items": { + "type": "integer", + "minimum": 0 + }, + "minItems": 2, + "maxItems": 2 + }, + "bboxes": { + "type": "array", + "description": "Bounding boxes for detected objects in the format [left, top, right, bottom] coordinates. Each bounding box is an array of 4 integers representing pixel coordinates. Used by detection and instance segmentation models.", + "items": { + "type": "array", + "items": { + "type": "integer" + }, + "minItems": 4, + "maxItems": 4 + } + }, + "probs": { + "type": "array", + "description": "Confidence scores (probabilities) for each detected object or classification result. Values typically range from 0.0 to 1.0, with higher values indicating greater confidence.", + "items": { + "type": "number", + "minimum": 0.0, + "maximum": 1.0 + } + }, + "labels": { + "type": "array", + "description": "Classification labels as an array of arrays (list[list[str]]). The outer array has one entry per detected object (aligned by index with 'bboxes', 'probs', etc.). The inner array contains one or more label strings for that object, since a single object may have multiple labels (e.g., ['car', 'red', 'moving']). For single-label models, each inner array contains exactly one string (e.g., [['car'], ['person']]).", + "items": { + "type": "array", + "items": { + "type": "string" + } + } + }, + "seg_maps": { + "type": "array", + "description": "Segmentation maps containing pixel-wise class predictions or instance masks. Each element is a 2D array (represented as nested arrays or numpy arrays) where values correspond to class IDs or instance IDs. Used by semantic segmentation and instance segmentation models.", + "items": { + "type": "array" + } + }, + "objects": { + "type": "array", + "description": "Object tracking IDs assigned by the DeepStream tracker. Each integer uniquely identifies a tracked object across frames. Only populated when tracker is enabled in the pipeline configuration.", + "items": { + "type": "integer", + "minimum": 0 + } + }, + "timestamp": { + "type": "integer", + "description": "Buffer presentation timestamp (PTS) in nanoseconds. Represents the temporal position of the frame in the video stream. Useful for synchronization and temporal analysis.", + "minimum": 0 + } + } + } + } +} + diff --git a/tools/inference_builder/schemas/index.json b/tools/inference_builder/schemas/index.json new file mode 100644 index 00000000..e6975c3a --- /dev/null +++ b/tools/inference_builder/schemas/index.json @@ -0,0 +1,105 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/index.json", + "title": "Inference Builder Schema Index", + "description": "Central index for all Inference Builder schemas", + "version": "1.0.0", + "schemas": { + "main": { + "id": "config.schema.json", + "description": "Main configuration schema for inference builder", + "path": "./config.schema.json" + }, + "backends": { + "deepstream": { + "id": "backends/deepstream.schema.json", + "description": "DeepStream backend configuration", + "path": "./backends/deepstream.schema.json", + "parameters": "./backends/parameters/deepstream-parameters.schema.json", + "backend_type": "deepstream/nvinfer" + }, + "triton": { + "id": "backends/triton.schema.json", + "description": "Triton Inference Server backend configuration", + "path": "./backends/triton.schema.json", + "parameters": "./backends/parameters/triton-parameters.schema.json", + "backend_types": [ + "triton/python", + "triton/tensorrt", + "triton/onnx", + "triton/pytorch" + ] + }, + "vllm": { + "id": "backends/vllm.schema.json", + "description": "vLLM backend configuration for large language models", + "path": "./backends/vllm.schema.json", + "parameters": "./backends/parameters/vllm-parameters.schema.json", + "backend_type": "vllm" + }, + "tensorrtllm": { + "id": "backends/tensorrtllm.schema.json", + "description": "TensorRT-LLM backend configuration", + "path": "./backends/tensorrtllm.schema.json", + "parameters": "./backends/parameters/tensorrtllm-parameters.schema.json", + "backend_types": [ + "tensorrtllm", + "tensorrtllm/pytorch" + ] + }, + "polygraphy": { + "id": "backends/polygraphy.schema.json", + "description": "Polygraphy (TensorRT) backend configuration", + "path": "./backends/polygraphy.schema.json", + "parameters": "./backends/parameters/polygraphy-parameters.schema.json", + "backend_type": "polygraphy" + }, + "dummy": { + "id": "backends/dummy.schema.json", + "description": "Dummy backend for testing", + "path": "./backends/dummy.schema.json", + "parameters": "./backends/parameters/dummy-parameters.schema.json", + "backend_type": "dummy" + }, + "pytorch": { + "id": "backends/pytorch.schema.json", + "description": "PyTorch backend configuration (Huggingface Transformers)", + "path": "./backends/pytorch.schema.json", + "parameters": "./backends/parameters/pytorch-parameters.schema.json", + "backend_type": "pytorch" + } + }, + "common": { + "base_model": { + "id": "common/base-model.schema.json", + "description": "Base model configuration (shared structure)", + "path": "./common/base-model.schema.json" + }, + "base_processor": { + "id": "common/base-processor.schema.json", + "description": "Base processor configuration (shared by preprocessors and postprocessors)", + "path": "./common/base-processor.schema.json" + }, + "definitions": { + "id": "common/definitions.schema.json", + "description": "Common definitions and reusable schemas", + "path": "./common/definitions.schema.json" + }, + "preprocessors": { + "id": "common/preprocessors.schema.json", + "description": "Preprocessor configurations", + "path": "./common/preprocessors.schema.json" + }, + "postprocessors": { + "id": "common/postprocessors.schema.json", + "description": "Postprocessor configurations", + "path": "./common/postprocessors.schema.json" + } + } + }, + "documentation": { + "readme": "./README.md", + "samples": "../builder/samples/" + } +} + diff --git a/tools/inference_builder/schemas/validate_config.py b/tools/inference_builder/schemas/validate_config.py new file mode 100644 index 00000000..d813037e --- /dev/null +++ b/tools/inference_builder/schemas/validate_config.py @@ -0,0 +1,220 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Validate Inference Builder configuration files against JSON schemas. + +Usage: + python validate_config.py config.yaml + python validate_config.py --schema custom_schema.json config.yaml +""" + +import argparse +import json +import sys +from pathlib import Path +from typing import Dict, Any + +try: + import yaml +except ImportError: + print("Error: PyYAML is required. Install with: pip install pyyaml") + sys.exit(1) + +try: + from jsonschema import validate, ValidationError, Draft7Validator +except ImportError: + print("Error: jsonschema is required. Install with: pip install jsonschema") + sys.exit(1) + + +def load_yaml(file_path: Path) -> Dict[Any, Any]: + """Load YAML configuration file. + + Args: + file_path: Path to YAML file + + Returns: + Parsed YAML as dictionary + """ + try: + with open(file_path, 'r') as f: + return yaml.safe_load(f) + except yaml.YAMLError as e: + print(f"Error parsing YAML file: {e}") + sys.exit(1) + except FileNotFoundError: + print(f"Error: File not found: {file_path}") + sys.exit(1) + + +def load_schema(schema_path: Path) -> Dict[Any, Any]: + """Load JSON schema file. + + Args: + schema_path: Path to schema file + + Returns: + Parsed schema as dictionary + """ + try: + with open(schema_path, 'r') as f: + return json.load(f) + except json.JSONDecodeError as e: + print(f"Error parsing schema file: {e}") + sys.exit(1) + except FileNotFoundError: + print(f"Error: Schema file not found: {schema_path}") + sys.exit(1) + + +def validate_config(config: Dict[Any, Any], schema: Dict[Any, Any], verbose: bool = False) -> bool: + """Validate configuration against schema. + + Args: + config: Configuration dictionary + schema: JSON schema dictionary + verbose: Enable verbose output + + Returns: + True if valid, False otherwise + """ + try: + # Create validator + validator = Draft7Validator(schema) + + # Check for errors + errors = list(validator.iter_errors(config)) + + if not errors: + print("✓ Configuration is valid!") + return True + + print(f"✗ Configuration validation failed with {len(errors)} error(s):\n") + + for i, error in enumerate(errors, 1): + print(f"Error {i}:") + print(f" Path: {'.'.join(str(p) for p in error.path)}") + print(f" Message: {error.message}") + + if verbose and error.context: + print(f" Context:") + for ctx_error in error.context: + print(f" - {ctx_error.message}") + + print() + + return False + + except Exception as e: + print(f"Error during validation: {e}") + return False + + +def get_backend_schema(config: Dict[Any, Any], schema_dir: Path) -> Path: + """Determine the appropriate backend schema based on config. + + Args: + config: Configuration dictionary + schema_dir: Directory containing schemas + + Returns: + Path to backend-specific schema or main schema + """ + if 'models' not in config or not config['models']: + return schema_dir / 'config.schema.json' + + # Get the backend from the first model + backend = config['models'][0].get('backend', '') + + backend_map = { + 'deepstream/nvinfer': 'deepstream.schema.json', + 'triton': 'triton.schema.json', + 'vllm': 'vllm.schema.json', + 'tensorrtllm': 'tensorrtllm.schema.json', + 'polygraphy': 'polygraphy.schema.json', + 'dummy': 'dummy.schema.json', + 'pytorch': 'pytorch.schema.json' + } + + for prefix, schema_file in backend_map.items(): + if backend.startswith(prefix): + backend_schema = schema_dir / 'backends' / schema_file + if backend_schema.exists(): + return backend_schema + + return schema_dir / 'config.schema.json' + + +def main(): + """Main entry point.""" + parser = argparse.ArgumentParser( + description='Validate Inference Builder configuration files' + ) + parser.add_argument( + 'config', + type=Path, + help='Path to configuration YAML file' + ) + parser.add_argument( + '-s', '--schema', + type=Path, + help='Path to JSON schema file (default: auto-detect from config)' + ) + parser.add_argument( + '-v', '--verbose', + action='store_true', + help='Enable verbose output' + ) + parser.add_argument( + '--schema-dir', + type=Path, + default=Path(__file__).parent, + help='Directory containing schema files' + ) + + args = parser.parse_args() + + # Load configuration + if args.verbose: + print(f"Loading configuration from: {args.config}") + config = load_yaml(args.config) + + # Determine schema to use + if args.schema: + schema_path = args.schema + else: + schema_path = get_backend_schema(config, args.schema_dir) + if args.verbose: + print(f"Auto-detected schema: {schema_path}") + + # Load schema + if args.verbose: + print(f"Loading schema from: {schema_path}") + schema = load_schema(schema_path) + + # Validate + if args.verbose: + print("\nValidating configuration...\n") + + is_valid = validate_config(config, schema, args.verbose) + + sys.exit(0 if is_valid else 1) + + +if __name__ == '__main__': + main() + diff --git a/tools/inference_builder/templates/api_server/fastapi/responder.jinja.py b/tools/inference_builder/templates/api_server/fastapi/responder.jinja.py new file mode 100644 index 00000000..9cda8a30 --- /dev/null +++ b/tools/inference_builder/templates/api_server/fastapi/responder.jinja.py @@ -0,0 +1,88 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} +{{ license }} + +import json +from dataclasses import asdict +from config import global_config +from .model import GenericInference +from lib.utils import create_jinja2_env, convert_list +from lib.responder import ResponderBase +from lib.inference import Stop +from lib.errors import EnhancedError +import re +import numpy as np +import torch +from typing import Dict, Any +from fastapi.responses import StreamingResponse +import asyncio + +class Responder(ResponderBase): + def __init__(self): + super().__init__() + self._inference = GenericInference() + self._inference.initialize() + + # initialize the action map + {% for responder in responders %} + self._action_map["{{ responder.operation }}"] = self.{{ responder.name }} + {% endfor %} + + def process_request( + self, + responder : str, + request, + ): + # the helper function transforms the HTTP request to a dictionary that satisfies the inference input schema + return super().process_request(responder, request) + + def process_response(self, responder: str, request, response: Dict[str, Any]): + accept = request.headers.get("accept", "") + streaming = "application/x-ndjson" in accept + # transform numpy ndarray or tensor to universal value types for inference + if responder == "infer": + type_map = { i.name: i.data_type for i in global_config.output} + for name in response: + if not name in type_map: + self.logger.error(f"Unexpected output: {name}") + continue + expected_type = type_map[name] + value = response[name] + if isinstance(value, np.ndarray) or isinstance(value, torch.Tensor): + l = value.tolist() + if expected_type == "TYPE_STRING" and value.dtype != np.string_: + response[name] = convert_list(l, lambda i: i.decode("utf-8", "ignore")) + elif len(response[name].shape) == 1 and len(l) == 1: + response[name] = l[0] + else: + response[name] = l + + # the helper function transforms the inference output to a json string + json_string = super().process_response(responder, request, response) + self.logger.debug(f"Sending json payload: {json_string}") + + return (json.dumps(json.loads(json_string), separators=(',', ':')) + "\n") if streaming else json.loads(json_string) + + async def take_action(self, action_name:str, **kwargs): + action = self._action_map.get(action_name, None) + if not action: + raise ValueError(f"Unknown action: {action_name}") + return await action(**kwargs) + +{% for responder in responders %} +{{ responder.implementation }} +{% endfor %} diff --git a/tools/inference_builder/templates/api_server/fastapi/route/inference.jinja2 b/tools/inference_builder/templates/api_server/fastapi/route/inference.jinja2 new file mode 100644 index 00000000..65fca099 --- /dev/null +++ b/tools/inference_builder/templates/api_server/fastapi/route/inference.jinja2 @@ -0,0 +1,76 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} + +from typing import Any +{{imports}} +from contextlib import asynccontextmanager +from fastapi import Request, HTTPException, FastAPI, APIRouter +import uvicorn +from .responder import Responder +from lib.utils import get_logger +import os + +class FastAPIInterface: + + def __init__(self, *args, **kwargs): + self.app = FastAPI() + self.router = APIRouter() + self.responder = Responder() + self.logger = get_logger(__name__) + self.register_routes() + self.app.include_router(self.router) + + def register_routes(self): + {% for operation in operations %} + self.router.{{operation.type}}("{{operation.path}}")(self.{{operation.function_name}}) + {% endfor %} + + + def serve(self, **kwargs): + http_port = int(os.getenv("HTTP_PORT", "8000")) + uvicorn.run(self.app, host="0.0.0.0", port=http_port) + + {% for operation in operations %} + async def {{operation.function_name}}(self, request: Request, {{operation.snake_case_arguments}}) -> {{operation.response}}: + self.logger.info("{{operation.function_name}} called") + kwargs = dict(locals()) + kwargs.pop("self") + status, response = await self.responder.take_action("{{operation.function_name}}", **kwargs) + if status != 200: + raise HTTPException(status_code=status, detail=response) + self.logger.debug(f"response generated as {response}") + return response + {% endfor %} + +def get_openapi_schema(file_dump=True, pprint=True): + """ + Utility function to get API schema in YAML format. + This is used after inference.py has been completed with custom user APIs. + """ + import yaml + interface_instance = Interface() + schema: dict = interface_instance.get_openapi_schema() + if pprint: + print(yaml.dump(schema, sort_keys=False)) + if file_dump: + with open("api_spec.yaml", "w+") as file: + yaml.dump(schema, file, sort_keys=False) + + +def main(): + interface = FastAPIInterface() + interface.serve() diff --git a/tools/inference_builder/templates/backend/deepstream.jinja.py b/tools/inference_builder/templates/backend/deepstream.jinja.py new file mode 100644 index 00000000..1358b319 --- /dev/null +++ b/tools/inference_builder/templates/backend/deepstream.jinja.py @@ -0,0 +1,1178 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} + +from pyservicemaker import Pipeline, Flow, BufferProvider, Buffer, RenderMode, BufferRetriever, as_tensor, StateTransitionMessage, DynamicSourceMessage, SourceConfig, BatchMetadataOperator, Probe +from typing import Dict, List +from queue import Queue, Empty, Full +from dataclasses import dataclass +import base64 +import numpy as np +from abc import ABC, abstractmethod +import yaml +import tempfile +import os + +png_data = base64.b64decode("iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAIAAACQd1PeAAAAEElEQVR4nGK6HcwNCAAA//8DTgE8HuxwEQAAAABJRU5ErkJggg==") +jpg_data = base64.b64decode("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") + + +@dataclass +class PerfConfig: + enable_fps_logs: bool = False + enable_latency_logs: bool = False + +@dataclass +class RenderConfig: + enable_display: bool = False + enable_osd: bool = False + enable_stream: bool = False + rtsp_mount_point: str | None = "/ds-test" + rtsp_port: int | None = 8554 + seg_mask_config: dict | None = None + + def __bool__(self) -> bool: + """Check if render configuration is valid.""" + if self.enable_osd and not self.enable_display and not self.enable_stream: + logger.warning("RenderConfig: enable_osd is True but both enable_display and enable_stream are False. OSD requires an output method.") + return False + if self.enable_stream and (self.rtsp_mount_point is None or self.rtsp_port is None): + logger.warning("RenderConfig: enable_stream is True but rtsp_mount_point or rtsp_port is None.") + return False + return True + +@dataclass +class TrackerConfig: + config_path: str | None = None + lib_path: str | None = None + width: int = 1920 + height: int = 1088 + display_tracking_id: bool = False + + def __bool__(self) -> bool: + """Check if all required tracker configuration fields are set.""" + # Check if width/height is set than we need to have both, set default values if not set + if (self.width is not None and self.height is None) or (self.width is None and self.height is not None): + logger.warning("TrackerConfig: width and height must be set together, setting default values") + return self.config_path is not None and self.lib_path is not None + +@dataclass +class MessageBrokerConfig: + proto_lib_path: str | None = None + conn_str: str | None = None + topic: str | None = None + msgconv_config_path: str | None = None + msgconv_payload_type: int = 0 + msgconv_msg2p_new_api: bool = False + msgconv_frame_interval: int = 30 + msgconv_msg2p_lib: str | None = None + + def __bool__(self) -> bool: + """Check if all required message broker configuration fields are set.""" + if self.msgconv_msg2p_lib is not None and not os.path.exists(self.msgconv_msg2p_lib): + logger.warning(f"MessageBrokerConfig: msgconv_msg2p_lib does not exist: {self.msgconv_msg2p_lib}") + return False + if self.msgconv_config_path is not None and not os.path.exists(self.msgconv_config_path): + logger.warning(f"MessageBrokerConfig: msgconv_config_path does not exist: {self.msgconv_config_path}") + return False + if self.proto_lib_path is not None and not os.path.exists(self.proto_lib_path): + logger.warning(f"MessageBrokerConfig: proto_lib_path does not exist: {self.proto_lib_path}") + return False + return ( + self.proto_lib_path is not None and + self.msgconv_config_path is not None and + self.conn_str is not None and + self.topic is not None + ) + +@dataclass +class AnalyticsConfig: + """Configuration for nvdsanalytics (ROI, line-crossing, overcrowding, direction).""" + config_path: str | None = None + gpu_id: int = 0 + + def __bool__(self) -> bool: + """Check if analytics is configured (config_path set).""" + if self.config_path is None: + return False + if not os.path.exists(self.config_path): + logger.warning(f"AnalyticsConfig: config_path does not exist: {self.config_path}") + return False + return True + +@dataclass +class KittiConfig: + infer_kitti_output_dir: str | None = None + tracker_kitti_output_dir: str | None = None + + def __bool__(self) -> bool: + """Check if any required kitti configuration fields are set.""" + if self.infer_kitti_output_dir is None and self.tracker_kitti_output_dir is None: + logger.warning("KittiConfig: No kitti output directory specified") + return False + if self.infer_kitti_output_dir is not None and not os.path.exists(self.infer_kitti_output_dir): + logger.warning(f"KittiConfig: infer_kitti_output_dir does not exist: {self.infer_kitti_output_dir}") + return False + if self.tracker_kitti_output_dir is not None and not os.path.exists(self.tracker_kitti_output_dir): + logger.warning(f"KittiConfig: tracker_kitti_output_dir does not exist: {self.tracker_kitti_output_dir}") + return False + return True + +class ImageTensorInput(BufferProvider): + + def __init__(self, height, width, format): + super().__init__() + self.width = width + self.height = height + self.format = format + self.framerate = 1 + self.device = 'cpu' + self.queue = Queue(maxsize=10) + + def generate(self, size): + tensor = self.queue.get() + if isinstance(tensor, np.ndarray): + return Buffer(tensor.tolist()) + elif isinstance(tensor, Stop): + # EOS + return Buffer() + else: + logger.exception("Unexpected input tensor data") + return Buffer() + + def send(self, data): + self.queue.put(data) + +class GenericTensorInput(): + def __init__(self, device_id): + self.queue = Queue(maxsize=10) + self._device_id = device_id + + def generate(self, n): + try: + tensors = self.queue.get(timeout=1) + except Empty: + logger.warning("No tensor data to generate") + return dict() + result = {k: as_tensor(v, "").to_gpu(self._device_id) for k, v in tensors.items()} + return result + + def send(self, data): + self.queue.put(data) + +class StaticTensorInput(): + def __init__(self, device_id): + self._tensors = {} + self._device_id = device_id + + def generate(self, n): + result = {k: as_tensor(v, "").to_gpu(self._device_id) for k, v in self._tensors.items()} + return result + + def set(self, data): + self._tensors.update(data) + +class TensorInputPool(ABC): + @abstractmethod + def submit(self, data: List): + pass + @abstractmethod + def stop(self, reason: str): + pass + +class ImageTensorInputPool(TensorInputPool): + + def __init__(self, height, width, formats, batch_size, image_tensor_name, media_url_tensor_name, mime_tensor_name, device_id, require_extra_input): + self._image_inputs = [ImageTensorInput(width, height, format) for format in formats for _ in range(batch_size)] + self._media_url_tensor_name = media_url_tensor_name + self._mime_tensor_name = mime_tensor_name + self._image_tensor_name = image_tensor_name + self._generic_input = GenericTensorInput(device_id) if require_extra_input else None + self._batch_size = batch_size + + @property + def image_inputs(self): + return self._image_inputs + + @property + def generic_input(self): + return self._generic_input + + def submit(self, data: List): + indices = [] + for item in data: + mime_type = item.pop(self._mime_tensor_name, None) + if mime_type is None: + logger.error("MIME type is not specified") + continue + mime_type = mime_type.split('/'); + if mime_type[0] == 'image': + format = mime_type[1].upper() + # try find the free slot for the specific format + i, image_tensor_input = next(((i, x) for i,x in enumerate(self._image_inputs) if x.format == format and x.queue.empty()), (-1, None)) + if image_tensor_input is None: + i, image_tensor_input = next(((i, x) for i,x in enumerate(self._image_inputs) if x.format == format), (-1, None)) + if image_tensor_input is not None: + if self._image_tensor_name in item: + image_tensor = item.pop(self._image_tensor_name) + image_tensor_input.send(image_tensor) + indices.append(i) + elif self._media_url_tensor_name in item: + image_url = item.pop(self._media_url_tensor_name) + with open(image_url, 'rb') as f: + image_tensor = np.frombuffer(f.read(), dtype=np.uint8) + image_tensor_input.send(image_tensor) + indices.append(i) + else: + logger.error(f"image tensor or media url is missing: {item}") + else: + logger.error(f"Unable to find free slot for format {format}") + else: + logger.error(f"Unsupported MIME type {mime_type}") + continue + if self._generic_input: + data = [data[i:i + self._batch_size] for i in range(0, len(data), self._batch_size)] + for d in data: + self._generic_input.send(stack_tensors_in_dict(d)) + # batched indices for each input + return indices + + def stop(self, reason: str): + for input in self._image_inputs: + input.send(Stop(reason)) + if self._generic_input: + self._generic_input.send(Stop(reason)) + +class BulkVideoInputPool(TensorInputPool): + def __init__(self, + max_batch_size, + batch_timeout, + media_url_tensor_name, + source_tensor_name, + mime_tensor_name, + infer_config_paths, + preprocess_config_paths, + tracker_config: TrackerConfig, + analytics_config: AnalyticsConfig, + msgbroker_config: MessageBrokerConfig, + render_config: RenderConfig, + perf_config: PerfConfig, + kitti_config: KittiConfig, + output, + device_id, + require_extra_input, + engine_file_names, + dims, + label_file_path, + model_home + ): + self._batch_size = max_batch_size + self._batch_timeout = batch_timeout + self._media_url_tensor_name = media_url_tensor_name + self._source_tensor_name = source_tensor_name + self._mime_tensor_name = mime_tensor_name + self._infer_config_paths = infer_config_paths + self._engine_file_names = engine_file_names + self._preprocess_config_paths = preprocess_config_paths + self._pipeline = None + self._output = output + self._generic_input = StaticTensorInput(device_id) if require_extra_input else None + self._device_id = device_id + self._dims = dims + self._tracker_config = tracker_config + self._analytics_config = analytics_config + self._msgbroker_config = msgbroker_config + self._render_config = render_config + self._perf_config = perf_config + self._kitti_config = kitti_config + self._label_file_path = label_file_path + self._model_home = model_home + + def submit(self, data: List): + def on_message(message, total_streams, output): + # handle the dynamic source messages only in case the source config is set + if not isinstance(message, DynamicSourceMessage): + return + logger.info(f"Received source added/removed message: {message.source_added}") + if message.source_added: + total_streams += 1 + else: + total_streams -= 1 + if total_streams <= 0: + output.reset() + + url_list = [] + source_config_file = None + total_streams = 0 + for item in data: + if self._mime_tensor_name and self._mime_tensor_name in item: + item.pop(self._mime_tensor_name) + if self._media_url_tensor_name and self._media_url_tensor_name in item: + url_list.append(str(item.pop(self._media_url_tensor_name))) + elif self._source_tensor_name and self._source_tensor_name in item: + source_config_file = item.pop(self._source_tensor_name) + if not isinstance(source_config_file, str): + logger.error( + f"Source config value must be a string, got {type(source_config_file).__name__}: {source_config_file}" + ) + return [] + if not source_config_file.lower().endswith(('.yml', '.yaml')): + logger.error( + f"Source config file must be a YAML file: {source_config_file}" + ) + return [] + # Correct relative path to absolute path relative to model_home + if not os.path.isabs(source_config_file): + source_config_file = os.path.join(self._model_home, source_config_file) + break + else: + logger.error(f"Invalid input data: {data}") + continue + + if len(url_list) > self._batch_size: + logger.warning( + f"Number of media urls ({len(url_list)}) > " + f"batch size ({self._batch_size}), " + f"only the first {self._batch_size} will be used" + ) + url_list = url_list[:self._batch_size] + + pipeline = Pipeline(f"deepstream-video-batch") + if self._generic_input and data: + self._generic_input.set(stack_tensors_in_dict(data)) + + # Use appropriate batch_capture method based on input type + if source_config_file: + sc = SourceConfig() + sc.load(source_config_file) + total_streams = len(sc.sensor_list) + flow = Flow(pipeline).batch_capture( + input=source_config_file, + width=self._dims[1], height=self._dims[0], + batch_size=self._batch_size, + batched_push_timeout=self._batch_timeout + ) + else: + total_streams = len(url_list) + flow = Flow(pipeline).batch_capture( + url_list, + width=self._dims[1], + height=self._dims[0], + batched_push_timeout=self._batch_timeout) + + for config in self._preprocess_config_paths: + flow = flow.preprocess(config, None if not self._generic_input else self._generic_input.generate) + for config_path, engine_file in zip(self._infer_config_paths, self._engine_file_names): + if engine_file: + flow = flow.infer(config_path, batch_size=self._batch_size, model_engine_file=engine_file) + else: + flow = flow.infer(config_path, batch_size=self._batch_size) + if self._kitti_config.infer_kitti_output_dir: + flow = flow.attach(what="kitti_dump_probe", name="inference_kitti_dump", properties={"kitti-dir": self._kitti_config.infer_kitti_output_dir}) + if self._tracker_config: + flow = flow.track( + ll_config_file=self._tracker_config.config_path, + ll_lib_file=self._tracker_config.lib_path, + gpu_id=self._device_id, + tracker_width=self._tracker_config.width, + tracker_height=self._tracker_config.height, + display_tracking_id=self._tracker_config.display_tracking_id + ) + if self._kitti_config.tracker_kitti_output_dir: + flow = flow.attach(what="kitti_dump_probe", name="tracker_kitti_dump", properties={"tracker-kitti-output": True,"kitti-dir": self._kitti_config.tracker_kitti_output_dir}) + if self._analytics_config: + flow = flow.analyze( + self._analytics_config.config_path, + enable=1, + gpu_id=self._analytics_config.gpu_id + ) + + if self._perf_config.enable_fps_logs: + flow = flow.attach(what="measure_fps_probe", name="fps_probe") + if self._perf_config.enable_latency_logs: + flow = flow.attach(what="measure_latency_probe", name="latency_probe") + n_sink = 0 + if self._msgbroker_config: + flow = flow.attach( + what="add_message_meta_probe", + name="message_generator", + properties={ + "label-file": self._label_file_path if self._label_file_path else "", + "source-config": source_config_file if source_config_file else "" + } + ) + flow = flow.fork() + n_sink += 1 + publish_params = { + 'msg_broker_proto_lib': self._msgbroker_config.proto_lib_path, + 'msg_broker_conn_str': self._msgbroker_config.conn_str, + 'topic': self._msgbroker_config.topic, + 'msg_conv_config': self._msgbroker_config.msgconv_config_path, + 'msg_conv_payload_type': self._msgbroker_config.msgconv_payload_type, + 'msg_conv_msg2p_new_api': self._msgbroker_config.msgconv_msg2p_new_api, + 'msg_conv_frame_interval': self._msgbroker_config.msgconv_frame_interval, + 'sync': False + } + if self._msgbroker_config.msgconv_msg2p_lib: + publish_params['msg_conv_msg2p_lib'] = self._msgbroker_config.msgconv_msg2p_lib + + flow.publish(**publish_params) + if self._render_config.enable_stream: + if n_sink == 0: + flow = flow.fork() + n_sink += 1 + flow.render(RenderMode.STREAM, + enable_osd=self._render_config.enable_osd, + rtsp_mount_point=self._render_config.rtsp_mount_point, + rtsp_port=self._render_config.rtsp_port, + sync=False) + if self._render_config.enable_display: + if n_sink == 0: + flow = flow.fork() + n_sink += 1 + seg_mask_config = dict(self._render_config.seg_mask_config) if self._render_config.seg_mask_config else None + flow.render(RenderMode.DISPLAY, enable_osd=self._render_config.enable_osd, seg_mask_config=seg_mask_config, sync=False) + + flow = flow.attach(what=Probe("metadata_probe", self._output)).retrieve(self._output, **{'async': False}) + + if self._pipeline is not None: + self._pipeline.wait() + + if source_config_file: + # monitor the total streams and reset the output when the total streams is 0 + pipeline.start(on_message=lambda message: on_message(message, total_streams, self._output)) + else: + pipeline.start() + self._pipeline = pipeline + return list(range(len(url_list))) if url_list else list(range(self._batch_size)) + + def stop(self, reason: str): + if self._pipeline: + self._pipeline.stop() + self._pipeline.wait() + + +class BaseTensorOutput(BufferRetriever, BatchMetadataOperator): + """Base class for tensor outputs that can work as both BufferRetriever (appsink) and + BatchMetadataOperator (probe). + + Subclasses override _extract_metadata() to yield results. + Both consume() (appsink) and handle_metadata() (probe) iterate and deposit. + """ + def __init__(self, n_outputs, metadata_output_name: str = None, image_output_name: str = None): + """Initialize the base tensor output handler. + + Args: + n_outputs: Maximum number of output slots (typically max_batch_size + or max_batch_size * num_formats). Each slot corresponds to a + pad index in the DeepStream pipeline. + metadata_output_name: When set, collected results are wrapped in a + dict keyed by this name (e.g. {"output": }). Used by + MetadataOutput / PreprocessMetadataOutput for DS metadata + outputs (TYPE_CUSTOM_DS_METADATA). None for raw tensor outputs. + image_output_name: When set, indicates that decoded image frames + should be extracted from the DeepStream pipeline and returned + as RGB uint8 tensors in HWC layout (TYPE_CUSTOM_DS_IMAGE + output). The value is the output tensor name to use. + """ + BufferRetriever.__init__(self) + BatchMetadataOperator.__init__(self) + self._n_outputs = n_outputs + self._queue = Queue() + self._stashed = [] + self._metadata_output_name = metadata_output_name + self._image_output_name = image_output_name + self._image_queue = Queue() # separate queue for decoded image frames + self._image_stashed = [] # stashed image batches for cross-collect reuse + + def consume(self, buffer): + """Called when used as appsink retriever (BufferRetriever interface). + + When image output is enabled, extracts decoded RGB HWC frames from + the buffer and converts them to CPU numpy arrays via dlpack before + queuing. The dlpack conversion must happen here while the buffer's + GPU memory is still valid — once consume() returns, the buffer is + released and the GPU tensor data becomes stale. + + Obtains batch_meta from the buffer to get the batch_id → pad_index + mapping, then calls buffer.extract(batch_id) for each frame. + Note: batch_id is assigned by arrival order, not pad index — e.g. if + only pads 0, 2, 3 deliver frames, they get batch_ids 0, 1, 2. + """ + if self._image_output_name: + received = [None] * self._n_outputs + batch_meta = buffer.batch_meta + for frame_meta in batch_meta.frame_items: + frame = buffer.extract(frame_meta.batch_id) + if frame is not None: + # Clone via dlpack while buffer GPU memory is still valid; + # clone() copies to a new GPU allocation so the tensor + # survives after the buffer is released. + frame = torch.utils.dlpack.from_dlpack(frame).clone() + received[frame_meta.pad_index] = (frame, frame_meta.buffer_pts) + self._image_queue.put(received) + return 1 + + def handle_metadata(self, batch_meta): + """Called when used as probe (BatchMetadataOperator interface).""" + for metadata in self._extract_metadata(batch_meta): + self._deposit(metadata) + + def _extract_metadata(self, batch_meta): + """Override in subclasses to extract metadata. + + Yields a list (indexed by pad_index) where each non-None element is + a dict containing the extracted data. The dict should include a + "timestamp" key (buffer_pts) for synchronization with image frames + in collect(). DS metadata dicts already carry this; tensor outputs + insert it explicitly. + """ + return + yield # Make this a generator + + def collect(self, indices: List, timeout=None) -> List | None: + def move_data(data: list, collected: list): + for i, d in enumerate(data): + if d is not None: + if collected[i] is None: + collected[i] = d + data[i] = None + + def _drain(queue, stashed, collected, indices): + """Drain a queue into collected slots using stash for leftovers.""" + while stashed: + data = stashed.pop(0) + move_data(data, collected) + while not all(collected[i] is not None for i in indices): + try: + data = queue.get(timeout=timeout) + if data is None: + break + move_data(data, collected) + if any(d is not None for d in data): + stashed.append(data) + except Empty: + break + + def _drain_images(queue, stashed, collected, indices, target_pts): + """Drain image queue, discarding images older than metadata timestamps.""" + def process(data): + for i, d in enumerate(data): + if d is None: + continue + # Always discard stale images even if this pad is already collected + if i in target_pts: + _, img_pts = d + if img_pts < target_pts[i]: + data[i] = None + continue + if collected[i] is None: + collected[i] = d + data[i] = None + + remaining = [] + for data in stashed: + process(data) + if any(d is not None for d in data): + remaining.append(data) + stashed.clear() + stashed.extend(remaining) + + while not all(collected[i] is not None for i in indices): + try: + data = queue.get(timeout=timeout) + if data is None: + break + process(data) + if any(d is not None for d in data): + stashed.append(data) + except Empty: + break + + n_slots = max(indices) + 1 + + # Drain metadata queue first — metadata drives collection. + meta_collected = [None] * n_slots + _drain(self._queue, self._stashed, meta_collected, indices) + + if self._image_output_name: + # Build target timestamps from metadata for image synchronization. + # Images with pts older than the metadata are stale and discarded. + target_pts = {} + for i in indices: + meta = meta_collected[i] + if meta is not None and isinstance(meta, dict): + pts = meta.get("timestamp") + if pts is not None: + target_pts[i] = pts + + image_collected = [None] * n_slots + _drain_images(self._image_queue, self._image_stashed, image_collected, indices, target_pts) + + if all(i is None for i in meta_collected) and all(i is None for i in image_collected): + return None + + results = [] + for idx in indices: + result = {} + metadata = meta_collected[idx] + image_entry = image_collected[idx] + + if metadata is not None: + if self._metadata_output_name is not None: + result[self._metadata_output_name] = metadata + elif isinstance(metadata, dict): + result.update(metadata) + else: + result = metadata + + if image_entry is not None: + frame, _ = image_entry + if not isinstance(result, dict): + result = {} + result[self._image_output_name] = frame + + results.append(result if result else None) + return results + else: + # --- Metadata-only path --- + if all(i is None for i in meta_collected): + return None + collected = [meta_collected[i] for i in indices] + return collected if self._metadata_output_name is None else [ + {self._metadata_output_name: r} for r in collected + ] + + def reset(self): + # wake up the old queue first + self._queue.put(None) + self._queue = Queue(maxsize=self._queue.maxsize) + self._stashed = [] + self._image_queue = Queue() + self._image_stashed = [] + + def _deposit(self, received: list): + logger.debug("DeepstreamBackend: Depositing data: %s", received) + try: + self._queue.put(received) + except Full: + logger.warning(f"DeepstreamBackend: Queue is full, dropping data: {received}") + + +class TensorOutput(BaseTensorOutput): + def __init__(self, n_outputs, preprocess_config_path, image_output_name=None): + super().__init__(n_outputs, metadata_output_name=None, image_output_name=image_output_name) + self._preprocess_config_path = preprocess_config_path + + def _extract_metadata(self, batch_meta): + received = [None] * self._n_outputs + if self._preprocess_config_path: + for meta in batch_meta.preprocess_batch_items: + preprocess_batch = meta.as_preprocess_batch() + if not preprocess_batch: + continue + for roi in preprocess_batch.rois: + result = dict() + for user_meta in roi.tensor_items: + tensor_output = user_meta.as_tensor_output() + if tensor_output : + for n, tensor in tensor_output.get_layers().items(): + torch_tensor = torch.utils.dlpack.from_dlpack(tensor).to('cpu') + result[n] = torch_tensor + result["timestamp"] = roi.frame_meta.buffer_pts + received[roi.frame_meta.pad_index] = result + else: + for frame_meta in batch_meta.frame_items: + result = dict() + for user_meta in frame_meta.tensor_items: + tensor_output = user_meta.as_tensor_output() + if tensor_output : + for n, tensor in tensor_output.get_layers().items(): + torch_tensor = torch.utils.dlpack.from_dlpack(tensor).to('cpu') + result[n] = torch_tensor + result["timestamp"] = frame_meta.buffer_pts + received[frame_meta.pad_index] = result + yield received + +class PreprocessMetadataOutput(BaseTensorOutput): + """Output handler for preprocess metadata.""" + def __init__(self, n_outputs, output_name, dims, image_output_name=None): + super().__init__(n_outputs, metadata_output_name=output_name, image_output_name=image_output_name) + self._n_outputs = n_outputs + self._shape = dims + + def _extract_metadata(self, batch_meta): + for meta in batch_meta.preprocess_batch_items: + preprocess_batch = meta.as_preprocess_batch() + if not preprocess_batch: + continue + received = preprocess_batch.extract(self._n_outputs) + for roi in preprocess_batch.rois: + idx = roi.frame_meta.pad_index + if received[idx] is not None: + received[idx]["timestamp"] = roi.frame_meta.buffer_pts + yield received + +class MetadataOutput(BaseTensorOutput): + def __init__(self, n_outputs, output_name, dims, image_output_name=None): + super().__init__(n_outputs, metadata_output_name=output_name, image_output_name=image_output_name) + self._n_outputs = n_outputs + self._shape = dims + + def _extract_metadata(self, batch_meta): + # DS metadata from batch_meta.extract() already carries pts internally + yield batch_meta.extract(self._n_outputs) + +class DeepstreamBackend(ModelBackend): + """Deepstream backend using pyservicemaker""" + def __init__(self, model_config:Dict, model_home: str, device_id: int=0): + super().__init__(model_config, model_home, device_id) + self._max_batch_size = model_config["max_batch_size"] + self._model_name = model_config["name"] + self._output_names = [o['name'] for o in model_config['output']] + self._output_types = [o['data_type'] for o in model_config['output']] + self._image_tensor_name = None + self._media_url_tensor_name = None + self._mime_tensor_name = None + self._source_tensor_name = None + self._image_output_name = None + self._inference_timeout = model_config["parameters"].get("inference_timeout", None) + self._in_pools = {} + self._outputs = {} + self._pipelines = {} + + tensor_output = False if self._output_types[0] == "TYPE_CUSTOM_DS_METADATA" else True + for o in model_config['output']: + if o['data_type'] == 'TYPE_CUSTOM_DS_IMAGE': + self._image_output_name = o['name'] + break + dims = (0, 0) + + if "parameters" not in model_config or "infer_config_path" not in model_config["parameters"]: + raise Exception("Deepstream pipeline requires infer_config_path") + + infer_config_paths = self._correct_config_paths(model_config["parameters"]['infer_config_path']) + if not infer_config_paths: + raise Exception("Deepstream pipeline requires infer_config_path") + + preprocess_config_paths = [] + if "preprocess_config_path" in model_config["parameters"]: + preprocess_config_paths = self._correct_config_paths(model_config["parameters"]['preprocess_config_path']) + + if "tracker_config" in model_config["parameters"]: + tracker_config = TrackerConfig( + config_path=self._correct_config_paths( + [model_config["parameters"]["tracker_config"].get("ll_config_file")] + )[0] if model_config["parameters"]["tracker_config"].get("ll_config_file") else None, + lib_path=self._correct_config_paths( + [model_config["parameters"]["tracker_config"]["ll_lib_file"]] + )[0], + width=model_config["parameters"]["tracker_config"].get("width", 1920), + height=model_config["parameters"]["tracker_config"].get("height", 1088), + display_tracking_id=model_config["parameters"]["tracker_config"].get("display_tracking_id", False) + ) + if not tracker_config: + logger.warning("DeepstreamBackend: tracker_config is not properlyconfigured") + else: + tracker_config = TrackerConfig() + + if "msgbroker_config" in model_config["parameters"]: + msgbroker_config = MessageBrokerConfig( + proto_lib_path=self._correct_config_paths( + [model_config["parameters"]["msgbroker_config"]["msgbroker_proto_lib_path"]] + )[0], + conn_str=model_config["parameters"]["msgbroker_config"]["msgbroker_conn_str"], + topic=model_config["parameters"]["msgbroker_config"]["msgbroker_topic"], + msgconv_config_path=self._correct_config_paths( + [model_config["parameters"]["msgbroker_config"]["msgconv_config_path"]] + )[0] + ) + if "msgconv_payload_type" in model_config["parameters"]["msgbroker_config"]: + msgbroker_config.msgconv_payload_type = model_config["parameters"]["msgbroker_config"]["msgconv_payload_type"] + if "msgconv_msg2p_new_api" in model_config["parameters"]["msgbroker_config"]: + msgbroker_config.msgconv_msg2p_new_api = model_config["parameters"]["msgbroker_config"]["msgconv_msg2p_new_api"] + if "msgconv_frame_interval" in model_config["parameters"]["msgbroker_config"]: + msgbroker_config.msgconv_frame_interval = model_config["parameters"]["msgbroker_config"]["msgconv_frame_interval"] + if "msgconv_msg2p_lib" in model_config["parameters"]["msgbroker_config"]: + msgbroker_config.msgconv_msg2p_lib = self._correct_config_paths( + [model_config["parameters"]["msgbroker_config"]["msgconv_msg2p_lib"]] + )[0] + if not msgbroker_config: + logger.warning("DeepstreamBackend: msgbroker_config is not properly configured") + else: + msgbroker_config = MessageBrokerConfig() + + if "render_config" in model_config["parameters"]: + render_config = RenderConfig( + enable_display=model_config["parameters"]["render_config"].get("enable_display", False), + enable_osd=model_config["parameters"]["render_config"].get("enable_osd", False), + enable_stream=model_config["parameters"]["render_config"].get("enable_stream", False), + rtsp_mount_point=model_config["parameters"]["render_config"].get("rtsp_mount_point", "/ds-test"), + rtsp_port=model_config["parameters"]["render_config"].get("rtsp_port", 8554), + seg_mask_config=model_config["parameters"]["render_config"].get("seg_mask_visualization", None) + ) + else: + render_config = RenderConfig() + + if "perf_config" in model_config["parameters"]: + perf_config = PerfConfig( + enable_fps_logs=model_config["parameters"]["perf_config"].get("enable_fps_logs", False), + enable_latency_logs=model_config["parameters"]["perf_config"].get("enable_latency_logs", False) + ) + else: + perf_config = PerfConfig() + + if "kitti_output_path" in model_config["parameters"]: + kitti_config = KittiConfig( + infer_kitti_output_dir=model_config["parameters"]["kitti_output_path"].get("infer", None), + tracker_kitti_output_dir=model_config["parameters"]["kitti_output_path"].get("tracker", None) + ) + if not kitti_config: + logger.warning("DeepstreamBackend: kitti_config is not properly configured") + else: + kitti_config = KittiConfig() + + if "analytics_config" in model_config["parameters"]: + ac = model_config["parameters"]["analytics_config"] + config_path = self._correct_config_paths([ac["config_file_path"]])[0] if ac.get("config_file_path") else None + analytics_config = AnalyticsConfig( + config_path=config_path, + gpu_id=ac.get("gpu_id", device_id) + ) + if not analytics_config: + logger.warning("DeepstreamBackend: analytics_config is not properly configured") + else: + analytics_config = AnalyticsConfig() + + infer_element = model_config['backend'].split('/')[-1] + with_triton = infer_element == 'nvinferserver' + require_extra_input = False + warmup_data_0 = dict() + warmup_data_1 = dict() + for input in model_config['input']: + if input['data_type'] == 'TYPE_CUSTOM_DS_IMAGE': + self._image_tensor_name = input['name'] + elif input['data_type'] == 'TYPE_CUSTOM_DS_MEDIA_URL': + self._media_url_tensor_name = input['name'] + elif input['data_type'] == 'TYPE_CUSTOM_DS_MIME': + self._mime_tensor_name = input['name'] + elif input['data_type'] == 'TYPE_CUSTOM_DS_SOURCE_CONFIG': + self._source_tensor_name = input['name'] + elif not ('optional' in input and input['optional']): + tensor_name = input['name'] + require_extra_input = True + np_type = np_datatype_mapping[input['data_type']] + warmup_data_0[tensor_name] = np.random.rand(*input['dims']).astype(np_type) + warmup_data_1[tensor_name] = np.random.rand(*input['dims']).astype(np_type) + if (self._image_tensor_name is None and + self._media_url_tensor_name is None and + self._source_tensor_name is None): + raise ValueError( + "Deepstream pipeline requires at least one " + "TYPE_CUSTOM_DS_IMAGE or TYPE_CUSTOM_DS_MEDIA_URL input " + "or TYPE_CUSTOM_DS_SOURCE_CONFIG input" + ) + if ((self._image_tensor_name or self._media_url_tensor_name) and + self._mime_tensor_name is None): + raise ValueError("Deepstream pipeline requires TYPE_CUSTOM_DS_MIME input") + + primary_infer_config_property = {} + try: + primary_infer_config_path = infer_config_paths[0] + with open(primary_infer_config_path, 'r') as f: + primary_infer_config = yaml.safe_load(f) + if "property" in primary_infer_config: + primary_infer_config_property = primary_infer_config["property"] + except Exception as e: + raise RuntimeError(f"Failed to load primary inference config: {e}") from e + + if "resize_video" in model_config["parameters"] and len(model_config["parameters"]["resize_video"]) == 2: + resize_to = model_config["parameters"]["resize_video"] + dims = (resize_to[0], resize_to[1]) + logger.info(f"DeepstreamBackend: setting video size to {dims}") + else: + # video resized to network dimensions from the primary inference config by default + if "infer-dims" in primary_infer_config_property: + infer_dims = [int(dim) for dim in primary_infer_config_property["infer-dims"].split(";")] + if len(infer_dims) == 3: + if "network-input-order" in primary_infer_config_property and primary_infer_config_property["network-input-order"] == 1: + dims = (infer_dims[0], infer_dims[1]) + else: + dims = (infer_dims[1], infer_dims[2]) + logger.info(f"DeepstreamBackend: setting video size to network dimensions: {dims}") + + if dims[0] == 0 or dims[1] == 0: + raise ValueError( + "DeepstreamBackend: unable to find network dimensions: " + "infer-dims missing in the config?" + ) + + # get the label file path from the primary inference config + label_file_path = None + if "labelfile-path" in primary_infer_config_property: + label_path = primary_infer_config_property["labelfile-path"] + # Convert to absolute path if it's relative + if not os.path.isabs(label_path): + label_file_path = os.path.join(self._model_home, label_path) + else: + label_file_path = label_path + + # construct the input pools, outputs and pipelines + if self._image_tensor_name is not None: + # image input support + media = "image" + formats = ["JPEG", "PNG"] + in_pool = ImageTensorInputPool(dims[0], dims[1], formats, self._max_batch_size, self._image_tensor_name, self._media_url_tensor_name, self._mime_tensor_name, device_id, require_extra_input) + n_output = self._max_batch_size * len(formats) + if tensor_output: + output = TensorOutput(n_output, preprocess_config_paths, image_output_name=self._image_output_name) + elif preprocess_config_paths: + output = PreprocessMetadataOutput(n_output, self._output_names[0], dims, image_output_name=self._image_output_name) + else: + output = MetadataOutput(n_output, self._output_names[0], dims, image_output_name=self._image_output_name) + # create the pipeline + pipeline = Pipeline(f"deepstream-{self._model_name}-{media}") + + self._pipelines[media] = pipeline + self._in_pools[media] = in_pool + self._outputs[media] = output + + # build the inference flow + flow = Flow(pipeline) + batch_timeout = model_config["parameters"].get("batch_timeout", 1000 * self._max_batch_size) + flow = flow.inject(in_pool.image_inputs).decode().batch(batch_size=self._max_batch_size, batched_push_timeout=batch_timeout, live_source=False, width=dims[1], height=dims[0]) + for config_file in preprocess_config_paths: + input = self._in_pools[media].generic_input if require_extra_input else None + flow = flow.preprocess(config_file, None if not input else input.generate) + for config_file in infer_config_paths: + engine_file = self._generate_engine_name(config_file, device_id, self._max_batch_size) + if engine_file: + flow = flow.infer(config_file, with_triton, batch_size=self._max_batch_size, model_engine_file=engine_file) + else: + flow = flow.infer(config_file, with_triton, batch_size=self._max_batch_size) + + flow = flow.attach(what=Probe("metadata_probe", output)).retrieve(output, **{'async': False}) + + # warm up + warmup_data_0[self._image_tensor_name] = np.frombuffer(png_data, dtype=np.uint8) + warmup_data_0[self._mime_tensor_name] = "image/png" + warmup_data_1[self._image_tensor_name] = np.frombuffer(jpg_data, dtype=np.uint8) + warmup_data_1[self._mime_tensor_name] = "image/jpeg" + + logger.info("Image decoder warming up - pre-filling all queues") + # Submit BOTH PNG and JPEG warmup data BEFORE starting pipeline + # This ensures all 4 sources have data when streaming threads start + indices_0 = in_pool.submit([warmup_data_0.copy() for _ in range(self._max_batch_size)]) + indices_1 = in_pool.submit([warmup_data_1.copy() for _ in range(self._max_batch_size)]) + + # Now start pipeline - all queues have data + pipeline.start() + + # Collect all results together (PNG + JPEG) + all_indices = indices_0 + indices_1 + results = output.collect(all_indices) + output.reset() + logger.info(f"Warm up done: {results}") + + if (self._media_url_tensor_name is not None or + self._source_tensor_name is not None): + # video input support + media = "video" + if tensor_output: + output = TensorOutput(self._max_batch_size, preprocess_config_paths, image_output_name=self._image_output_name) + elif preprocess_config_paths: + output = PreprocessMetadataOutput(self._max_batch_size, self._output_names[0], dims, image_output_name=self._image_output_name) + else: + output = MetadataOutput(self._max_batch_size, self._output_names[0], dims, image_output_name=self._image_output_name) + engine_files = [ + self._generate_engine_name( + config_file, + device_id, + self._max_batch_size + ) + for config_file in infer_config_paths + ] + in_pool = BulkVideoInputPool( + max_batch_size=self._max_batch_size, + batch_timeout=model_config["parameters"].get("batch_timeout", 1000 * self._max_batch_size), + media_url_tensor_name=self._media_url_tensor_name, + source_tensor_name=self._source_tensor_name, + mime_tensor_name=self._mime_tensor_name, + infer_config_paths=infer_config_paths, + preprocess_config_paths=preprocess_config_paths, + tracker_config=tracker_config, + analytics_config=analytics_config, + msgbroker_config=msgbroker_config, + render_config=render_config, + perf_config=perf_config, + kitti_config=kitti_config, + output=output, + device_id=device_id, + require_extra_input=require_extra_input, + engine_file_names=engine_files, + dims=dims, + label_file_path=label_file_path, + model_home=self._model_home + ) + + # build tensorrt engine if not exist + if not all (os.path.exists(e) for e in engine_files): + with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as f: + f.write(jpg_data) + warmup_video_path = f.name + # Disable perf/render probes for warmup to avoid leaked FPS reporters + in_pool._perf_config = PerfConfig() + in_pool._render_config = RenderConfig() + try: + logger.info("Video decoder warming up - building TensorRT engine") + warmup_item = {self._media_url_tensor_name: warmup_video_path} + if self._mime_tensor_name: + warmup_item[self._mime_tensor_name] = "video/mp4" + indices = in_pool.submit([warmup_item]) + if indices: + results = output.collect(indices) + logger.info(f"Video warm up done: {results}") + output.reset() + in_pool.stop("warmup") + finally: + in_pool._perf_config = perf_config + in_pool._render_config = render_config + os.unlink(warmup_video_path) + + self._in_pools[media] = in_pool + self._outputs[media] = output + + logger.info( + f"DeepstreamBackend created for {self._model_name} to generate " + f"{self._output_names}, output tensor: {tensor_output}" + ) + + + def __call__(self, *args, **kwargs): + in_data_list = args if args else split_tensor_in_dict(kwargs) + media = None + explicit_batch = True if args else False + + # analyze the input batch + for data in in_data_list: + # convert numpy types to native Python types + for k, v in data.items(): + if isinstance(v, np.generic): + v = v.item() + if isinstance(v, bytes): + v = v.decode() + data[k] = v + # get the media type + if ((self._image_tensor_name in data or self._media_url_tensor_name in data) and + not self._mime_tensor_name in data): + logger.error(f"MIME type is not specified for input {data}") + return + if self._source_tensor_name and self._source_tensor_name in data: + media = "video" + explicit_batch = True + break # only video source is supported through source config + + current_media = data[self._mime_tensor_name].split('/')[0] + if media is None: + media = current_media + elif media != current_media: + logger.error( + f"Mixed media types are not supported in a single batch, " + f"got {media} and {current_media}" + ) + return + + # validate the media type + if media not in self._in_pools: + if media == "image": + logger.error("TYPE_CUSTOM_DS_IMAGE input must be added to the pipeline for image support") + elif media == "video": + logger.error("TYPE_CUSTOM_DS_MEDIA_URL input must be added to the pipeline for video support") + else: + logger.error("Unknown media type : ", media) + return + + # submit the data to the pipeline which supports the media type + indices = self._in_pools[media].submit(in_data_list) + # collect the results + while True: + # TODO: timeout should be runtime configurable + results = self._outputs[media].collect(indices, timeout=self._inference_timeout) + if not results: + logger.info("DeepstreamBackend: No more data from this batch") + break + out_data_list = [] + for result in results: + out_data = dict() + for o in self._output_names: + if o in result: + out_data[o] = result[o] + else: + out_data[o] = None + out_data_list.append(out_data) + yield out_data_list if explicit_batch else out_data_list[0] + # No consecutive inference results for image + if media == "image": + break + + def stop(self): + for input_pool in self._in_pools.values(): + input_pool.stop("Finalized") + for pipeline in self._pipelines.values(): + pipeline.stop() + pipeline.wait() + self._in_pools.clear() + self._pipelines.clear() + super().stop() + + def __del__(self): + self.stop() + + def _generate_engine_name(self, config_path: str, device_id: int, batch_size: int): + def network_mode_to_string(network_mode: int): + if network_mode == 0: + return "fp32" + elif network_mode == 1: + return "int8" + elif network_mode == 2: + return "fp16" + else: + return "" + network_mode = "fp16" + onnx_file = "model.onnx" + with open(config_path, 'r') as f: + config = yaml.safe_load(f) + if not "property" in config: + return None + property = config["property"] + if not "onnx-file" in property: + return None + onnx_file = property["onnx-file"] + if "network-mode" in property: + mode = network_mode_to_string(property["network-mode"]) + if mode: + network_mode = mode + engine_file = f"{onnx_file}_b{batch_size}_gpu{device_id}_{network_mode}.engine" + return os.path.join(self._model_home, engine_file) + + + def _correct_config_paths(self, config_paths: List[str]) -> List[str]: + if not config_paths: + return [] + for i, path in enumerate(config_paths): + if not os.path.isabs(path): + config_paths[i] = os.path.join(self._model_home, path) + return config_paths diff --git a/tools/inference_builder/templates/backend/dummy.jinja.py b/tools/inference_builder/templates/backend/dummy.jinja.py new file mode 100644 index 00000000..b4ff1f1a --- /dev/null +++ b/tools/inference_builder/templates/backend/dummy.jinja.py @@ -0,0 +1,107 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} + +import numpy as np + +class DummyBackend(ModelBackend): + """Python TensorRT Backend from polygraph""" + def __init__(self, model_config:Dict, model_home: str, device_id: int=0): + super().__init__(model_config, model_home, device_id) + self._model_name = model_config["name"] + self._input_config = [i for i in model_config['input']] + self._output_config = [o for o in model_config['output']] + self._params = model_config["parameters"] if "parameters" in model_config else {} + n_workers = self._params.get("thread_concurrency", 0) + # enable thread pool for concurrent inference + self._thread_pool = ThreadPoolExecutor(max_workers=n_workers) if n_workers > 0 else None + logger.info(f"DummyBackend created for {self._model_name} to generate {[o['name'] for o in self._output_config]}") + + def __call__(self, *args, **kwargs): + def generate_sync(in_data): + for _ in in_data: + yield self._generate_dummy_data() + + def generate_async(in_data): + return [self._generate_dummy_data() for _ in in_data] + + in_data = list(args) if args else [kwargs] + if self._thread_pool is not None: + return self._thread_pool.submit(generate_async, in_data) + else: + return generate_sync(in_data) + + def _generate_dummy_data(self): + dummy_data = {} + for config in self._output_config: + name = config['name'] + dims = config['dims'] + data_type = config.get('data_type', 'TYPE_FP32') + + # Generate random data based on type with correct numpy dtype + if data_type == 'TYPE_BOOL': + data = np.random.choice([True, False], size=dims).astype(np.bool_) + elif data_type == 'TYPE_INT8': + data = np.random.randint(-128, 127, size=dims, dtype=np.int8) + elif data_type == 'TYPE_INT16': + data = np.random.randint(-32768, 32767, size=dims, dtype=np.int16) + elif data_type == 'TYPE_INT32': + data = np.random.randint(-100, 100, size=dims, dtype=np.int32) + elif data_type == 'TYPE_INT64': + data = np.random.randint(-100, 100, size=dims, dtype=np.int64) + elif data_type == 'TYPE_UINT8': + data = np.random.randint(0, 255, size=dims, dtype=np.uint8) + elif data_type == 'TYPE_UINT16': + data = np.random.randint(0, 65535, size=dims, dtype=np.uint16) + elif data_type == 'TYPE_UINT32': + data = np.random.randint(0, 100, size=dims, dtype=np.uint32) + elif data_type == 'TYPE_UINT64': + data = np.random.randint(0, 100, size=dims, dtype=np.uint64) + elif data_type == 'TYPE_FP16': + data = np.random.randn(*dims).astype(np.float16) + elif data_type == 'TYPE_FP32': + data = np.random.randn(*dims).astype(np.float32) + elif data_type == 'TYPE_FP64': + data = np.random.randn(*dims).astype(np.float64) + elif 'TYPE_STRING' in data_type or 'TYPE_BYTES' in data_type: + # Generate random strings + flat_size = np.prod(dims) if dims else 1 + data = np.array([''.join(np.random.choice(list('abcdefghijklmnopqrstuvwxyz0123456789'), size=10)) + for _ in range(flat_size)]).reshape(dims) if dims else np.array(['random_str']) + else: + # Default to FP32 for unknown types + data = np.random.randn(*dims).astype(np.float32) + + dummy_data[name] = data + return dummy_data + + def _valicate_inputs(self, data): + i_names = [i['name'] for i in self._input_config] + i_shapes = {i['name']: i['dims'] for i in self._input_config} + i_types = {i['name']: i['data_type'] for i in self._input_config} + for name, tensor in data.items(): + if name not in i_names: + raise ValueError(f"Input {name} not found in the input config") + if len(tensor.shape) != len(i_shapes[name]): + raise ValueError(f"Input {name} has invalid shape: {tensor.shape}") + for i, s in enumerate(tensor.shape): + + if i_shapes[name][i] != -1 and s != i_shapes[name][i]: + raise ValueError(f"Input {name} has invalid shape: {tensor.shape}") + if isinstance(tensor, np.ndarray) and tensor.dtype != np_datatype_mapping[i_types[name]]: + raise ValueError(f"Input {name} has invalid type: {tensor.dtype}") + if isinstance(tensor, torch.Tensor) and tensor.dtype != torch_datatype_mapping[i_types[name]]: + raise ValueError(f"Input {name} has invalid type: {tensor.dtype}") diff --git a/tools/inference_builder/templates/backend/polygraphy.jinja.py b/tools/inference_builder/templates/backend/polygraphy.jinja.py new file mode 100644 index 00000000..06bac46d --- /dev/null +++ b/tools/inference_builder/templates/backend/polygraphy.jinja.py @@ -0,0 +1,50 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} +from polygraphy.backend.common import BytesFromPath +from polygraphy.backend.trt import EngineFromBytes, TrtRunner + +class PolygraphBackend(ModelBackend): + """Python TensorRT Backend from polygraph""" + def __init__(self, model_config:Dict, model_home: str, device_id: int=0): + super().__init__(model_config, model_home, device_id) + self._model_name = model_config["name"] + self._output_names = [o['name'] for o in model_config['output']] + + logger.debug(f"PolygraphBackend created for {self._model_name} to generate {self._output_names}") + if "parameters" not in model_config or "tensorrt_engine" not in model_config["parameters"]: + raise("PolygraphBackend requires a path to tensorrt_engine") + engine_file = model_config["parameters"]["tensorrt_engine"] + if not os.path.isabs(engine_file): + engine_file = os.path.join(self._model_home, engine_file) + engine = EngineFromBytes(BytesFromPath(engine_file)) + self._trt_runner = TrtRunner(engine) + self._trt_runner.activate() + logger.info(f"TensorRT runtime created from {engine_file}") + + + def __call__(self, *args, **kwargs): + in_data = stack_tensors_in_dict(args) if args else dict(kwargs) + for k, v in in_data.items(): + if isinstance(v, np.ndarray): + in_data[k] = torch.from_numpy(v).to(self._device_id) + result = self._trt_runner.infer(in_data) + if not all([key in result for key in self._output_names]): + logger.error(f"Not all the expected output in {self._output_names} are not found in the result") + return + o_data = { o: result[o] for o in self._output_names} + yield o_data + diff --git a/tools/inference_builder/templates/backend/pytorch.jinja.py b/tools/inference_builder/templates/backend/pytorch.jinja.py new file mode 100644 index 00000000..62212919 --- /dev/null +++ b/tools/inference_builder/templates/backend/pytorch.jinja.py @@ -0,0 +1,41 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} + +import importlib + +class PytorchBackend(ModelBackend): + """Pytorch Backend to run models from Huggingface""" + def __init__(self, model_config:Dict, model_home: str, device_id: int=0): + super().__init__(model_config, model_home, device_id) + self._model_name = model_config["name"] + self._device = f"cuda:{device_id}" + self._output_names = [o["name"] for o in model_config["output"]] + self._model_class = model_config["parameters"].get("model_class", "AutoModelForCausalLM") + self._module = importlib.import_module("transformers") + model_class = getattr(self._module, self._model_class) + logger.info(f"Loading pre-trained model {self._model_name} of type {self._model_class} from {self._model_home}") + self._model = model_class.from_pretrained(self._model_home, torch_dtype="auto", device_map="auto") + self._model.to(self._device) + logger.info(f"Model {self._model_name} loaded from {self._model_home}") + + def __call__(self, *args, **kwargs): + in_data_list = args if args else [kwargs] + for in_data in in_data_list: + result = self._model.generate(**in_data) + if len(self._output_names) != len(result): + raise ValueError(f"Number of output names ({len(self._output_names)}) does not match the number of output tensors ({len(result)})") + yield {self._output_names[i]: tensor for i, tensor in enumerate(result)} diff --git a/tools/inference_builder/templates/backend/tensorrtllm.jinja.py b/tools/inference_builder/templates/backend/tensorrtllm.jinja.py new file mode 100644 index 00000000..6cf8a661 --- /dev/null +++ b/tools/inference_builder/templates/backend/tensorrtllm.jinja.py @@ -0,0 +1,199 @@ +from typing import List, Dict +from lib.inference import ModelBackend +import tensorrt_llm +from tensorrt_llm.runtime import Session, TensorInfo +import tensorrt as trt +import numpy +import os +import json +def trt_dtype_to_torch(dtype): + ''' + Convert TRT data type to PyTorch data type + ''' + if dtype == trt.float16: + return torch.float16 + elif dtype == trt.float32: + return torch.float32 + elif dtype == trt.int32: + return torch.int32 + else: + raise TypeError("%s is not supported" % dtype) + +def get_trt_dtype(dtype): + if dtype == torch.float16: + return trt.float16 + elif dtype == torch.float32: + return trt.float32 + elif dtype == torch.int32: + return trt.int32 + else: + raise TypeError("%s is not supported" % dtype) + +class TensorRTSession: + def __init__(self, stream, device, engine_file, input_dtype): + self._stream = stream + self._device = device + self._input_dtype = input_dtype + torch.cuda.set_stream(self._stream) + logger.info(f"Loading TensorRT Engine from {engine_file}...") + with open(engine_file, 'rb') as f: + engine_buffer = f.read() + self._trt_session = Session.from_serialized_engine(engine_buffer) + logger.info("TensorRT Engine loaded") + + def infer(self, in_data: Dict): + tensor_infos = [] + for key in in_data: + tensor = in_data[key] + if isinstance(tensor, numpy.ndarray): + tensor = torch.from_numpy(tensor).to(self._device) + if not isinstance(tensor, torch.Tensor): + logger.error(f"Input tensor must be a numpy array or a torch tensor, but got {type(tensor)}") + return {} + dtype = self._input_dtype[key] + in_data[key] = tensor.to(dtype=dtype) + tensor_infos.append(TensorInfo(key, get_trt_dtype(dtype), tensor.shape)) + output_info = self._trt_session.infer_shapes(tensor_infos) + trt_out = { t.name: torch.empty(tuple(t.shape), dtype=trt_dtype_to_torch(t.dtype), device=tensor.device) for t in output_info } + ok = self._trt_session.run(in_data, trt_out, self._stream.cuda_stream) + assert ok, "Runtime execution failed for vision encoder session" + self._stream.synchronize() + # TODO associate trt output names with triton output names + return trt_out + + +class TensorRTLLMBackend(ModelBackend): + """Python TensorRT Backend""" + def __init__(self, model_config:Dict, model_home: str, device_id: int=0): + super().__init__(model_config, model_home, device_id) + self._model_name = model_config["name"] + self._output_names = [o['name'] for o in model_config['output']] + self._device = f"cuda:{device_id}" + self._stream = torch.cuda.Stream(self._device) + self._params = model_config["parameters"] if "parameters" in model_config else {} + self._inputs = model_config["input"] if "input" in model_config else [] + llm_backend = model_config["backend"].split("/")[-1] + + if not self._inputs: + raise ValueError("No input tensors found in model config") + + if llm_backend == "pytorch": + from tensorrt_llm import SamplingParams + from tensorrt_llm import LLM + from tensorrt_llm.llmapi import (DraftTargetDecodingConfig, EagleDecodingConfig, + KvCacheConfig, MTPDecodingConfig, + NGramDecodingConfig, TorchCompileConfig) + + in_names = [i['name'] for i in model_config['input']] + if "max_tokens" not in in_names or "temperature" not in in_names or "top_p" not in in_names or "top_k" not in in_names: + raise ValueError("max_tokens, temperature, top_p, and top_k must be provided for tensorrtllmpytorch backend") + self._trt_session = None + default_kv_cache_config = { + "enable_block_reuse": True + } + if "kv_cache_config" in self._params: + default_kv_cache_config.update(self._params.pop("kv_cache_config")) + kv_cache_config = KvCacheConfig(**default_kv_cache_config) + + spec_decode_algo = self._params.pop("spec_decode_algo", None) + if spec_decode_algo is not None: + spec_decode_algo = spec_decode_algo.upper() + + if spec_decode_algo == 'MTP': + spec_config = MTPDecodingConfig( + num_nextn_predict_layers=self._params.pop("spec_decode_nextn", 1), + use_relaxed_acceptance_for_thinking=self._params.pop("use_relaxed_acceptance_for_thinking", False), + relaxed_topk=self._params.pop("relaxed_topk", 1), + relaxed_delta=self._params.pop("relaxed_delta", 0.0)) + elif spec_decode_algo == "EAGLE3": + spec_config = EagleDecodingConfig( + max_draft_len=self._params.pop("spec_decode_nextn", 1), + pytorch_weights_path=self._params.pop("draft_model_dir", None), + eagle3_one_model=self._params.pop("use_one_model", None)) + elif spec_decode_algo == "DRAFT_TARGET": + spec_config = DraftTargetDecodingConfig( + max_draft_len=self._params.pop("spec_decode_nextn", 1), + pytorch_weights_path=self._params.pop("draft_model_dir", None)) + elif spec_decode_algo == "NGRAM": + spec_config = NGramDecodingConfig( + prompt_lookup_num_tokens=self._params.pop("spec_decode_nextn", 1), + max_matching_ngram_size=self._params.pop("max_matching_ngram_size", 1), + is_keep_all=True, + is_use_oldest=True, + is_public_pool=True, + ) + else: + spec_config = None + + use_torch_compile = self._params.pop("use_torch_compile", False) + torch_compile_config = TorchCompileConfig( + enable_fullgraph=True, + enable_inductor=True, + enable_piecewise_cuda_graph=self._params.pop("use_piecewise_cuda_graph", False) + ) if use_torch_compile else None + + self._llm = LLM( + model=self._model_home, + backend="pytorch", + kv_cache_config=kv_cache_config, + speculative_config=spec_config, + torch_compile_config=torch_compile_config, + **self._params + ) + self._sample_params_cls = SamplingParams + self._trtllm_input_name = next((i["name"] for i in self._inputs if i["data_type"] == "TYPE_CUSTOM_VLM_INPUT"), None) + if self._trtllm_input_name is None: + raise ValueError("TYPE_CUSTOM_VLM_INPUT must be provided for tensorrtllm/pytorch backend") + + logger.debug(f"TensorRTLLMBackend with pytorch created for {self._model_name} to generate {self._output_names}") + else: + self._llm = None + if "tensorrt_engine" not in model_config: + raise("TensorRTLLM backend requires a path to tensorrt_engine") + engine_file = model_config["parameters"]["tensorrt_engine"] + if not os.path.isabs(engine_file): + engine_file = os.path.join(self._model_home, engine_file) + input_dtype = { i['name']: torch_datatype_mapping[i['data_type']] for i in model_config['input']} + self._trt_session = TensorRTSession(self._stream, self._device, engine_file, input_dtype) + logger.debug(f"TensorRTBackend created for {self._model_name} to generate {self._output_names}") + + def __call__(self, *args, **kwargs): + if self._llm is not None: + params = args[0] if args else kwargs + sample_params = self._sample_params_cls( + max_tokens=params.get("max_tokens", 1024), + temperature=params.get("temperature", 0.7), + top_p=params.get("top_p", 0.95), + top_k=params.get("top_k", 0) + ) + inputs = [] + if args: + for i in args: + if self._trtllm_input_name in i: + input = i[self._trtllm_input_name] + inputs.extend(input) if isinstance(input, list) else inputs.append(input) + else: + logger.warning(f"TensorRTLLMBackend: Input {self._trtllm_input_name} not found in {i}") + elif kwargs: + if self._trtllm_input_name in kwargs: + inputs = kwargs[self._trtllm_input_name] + else: + logger.warning(f"TensorRTLLMBackend: Input {self._trtllm_input_name} not found in {kwargs}") + else: + logger.warning("TensorRTLLMBackend: No inputs provided") + if not inputs: + return + + results = self._llm.generate(inputs, sample_params) + yield [{"outputs": r} for r in results] + elif self._trt_session is not None: + # TensorRT LLM uses implicit batching, so we need to stack the input tensors + in_data = stack_tensors_in_dict(args) if args else dict(kwargs) + yield self._trt_session.infer(in_data) + else: + raise Exception("TensorRTLLM backend is not correctly initialized") + + + + + diff --git a/tools/inference_builder/templates/backend/triton.jinja.py b/tools/inference_builder/templates/backend/triton.jinja.py new file mode 100644 index 00000000..64c8a081 --- /dev/null +++ b/tools/inference_builder/templates/backend/triton.jinja.py @@ -0,0 +1,180 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} + +{% if server_type == "triton" %} + +class TritonBackend(ModelBackend): + """Triton Python Backend""" + def __init__(self, model_config: Dict, model_home: str): + logger.debug(f"model_config: {model_config}, model_home: {model_home}") + super().__init__(model_config, model_home) + self._model_name = model_config["name"] + self._input_names = [i['name'] for i in model_config['input']] + self._output_names = [o['name'] for o in model_config['output']] + logger.debug(f"TritonBackend created for {self._model_name} with inputs {self._input_names} and outputs {self._output_names}") + + def __call__(self, *args, **kwargs): + in_data_list = args if args else [kwargs] + input_config = self._model_config["input"] + # to determine if we need to stack the input, TODO: below logic needs be more generic + need_stack = False + for in_data in in_data_list: + for key, value in in_data.items(): + input_config = next((i for i in self._model_config["input"] if i['name'] == key), None) + if input_config is None: + logger.error(f"Unexpected input: {key}") + continue + expected_dims = len(input_config["dims"]) + if expected_dims == len(value.shape) + 1: + need_stack = True + break + if need_stack: + in_data_list = [stack_tensors_in_dict(in_data_list)] + for in_data in in_data_list: + tensors = [] + for k in in_data: + tensor = in_data[k] + batched = "max_batch_size" in self._model_config and self._model_config["max_batch_size"] > 0 + if isinstance(tensor, np.ndarray): + tensor = pb_utils.Tensor(k, np.expand_dims(tensor, 0)) if batched else pb_utils.Tensor(k, tensor) + elif isinstance(tensor, torch.Tensor): + if batched: + tensor = tensor.unsqueeze(0) + tensor = pb_utils.Tensor.from_dlpack(k, torch.utils.dlpack.to_dlpack(tensor)) + elif hasattr(tensor, "__dlpack__") and hasattr(tensor, "__dlpack_device__"): + tensor = pb_utils.Tensor.from_dlpack(k, tensor) + else: + yield Error(message="Unsupported input tensor format") + return + tensors.append(tensor) + + llm_request = pb_utils.InferenceRequest( + model_name = self._model_name, + requested_output_names = self._output_names, + inputs = tensors + ) + finish_reason = "done" + for idx, response in enumerate(llm_request.exec(decoupled=True)): + expected = {n: None for n in self._output_names} + if response.has_error(): + yield Error(message=f"{response.error().message()}, stream_id={idx}") + if not response.output_tensors(): + continue + for name in expected: + config = next((c for c in self._model_config['output'] if c['name'] == name), None) + dims = config['dims'] + output = pb_utils.get_output_tensor_by_name(response, name) + if not output: + continue + if pb_utils.Tensor.is_cpu(output): + tensor = output.as_numpy() + if tensor.size != 0: + expected[name] = np.squeeze(tensor, 0) if len(tensor.shape) == (len(dims)+1) else tensor + else: + tensor = torch.utils.dlpack.from_dlpack(output.to_dlpack()) + if tensor.numel() != 0: + expected[name] = torch.squeeze(tensor, 0) if len(tensor.shape) == (len(dims)+1) else tensor + logger.debug(f"TritonBackend saved inference results to: {expected}") + if all([expected[k] is not None for k in expected]): + yield expected + logger.info(f"Infernece with TritonBackend {self._model_name} accomplished") + +{% else %} + +import tritonclient.http as httpclient +from tritonclient.utils import * + +class TritonBackend(ModelBackend): + """Triton Python Backend""" + def __init__(self, model_config: Dict, model_home: str): + logger.debug(f"model_config: {model_config}") + super().__init__(model_config, model_home) + self._model_name = model_config["name"] + self._input_names = [i['name'] for i in model_config['input']] + self._output_names = [o['name'] for o in model_config['output']] + + # Wait for Triton server to be ready + max_retries = 60 + retry_interval = 1 + for attempt in range(max_retries): + try: + with httpclient.InferenceServerClient("localhost:8000") as client: + if client.is_server_ready(): + logger.info(f"Triton server is ready after {attempt + 1} attempt(s)") + break + except Exception as e: + logger.debug(f"Attempt {attempt + 1}/{max_retries}: Triton server not ready yet - {e}") + if attempt < max_retries - 1: + import time + time.sleep(retry_interval) + else: + logger.error(f"Triton server did not become ready after {max_retries} attempts") + raise RuntimeError("Triton server is not ready") + + logger.debug(f"TritonBackend created for {self._model_name} with inputs {self._input_names} and outputs {self._output_names}") + + def __call__(self, *args, **kwargs): + in_data_list = args if args else [kwargs] + input_config = self._model_config["input"] + # to determine if we need to stack the input, TODO: below logic needs be more generic + need_stack = False + for in_data in in_data_list: + for key, value in in_data.items(): + input_config = next((i for i in self._model_config["input"] if i['name'] == key), None) + if input_config is None: + logger.error(f"Unexpected input: {key}") + continue + expected_dims = len(input_config["dims"]) + if expected_dims == len(value.shape) + 1: + need_stack = True + break + if need_stack: + in_data_list = [stack_tensors_in_dict(in_data_list)] + for in_data in in_data_list: + inputs = [] + for k in in_data: + tensor = in_data[k] + batched = "max_batch_size" in self._model_config and self._model_config["max_batch_size"] > 0 + if isinstance(tensor, torch.Tensor): + tensor = tensor.cpu().numpy() + if batched: + tensor = np.expand_dims(tensor, 0) + elif not isinstance(tensor, np.ndarray): + yield Error(message="Unsupported input tensor format") + return + input = httpclient.InferInput(k, tensor.shape, np_to_triton_dtype(tensor.dtype)) + input.set_data_from_numpy(tensor) + inputs.append(input) + outputs = [httpclient.InferRequestedOutput(n) for n in self._output_names] + with httpclient.InferenceServerClient("localhost:8000") as client: + response = client.infer(self._model_name, inputs, request_id=str(1), outputs=outputs) + + result = response.get_response() + expected = {n: None for n in self._output_names} +# if response.has_error(): +# yield Error(message=f"{response.error().message()}, stream_id={idx}") + for name in expected: + config = next((c for c in self._model_config['output'] if c['name'] == name), None) + dims = config['dims'] + output = response.as_numpy(name) + expected[name] = np.squeeze(output, 0) if len(output.shape) == (len(dims)+1) else output + logger.debug(f"TritonBackend saved inference results to: {expected}") + if all([expected[k] is not None for k in expected]): + yield expected + logger.info(f"Inference with TritonBackend {self._model_name} accomplished") + +{% endif %} \ No newline at end of file diff --git a/tools/inference_builder/templates/backend/vllm.jinja.py b/tools/inference_builder/templates/backend/vllm.jinja.py new file mode 100644 index 00000000..4652e0d5 --- /dev/null +++ b/tools/inference_builder/templates/backend/vllm.jinja.py @@ -0,0 +1,202 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} + +import asyncio, threading, uuid, queue +from asyncio import run_coroutine_threadsafe +from typing import Callable, Any, Optional, List, Dict +from concurrent.futures import Future +from vllm import LLM, SamplingParams + +class VLLMSyncFacade: + """ + Wrapper around AsyncLLMEngine that provides a synchronous interface + for submitting inference requests that can be processed concurrently. + """ + def __init__(self, **engine_kwargs): + self.loop = asyncio.new_event_loop() + self.out_q = queue.Queue(maxsize=1000) + self._ready = threading.Event() + + def runner(): + from vllm.engine.async_llm_engine import AsyncLLMEngine + try: + from vllm.engine.async_llm_engine import AsyncEngineArgs + except ImportError: + from vllm.engine.arg_utils import AsyncEngineArgs + asyncio.set_event_loop(self.loop) + self.engine = AsyncLLMEngine.from_engine_args(AsyncEngineArgs(**engine_kwargs)) + self._ready.set() + self.loop.run_forever() + + threading.Thread(target=runner, daemon=True).start() + self._ready.wait() + logger.info("VLLMSyncFacade: AsyncLLMEngine initialized and ready") + + def submit(self, + prompts: List[Any], + sampling_params: SamplingParams, + result_filter: Callable[[Any], Dict[str, Any]], + job_id_prefix: Optional[str] = None) -> Future: + """ + Submit requests for inference. AsyncEngine processes one prompt at a time, + but can handle multiple concurrent requests with different request_ids. + + Args: + prompts: List of prompts (always a list from __call__) + sampling_params: SamplingParams object for this request + result_filter: Function that takes a result and returns a dict + job_id_prefix: Optional prefix for request IDs + Returns: + Future that resolves to list of filtered outputs (list of dicts) + """ + if not callable(result_filter): + raise ValueError("result_filter must be a callable") + + job_id_prefix = job_id_prefix or f"batch_{uuid.uuid4().hex[:8]}" + + async def _run_single(prompt, request_id): + """Process a single prompt with unique request_id""" + final_output = None + async for out in self.engine.generate(prompt, sampling_params, request_id): + final_output = out + # Apply result_filter to each output + return final_output + + async def _run_all(): + """Submit all prompts concurrently and collect results""" + # Create tasks for all prompts with unique request_ids + tasks = [] + for idx, prompt in enumerate(prompts): + request_id = f"{job_id_prefix}_req{idx}" + logger.debug(f"VLLMSyncFacade: Creating task for request {request_id}") + task = _run_single(prompt, request_id) + tasks.append(task) + + # Run all tasks concurrently and wait for all to complete + results = await asyncio.gather(*tasks, return_exceptions=True) + + # Extract outputs from RequestOutput objects and apply filter + all_outputs = [] + for idx, result in enumerate(results): + if isinstance(result, Exception): + logger.error(f"VLLMSyncFacade: Request {idx} failed: {result}") + # Add empty result for failed request + all_outputs.append({}) + elif result is not None: + all_outputs.append(result_filter(result)) + else: + logger.warning(f"VLLMSyncFacade: Request {idx} returned unexpected result: {result}") + + return all_outputs + + return run_coroutine_threadsafe(_run_all(), self.loop) + +class VLLMBackend(ModelBackend): + """VLLM Backend""" + def __init__(self, model_config:Dict, model_home: str, device_id: int=0): + super().__init__(model_config, model_home, device_id) + self._model_name = model_config["name"] + self._output_names = [o['name'] for o in model_config['output']] + self._params = model_config["parameters"] if "parameters" in model_config else {} + self._inputs = model_config["input"] if "input" in model_config else [] + self._vlm_input_name = next((i["name"] for i in self._inputs if i["data_type"] == "TYPE_CUSTOM_VLM_INPUT"), None) + self._async_mode = self._params.get("async_mode", False) + + llm_kwargs = {} + for key in [ + "tensor_parallel_size", + "gpu_memory_utilization", + "dtype", + "max_model_len", + "max_num_batched_tokens", + "max_num_seqs", + "kv_cache_dtype", + "pipeline_parallel_size", + "max_parallel_loading_workers", + "enforce_eager", + ]: + if key in self._params: + llm_kwargs[key] = self._params.pop(key) + + if self._async_mode: + self._llm = VLLMSyncFacade( + model=model_home, + **llm_kwargs + ) + else: + self._llm = LLM( + model=model_home, + **llm_kwargs + ) + logger.info(f"Model {self._model_name} loaded from {model_home}") + + def extract_outputs(self, result: Any) -> Dict[str, Any]: + """ + Extracts the outputs from the result based on the configured output names + Args: + result: The result object from vLLM generation + Returns: + Dictionary mapping output names to their values + """ + outputs = {n: None for n in self._output_names} + for name in self._output_names: + if hasattr(result, name): + outputs[name] = getattr(result, name) + else: + logger.warning(f"VLLMBackend: Output {name} not found in {result}") + return outputs + + def __call__(self, *args, **kwargs): + def assemble_input(vlm_input_name: str, vlm_input: List[Any], all_input: Dict[str, Any]) -> Any: + if vlm_input_name in all_input: + i = all_input.pop(vlm_input_name) + if isinstance(i, list): + vlm_input.extend(i) + else: + vlm_input.append(i) + else: + logger.warning(f"VLLMBackend: Input {vlm_input_name} not found in {all_input}") + + def generate_sync(inputs, sampling_params): + results = self._llm.generate(inputs, sampling_params) + for result in results: + yield self.extract_outputs(result) + + # Extract inputs and parameters + inputs = [] + params = {} + if args: + for i in args: + assemble_input(self._vlm_input_name, inputs, i) + params = args[0] + elif kwargs: + assemble_input(self._vlm_input_name, inputs, kwargs) + params = kwargs + + # Convert numpy scalars to Python scalars + for key, value in params.items(): + if isinstance(value, np.ndarray) and not value.shape: + params[key] = value.item() + + sampling_params = SamplingParams(**params) + + # Use async facade or sync LLM based on mode + if self._async_mode: + return self._llm.submit(inputs, sampling_params, self.extract_outputs) + else: + return generate_sync(inputs, sampling_params) + diff --git a/tools/inference_builder/templates/client/test_runner.jinja.py b/tools/inference_builder/templates/client/test_runner.jinja.py new file mode 100644 index 00000000..e27c74d0 --- /dev/null +++ b/tools/inference_builder/templates/client/test_runner.jinja.py @@ -0,0 +1,165 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} + +import unittest +import json +import yaml +import os +import sys +import logging +from pathlib import Path + +# Configure logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(levelname)s - %(message)s', + datefmt='%Y-%m-%d %H:%M:%S' +) +logger = logging.getLogger(__name__) + +# # Add builder/validator.py path to Python path +# VALIDATOR_DIR = Path("{{ validator_dir }}") +# if str(VALIDATOR_DIR) not in sys.path: +# sys.path.append(str(VALIDATOR_DIR)) + +# Will load validate.py in the same directory +import validate + +# Common paths relative to test runner location +GENERATED_CLIENT_DIR = Path(__file__).parent / validate.GENERATED_CLIENT_DIR +TEST_CASES_FILE = Path(__file__).parent / validate.TEST_CASES_FILE + +# Setup environment before imports +if not validate.check_client_exists(): + if not validate.setup_environment(GENERATED_CLIENT_DIR): + logger.error("Failed to setup client environment") + sys.exit(1) + +from openapi_client.api.nvidiametropolisinferenceapi_api import NVIDIAMETROPOLISINFERENCEAPIApi +from openapi_client.api_client import ApiClient +from openapi_client.configuration import Configuration +from openapi_client.models.inference_request import InferenceRequest + +class TestInference(unittest.TestCase): + @classmethod + def setUpClass(cls): + """Set up test environment once for all test methods. + + This is a class method because it sets up shared resources (host, api client) + that will be used by all test methods. It runs once before any tests. + """ + cls.host = os.getenv("TEST_HOST", "http://127.0.0.1:8800") + cls.tolerance = float(os.getenv("TEST_TOLERANCE", "1e-5")) + + logger.info("Setting up test environment") + logger.info(f"Server host: {cls.host}") + logger.info(f"Tolerance: {cls.tolerance}") + + with open(TEST_CASES_FILE) as f: + cls.test_cases = yaml.safe_load(f) + logger.info(f"Loaded {len(cls.test_cases)} test cases from {TEST_CASES_FILE}") + + config = Configuration(host=cls.host) + api_client = ApiClient(config) + cls.api = NVIDIAMETROPOLISINFERENCEAPIApi(api_client) + + def setUp(self): + """Set up test fixtures before each test method. + + Instance method because it's specific to each test run. + Creates a logger for the specific test instance. + """ + self.logger = logging.getLogger(f"{__name__}.{self.id()}") + self.logger.info(f"Starting test: {self._testMethodName}") + + def tearDown(self): + """Clean up after each test method. + + Instance method because it's specific to each test run. + """ + self.logger.info(f"Completed test: {self._testMethodName}") + + def test_health_check(self): + """Test health check endpoint""" + # self.logger.info("Running health check...") + # response = self.api.health_ready_v1_health_ready_get_with_http_info() + # self.assertEqual(response.status_code, 200) + # self.assertEqual(response.data["status"], "ready") + # self.logger.info("Health check successful") + self.logger.info(f"Skip health check for {self.host}") + + def test_inference(self): + """Test inference with pre-built requests""" + + for test in self.test_cases: + with self.subTest(test=test["name"]): + test_logger = logging.getLogger(f"{__name__}.{self.id()}.{test['name']}") + test_logger.info(f"Starting test case: {test['name']}") + try: + # Load pre-built request + request_path = Path(test["request"]) + test_logger.debug(f"Loading request from: {request_path}") + with open(request_path) as f: + request_data = json.load(f) + request = InferenceRequest.from_dict(request_data) + + # Load expected output + expected_path = Path(test["expected"]) + test_logger.debug(f"Loading expected output from: {expected_path}") + with open(expected_path) as f: + expected = json.load(f) + + # Run inference + test_logger.info("Executing inference request...") + response = self.api.inference(request) + actual = response.to_dict() + + # Compare results + if expected: + test_logger.info("Validating response...") + self.assertTrue( + # TODO: We can template below line for different validators + # validator = {{ my_validator }} + # validator.compare_responses(actual, expected, self.tolerance) + # or make abstract validator class and ask users to implement their own validator + validate.CvValidator.compare_responses(actual, expected, self.tolerance), + f"Test {test['name']}: Response doesn't match expected output under tolerance {self.tolerance}. " + f"Actual: {actual}, Expected: {expected}" + ) + else: + test_logger.info("No expected output, skipping validation") + test_logger.info(f"Test case '{test['name']}' passed successfully") + + except Exception as e: + test_logger.error(f"Test case '{test['name']}' failed: {str(e)}") + raise + +if __name__ == '__main__': + # Create a test runner that outputs both to console and file + runner = unittest.TextTestRunner( + verbosity=2, # Increased verbosity + stream=sys.stdout, # Output to console + ) + + # Create test suite + suite = unittest.TestLoader().loadTestsFromTestCase(TestInference) + + # Run tests + result = runner.run(suite) + + # Exit with appropriate code so the test framework detects failures + sys.exit(0 if result.wasSuccessful() else 1) \ No newline at end of file diff --git a/tools/inference_builder/templates/common/config.jinja.py b/tools/inference_builder/templates/common/config.jinja.py new file mode 100644 index 00000000..317bf6ff --- /dev/null +++ b/tools/inference_builder/templates/common/config.jinja.py @@ -0,0 +1,89 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} +{{ license }} + +import os +import re +from omegaconf import OmegaConf + + +def _resolve_env_vars(config_dict): + """ + Recursively resolve environment variable references in the config. + + Supports the format: $ENV_VAR|default_value + If ENV_VAR exists, uses its value. Otherwise, uses default_value. + + Type conversion is performed based on the default value: + - "true"/"false" (case-insensitive) -> bool + - numeric strings -> int or float + - everything else -> string + """ + env_pattern = re.compile(r'^\$([A-Za-z_][A-Za-z0-9_]*)\|(.*)$') + + def convert_value(value_str): + """Convert string value to appropriate type.""" + # Handle boolean strings + if value_str.lower() in ('true', 'false'): + return value_str.lower() == 'true' + + # Try to convert to int + try: + if '.' not in value_str: + return int(value_str) + except ValueError: + pass + + # Try to convert to float + try: + return float(value_str) + except ValueError: + pass + + # Return as string + return value_str + + def process_value(value): + """Process a single value for environment variable substitution.""" + if isinstance(value, str): + match = env_pattern.match(value) + if match: + env_var = match.group(1) + default_value = match.group(2) + + # Check if environment variable exists + if env_var in os.environ: + return convert_value(os.environ[env_var]) + else: + return convert_value(default_value) + elif isinstance(value, dict): + return {k: process_value(v) for k, v in value.items()} + elif isinstance(value, list): + return [process_value(item) for item in value] + + return value + + return process_value(config_dict) + + +# Load the base configuration +_base_config = OmegaConf.create("""{{ config }}""") + +# Convert to dict, resolve environment variables, and convert back to OmegaConf +_config_dict = OmegaConf.to_container(_base_config) +_resolved_config = _resolve_env_vars(_config_dict) +global_config = OmegaConf.create(_resolved_config) \ No newline at end of file diff --git a/tools/inference_builder/templates/common/custom.__init__.jinja.py b/tools/inference_builder/templates/common/custom.__init__.jinja.py new file mode 100644 index 00000000..2ff619e8 --- /dev/null +++ b/tools/inference_builder/templates/common/custom.__init__.jinja.py @@ -0,0 +1,50 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} + +import importlib +from pathlib import Path +from lib.utils import get_logger + +logger = get_logger(__name__) + +custom_module_table = { + {% for item in classes %} + "{{ item.name }}" : { + "module": "{{ item.module }}", + "class": "{{ item.class_name }}" + }{% if not loop.last %},{% endif %} + {% endfor %} +} + +def create_instance(name:str, config): + if not name in custom_module_table: + logger.error(f"{name} not found") + return None + module_name = custom_module_table[name]["module"] + class_name = custom_module_table[name]["class"] + try: + # Dynamically import the module + module = importlib.import_module(f".{module_name}", __package__) + + # Get the class from the module + cls = getattr(module, class_name) + + # Instantiate and return the class object + return cls(config) + except Exception as e: + logger.error(f"Error creating class '{class_name}' from module '{module_name}': {e}") + return None \ No newline at end of file diff --git a/tools/inference_builder/templates/generic/model.jinja.py b/tools/inference_builder/templates/generic/model.jinja.py new file mode 100644 index 00000000..4098fc94 --- /dev/null +++ b/tools/inference_builder/templates/generic/model.jinja.py @@ -0,0 +1,227 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} +{{ license }} + +from config import global_config +from lib.inference import * +from lib.utils import * +from lib.errors import ErrorFactory +from omegaconf import OmegaConf +import json +import os +from typing import List, Dict +import asyncio +import numpy as np +import torch +from pathlib import Path +import dataclasses + +logger = get_logger(__name__) + +{% for backend in backends %} +{{ backend }} +{% endfor %} + +class GenericInference(InferenceBase): + """The class that drives a generic inference flow""" + + def _create_backend(self, backend_spec: List[str], model_config: Dict, model_home: str): + if backend_spec[0] == 'triton': + backend_class = TritonBackend + elif backend_spec[0] == 'deepstream': + backend_class = DeepstreamBackend + elif backend_spec[0] == 'polygraphy': + backend_class = PolygraphBackend + elif backend_spec[0] == 'tensorrtllm': + backend_class = TensorRTLLMBackend + elif backend_spec[0] == 'dummy': + backend_class = DummyBackend + elif backend_spec[0] == 'pytorch': + backend_class = PytorchBackend + elif backend_spec[0] == 'vllm': + backend_class = VLLMBackend + else: + return None + return backend_class(model_config, model_home) + + async def execute(self, request): + """ execute a list of requests""" + if self._async_dispatcher is None: + self._async_dispatcher = AsyncDispatcher(self._collector, loop=asyncio.get_running_loop()) + + logger.debug(f"Received request {request}") + if not self._inject_tensors(request): + error = ErrorFactory.create( + "ERR_DF_006", + caller=self, + input_data={"request_keys": list(request.keys())}, + tensor_names=[n for i in self._inputs for n in i.in_names] + ) + error.log(logger) + InferenceBase._export_errors() + yield error + return + + async_queue = asyncio.Queue() + self._async_dispatcher.append_async_queue(async_queue) + # Wait for all the results from one inference request + while not self._stop_event.is_set(): + try: + logger.debug("Waiting for tensors from async queue") + result = await async_queue.get() + if isinstance(result, Stop): + logger.info(f"Got Stop: {result.reason}") + return + if isinstance(result, Error): + InferenceBase._export_errors() + else: + logger.debug(f"Got result: {result}") + # post-process the data + result = self._post_process(result) + # Yield control to allow other requests to be processed + await asyncio.sleep(0) + yield result + except Exception as e: + error = ErrorFactory.from_exception(e, caller=self) + error.log(logger) + yield error + + def exec_sync(self, request): + """ execute a list of requests synchronously""" + from queue import Empty + + logger.debug(f"Received request {request}") + if not self._inject_tensors(request): + error = ErrorFactory.create( + "ERR_DF_006", + caller=self, + input_data={"request_keys": list(request.keys())}, + tensor_names=[n for i in self._inputs for n in i.in_names] + ) + error.log(logger) + yield error + return + + # Wait for all the results from one inference request + while not self._stop_event.is_set(): + try: + logger.debug("Waiting for tensors from collector") + result = self._collector.collect() + if isinstance(result, Stop): + logger.info(f"Got Stop: {result.reason}") + return + if isinstance(result, Error): + InferenceBase._export_errors() + else: + logger.debug(f"Got result: {result}") + # post-process the data + result = self._post_process(result) + yield result + except Empty: + continue + except Exception as e: + error = ErrorFactory.from_exception(e, caller=self) + error.log(logger) + yield error + + def finalize(self): + self._stop_event.set() + super().finalize() + + def _inject_tensors(self, request: Dict) -> bool: + """Inject tensors into the input flows""" + injected = False + matched = [[n for n in i.in_names if n in request and request[n]] for i in self._inputs] + reshuffled = sorted(range(len(matched)), key=lambda x: len(matched[x]), reverse=True) + for i in reshuffled: + input_flow = self._inputs[i] + tensors = {} + # select and transform the tensors for the input + for n in matched[i]: + if n in request: + tensors[n] = np.array(request.pop(n)) + if tensors: + logger.debug(f"Injecting tensors {tensors}") + input_flow.put(tensors) + input_flow.put(Stop(reason="end")) + injected = True + return injected + + def _post_process(self, data: Dict): + processed = {k: v for k, v in data.items()} + data_type_names = { i['name']: i['data_type'] for i in self._output_config} + for processor in self._processors: + if not all([i in data for i in processor.input]): + logger.warning(f"Input settings invalid for the processor: {processor.name}") + continue + input = [processed.pop(i) for i in processor.input] + logger.debug(f"Post-processor invoked with given input {input}") + output = processor(*input) + logger.debug(f"Post-processor generated output {output}") + if len(output) != len(processor.output): + logger.warning(f"Number of postprocessing output doesn't match the configuration, expecting {len(processor.output)}, while getting {len(output)}") + continue + # update as processed + for key, value in zip(processor.output, output): + processed[key] = value + # correct data type + if key not in data_type_names: + logger.warning(f"Invalid output parsed: {key}") + continue + data_type = data_type_names[key] + if isinstance(value, np.ndarray): + data_type = np_datatype_mapping[data_type] + if value.dtype != data_type: + processed[key] = value.astype(data_type) + elif isinstance(value, torch.Tensor): + data_type = torch_datatype_mapping[data_type] + if value.dtype != data_type: + processed[key] = value.to(data_type) + else: + processed[key] = value + # convert numpy and torch tensors to list for server to process + def convert_to_list(v, is_string_type): + """Convert numpy/torch tensor to list with proper string decoding""" + if isinstance(v, np.ndarray): + if is_string_type and (np.issubdtype(v.dtype, np.bytes_) or np.issubdtype(v.dtype, np.str_) or v.dtype == np.object_): + # Decode string arrays + flat_list = v.flatten().tolist() + decoded = [i.decode("utf-8", "ignore") if isinstance(i, bytes) else str(i) for i in flat_list] + return np.array(decoded).reshape(v.shape).tolist() + else: + return v.tolist() + elif isinstance(v, torch.Tensor): + if v.dtype == torch.uint8 and is_string_type: + # Decode byte tensors as strings + flat_list = v.flatten().tolist() + decoded = [bytes([i]).decode("utf-8", "ignore") if isinstance(i, int) else str(i) for i in flat_list] + return decoded if v.dim() == 1 else np.array(decoded).reshape(v.shape).tolist() + else: + return v.tolist() + elif dataclasses.is_dataclass(v): + return dataclasses.asdict(v) + else: + return v + + for key, value in processed.items(): + is_string_type = key in data_type_names and 'TYPE_STRING' in data_type_names[key] + if isinstance(value, list): + # this is a batch of data + processed[key] = [convert_to_list(v, is_string_type) for v in value] + else: + processed[key] = convert_to_list(value, is_string_type) + return processed diff --git a/tools/inference_builder/templates/responder/add_file.jinja.py b/tools/inference_builder/templates/responder/add_file.jinja.py new file mode 100644 index 00000000..282cdf2d --- /dev/null +++ b/tools/inference_builder/templates/responder/add_file.jinja.py @@ -0,0 +1,27 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} + + async def {{ name }}(self, request, file): + if not file: + return 400, "No file provided" + try: + asset = self._asset_manager.save_file(file.file, file.filename, file.content_type) + except Exception as e: + return 500, str(e) + response = self.process_response("{{ name }}", request, asset.to_dict()) + return 200, response + diff --git a/tools/inference_builder/templates/responder/add_live_stream.jinja.py b/tools/inference_builder/templates/responder/add_live_stream.jinja.py new file mode 100644 index 00000000..2bfe8403 --- /dev/null +++ b/tools/inference_builder/templates/responder/add_live_stream.jinja.py @@ -0,0 +1,32 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} + + async def {{ name }}(self, request, body, **kwargs): + stream_info = self.process_request("{{ name }}", body) + if "url" not in stream_info or not stream_info["url"]: + return 400, "No url provided" + try: + asset = self._asset_manager.add_live_stream( + stream_info["url"], + stream_info["description"], + stream_info["username"], + stream_info["password"]) + except Exception as e: + return 500, str(e) + response = self.process_response("{{ name }}", request, asset.to_dict()) + return 200, response + diff --git a/tools/inference_builder/templates/responder/del_file.jinja.py b/tools/inference_builder/templates/responder/del_file.jinja.py new file mode 100644 index 00000000..0ed61407 --- /dev/null +++ b/tools/inference_builder/templates/responder/del_file.jinja.py @@ -0,0 +1,25 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} + + async def {{ name }}(self, request, **kwargs): + asset_id = list(kwargs.values())[0] + try: + status = self._asset_manager.delete_asset(asset_id) + except Exception as e: + return 500, str(e) + response = self.process_response("{{ name }}", request, {"status": status}) + return 200, response diff --git a/tools/inference_builder/templates/responder/del_live_stream.jinja.py b/tools/inference_builder/templates/responder/del_live_stream.jinja.py new file mode 100644 index 00000000..0ed61407 --- /dev/null +++ b/tools/inference_builder/templates/responder/del_live_stream.jinja.py @@ -0,0 +1,25 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} + + async def {{ name }}(self, request, **kwargs): + asset_id = list(kwargs.values())[0] + try: + status = self._asset_manager.delete_asset(asset_id) + except Exception as e: + return 500, str(e) + response = self.process_response("{{ name }}", request, {"status": status}) + return 200, response diff --git a/tools/inference_builder/templates/responder/healthy_ready.jinja.py b/tools/inference_builder/templates/responder/healthy_ready.jinja.py new file mode 100644 index 00000000..ef32a650 --- /dev/null +++ b/tools/inference_builder/templates/responder/healthy_ready.jinja.py @@ -0,0 +1,22 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} + + async def {{ name }}(self, request, **kwargs): + if super().healthy_ready(): + return 200, "Ready" + else: + return 503, "Service Unavailable" diff --git a/tools/inference_builder/templates/responder/infer.jinja.py b/tools/inference_builder/templates/responder/infer.jinja.py new file mode 100644 index 00000000..2d1b5f4b --- /dev/null +++ b/tools/inference_builder/templates/responder/infer.jinja.py @@ -0,0 +1,64 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} + + async def {{ name }}(self, request, body, **kwargs): + accept = request.headers.get("accept", "") + streaming = "application/x-ndjson" in accept + try: + in_data = self.process_request("{{ name }}", body) + self.logger.debug(f"request processed as {in_data}") + except Exception as e: + self.logger.error(f"Request processing failed: {type(e).__name__}: {e}") + return 400, str(e) + + def get_error_response(err): + """Get structured error response for EnhancedError, or simple dict for Error.""" + if isinstance(err, EnhancedError): + return err.to_dict(include_stack_trace=False) + return {"error": str(err)} + + try: + if streaming: + async def generate_stream(): + async for result in self._inference.execute(in_data): + # Yield control to allow other requests to be processed + await asyncio.sleep(0) + if not result: + if isinstance(result, Stop): + return # Normal termination + self.logger.error(f"Inference error: {result}") + yield get_error_response(result) + return + yield self.process_response("infer", request, result) + + return 200, StreamingResponse(generate_stream(), media_type="application/x-ndjson") + else: + # If not streaming, process and return the last result + response = None + async for result in self._inference.execute(in_data): + # Yield control to allow other requests to be processed + await asyncio.sleep(0) + if not result: + if isinstance(result, Stop): + break # Normal termination + self.logger.error(f"Inference error: {result}") + return 500, get_error_response(result) + response = self.process_response("infer", request, result) + return (200, response) if response else (500, "No results generated from inference") + except Exception as e: + self.logger.error(f"Inference failed: {type(e).__name__}: {e}") + return 500, str(e) diff --git a/tools/inference_builder/templates/responder/list_files.jinja.py b/tools/inference_builder/templates/responder/list_files.jinja.py new file mode 100644 index 00000000..85da82ea --- /dev/null +++ b/tools/inference_builder/templates/responder/list_files.jinja.py @@ -0,0 +1,24 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} + + async def {{ name }}(self, request): + try: + assets = [asset for asset in self._asset_manager.list_assets() if asset.file_name] + except Exception as e: + return 500, str(e) + response = self.process_response("{{ name }}", request, {"assets": assets}) + return 200, response diff --git a/tools/inference_builder/templates/responder/list_live_streams.jinja.py b/tools/inference_builder/templates/responder/list_live_streams.jinja.py new file mode 100644 index 00000000..d7416ddb --- /dev/null +++ b/tools/inference_builder/templates/responder/list_live_streams.jinja.py @@ -0,0 +1,24 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} + + async def {{ name }}(self, request): + try: + assets = [asset for asset in self._asset_manager.list_assets() if not asset.file_name] + except Exception as e: + return 500, str(e) + response = self.process_response("{{ name }}", request, {"assets": assets}) + return 200, response diff --git a/tools/inference_builder/templates/serverless/inference.jinja.py b/tools/inference_builder/templates/serverless/inference.jinja.py new file mode 100644 index 00000000..79c8cb25 --- /dev/null +++ b/tools/inference_builder/templates/serverless/inference.jinja.py @@ -0,0 +1,137 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} +{{ license }} + +import argparse +from config import global_config +from typing import List, Optional +from lib.inference import py_datatype_mapping +from .model import GenericInference +import json +import sys +from lib.utils import NumpyFlatEncoder + + +def create_parser(inputs: List) -> argparse.ArgumentParser: + """Create and configure the argument parser for the application.""" + parser = argparse.ArgumentParser( + description='Command line interface for{{ service_name }}' + ) + for input_item in inputs: + input_name = input_item.name.replace("_", "-") + nargs = 1 + optional = input_item.optional if hasattr(input_item, "optional") else False + if len(input_item.dims) == 1: + if input_item.dims[0] == -1: + nargs = "+" + elif input_item.dims[0] == 1 and optional: + nargs = "?" + else: + nargs = input_item.dims[0] + parser.add_argument( + f'--{input_name}', + type=py_datatype_mapping[input_item.data_type], + nargs=nargs, + required=not optional + ) + parser.add_argument( + '-s', + '--save-to', + type=str, + nargs='?', + const='stdout', + help='Save results to a file, or use -s alone for stdout' + ) + return parser + + +def run_inference(args) -> Optional[int]: + """Run the inference service synchronously. + + Args: + args: Parsed command line arguments + + Returns: + Optional[int]: Exit code, 0 for success + """ + # Read and remove save_to argument before converting to dict + save_to = getattr(args, 'save_to', None) + if hasattr(args, 'save_to'): + delattr(args, 'save_to') + + # Convert remaining args to dictionary + inputs = vars(args) + + # Convert input names back to original format (replace '-' with '_') + inputs = {k.replace('-', '_'): v for k, v in inputs.items()} + + service = GenericInference() + service.initialize() + status = 0 + + # Determine output target: file, stdout, or None (no output) + output_file = None + close_file = False + if save_to: + if save_to in ('-', 'stdout'): + output_file = sys.stdout + else: + output_file = open(save_to, 'w', encoding='utf-8') + close_file = True + + try: + for result in service.exec_sync(inputs): + if not result: + status = -1 + break + # Unified logic: write JSON to output target if specified + if output_file: + json_str = json.dumps(result, indent=4, cls=NumpyFlatEncoder) + output_file.write(json_str) + output_file.write("\n") + if close_file: + output_file.flush() # Ensure data is written to disk + finally: + if close_file and output_file: + output_file.close() + + print("Inference completed.") + service.finalize() + return status + + +def main() -> int: + """Main entry point for the inference service. + + Returns: + Optional[int]: Exit code, None for success + """ + parser = create_parser(global_config.input) + args = parser.parse_args() + + try: + return run_inference(args) + except KeyboardInterrupt: + print("\nInterrupted by user") + return 130 + except Exception as e: + print(f"Error: {e}") + return 1 + + +if __name__ == "__main__": + exit(main()) diff --git a/tools/inference_builder/templates/triton/model.jinja.py b/tools/inference_builder/templates/triton/model.jinja.py new file mode 100644 index 00000000..2335740f --- /dev/null +++ b/tools/inference_builder/templates/triton/model.jinja.py @@ -0,0 +1,274 @@ +{# + SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. + SPDX-License-Identifier: Apache-2.0 + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +#} + +from config import global_config +import triton_python_backend_utils as pb_utils +from lib.inference import * +from lib.utils import * +from omegaconf import OmegaConf +import json +import os +from typing import List, Dict +import asyncio +from concurrent.futures import ThreadPoolExecutor +import numpy as np +import torch +from pathlib import Path + +logger = get_logger(__name__) + +CHECKPOINTS_DIR = os.getenv("CHECKPOINTS_DIR", Path(__file__).resolve().parent.parent.parent) + +triton_input_type_map = { + "TYPE_CUSTOM_BINARY_BASE64": "TYPE_STRING", + "TYPE_CUSTOM_IMAGE_BASE64": "TYPE_STRING" +} + +{% for backend in backends %} +{{ backend }} +{% endfor %} + +{% if top_level %} + +class TritonPythonModel(InferenceBase): + """The top level python model that drives the inference flow""" + + @staticmethod + def auto_complete_config(auto_complete_model_config): + # create a minimum config + auto_complete_model_config.set_max_batch_size(1) + auto_complete_model_config.set_dynamic_batching() + auto_complete_model_config.set_model_transaction_policy({"decoupled": True}) + for input in global_config.input: + input_config = OmegaConf.to_container(input) + data_type = input_config["data_type"] + if data_type.startswith("TYPE_CUSTOM_"): + data_type = triton_input_type_map[data_type] if data_type in triton_input_type_map else None + if data_type is None: + raise Exception(f"Unsupported input type: {data_type}") + input_config["data_type"] = data_type + auto_complete_model_config.add_input(input_config) + for output in global_config.output: + output_config = OmegaConf.to_container(output) + auto_complete_model_config.add_output(output_config) + logger.info(f"Model configuration completed as {auto_complete_model_config.as_dict()}") + return auto_complete_model_config + + def _create_backend(self, backend_spec: List[str], model_config: Dict, model_home: str): + if backend_spec[0] == 'triton': + return TritonBackend(model_config, model_home) + return None + + async def execute(self, requests): + """ execute a list of requests""" + async def async_put(queue, item): + await queue.put(item) + def thread_to_async_bridge(thread_queue, async_queue, loop): + while True: + try: + item = thread_queue.get() + asyncio.run_coroutine_threadsafe(async_put(async_queue, item), loop) + if isinstance(item, Stop) or isinstance(item, Error): + break + except Empty: + continue + + + logger.info(f"Received {len(requests)} request(s)") + for request in requests: + response_sender = request.get_response_sender() + if request.is_cancelled(): + response_sender.send(pb_utils.InferenceResponse( + error=pb_utils.TritonError("request is cancelled", pb_utils.TritonError.CANCELLED)), + flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL) + continue + for input in self._inputs: + # select the tensors for the input + tensors = {n: pb_utils.get_input_tensor_by_name(request, n) for n in input.in_names} + # the tensors need to be transformed to generic type + for name in tensors: + tensor = tensors[name] + config = next((c for c in self._input_config if c['name'] == name), None) + if config is None: + logger.warning(f"Invalid input parsed: {name}") + continue + dims = config['dims'] + if pb_utils.Tensor.is_cpu(tensor): + tensor = tensor.as_numpy() + # auto reshape + if len(tensor.shape) == (len(dims)+1): + tensor = np.squeeze(tensor, 0) + else: + tensor = torch.utils.dlpack.from_dlpack(tensor.to_dlpack()) + # auto reshape + if len(tensor.shape) == (len(dims)+1): + tensor = torch.squeeze(tensor, 0) + tensors[name] = tensor + if tensors: + logger.debug(f"Injecting tensors {tensors}") + input.put(tensors) + input.put(Stop(reason="end")) + # fetch result + loop = asyncio.get_event_loop() + for a_output, output in zip(self._async_outputs, self._outputs): + logger.error(f"Submitting {output._timeout} to async executor") + self._async_executor.submit(thread_to_async_bridge, output, a_output, loop) + stop = False + while not stop: + try: + logger.debug("Waiting for tensors from async queue") + response_data = dict() + results = await asyncio.gather(*(ao.get() for ao in self._async_outputs)) + for data in results: + logger.debug(f"Got output data: {data}") + if isinstance(data, Error): + error = pb_utils.TritonError(f"steam llm_response, error received: {data.message}") + response_sender.send( + pb_utils.InferenceResponse(error=error), + flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL + ) + return + elif isinstance(data, Stop): + stop = True + continue + # collect the output + for k, v in data.items(): + response_data[k] = v + response_data = self._post_process(response_data) + # response with partial data + response_sender.send(pb_utils.InferenceResponse( + output_tensors=[ + pb_utils.Tensor(name, tensor) for name, tensor in response_data.items() + ] + )) + except Exception as e: + logger.exception(e) + + response_sender.send(flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL) + logger.debug(f"Finalizing the response for requests: {request}") + logger.debug(f"All request done") + + def finalize(self): + super().finalize() + + def _post_process(self, data: Dict): + processed = {k: v for k, v in data.items()} + for processor in self._processors: + if not all([i in data for i in processor.input]): + logger.warning(f"Input settings invalid for the processor: {processor}") + continue + input = [processed.pop(i) for i in processor.input] + logger.debug(f"Post-processor invoked with given input {input}") + output = processor(*input) + logger.debug(f"Post-processor generated output {output}") + if len(output) != len(processor.output): + logger.warning(f"Number of postprocessing output doesn't match the configuration, expecting {len(processor.output)}, while getting {len(output)}") + continue + # update as processed + for key, value in zip(processor.output, output): + processed[key] = value + return processed + +{% else %} +class TritonPythonModel: + def initialize(self, args): + """ + This function allows + the model to initialize any state associated with this model. + """ + model_name = args["model_name"] + model_home = os.path.join(global_config.model_repo, model_name, "1") + model_config = next((m for m in global_config.models if m.name == model_name), None) + if model_config is None: + raise Exception("Model config not found") + self._model_backend = None + self._model_config = OmegaConf.to_container(model_config) + self._in_config = {i["name"]: i for i in self._model_config["input"]} + self._out_config = {o["name"]: o for o in self._model_config["output"]} + self._model_repo = args["model_repository"] + self._device_id = int(json.loads(args["model_instance_device_id"])) + # default model path + engine_path = os.path.join(self._model_repo, "1", "model.plan") + if "tensorrt_engine" in self._model_config: + self._model_config["tensorrt_engine"] = os.path.join(CHECKPOINTS_DIR, self._model_config["tensorrt_engine"]) + else: + self._model_config["tensorrt_engine"] = engine_path + # create backend + BackendClass = None + backend_spec = model_config.backend.split('/') + if backend_spec[-1] == "tensorrtllm": + BackendClass = TensorRTLLMBackend + elif backend_spec[-1] == "polygraphy": + BackendClass = PolygraphBackend + elif backend_spec[-1] == "dummy": + BackendClass = DummyBackend + if BackendClass is not None: + self._model_backend = BackendClass(self._model_config, model_home,self._device_id) + else: + raise Exception(f"Backend not supported: {model_config.backend}") + + + def execute(self, requests): + """ + execute each request + """ + logger.info(f"Model {self._model_config['name']} received {len(requests)} requests") + + r_list = [] + responses = [] + for request in requests: + inputs = {k: None for k in self._in_config} + for name, config in self._in_config.items(): + dims = config['dims'] + pb_tensor = pb_utils.get_input_tensor_by_name(request, name) + if pb_utils.Tensor.is_cpu(pb_tensor): + tensor = pb_tensor.as_numpy() + # auto reshape + if len(dims) > 1 and len(tensor.shape) == (len(dims)+1): + tensor = np.squeeze(tensor, 0) + else: + tensor = torch.utils.dlpack.from_dlpack(pb_tensor.to_dlpack()) + # auto reshape + if len(dims) > 1 and len(tensor.shape) == (len(dims)+1): + tensor = torch.squeeze(tensor, 0) + inputs[name] = tensor + + for output in self._model_backend(**inputs): + r_list.append(output) + for r in r_list: + logger.info(f"Model {self._model_config['name']} generating responses {r}") + for k in r: + v = r[k] + force_cpu = True if "force_cpu" in self._out_config[k] and self._out_config[k]["force_cpu"] else False + if isinstance(v, torch.Tensor): + tensor = pb_utils.Tensor(k, v.cpu().numpy()) if force_cpu else pb_utils.Tensor.from_dlpack(k, torch.utils.dlpack.to_dlpack(v)) + elif isinstance(v, np.ndarray): + tensor = pb_utils.Tensor(k, v) + elif hasattr(v, "__dlpack__") and hasattr(v, "__dlpack_device__"): + tensor = pb_utils.Tensor(k, np.from_dlpack(v)) if force_cpu else pb_utils.Tensor.from_dlpack(k, v) + else: + responses.append( + pb_utils.InferenceResponse( + pb_utils.TritonError(f"Unsupported tensor format from the backend. {v}") + ) + ) + break + r[k] = tensor + responses.append(pb_utils.InferenceResponse(output_tensors=[r[k] for k in r])) + + return responses +{% endif %} \ No newline at end of file diff --git a/tools/inference_builder/tools/trt_build.py b/tools/inference_builder/tools/trt_build.py new file mode 100644 index 00000000..04598a1a --- /dev/null +++ b/tools/inference_builder/tools/trt_build.py @@ -0,0 +1,110 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import tensorrt as trt + +def build_engine(onnx_file_path, min_batch_size, opt_batch_size, max_batch_size, max_text_len): + # Create a TensorRT logger and builder + TRT_LOGGER = trt.Logger(trt.Logger.WARNING) + trt.init_libnvinfer_plugins(TRT_LOGGER, namespace="") + builder = trt.Builder(TRT_LOGGER) + network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) + parser = trt.OnnxParser(network, TRT_LOGGER) + + # Read the ONNX file + with open(onnx_file_path, "rb") as model: + if not parser.parse(model.read()): + print("Failed to parse the ONNX file!") + for error in range(parser.num_errors): + print(parser.get_error(error)) + return None + + inputs = [network.get_input(i) for i in range(network.num_inputs)] + outputs = [network.get_output(i) for i in range(network.num_outputs)] + # Set builder configurations + config = builder.create_builder_config() + config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 2 << 30) # 2GB of workspace + config.set_flag(trt.BuilderFlag.FP16) +# builder.max_batch_size = max_batch_size + profile = builder.create_optimization_profile() + for model_input in inputs: + print(f"Input {model_input.name} with shape {model_input.shape} and dtype {model_input.dtype}") + input_shape = model_input.shape + input_name = model_input.name + if input_name == 'inputs': + real_shape_min = (min_batch_size, *input_shape[1:]) + real_shape_opt = (opt_batch_size, *input_shape[1:]) + real_shape_max = (max_batch_size, *input_shape[1:]) + elif input_name == 'text_token_mask': + real_shape_min = (min_batch_size, max_text_len, max_text_len) + real_shape_opt = (opt_batch_size, max_text_len, max_text_len) + real_shape_max = (max_batch_size, max_text_len, max_text_len) + else: + real_shape_min = (min_batch_size, max_text_len) + real_shape_opt = (opt_batch_size, max_text_len) + real_shape_max = (max_batch_size, max_text_len) + + profile.set_shape(input=input_name, + min=real_shape_min, + opt=real_shape_opt, + max=real_shape_max) + + config.add_optimization_profile(profile) + + for output in outputs: + print(f"Output {output.name} with shape {output.shape} and dtype {output.dtype}") + + # Build and return the engine + print("Building the engine. This might take a while...") + engine = builder.build_serialized_network(network, config) + if engine: + print("Engine built successfully!") + return engine + +def save_engine(engine, file_name): + with open(file_name, "wb") as f: + f.write(engine) + +def main(args): + onnx_file_path = args.input # Your ONNX file path + engine_file_path = args.output # Output TensorRT engine file + + engine = build_engine(onnx_file_path, args.min_batch_size, args.opt_batch_size, args.max_batch_size, args.max_text_len) + if engine: + save_engine(engine, engine_file_path) + print(f"Engine saved to {engine_file_path}") + +if __name__ == "__main__": + parser = argparse.ArgumentParser("TRT Builder") + parser.add_argument("input", type=str, help="Onnx input file") + parser.add_argument("-o", "--output", type=str, help="TensorRT engine file", default="model.plan") + parser.add_argument('--max_batch_size', + type=int, + default=1, + help="Maximum batch size for input images") + parser.add_argument('--min-batch-size', + type=int, + default=1, + help="Minimum batch size for input images") + parser.add_argument('--opt-batch-size', + type=int, + default=1, + help="Optimal batch size for input images") + parser.add_argument('--max-text-len', + type=int, + default=256, + help="Maximum text length for input prompt") + main(parser.parse_args()) From 47055cb972e37c3dbd0ff3ec672e91c6b61b140f Mon Sep 17 00:00:00 2001 From: ankitat Date: Thu, 14 May 2026 17:51:18 +0530 Subject: [PATCH 7/7] Revert "migrate: copy tools/inference_builder/ from mono-repo" This reverts commit dea1cc262f1019581011431a79c66152f7bcadab. --- tools/inference_builder/.Dockerfile.builder | 42 - tools/inference_builder/.gitattributes | 7 - tools/inference_builder/.gitignore | 12 - tools/inference_builder/.gitkeep | 0 tools/inference_builder/.gitmodules | 3 - tools/inference_builder/CONTRIBUTING.md | 44 - tools/inference_builder/LICENSE | 201 -- tools/inference_builder/README.md | 207 -- 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tools/inference_builder/templates/responder/del_live_stream.jinja.py delete mode 100644 tools/inference_builder/templates/responder/healthy_ready.jinja.py delete mode 100644 tools/inference_builder/templates/responder/infer.jinja.py delete mode 100644 tools/inference_builder/templates/responder/list_files.jinja.py delete mode 100644 tools/inference_builder/templates/responder/list_live_streams.jinja.py delete mode 100644 tools/inference_builder/templates/serverless/inference.jinja.py delete mode 100644 tools/inference_builder/templates/triton/model.jinja.py delete mode 100644 tools/inference_builder/tools/trt_build.py diff --git a/tools/inference_builder/.Dockerfile.builder b/tools/inference_builder/.Dockerfile.builder deleted file mode 100644 index a0e357cf..00000000 --- a/tools/inference_builder/.Dockerfile.builder +++ /dev/null @@ -1,42 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# Dockerfile.builder -FROM python:3.10-slim - -# Install system dependencies -RUN apt-get update && apt-get install -y \ - protobuf-compiler \ - git \ - default-jre \ - && rm -rf /var/lib/apt/lists/* - -# Configure pip to use NVIDIA's PyPI -ENV PIP_INDEX_URL=https://urm.nvidia.com/artifactory/api/pypi/nv-shared-pypi/simple - -# Install NIM tools and other Python dependencies -RUN pip install --no-cache-dir \ - openapi-generator-cli - -# Install project dependencies -COPY requirements.txt /tmp/ -RUN pip install --no-cache-dir -r /tmp/requirements.txt - -# Set JAVA_HOME -ENV JAVA_HOME=/usr/lib/jvm/java-11-openjdk-amd64 -ENV PATH="$JAVA_HOME/bin:$PATH" - -# Set working directory -WORKDIR /workspace diff --git a/tools/inference_builder/.gitattributes b/tools/inference_builder/.gitattributes deleted file mode 100644 index 58cee12d..00000000 --- a/tools/inference_builder/.gitattributes +++ /dev/null @@ -1,7 +0,0 @@ -builder/boilerplates/nim/** merge=ours -builder/boilerplates/triton/** merge=ours -templates/api_server/nim/** merge=ours -templates/api_server/triton/** merge=ours -builder/allowed_servers.txt merge=ours -.gitlab-ci.yml merge=ours -builder/samples/tao/ib_client.py merge=ours diff --git a/tools/inference_builder/.gitignore b/tools/inference_builder/.gitignore deleted file mode 100644 index ee1eb869..00000000 --- a/tools/inference_builder/.gitignore +++ /dev/null @@ -1,12 +0,0 @@ -**/__pycache__ -*.swp -*.vscode -.vscode/** -.vscode -dist -.venv -output -generated_client -*tmp* -*.tgz -.tmp diff --git a/tools/inference_builder/.gitkeep b/tools/inference_builder/.gitkeep deleted file mode 100644 index e69de29b..00000000 diff --git a/tools/inference_builder/.gitmodules b/tools/inference_builder/.gitmodules deleted file mode 100644 index 5cb67da8..00000000 --- a/tools/inference_builder/.gitmodules +++ /dev/null @@ -1,3 +0,0 @@ -[submodule "dependencies/triton-inference-server/common"] - path = dependencies/triton-inference-server/common - url = https://github.com/triton-inference-server/common diff --git a/tools/inference_builder/CONTRIBUTING.md b/tools/inference_builder/CONTRIBUTING.md deleted file mode 100644 index f8df0d30..00000000 --- a/tools/inference_builder/CONTRIBUTING.md +++ /dev/null @@ -1,44 +0,0 @@ -## Inference Builder OSS Contribution Rules - -#### Signing Your Work - -* We require that all contributors "sign-off" on their commits. This certifies that the contribution is your original work, or you have rights to submit it under the same license, or a compatible license. - - * Any contribution which contains commits that are not Signed-Off will not be accepted. - -* To sign off on a commit you simply use the `--signoff` (or `-s`) option when committing your changes: - ```bash - $ git commit -s -m "Add cool feature." - ``` - This will append the following to your commit message: - ``` - Signed-off-by: Your Name - ``` - -* Full text of the DCO: - - ``` - Developer Certificate of Origin - Version 1.1 - - Copyright (C) 2004, 2006 The Linux Foundation and its contributors. - 1 Letterman Drive - Suite D4700 - San Francisco, CA, 94129 - - Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. - ``` - - ``` - Developer's Certificate of Origin 1.1 - - By making a contribution to this project, I certify that: - - (a) The contribution was created in whole or in part by me and I have the right to submit it under the open source license indicated in the file; or - - (b) The contribution is based upon previous work that, to the best of my knowledge, is covered under an appropriate open source license and I have the right under that license to submit that work with modifications, whether created in whole or in part by me, under the same open source license (unless I am permitted to submit under a different license), as indicated in the file; or - - (c) The contribution was provided directly to me by some other person who certified (a), (b) or (c) and I have not modified it. - - (d) I understand and agree that this project and the contribution are public and that a record of the contribution (including all personal information I submit with it, including my sign-off) is maintained indefinitely and may be redistributed consistent with this project or the open source license(s) involved. - ``` diff --git a/tools/inference_builder/LICENSE b/tools/inference_builder/LICENSE deleted file mode 100644 index 56ee3c8c..00000000 --- a/tools/inference_builder/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [yyyy] [name of copyright owner] - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. \ No newline at end of file diff --git a/tools/inference_builder/README.md b/tools/inference_builder/README.md deleted file mode 100644 index 17fc7972..00000000 --- a/tools/inference_builder/README.md +++ /dev/null @@ -1,207 +0,0 @@ -# Inference Builder - -## Overview - -Inference Builder is a tool that automatically generates inference pipelines and integrates them into either a microservice or a standalone application. It takes an inference configuration file and an OpenAPI specification (when integrated with an HTTP server) as inputs, and may also require custom code snippets in certain cases. - -The output of the tool is a Python package that can be used to build a microservice container image with a customized Dockerfile. - -![Overview](overview.png) - -The Inference Builder consists of three major components: - -- Code templates: These are reusable modules for various inference backends and frameworks, as well as for API servers. They are optimized and tested, making them suitable for any model with specified inputs, outputs, and configuration parameters. -- Common inference flow: It serves as the core logic that standardizes the end-to-end inference process—including data loading and pre-processing, model inference, post-processing, and integration with the API server. It supports pluggable inference backends and frameworks, enabling flexibility and performance optimization. -- Command line tool: It generates a source code package by combining predefined code templates with the Common Inference Flow. It also automatically produces corresponding test cases and evaluation scripts to support validation and performance assessment. - -Visit our [documentation](doc) for more details: - -- [Inference Builder Usage](doc/usage.md) -- [Inference Builder Architecture](doc/architecture.md) - -## Getting started - -First, be sure your system meets the requirement. - -| Operating System | Python | CPU | GPU[^1] | -|:-------------------|:-------|:---------------|:-----------------------------| -|Ubuntu 24.04 |3.12 | x86, aarch64 |Nvidia ADA, Hopper, Blackwell | - -[^1]: If you only generate the inference pipeline without running it, no GPU is required. - -Next, follow these steps to get started: - -### Install prerequisites - -```bash -sudo apt update -sudo apt install protobuf-compiler -sudo apt install python3.12-venv python3.12-dev -``` - -**Note for TEGRA users:** If you're using a TEGRA device, you'll also need to install the Docker buildx plugin: - -```bash -sudo apt install docker-buildx -``` - -### Clone the repository - -```bash -git clone https://github.com/NVIDIA-AI-IOT/inference_builder -``` - -### Set up the virtual environment - -```bash -cd inference_builder -git submodule update --init --recursive -python3 -m venv .venv -source .venv/bin/activate -pip3 install -r requirements.txt -``` - -## Play with the examples - -Docker environment must be properly set up with below packages for building and running the examples: - -- **Docker**: [Installation Guide](https://docs.docker.com/desktop/setup/install/linux/ubuntu/) -- **Docker Compose**: [Installation Guide](https://docs.docker.com/desktop/setup/install/linux/ubuntu/) -- **NVIDIA Container Toolkit**: [Installation Guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) - - -Ensure nvidia runtime added to `/etc/docker/daemon.json` to run GPU-enabled containers - -```bash -{ - "runtimes": { - "nvidia": { - "args": [], - "path": "nvidia-container-runtime" - } - } -} -``` - -The `docker` group must exist in your system, please check if it has been created using `getent group docker`. Your current user must belong to the `docker` group, If not, run the command below, then log out and back in for the group change to take effect. - -```bash -sudo usermod -aG docker $USER -``` - -Now you can try [our examples](builder/samples/README.md) to learn more. These examples span all supported backends and demonstrate their distinct inference flows. - -![Inference FLow of Different Backend](doc/assets/inference_flow.png) - -## Benefit of using Inference Builder - -Compared to manually crafting inference source code, Inference Builder offers developers the following advantages: -- Separation of concerns: Introduces a new programming paradigm that decouples inference data flow and server logic from the model implementation, allowing developers to focus solely on model behavior. -- Backend flexibility: Standardizes data flow across different inference backends, enabling developers to switch to the optimal backend for their specific requirement without rewriting the entire pipeline. -- Hardware acceleration: Automatically enables GPU-accelerated processing to boost performance. -- Streaming support: Provides built-in support for streaming protocols such as RTSP with minimal configuration. -- Standardized testing: Automates and standardizes test case generation to simplify validation and evaluation workflows. - -## MCP Integration - -Inference Builder includes MCP (Model Context Protocol) integration, enabling AI-assisted pipeline generation directly within Cursor or Claude Code. With MCP, you can use natural language to generate inference pipelines, build Docker images, and explore sample configurations. - -For detailed tool reference and advanced usage, see [mcp/README-MCP.md](mcp/README-MCP.md). - -### Prerequisites - -Before setting up MCP, ensure you have completed the [Getting Started](#getting-started) steps above. - -Ensure that you are in the repository's root directory. - -### Set Up for Cursor - -1. **Generate the MCP configuration** - - For a global configuration (available across all projects): - ```bash - python3 mcp/setup_mcp.py ~/.cursor/mcp.json - ``` - - For a project-specific configuration: - ```bash - python3 mcp/setup_mcp.py /path/to/your_project/.cursor/mcp.json - ``` - -2. **Open your project folder and verify the MCP server is successfully loaded** - - In Cursor, navigate to **File > Preferences > Cursor Settings > MCP**. You should see: - - ![MCP Server](mcp.png) - - A green status icon next to `deepstream-inference-builder` indicates the server is connected. - -3. **Start using the MCP tools:** - - Start a new Cursor agent and invoke the tools by mentioning "deepstream inference builder" in your prompt: - - "Show me what sample configurations are available from the inference builder?" - - "Generate a DeepStream object detection pipeline using the inference builder with PeopleNet transformer model from NGC." - -### Set Up for Claude Code - -1. **Generate the MCP configuration:** - - For a global configuration: - ```bash - python3 mcp/setup_mcp.py ~/.claude/.mcp.json - ``` - - For a project-specific configuration: - ```bash - python3 mcp/setup_mcp.py /path/to/your_project/.mcp.json - ``` - -2. **Start Claude Code from your project folder and Verify the MCP server is connected:** - - Run `/mcp` in the Claude Code console. You should see: - ``` - ❯ deepstream-inference-builder · ✔ connected - ``` - -3. **Start using the MCP tools:** - - Simply ask Claude Code to perform tasks by mentioning "deepstream inference builder" in your prompt: - - "Show me what sample configurations are available from the inference builder?" - - "Generate a DeepStream object detection pipeline using the inference builder with PeopleNet transformer model from NGC." - -### Available MCP Tools - -| Tool | Description | -|------|-------------| -| `generate_inference_pipeline` | Generate inference pipelines from YAML configuration files | -| `build_docker_image` | Build Docker images from generated pipelines | -| `docker_run_image` | Run Docker images for testing and troubleshooting | -| `prepare_model_repository` | Download models from NGC or HuggingFace and prepare repositories | -| `generate_nvinfer_config` | Generate DeepStream nvinfer runtime configuration files | - -### Available MCP Resources - -| Resource | Description | -|----------|-------------| -| `docs://README.md` | Project documentation | -| `docs://mcp/README-MCP.md` | MCP integration documentation | -| `schema://config.schema.json` | Configuration schema reference | -| `samples://config/*` | Sample pipeline configurations | -| `samples://dockerfile/*` | Sample Dockerfiles | -| `samples://processor/*` | Sample preprocessors and postprocessors | - -### Sample Prompts - -You can now try out the [example prompts](mcp/examples.md) using either Cursor or Claude Code. - -## Contributing - -Contributions are welcome! Please feel free to submit a PR. - - -## Project status and roadmap - -The project is under active development and the following features are expected to be supported in the near future: - -- Support for more backends and frameworks such as SGLANG. -- Support for more model types such as speech models. diff --git a/tools/inference_builder/VERSION b/tools/inference_builder/VERSION deleted file mode 100644 index 0ea3a944..00000000 --- a/tools/inference_builder/VERSION +++ /dev/null @@ -1 +0,0 @@ -0.2.0 diff --git a/tools/inference_builder/builder/__init__.py b/tools/inference_builder/builder/__init__.py deleted file mode 100644 index 3159bfe6..00000000 --- a/tools/inference_builder/builder/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tools/inference_builder/builder/allowed_servers.txt b/tools/inference_builder/builder/allowed_servers.txt deleted file mode 100644 index 08e5d42f..00000000 --- a/tools/inference_builder/builder/allowed_servers.txt +++ /dev/null @@ -1,3 +0,0 @@ -fastapi -serverless - diff --git a/tools/inference_builder/builder/boilerplates/fastapi/cookiecutter.json b/tools/inference_builder/builder/boilerplates/fastapi/cookiecutter.json deleted file mode 100644 index ebeca75a..00000000 --- a/tools/inference_builder/builder/boilerplates/fastapi/cookiecutter.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "service_name": "my-service" -} diff --git a/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/__main__.py b/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/__main__.py deleted file mode 100644 index b5442ae4..00000000 --- a/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/__main__.py +++ /dev/null @@ -1,19 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import server.inference - -if __name__ == "__main__": - server.inference.main() \ No newline at end of file diff --git a/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/config/__init__.py b/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/config/__init__.py deleted file mode 100644 index f9c99939..00000000 --- a/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/config/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. \ No newline at end of file diff --git a/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/custom/__init__.py b/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/custom/__init__.py deleted file mode 100644 index f9c99939..00000000 --- a/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/custom/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. \ No newline at end of file diff --git a/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/lib/__init__.py b/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/lib/__init__.py deleted file mode 100644 index f9c99939..00000000 --- a/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/lib/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. \ No newline at end of file diff --git a/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/server/__init__.py b/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/server/__init__.py deleted file mode 100644 index f9c99939..00000000 --- a/tools/inference_builder/builder/boilerplates/fastapi/{{cookiecutter.service_name}}/server/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. \ No newline at end of file diff --git a/tools/inference_builder/builder/boilerplates/serverless/cookiecutter.json b/tools/inference_builder/builder/boilerplates/serverless/cookiecutter.json deleted file mode 100644 index ebeca75a..00000000 --- a/tools/inference_builder/builder/boilerplates/serverless/cookiecutter.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "service_name": "my-service" -} diff --git a/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/__main__.py b/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/__main__.py deleted file mode 100644 index 613fefcd..00000000 --- a/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/__main__.py +++ /dev/null @@ -1,19 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import app.inference - -if __name__ == "__main__": - app.inference.main() \ No newline at end of file diff --git a/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/app/__init__.py b/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/app/__init__.py deleted file mode 100644 index 3159bfe6..00000000 --- a/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/app/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/config/__init__.py b/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/config/__init__.py deleted file mode 100644 index 3159bfe6..00000000 --- a/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/config/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/custom/__init__.py b/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/custom/__init__.py deleted file mode 100644 index 3159bfe6..00000000 --- a/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/custom/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/lib/__init__.py b/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/lib/__init__.py deleted file mode 100644 index 3159bfe6..00000000 --- a/tools/inference_builder/builder/boilerplates/serverless/{{cookiecutter.service_name}}/lib/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tools/inference_builder/builder/main.py b/tools/inference_builder/builder/main.py deleted file mode 100644 index e8085c30..00000000 --- a/tools/inference_builder/builder/main.py +++ /dev/null @@ -1,677 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import base64 -import tempfile -import shutil -from omegaconf import OmegaConf -import cookiecutter.main -import cookiecutter -import logging -from typing import Dict, List -from pathlib import Path -from utils import get_resource_path, copy_files, create_tar_gz -from triton.utils import generate_pbtxt -from omegaconf.errors import ConfigKeyError -from jinja2 import Environment, FileSystemLoader -import ast -import os -import sys -import subprocess -import validate -import re -import json -import sys - -# Import schema validation functions -try: - from jsonschema import Draft7Validator -except ImportError: - Draft7Validator = None - logging.warning("jsonschema not installed. Schema validation will be skipped. Install with: pip install jsonschema") - -""" -Inference Builder Main Module - -Security Features: -- Input validation for file paths and directory paths to prevent directory traversal attacks -- Server type validation against allowed values to prevent injection -- Safe subprocess calls using argument lists instead of shell string interpolation -- Comprehensive argument validation in main function to prevent command injection -- Logging of all executed commands for audit trails -""" - -LICENSE_HEADER = """ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" - - -def load_allowed_servers(file_path: str = None) -> List[str]: - """Load allowed server types from a text file. - - Args: - file_path: Path to the text file containing allowed server types. - If None, uses the default 'allowed_servers.txt' - in the builder directory. - - Returns: - List of allowed server type strings. - - Raises: - FileNotFoundError: If the configuration file is not found. - ValueError: If the file is empty or contains invalid entries. - """ - if file_path is None: - # Use default file in the same directory as this script - script_dir = Path(__file__).parent - file_path = script_dir / "allowed_servers.txt" - else: - file_path = Path(file_path) - - if not file_path.exists(): - raise FileNotFoundError( - f"Allowed servers configuration file not found: {file_path}" - ) - - allowed_servers = [] - with open(file_path, 'r', encoding='utf-8') as f: - for line in f: - # Strip whitespace and ignore empty lines and comments - line = line.strip() - if line and not line.startswith('#'): - allowed_servers.append(line) - - if not allowed_servers: - raise ValueError(f"No valid server types found in {file_path}") - - return allowed_servers - - -# Load allowed server types from configuration file -ALLOWED_SERVER = load_allowed_servers() - - -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger("Main") -OmegaConf.register_new_resolver("multiline", lambda x: x, replace=False) - - -def validate_file_path(file_path: str) -> str: - """Validate and sanitize a file path to prevent directory traversal and command injection. - - Returns: - str: Validated and resolved absolute file path. - Raises: - ValueError: If path is invalid or potentially dangerous. - """ - if not file_path: - raise ValueError("File path cannot be empty") - - try: - abs_path = os.path.abspath(file_path) - resolved_path = os.path.realpath(abs_path) - except (OSError, ValueError) as e: - raise ValueError(f"Invalid file path: {e}") - - if ".." in os.path.normpath(file_path): - raise ValueError("Path traversal detected in file path") - - invalid_chars = ['<', '>', ':', '"', '|', '?', '*'] - if any(char in file_path for char in invalid_chars): - raise ValueError("Invalid characters in file path") - - system_dirs = ['/etc', '/sys', '/proc', '/dev', '/boot', '/usr/bin', '/usr/sbin'] - for sys_dir in system_dirs: - if resolved_path.startswith(sys_dir): - raise ValueError(f"Access to system directory {sys_dir} is not allowed") - - path = Path(resolved_path) - if not path.exists() or not path.is_file(): - raise ValueError(f"File not found or not a regular file: {resolved_path}") - - return resolved_path - - -def validate_directory_path(dir_path: str) -> str: - """Validate and sanitize a directory path to prevent directory traversal attacks. - - Returns: - str: Validated and resolved absolute directory path. - Raises: - ValueError: If path is invalid or potentially dangerous. - """ - if not dir_path: - raise ValueError("Directory path cannot be empty") - - try: - abs_path = os.path.abspath(dir_path) - resolved_path = os.path.realpath(abs_path) - except (OSError, ValueError) as e: - raise ValueError(f"Invalid directory path: {e}") - - if ".." in os.path.normpath(dir_path): - raise ValueError("Path traversal detected in directory path") - - invalid_chars = ['<', '>', ':', '"', '|', '?', '*'] - if any(char in dir_path for char in invalid_chars): - raise ValueError("Invalid characters in directory path") - - system_dirs = ['/etc', '/sys', '/proc', '/dev', '/boot', '/usr/bin', '/usr/sbin'] - for sys_dir in system_dirs: - if resolved_path.startswith(sys_dir): - raise ValueError(f"Access to system directory {sys_dir} is not allowed") - - return resolved_path - - -def validate_server_type(server_type: str) -> bool: - """Validate server type against allowed values.""" - return server_type in ALLOWED_SERVER - - -def validate_config_against_schema(config_dict: Dict, schema_dir: Path = None) -> bool: - """Validate configuration against JSON schema. - - Args: - config_dict: Configuration dictionary to validate - schema_dir: Directory containing schema files (default: ../schemas from this file) - - Returns: - True if valid, False otherwise - """ - if Draft7Validator is None: - logger.warning("Skipping schema validation (jsonschema not installed)") - return True - - # Determine schema directory - if schema_dir is None: - schema_dir = Path(__file__).parent.parent / "schemas" - - if not schema_dir.exists(): - logger.warning(f"Schema directory not found: {schema_dir}. Skipping schema validation.") - return True - - # Load main schema - schema_path = schema_dir / "config.schema.json" - try: - with open(schema_path, 'r') as f: - schema = json.load(f) - except FileNotFoundError: - logger.warning(f"Schema file not found: {schema_path}. Skipping schema validation.") - return True - except json.JSONDecodeError as e: - logger.error(f"Invalid schema file: {e}") - return False - - # Validate configuration - try: - validator = Draft7Validator(schema) - errors = list(validator.iter_errors(config_dict)) - - if not errors: - logger.info("✓ Configuration schema validation passed") - return True - - logger.error(f"✗ Configuration validation failed with {len(errors)} error(s):") - for i, error in enumerate(errors, 1): - path = '.'.join(str(p) for p in error.path) if error.path else 'root' - logger.error(f" Error {i} at '{path}': {error.message}") - - if error.context: - for ctx_error in error.context: - logger.error(f" - {ctx_error.message}") - - return False - - except Exception as e: - logger.error(f"Error during schema validation: {e}") - return False - - -def get_version(): - version_file = Path(__file__).parent.parent / "VERSION" - with open(version_file, 'r') as f: - return f.read().strip() - - -def build_args(parser): - parser.add_argument( - "-v", - "--version", - action="version", - version=f"%(prog)s {get_version()}" - ) - parser.add_argument( - "--server-type", - type=str, - nargs='?', - default='fastapi', - choices=ALLOWED_SERVER, - help="Choose the server type" - ) - parser.add_argument( - "-o", - "--output-dir", - type=str, - nargs='?', - default='.', - help="Output directory" - ) - parser.add_argument( - "-a", - "--api-spec", - type=argparse.FileType('r'), - nargs='?', - help="File for OpenAPI specification" - ) - parser.add_argument( - "-c", - "--custom-module", - type=argparse.FileType('r'), - nargs='*', - help="Custom python modules" - ) - parser.add_argument( - "-x", - "--exclude-lib", - action='store_true', - help="Don't include common lib to the generated code." - ) - parser.add_argument( - "-t", - "--tar-output", - action='store_true', - help="Zip the output to a single file" - ) - parser.add_argument("config", type=str, help="Path the the configuration") - parser.add_argument( - "--validation-dir", - type=str, - help="valid validation directory path to build validator" - ) - parser.add_argument( - "--no-docker", - action="store_true", - help="Use local OpenAPI Generator instead of Docker for OpenAPI client generation") - parser.add_argument( - "--test-cases-abs-path", - action="store_true", - help="Use absolute paths in generated test_cases.yaml" - ) - -def build_tree(server_type, config, temp_dir): - cookiecutter.main.cookiecutter( - get_resource_path(f"builder/boilerplates/{server_type}"), - no_input=True, - extra_context={"service_name": config.name}, - output_dir=temp_dir) - return Path(temp_dir) / Path(config.name) - -def build_custom_modules(custom_modules: List, tree): - cls_list = [] - tpl_dir = get_resource_path("templates") - jinja_env = Environment(loader=FileSystemLoader(tpl_dir)) - tree = tree / "custom" - for m in custom_modules: - filename = os.path.basename(m.name) - module_id = os.path.splitext(filename)[0] - with m as f: - valid = False - source = f.read() - py = ast.parse(source) - for node in ast.walk(py): - if isinstance(node, ast.ClassDef): - name = None - has_call = False - for cls_node in node.body: - if isinstance(cls_node, ast.Assign): - for target in cls_node.targets: - if isinstance(target, ast.Name) and target.id == "name": - name = ast.literal_eval(cls_node.value) - if isinstance(cls_node, ast.FunctionDef): - if cls_node.name == "__call__": - has_call = True - if name and has_call: - if next((i for i in cls_list if i["name"] == name), None): - logger.warning(f"Custom class {name} defined more than once") - continue - cls_list.append({ - "name": name, - "module": module_id, - "class_name": node.name - }) - valid = True - if valid: - # write the python module - target_path = tree / f"{module_id}.py" - with open(target_path, "w") as t: - t.write(source) - custom_tpl = jinja_env.get_template('common/custom.__init__.jinja.py') - output = custom_tpl.render(classes=cls_list) - with open(tree/"__init__.py", 'w') as f: - f.write(output) - -def build_inference(server_type, config, output_dir: Path): - tpl_dir = get_resource_path("templates") - jinja_env = Environment(loader=FileSystemLoader(tpl_dir)) - triton_model_repo_dir = output_dir/'model_repo' - triton_tpl = jinja_env.get_template("triton/model.jinja.py") - generic_tpl = jinja_env.get_template("generic/model.jinja.py") - - # collect the backend: build model.py and pbtxt if the backend is triton - t_backends = [] - for model in config.models: - backend_spec = model.backend.split('/') - if backend_spec[0] == "triton": - os.makedirs(triton_model_repo_dir/f"{model.name}/1", exist_ok=True) - if len(backend_spec) < 2: - raise Exception("Triton backend needs a triton backend type") - if backend_spec[1] == "python": - if len(backend_spec) < 3: - raise Exception("Triton python backend needs an implementation type") - # generating triton model for triton backend - target_dir = triton_model_repo_dir/f"{model.name}/1" - # Triton python backend needs a model.py - backend_tpl = jinja_env.get_template(f"backend/{backend_spec[2]}.jinja.py") - backend = backend_tpl.render(server_type=server_type) - output = triton_tpl.render(backends=[backend], top_level=False, license=LICENSE_HEADER) - with open (target_dir/"model.py", 'w') as o: - o.write(output) - # triton python backend to communicate with triton fastapi server - if "triton" not in t_backends: - t_backends.append("triton") - # write the pbtxt - pbtxt_str = generate_pbtxt(OmegaConf.to_container(model), backend_spec[1] ) - pbtxt_path = triton_model_repo_dir/model.name/"config.pbtxt" - with open(pbtxt_path, 'w') as f: - f.write(pbtxt_str) - else: - bare_backend = backend_spec[0] - if bare_backend not in t_backends: - t_backends.append(bare_backend) - - # create backends and model.py - backends = [] - if server_type == "serverless": - target_dir = output_dir / "app" - elif server_type == "triton": - target_dir = triton_model_repo_dir/f"{config.name}"/"1/" - else: - target_dir = output_dir / "server" - for backend in t_backends: - backend_tpl = jinja_env.get_template(f"backend/{backend}.jinja.py") - backends.append(backend_tpl.render(server_type=server_type)) - if server_type == "triton": - # render top level triton backend - output = triton_tpl.render(backends=backends, top_level=True) - else: - # render generic model.y - output = generic_tpl.render(backends=backends, license=LICENSE_HEADER) - with open (target_dir/"model.py", 'w') as o: - o.write(output) - -def build_serverless(name: str, output_dir: Path): - output_dir = output_dir / "app" - tpl_dir = get_resource_path("templates") - jinja_env = Environment(loader=FileSystemLoader(tpl_dir)) - app_tpl = jinja_env.get_template("serverless/inference.jinja.py") - output = app_tpl.render(service_name=name, license=LICENSE_HEADER) - with open(output_dir/"inference.py", 'w') as f: - f.write(output) - -def build_server(server_type, model_name, api_spec, config: Dict, output_dir): - output_dir = output_dir / "server" - # generate pydantic data models and inference base class from swagger spec - tpl_dir = get_resource_path("templates") - jinja_env = Environment(loader=FileSystemLoader(tpl_dir)) - api_tpl_dir = get_resource_path(f"templates/api_server/{server_type}/route") - - # Use safer subprocess call with argument list instead of shell string interpolation - # Find fastapi-codegen in the same directory as the current Python executable - python_dir = Path(sys.executable).parent - fastapi_codegen = python_dir / "fastapi-codegen" - - command = [ - str(fastapi_codegen), - "--input", api_spec.name, - "--output", str(output_dir), - "--output-model-type", "pydantic_v2.BaseModel", - "--template-dir", str(api_tpl_dir), - "-m", "data_model.py", - "--disable-timestamp" - ] - - logger.info(f"Executing command: {' '.join(command)}") - result = subprocess.run(command, capture_output=True, text=True) - if result.returncode != 0: - raise Exception(f"Failed to generate fastapi data models: {result.stderr}") - - responders = [] - for name, r in config["responders"].items(): - responder = { - "name": name, - "operation": r["operation"], - } - tpl = jinja_env.get_template(f"responder/{name}.jinja.py") - responder["implementation"] = tpl.render(**responder) - responders.append(responder) - svr_tpl = jinja_env.get_template(f"api_server/{server_type}/responder.jinja.py") - # render the responder.py - if server_type == "triton": - req_cls = [k for k in config["responders"]["infer"]["requests"].keys()] - res_cls = [k for k in config["responders"]["infer"]["responses"].keys()] - triton_config = { - "request_class": req_cls[0], - "response_class": res_cls[0], - "streaming_response_class": res_cls[1] if len(res_cls) > 1 else res_cls[0] - } - output = svr_tpl.render( - service_name=model_name, - license=LICENSE_HEADER, - responders=responders, - triton=triton_config - ) - elif server_type == "fastapi": - output = svr_tpl.render( - service_name=model_name, - license=LICENSE_HEADER, - responders=responders - ) - else: - raise ValueError(f"Unsupported server type: {server_type}") - with open(output_dir/"responder.py", 'w') as f: - f.write(output) - - -def generate_configuration(config, tree): - def encode_templates(templates): - encoded_templates = dict() - for key, value in templates.items(): - # these are json templates and need be encoded before being - # embeded as yaml strings - if isinstance(value, str): - encoded_templates[key] = base64.b64encode(value.encode()) - else: - encoded_templates[key] = value - return encoded_templates - # base64 encode the templates if found in the config - config_map = OmegaConf.to_container(config) - if "input" not in config_map: - config_map["input"] = [] - for m in config_map["models"]: - for i in m["input"]: - config_map["input"].append(i) - if "output" not in config_map: - config_map["output"] = [] - for m in config_map["models"]: - for o in m["output"]: - config_map["output"].append(o) - if "server" in config_map: - try: - for responder in config_map["server"]["responders"].values(): - input_templates = responder.get("requests", None) - output_templates = responder.get("responses", None) - if input_templates: - responder["requests"] = encode_templates(input_templates) - if output_templates: - responder["responses"] = encode_templates(output_templates) - except ConfigKeyError: - raise ValueError("Server config error: responders not found") - # write the config to a python file - tpl_dir = get_resource_path("templates") - jinja_env = Environment(loader=FileSystemLoader(tpl_dir)) - config_tpl = jinja_env.get_template('common/config.jinja.py') - config = OmegaConf.create(config_map) - output = config_tpl.render(config=OmegaConf.to_yaml(config), license=LICENSE_HEADER) - with open(tree/"config/__init__.py", 'w') as f: - f.write(output) - - -def main(args): - # Defense-in-depth: validate and sanitize all path arguments - try: - args.config = validate_file_path(args.config) - except ValueError as e: - raise ValueError(f"Invalid config file path: {e}") - - if not validate_server_type(args.server_type): - raise ValueError(f"Invalid server type: {args.server_type}") - - try: - args.output_dir = validate_directory_path(args.output_dir) - except ValueError as e: - raise ValueError(f"Invalid output directory: {e}") - - if args.validation_dir: - try: - args.validation_dir = validate_directory_path(args.validation_dir) - except ValueError as e: - raise ValueError(f"Invalid validation directory: {e}") - - if args.custom_module: - for module in args.custom_module: - try: - validate_file_path(module.name) - except ValueError as e: - raise ValueError(f"Invalid custom module file path: {e}") - - # Load configuration - logger.info(f"Loading configuration from: {args.config}") - config = OmegaConf.load(args.config) - - # Validate configuration against JSON schema - logger.info("Validating configuration against JSON schema...") - config_dict = OmegaConf.to_container(config, resolve=True) - - if not validate_config_against_schema(config_dict): - logger.error("Configuration validation failed. Fix the errors above and try again.") - sys.exit(1) - - with tempfile.TemporaryDirectory() as temp_dir: - tree = build_tree(args.server_type, config, temp_dir) - if args.server_type == "serverless": - build_serverless(config.name, tree) - else: - build_server(args.server_type, config.name, args.api_spec, OmegaConf.to_container(config.server), tree) - build_inference(args.server_type, config, tree) - generate_configuration(config, tree) - if not args.exclude_lib : - copy_files(get_resource_path("lib"), tree/"lib") - if args.custom_module: - build_custom_modules(args.custom_module, tree) - if args.tar_output: - target = Path(args.output_dir).resolve() / f"{config.name}.tgz" - create_tar_gz(target, tree) - else: - try: - target = Path(args.output_dir).resolve() / config.name - shutil.copytree(tree, target, dirs_exist_ok=True) - except FileExistsError: - logging.error(f"{target} already exists in the output directory") - # Build validator if validation dir provided - if args.validation_dir: - try: - validation_dir = Path(args.validation_dir).resolve() - api_spec_path = Path(args.api_spec.name).resolve() - if validate.build_validation(api_spec_path, validation_dir, not args.no_docker, args.test_cases_abs_path): - logger.info("✓ Successfully built validation") - else: - logger.error("✗ Failed to build validation") - except Exception as e: - logger.error(f"validation build failed: {e}") - # Continue with other builds - # Continue to finish the build without validation - print("Build completed successfully") - - - -if __name__ == "__main__": - parser = argparse.ArgumentParser("Inference Builder") - build_args(parser) - - try: - args = parser.parse_args() - except Exception as e: - print(f"Error: Argument parsing failed: {str(e)}") - sys.exit(1) - - # Comprehensive security validation - validation_errors = [] - - # Validate and sanitize config file path - try: - args.config = validate_file_path(args.config) - except ValueError as e: - validation_errors.append(f"Invalid config file path: {e}") - - # Validate server type (already constrained by argparse choices) - if not validate_server_type(args.server_type): - validation_errors.append(f"Invalid server type: {args.server_type}") - - # Validate and sanitize output directory - try: - args.output_dir = validate_directory_path(args.output_dir) - except ValueError as e: - validation_errors.append(f"Invalid output directory: {e}") - - # Validate and sanitize validation directory - if args.validation_dir: - try: - args.validation_dir = validate_directory_path(args.validation_dir) - except ValueError as e: - validation_errors.append(f"Invalid validation directory: {e}") - - # Exit if any validation errors - if validation_errors: - print("Security validation failed:") - for error in validation_errors: - print(f" - {error}") - sys.exit(1) - - main(args) \ No newline at end of file diff --git a/tools/inference_builder/builder/openapitools.json b/tools/inference_builder/builder/openapitools.json deleted file mode 100644 index b3ae3aa4..00000000 --- a/tools/inference_builder/builder/openapitools.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "$schema": "./node_modules/@openapitools/openapi-generator-cli/config.schema.json", - "spaces": 2, - "generator-cli": { - "version": "7.14.0" - } -} - diff --git a/tools/inference_builder/builder/samples/README.md b/tools/inference_builder/builder/samples/README.md deleted file mode 100644 index c90fb155..00000000 --- a/tools/inference_builder/builder/samples/README.md +++ /dev/null @@ -1,49 +0,0 @@ -## Introduction - -The examples in this folder demonstrates how to use Inference Builder to create inference pipelines with various backend, such as Deepstream, TensorRT, Triton, TensorRT-LLM, etc. - -With the provided Dockerfile, you can package the generated pipeline into a container image and run it as a standalone app or a microservice. Build steps vary by backend. Check the corresponding README.md in each example folder for exact instructions. For examples that run as microservices, we've provided an all-in-one [docker-compose.yml](./docker-compose.yml) to manage them together. You can customize the container behavior by changing the configurations there accordingly. - -## Models - -Some of the models for examples are from the NVIDIA GPU Cloud (NGC) repository, and certain models from NGC require active subscription. Please download and install the NGC CLI from the [NGC page](https://org.ngc.nvidia.com/setup/installers/cli) and follow the [NGC CLI Guide](https://docs.ngc.nvidia.com/cli/index.html) to set up the tool. - -## List of Examples - -### DeepStream Backend Examples - -| Name | Description | Models | Backend | Output | -|------|-------------|---------|---------|---------| -| [ds_app](./ds_app/) | examples of building standalone deepstream application | TAO Computer Vision models | Deepstream | command line interface application | -| [tao](./tao/) | examples of building inference microservices using deepstream pipeline and fastapi | TAO Computer Vision models | Deepstream | microservice | - -### Triton Backend Examples - -| Name | Description | Models | Backend | Output | -|------|-------------|---------|---------|---------| -| [changenet](./changenet/) | example of building inference microservices with triton server | Visual ChangeNet | Triton/TensorRT | microservice | - -### TensorRT Backend Examples - -| Name | Description | Models | Backend | Output | -|------|-------------|---------|---------|---------| -| [nvclip](./nvclip/) | example of building inference microservices with TensorRT backend | NVCLIP | TensorRT | microservice | - -### TensorRT-LLM Backend Examples - -| Name | Description | Models | Backend | Output | -|------|-------------|---------|---------|---------| -| [qwen](./qwen/) | example of building inference microservices with TensorRT-LLM backend for vlm models | Qwen 2.5 VL models | TensorRT-LLM, Pytorch | microservice | - -### Multiple Model Examples - -| Name | Description | Models | Backend | Output | -|------|-------------|---------|---------|---------| -| [cradio](./cradio/) | two-stage pipeline: PeopleNet Transformer (detection) then C-RADIOv3-H (per-detection embeddings) | PeopleNet Transformer, C-RADIOv3-H | DeepStream/nvinfer, TensorRT (polygraphy) | command line interface application | - -### VLLM Backend Examples - -| Name | Description | Models | Backend | Output | -|------|-------------|---------|---------|---------| -| [vllm](./vllm/) | example of building inference microservices with vLLM backend and DeepStream MediaExtractor | Cosmos-Reason2-2B, Qwen3-VL-2B-Instruct | vLLM, DeepStream | microservice | - diff --git a/tools/inference_builder/builder/samples/changenet/Dockerfile b/tools/inference_builder/builder/samples/changenet/Dockerfile deleted file mode 100644 index 0ca172a0..00000000 --- a/tools/inference_builder/builder/samples/changenet/Dockerfile +++ /dev/null @@ -1,85 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# A sample Dockerfile for a Triton inference backend with DeepStream- -# accelerated ImageInputDataFlow and CVCUDA for pre-processing. - -# Pull the base image from Deepstream Triton multiarch image. -FROM "nvcr.io/nvidia/deepstream:9.0-triton-multiarch" AS builder_base - -# Install the Python dependencies required by THIS changenet sample -# backend. These versions are pinned for reproducibility and may be -# adjusted for other pipelines or environments. -RUN --mount=type=cache,target=/root/.cache/pip \ - pip install \ - requests==2.32.3 \ - python-multipart==0.0.9 \ - aiofiles==23.2.1 \ - aiohttp==3.11.0 \ - annotated-types==0.7.0 \ - python-magic==0.4.27 \ - torch==2.7.0 --extra-index-url https://download.pytorch.org/whl/cu128 \ - torchvision==0.22.0 \ - Pillow==11.1.0 \ - open_clip_torch==2.30.0 \ - numpy==1.26.4 \ - omegaconf==2.3.0 \ - transformers==4.48.0 \ - fastapi==0.115.12 \ - uvicorn==0.29.0 \ - tritonclient[all]==2.63.0 - -# Install CVCUDA for pre-processing. -# NOTE: These .deb files are built for x86_64 and specific CUDA/Python -# versions. For different architectures or CUDA/Python versions you may -# need to update these URLs to a compatible CVCUDA release. -RUN wget https://github.com/CVCUDA/CV-CUDA/releases/download/v0.16.0/cvcuda-lib-0.16.0-cuda13-x86_64-linux.deb \ - https://github.com/CVCUDA/CV-CUDA/releases/download/v0.16.0/cvcuda-dev-0.16.0-cuda13-x86_64-linux.deb \ - https://github.com/CVCUDA/CV-CUDA/releases/download/v0.16.0/cvcuda-python3.12-0.16.0-cuda13-x86_64-linux.deb \ - && dpkg -i *.deb - -ENV TRANSFORMERS_CACHE=/tmp - -WORKDIR /workspace - -# Copy the inference flow package generated by Inference Builder. -# This tarball (here: changenet.tgz) is typically the result of running -# the builder with tar ball output enabled, and its name should match the -# generated pipeline artifact you want to deploy. -ADD changenet.tgz /workspace - -RUN groupadd --gid 1000 --non-unique nvs && \ - useradd --create-home --shell /usr/sbin/nologin --uid 1000 --non-unique --gid 1000 nvs && \ - chown 1000.1000 /workspace - -USER nvs:1000 - -# Create the entrypoint script that runs the generated -# /workspace/__main__.py (the Inference Builder pipeline entrypoint) -# and then starts Triton with /workspace/model_repo as the model repository. -RUN touch /workspace/start_server.sh && \ - chmod a+rx /workspace/start_server.sh && \ - cat > /workspace/start_server.sh <<-EOF - #!/usr/bin/env bash - set -eu - - # SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. - # SPDX-License-Identifier: LicenseRef-NvidiaProprietary - python3 /workspace/__main__.py & tritonserver --model-repository /workspace/model_repo -# while true; do sleep 5; done -EOF - -ENTRYPOINT ["/workspace/start_server.sh"] -CMD [] \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/changenet/README.md b/tools/inference_builder/builder/samples/changenet/README.md deleted file mode 100644 index 4bdb3f23..00000000 --- a/tools/inference_builder/builder/samples/changenet/README.md +++ /dev/null @@ -1,96 +0,0 @@ -# Introduction - -This sample demonstrates how to build an inference pipeline for models that use triton backend. It also shows how to incorporate CVCUDA in customized processors to accelerate media processing. - -We provide a sample Dockerfile for the example, which you can use to build a Docker image and test the microservice on any x86 system with an NVIDIA Ampere, Hopper, and Blackwell GPU. - -# Prerequisites - -**Note:** Make sure you are in the root directory (`path/to/inference_builder`) to execute the commands in this README. All relative paths and commands assume you are running from the inference_builder root directory. Also ensure that your virtual environment is activated before running any commands. - -The model used in the sample can be found on NGC: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/visual_changenet_segmentation_landsatscd - -Before downloading the model file, you need to set up your model repository: - -```bash -mkdir -p ~/.cache/model-repo && chmod 777 ~/.cache/model-repo -export MODEL_REPO=~/.cache/model-repo -``` - -## Download the model file using NGC CLI: - -```bash -ngc registry model download-version "nvidia/tao/visual_changenet_segmentation_landsatscd:deployable_v1.2" -# Move the folder to the model-repo directory, and the sample uses ~/.cache/model-repo by default -mv visual_changenet_segmentation_landsatscd_vdeployable_v1.2 $MODEL_REPO/visual_changenet -# Change the access right for docker command -chmod 777 $MODEL_REPO/visual_changenet -chmod 666 $MODEL_REPO/visual_changenet/* # correct the permissions in case they're wrong -``` - -Run `ls $MODEL_REPO/visual_changenet -l`, be sure you have `changenet_768.onnx` in your model folder and the file permissions are correct. - -**Note:** If NGC commands fail, make sure you have access to the models you are trying to download. Some models require an active subscription. Ensure NGC is set up properly, or alternatively try using the NGC web UI to directly download the model from the links provided above within the prerequisites section. - -## Create the optimized TensorRT Engine: - -```bash -docker login nvcr.io -docker run -it --rm --gpus all \ --v ~/.cache/model-repo/visual_changenet/:/changenet \ -nvcr.io/nvidia/tensorrt:25.12-py3 \ -trtexec --onnx=/changenet/changenet_768.onnx --saveEngine=/changenet/model.plan --fp16 -``` - -Run `ls $MODEL_REPO/visual_changenet -l`, be sure you have `model.plan` generated in your model folder and the file permissions are correct. The process takes approximately 10 minutes and you only need to run it once unless the model file changes. - -# Build the inference flow: - -Assume you've followed the [top level instructions](../../../README.md#getting-started) to set up the environment, and you're already in the inference_builder folder. - -The following command activates the virtual environment and generates the inference source package in `builder/samples/changenet` - -```bash -source .venv/bin/activate -python builder/main.py \ - --server-type fastapi \ - -a builder/samples/changenet/openapi.yaml \ - -c builder/samples/changenet/processors.py \ - -o builder/samples/changenet -t \ - builder/samples/changenet/trt_changenet.yaml -``` - -# Build and run the docker image: - -The process takes about 10 minutes, though the actual time may vary depending on your hardware and network bandwidth. - -```bash -cd builder/samples -docker compose up --build ms-changenet -``` - -# Test the microservice with a client - -After the server has started successfully, open a new terminal in the inference_builder folder and launch the client to compare two sample images. A display environment is required for the client to visualize the difference between the two input pictures. - -To avoid errors related to display, you need to set the DISPLAY environment variable based on your system: - -```bash -export DISPLAY=:0 # or :1 depending on your system -``` - -Then, allow X server connections from any host: -```bash -xhost + -``` - -If the configuration is successful, you will see this message in the log: `access control disabled, clients can connect from any host`. - -Then start the client: - -```bash -source .venv/bin/activate && cd builder/samples/changenet -python ib_client.py --host 127.0.0.1 --port 8803 --file test_0.jpg golden_0.jpg -``` - -If the inference request is successfully processed, you'll get a HTTP 200 response and a new window shows the difference between two images. \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/changenet/golden_0.jpg b/tools/inference_builder/builder/samples/changenet/golden_0.jpg 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The application supports various RTSP stream formats including H.264, H.265, and MJPEG. - -```bash -# media-url: the path or URL to the input media. -# mime: the media type (e.g., "video/mp4" or "image/jpeg"). - -# Note: Replace rtsp:// with your actual RTSP stream URL - -docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ - -v $MODEL_REPO:/workspace/models \ - -v /tmp/.X11-unix/:/tmp/.X11-unix \ - -e DISPLAY=$DISPLAY \ - -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ - deepstream-app \ - --media-url rtsp:// \ - --mime video/mp4 -``` - -### Run with source config - -```bash -# source-config: path to the source configuration file that defines input sources -docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ - -v $MODEL_REPO:/workspace/models \ - -v /tmp/.X11-unix/:/tmp/.X11-unix \ - -e DISPLAY=$DISPLAY \ - -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ - deepstream-app \ - --source-config /opt/nvidia/deepstream/deepstream/service-maker/sources/apps/python/pipeline_api/deepstream_test5_app/source_list_dynamic.yaml -``` - -**Note:** To use a custom source configuration file, you need to mount your file into the docker container and reference it from within the container's filesystem. This allows you to use your own source configuration instead of the default one. - -```bash -# source-config: path to the source configuration file that defines input sources - -# Note: /workspace/inputs/source_list_dynamic.yaml is just a placeholder for any config present in $SAMPLE_INPUT directory - -docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ - -v $MODEL_REPO:/workspace/models \ - -v $SAMPLE_INPUT:/workspace/inputs \ - -v /tmp/.X11-unix/:/tmp/.X11-unix \ - -e DISPLAY=$DISPLAY \ - -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ - deepstream-app \ - --source-config /workspace/inputs/source_list_dynamic.yaml -``` - -### Run with image input - -```bash -# media-url: the path or URL to the input media. -# mime: the media type (e.g., "video/mp4" or "image/jpeg"). - -# Note: /sample_input/test_1.jpg is just a placeholder for any image present in $SAMPLE_INPUT directory - -docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ - -v $SAMPLE_INPUT:/sample_input \ - -v $MODEL_REPO:/workspace/models \ - -v /tmp/.X11-unix/:/tmp/.X11-unix \ - -e DISPLAY=$DISPLAY \ - -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ - deepstream-app \ - --media-url /sample_input/test_1.jpg \ - --mime image/jpeg -``` - - diff --git a/tools/inference_builder/builder/samples/ds_app/detection/ds_detect.yaml b/tools/inference_builder/builder/samples/ds_app/detection/ds_detect.yaml deleted file mode 100644 index 2f19b82d..00000000 --- a/tools/inference_builder/builder/samples/ds_app/detection/ds_detect.yaml +++ /dev/null @@ -1,95 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -name: "deepstream-app" -model_repo: "/workspace/models" - -input: -- name: media_url - data_type: TYPE_CUSTOM_LIST - dims: [ -1 ] - optional: true -- name: mime - data_type: TYPE_CUSTOM_DS_MIME - dims: [ -1 ] - optional: true -- name: source_config - data_type: TYPE_CUSTOM_DS_SOURCE_CONFIG - dims: [ 1 ] - optional: true - -output: - - name: output - data_type: TYPE_CUSTOM_DS_METADATA - dims: [ -1 ] - -models: -- name: rtdetr - backend: deepstream/nvinfer - max_batch_size: "$MAX_BATCH_SIZE|4" - input: - - name: images - data_type: TYPE_CUSTOM_DS_IMAGE - dims: [ -1 ] - optional: true - - name: media_url - data_type: TYPE_CUSTOM_DS_MEDIA_URL - dims: [ -1 ] - optional: true - - name: mime - data_type: TYPE_CUSTOM_DS_MIME - dims: [ -1 ] - optional: true - - name: source_config - data_type: TYPE_CUSTOM_DS_SOURCE_CONFIG - dims: [ 1 ] - optional: true - output: - - name: output - data_type: TYPE_CUSTOM_DS_METADATA - dims: [ -1 ] - parameters: - # timeout in microseconds for batching multiple inputs - batch_timeout: 33333 - infer_config_path: - - nvdsinfer_config.yaml - tracker_config: - ll_lib_file: /opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so - ll_config_file: /opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/config_tracker_NvDCF_perf.yml - width: 960 - height: 544 - display_tracking_id: true - # analytics_config: - # config_file_path: /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-nvdsanalytics-test/config_nvdsanalytics.txt - # gpu_id: 0 - # msgbroker_config: - # msgbroker_proto_lib_path: /opt/nvidia/deepstream/deepstream/lib/libnvds_kafka_proto.so - # msgbroker_conn_str: localhost;9092 - # msgbroker_topic: ds_app - # msgconv_config_path: /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-test5/configs/dstest5_msgconv_sample_config.txt - render_config: - enable_display: "$ENABLE_DISPLAY|true" - enable_osd: true - # enable_stream: true - # rtsp_mount_point: /ds_app - # rtsp_port: 8554 - perf_config: - enable_fps_logs: true - enable_latency_logs: true - # kitti_output_path: - # infer: /workspace/models/resnet/infer-kitti-dump/ - # tracker: /workspace/models/resnet/tracker-kitti-dump/ - inference_timeout: "$INFERENCE_TIMEOUT|5" diff --git a/tools/inference_builder/builder/samples/ds_app/detection/mask2former/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/detection/mask2former/nvdsinfer_config.yaml deleted file mode 100644 index ec9a1dc3..00000000 --- a/tools/inference_builder/builder/samples/ds_app/detection/mask2former/nvdsinfer_config.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -property: - gpu-id: 0 - net-scale-factor: 0.017507 - offsets: 123.675;116.280;103.53 - model-color-format: 0 - onnx-file: mask2former_swint.onnx - network-type: 3 ## 3 is for instance segmentation network - infer-dims: 3;800;800 - num-detected-classes: 2 - batch-size: 1 - ## 0=FP32, 1=INT8, 2=FP16 mode - network-mode: 2 - interval: 0 - gie-unique-id: 1 - #no cluster - ## 0=Group Rectangles, 1=DBSCAN, 2=NMS, 3= DBSCAN+NMS Hybrid, 4 = None(No clustering) - ## MRCNN supports only cluster-mode=4; Clustering is done by the model itself - cluster-mode: 4 - output-instance-mask: 1 - input-object-min-width: 32 - input-object-min-height: 32 - output-blob-names: pred_masks;pred_scores;pred_classes - parse-bbox-instance-mask-func-name: NvDsInferParseCustomMask2Former - custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so - scaling-compute-hw: 1 - layer-device-precision: 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- -class-attrs-all: - pre-cluster-threshold: 0.6 - - - - diff --git a/tools/inference_builder/builder/samples/ds_app/detection/peoplenet_transformer/labels.txt b/tools/inference_builder/builder/samples/ds_app/detection/peoplenet_transformer/labels.txt deleted file mode 100644 index 4b02dbbc..00000000 --- a/tools/inference_builder/builder/samples/ds_app/detection/peoplenet_transformer/labels.txt +++ /dev/null @@ -1,4 +0,0 @@ -BG -Person -Face -Bag diff --git a/tools/inference_builder/builder/samples/ds_app/detection/peoplenet_transformer/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/detection/peoplenet_transformer/nvdsinfer_config.yaml deleted file mode 100644 index 3f7ffc8c..00000000 --- a/tools/inference_builder/builder/samples/ds_app/detection/peoplenet_transformer/nvdsinfer_config.yaml +++ /dev/null @@ -1,38 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# Reference configuration for PeopleNetTransformer v2 - -class-attrs-all: - pre-cluster-threshold: 0.3 - topk: 20 - -property: - gpu-id: 0 - offsets: "123.675;116.28;103.53" - net-scale-factor: 0.0173520735728 - labelfile-path: labels.txt - onnx-file: resnet50_peoplenet_transformer_op17.onnx - - # 0=FP32, 1=INT8, 2=FP16 mode - network-mode: 2 - network-type: 0 # detection - num-detected-classes: 4 # BG, Person, Face, Bag - gie-unique-id: 1 - output-blob-names: pred_boxes;pred_logits - infer-dims: 3;544;960 - cluster-mode: 4 - parse-bbox-func-name: NvDsInferParseCustomDDETRTAO - custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so diff --git a/tools/inference_builder/builder/samples/ds_app/detection/resnet/labels.txt b/tools/inference_builder/builder/samples/ds_app/detection/resnet/labels.txt deleted file mode 100644 index 0181d7d4..00000000 --- a/tools/inference_builder/builder/samples/ds_app/detection/resnet/labels.txt +++ /dev/null @@ -1,4 +0,0 @@ -car -bicycle -person -road_sign \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/ds_app/detection/resnet/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/detection/resnet/nvdsinfer_config.yaml deleted file mode 100644 index 9f44632a..00000000 --- a/tools/inference_builder/builder/samples/ds_app/detection/resnet/nvdsinfer_config.yaml +++ /dev/null @@ -1,37 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -property: - gpu-id: 0 - net-scale-factor: 0.00392156862745098 - onnx-file: resnet18_trafficcamnet_pruned.onnx - model-engine-file: resnet18_trafficcamnet_pruned.onnx_b4_gpu0_fp32.engine - labelfile-path: labels.txt - process-mode: 1 - model-color-format: 0 - network-mode: 0 - num-detected-classes: 4 - interval: 0 - gie-unique-id: 1 - cluster-mode: 2 - infer-dims: 3;544;960 -class-attrs-all: - topk: 20 - nms-iou-threshold: 0.5 - pre-cluster-threshold: 0.2 -class-attrs-0: - topk: 20 - nms-iou-threshold: 0.5 - pre-cluster-threshold: 0.4 \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/ds_app/detection/rtdetr/labels.txt b/tools/inference_builder/builder/samples/ds_app/detection/rtdetr/labels.txt deleted file mode 100644 index 92284f3d..00000000 --- a/tools/inference_builder/builder/samples/ds_app/detection/rtdetr/labels.txt +++ /dev/null @@ -1,5 +0,0 @@ -background -bicycle -car -person -road_sign diff --git a/tools/inference_builder/builder/samples/ds_app/detection/rtdetr/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/detection/rtdetr/nvdsinfer_config.yaml deleted file mode 100644 index 18da65ac..00000000 --- a/tools/inference_builder/builder/samples/ds_app/detection/rtdetr/nvdsinfer_config.yaml +++ /dev/null @@ -1,36 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -class-attrs-all: - pre-cluster-threshold: 0.5 -property: - cluster-mode: 4 - gie-unique-id: 1 - custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so - data-format: channels_first - infer-dims: 3;544;960 - maintain-aspect-ratio: 1 - model-color-format: 0 - symmetric-padding: 1 - # std: 1 - net-scale-factor: 0.00392156863 - network-mode: 2 - network-type: 0 - num-detected-classes: 5 - onnx-file: resnet50_trafficamnet_rtdetr.onnx - model-engine-file: resnet50_trafficamnet_rtdetr.onnx_b4_gpu0_fp16.engine - parse-bbox-func-name: NvDsInferParseCustomDDETRTAO - workspace-size: 1048576 - labelfile-path: labels.txt diff --git a/tools/inference_builder/builder/samples/ds_app/gdino/README.md b/tools/inference_builder/builder/samples/ds_app/gdino/README.md deleted file mode 100644 index 25aa72b4..00000000 --- a/tools/inference_builder/builder/samples/ds_app/gdino/README.md +++ /dev/null @@ -1,184 +0,0 @@ -## Introduction - -This sample demonstrates how to build a deepstream application with Inference Builder using grounding dino models: -1. gdino: grounding_dino_swin_tiny_commercial_deployable_v1.0 from https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/grounding_dino -2. mask_gdino: mask_grounding_dino_swin_tiny_commercial_deployable_v1.0 from https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/mask_grounding_dino - -**Note:** For both gdino and mask_gdino models, the steps are the same as described below. Users just need to replace the model name from "gdino" to "mask_gdino" in the commands and directory names. - -## Prerequisites - -**Note:** Make sure you are in the root directory (`path/to/inference_builder`) to execute the commands in this README. All relative paths and commands assume you are running from the inference_builder root directory. Also ensure that your virtual environment is activated before running any commands. - -Model files are loaded from '/workspace/models/{MODEL_NAME}' within the container, thus the volume must be correctly mapped from the host. -You need to export MODEL_REPO environment variable to the path where you want to store the model files. - -```bash -mkdir ~/.cache/model-repo/ -sudo chmod -R 777 ~/.cache/model-repo/ -export MODEL_REPO=~/.cache/model-repo -``` - -You need first download the model files from the NGC catalog and put them in the $MODEL_REPO/{model-name}/ directory, then copy the other required configurations to the same directory: - -**Note:** If NGC commands fail, make sure you have access to the models you are trying to download. Some models require an active subscription. Ensure NGC is set up properly, or alternatively try using the NGC web UI to directly download the model from the links provided [here](../README.md#models-used-in-the-samples) - -### for gdino sample - -Please use `gdino` as the directory: - -```bash -ngc registry model download-version "nvidia/tao/grounding_dino:grounding_dino_swin_tiny_commercial_deployable_v1.0" -# Move the folder to the model-repo directory, and the sample uses ~/.cache/model-repo by default -mv grounding_dino_vgrounding_dino_swin_tiny_commercial_deployable_v1.0 $MODEL_REPO/gdino -chmod 777 $MODEL_REPO/gdino -cp -r builder/samples/ds_app/gdino/gdino/* $MODEL_REPO/gdino/ -``` - -### for mask_gdino sample - -Please use `mask_gdino` as the directory: - -```bash -ngc registry model download-version "nvidia/tao/mask_grounding_dino:mask_grounding_dino_swin_tiny_commercial_deployable_v1.0" -# Move the folder to the model-repo directory, and the sample uses ~/.cache/model-repo by default -mv mask_grounding_dino_vmask_grounding_dino_swin_tiny_commercial_deployable_v1.0 $MODEL_REPO/mask_gdino -chmod 777 $MODEL_REPO/mask_gdino -cp -r builder/samples/ds_app/gdino/mask_gdino/* $MODEL_REPO/mask_gdino/ -``` - -## Generate the deepstream application package and build it into a container image: - -Assume you've followed the [top level instructions](../../../README.md#getting-started) to set up the environment and be sure you're in the inference_builder folder, then activate your virtual environment: - -```bash -source .venv/bin/activate -``` - -### For x86 Architecture - -```bash -python builder/main.py builder/samples/ds_app/gdino/ds_gdino.yaml \ - -o builder/samples/ds_app \ - --server-type serverless \ - -c builder/samples/tao/processors.py \ - -t \ -&& docker build -t deepstream-app builder/samples/ds_app -``` - -### For Tegra Architecture - -```bash -python builder/main.py builder/samples/ds_app/gdino/ds_gdino.yaml \ - -o builder/samples/ds_app \ - --server-type serverless \ - -c builder/samples/tao/processors.py \ - -t \ -&& docker build \ - -t deepstream-app \ - -f builder/samples/ds_app/Dockerfile.tegra \ - builder/samples/ds_app -``` - -#### For Dgx Spark - -```bash -python builder/main.py builder/samples/ds_app/gdino/ds_gdino.yaml \ - -o builder/samples/ds_app \ - --server-type serverless \ - -c builder/samples/tao/processors.py \ - -t \ -&& docker build \ - -t deepstream-app \ - -f builder/samples/ds_app/Dockerfile.dgxspark \ - builder/samples/ds_app -``` - -## Run the deepstream app with different inputs: - -**Note:** The TensorRT engine is generated during the first time run and it takes several minutes. - -**Note:** You can optionally set the `$SAMPLE_INPUT` environment variable to point to your input media directory if you want to perform inference on media files stored on your host machine. - -**Note:** To save the inference results, append the `-s result.json` option to your `docker run` command. - -```bash -# Update this with your actual samples directory path -export SAMPLE_INPUT=/path/to/your/samples/directory -``` - -**Note:** When you set `enable_display: true` under the `render_config` section of -[ds_gdino.yaml](ds_gdino.yaml), you need to have a display on your host and run both commands in this order to give the container access to it. For more information about render configuration options, see the [render configuration section](../README.md#render-configuration). - - -First, set the display environment variable: -```bash -export DISPLAY=:0 # or :1 depending on your system -``` - -Then, allow X server connections from any host: -```bash -xhost + -``` - -If the configuration is successful, you will see this message in the log: `access control disabled, clients can connect from any host`. - -### Run with video input - -```bash -# media-url: the path or URL to the input media. -# mime: the media type (e.g., "video/mp4" or "image/jpeg"). -# text: the text prompt for object detection (e.g., "car,person"). -docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ - -v $MODEL_REPO:/workspace/models \ - -v /tmp/.X11-unix/:/tmp/.X11-unix \ - -e DISPLAY=$DISPLAY \ - -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ - deepstream-app \ - --media-url /opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4 \ - --mime video/mp4 \ - --text "car,person" -``` - -### Run with RTSP input - -**Note:** Replace `rtsp://` (which is just a placeholder) with your actual RTSP stream URL. The application supports various RTSP stream formats including H.264, H.265, and MJPEG. - -```bash -# media-url: the path or URL to the input media. -# mime: the media type (e.g., "video/mp4" or "image/jpeg"). -# text: the text prompt for object detection (e.g., "car,person"). - -# Note: Replace rtsp:// with your actual RTSP stream URL - -docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ - -v $MODEL_REPO:/workspace/models \ - -v /tmp/.X11-unix/:/tmp/.X11-unix \ - -e DISPLAY=$DISPLAY \ - -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ - deepstream-app \ - --media-url rtsp:// \ - --mime video/mp4 \ - --text "car,person" -``` - -### Run with image input - -```bash -# media-url: the path or URL to the input media. -# mime: the media type (e.g., "video/mp4" or "image/jpeg"). -# text: the text prompt for object detection (e.g., "car,person"). - -# Note: /sample_input/test.jpg is just a placeholder for any image present in $SAMPLE_INPUT directory - -docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ - -v $SAMPLE_INPUT:/sample_input \ - -v $MODEL_REPO:/workspace/models \ - -v /tmp/.X11-unix/:/tmp/.X11-unix \ - -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ - -e DISPLAY=$DISPLAY \ - deepstream-app \ - --media-url /sample_input/test.jpg \ - --mime image/jpeg \ - --text "car,person" -``` \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/ds_app/gdino/ds_gdino.yaml b/tools/inference_builder/builder/samples/ds_app/gdino/ds_gdino.yaml deleted file mode 100644 index 7862abcf..00000000 --- a/tools/inference_builder/builder/samples/ds_app/gdino/ds_gdino.yaml +++ /dev/null @@ -1,104 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -name: "deepstream-app" -model_repo: /workspace/models - -input: -- name: media_url - data_type: TYPE_CUSTOM_LIST - dims: [ -1 ] -- name: mime - data_type: TYPE_CUSTOM_DS_MIME - dims: [ -1 ] -- name: text - data_type: TYPE_STRING - dims: [ -1 ] - -output: - - name: output - data_type: TYPE_CUSTOM_DS_METADATA - dims: [ -1 ] - -models: -- name: gdino - backend: deepstream/nvinfer - max_batch_size: "$MAX_BATCH_SIZE|1" - input: - - name: images - data_type: TYPE_CUSTOM_DS_IMAGE - dims: [ -1 ] - optional: true - - name: media_url - data_type: TYPE_CUSTOM_DS_MEDIA_URL - dims: [ -1 ] - - name: mime - data_type: TYPE_CUSTOM_DS_MIME - dims: [ -1 ] - - name: input_ids - data_type: TYPE_INT64 - dims: [ 256 ] - - name: attention_mask - data_type: TYPE_BOOL - dims: [ 256 ] - - name: position_ids - data_type: TYPE_INT64 - dims: [ 256 ] - - name: token_type_ids - data_type: TYPE_INT64 - dims: [ 256 ] - - name: text_token_mask - data_type: TYPE_BOOL - dims: [ 256, 256 ] - output: - - name: pred_logits - data_type: TYPE_FP32 - dims: [ -1, -1 ] - - name: pred_boxes - data_type: TYPE_FP32 - dims: [ -1, 4 ] - - name: pred_masks - data_type: TYPE_FP32 - dims: [ -1, -1, -1, -1 ] - - name: decoded_frames - data_type: TYPE_CUSTOM_DS_IMAGE - dims: [ -1, -1, 3 ] - parameters: - # timeout in nanoseconds for batching multiple inputs - batch_timeout: -1 - infer_config_path: - - nvdsinfer_config.yaml - preprocess_config_path: - - nvdspreprocess_config.yaml - inference_timeout: "$INFERENCE_TIMEOUT|5" - preprocessors: - - kind: "custom" - name: "gdino-tokenizer" - input: ["text"] - output: ["input_ids", "attention_mask", "position_ids", "token_type_ids", "text_token_mask", "pos_map"] - postprocessors: - - kind: "custom" - name: "gdino-postprocessor" - input: ["pred_logits", "pred_boxes", "pred_masks", "pos_map"] - output: ["output"] - config: - infer_config_path: /workspace/models/gdino/nvdsinfer_config.yaml - - kind: "custom" - name: "bbox-overlay-visualizer" - input: ["output", "decoded_frames"] - output: ["output"] - config: - output_dir: /tmp/bbox_overlay - diff --git a/tools/inference_builder/builder/samples/ds_app/gdino/gdino/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/gdino/gdino/nvdsinfer_config.yaml deleted file mode 100644 index 0212835b..00000000 --- a/tools/inference_builder/builder/samples/ds_app/gdino/gdino/nvdsinfer_config.yaml +++ /dev/null @@ -1,32 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -class-attrs-all: - topk: 20 - pre-cluster-threshold: 0.2 -property: - gie-unique-id: 1 - # std: 0.5 - net-scale-factor: 0.0078431372549 - onnx-file: grounding_dino_swin_tiny_commercial_deployable.onnx - model-engine-file: grounding_dino_swin_tiny_commercial_deployable.onnx_b1_gpu0_fp16.engine - network-mode: 2 - network-type: 100 # detection - offsets: 127.5;127.5;127.5 - output-tensor-meta: 1 - input-tensor-from-meta: 1 - infer-dims: 3;544;960 - custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so - diff --git a/tools/inference_builder/builder/samples/ds_app/gdino/gdino/nvdspreprocess_config.yaml b/tools/inference_builder/builder/samples/ds_app/gdino/gdino/nvdspreprocess_config.yaml deleted file mode 100644 index d185608f..00000000 --- a/tools/inference_builder/builder/samples/ds_app/gdino/gdino/nvdspreprocess_config.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -property: - tensor-name: inputs - processing-width: 960 - processing-height: 544 - network-input-shape: 1;3;544;960 - network-color-format: 0 - network-input-order: 0 - tensor-data-type: 0 - maintain-aspect-ratio: 1 - symmetric-padding: 1 - scaling-pool-memory-type: 0 - scaling-pool-compute-hw: 0 - scaling-filter: 0 - scaling-buf-pool-size: 6 - tensor-buf-pool-size: 6 - target-unique-ids: 1 - process-on-frame: 1 - unique-id: 5 - custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/gst-plugins/libcustom2d_preprocess.so - custom-tensor-preparation-function: CustomTensorPreparation - enable: 1 -user-configs: - pixel-normalization-factor: 0.007843137 - offsets: 127.5;127.5;127.5 -group: - - src-ids: -1 - custom-input-transformation-function: CustomAsyncTransformation - process-on-roi: 0 - diff --git a/tools/inference_builder/builder/samples/ds_app/gdino/mask_gdino/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/gdino/mask_gdino/nvdsinfer_config.yaml deleted file mode 100644 index 4577bf84..00000000 --- a/tools/inference_builder/builder/samples/ds_app/gdino/mask_gdino/nvdsinfer_config.yaml +++ /dev/null @@ -1,32 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -class-attrs-all: - topk: 20 - pre-cluster-threshold: 0.2 -property: - gie-unique-id: 1 - # std: 0.5 - net-scale-factor: 0.0078431372549 - onnx-file: model_epoch_049.onnx - model-engine-file: model_epoch_049.onnx_b1_gpu0_fp16.engine - network-mode: 2 - network-type: 100 # detection - offsets: 127.5;127.5;127.5 - output-tensor-meta: 1 - input-tensor-from-meta: 1 - infer-dims: 3;544;960 - custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so - diff --git a/tools/inference_builder/builder/samples/ds_app/gdino/mask_gdino/nvdspreprocess_config.yaml b/tools/inference_builder/builder/samples/ds_app/gdino/mask_gdino/nvdspreprocess_config.yaml deleted file mode 100644 index 1ba6d07c..00000000 --- a/tools/inference_builder/builder/samples/ds_app/gdino/mask_gdino/nvdspreprocess_config.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -property: - tensor-name: inputs - processing-width: 960 - processing-height: 544 - network-input-shape: 1;3;544;960 - network-color-format: 0 - network-input-order: 0 - tensor-data-type: 0 - maintain-aspect-ratio: 1 - symmetric-padding: 1 - scaling-pool-memory-type: 0 - scaling-pool-compute-hw: 0 - scaling-filter: 0 - scaling-buf-pool-size: 6 - tensor-buf-pool-size: 6 - target-unique-ids: 1 - process-on-frame: 1 - unique-id: 5 - custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/gst-plugins/libcustom2d_preprocess.so - custom-tensor-preparation-function: CustomTensorPreparation - enable: 1 -user-configs: - pixel-normalization-factor: 0.007843137 - offsets: 127.5;127.5;127.5 -group: - - src-ids: -1 - custom-input-transformation-function: CustomAsyncTransformation - process-on-roi: 0 - diff --git a/tools/inference_builder/builder/samples/ds_app/segmentation/README.md b/tools/inference_builder/builder/samples/ds_app/segmentation/README.md deleted file mode 100644 index 96f4afd4..00000000 --- a/tools/inference_builder/builder/samples/ds_app/segmentation/README.md +++ /dev/null @@ -1,154 +0,0 @@ -## Introduction - -This sample demonstrates how to build a deepstream application with Inference Builder using segmentation models: -1. citysemsegformer: deployable_onnx_v1.0 from https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer - -## Prerequisites - -**Note:** Make sure you are in the root directory (`path/to/inference_builder`) to execute the commands in this README. All relative paths and commands assume you are running from the inference_builder root directory. Also ensure that your virtual environment is activated before running any commands. - -Model files are loaded from '/workspace/models/{MODEL_NAME}' within the container, thus the volume must be correctly mapped from the host. -You need to export MODEL_REPO environment variable to the path where you want to store the model files. - -```bash -mkdir ~/.cache/model-repo/ -sudo chmod -R 777 ~/.cache/model-repo/ -export MODEL_REPO=~/.cache/model-repo -``` - -You need first download the model files from the NGC catalog and put them in the $MODEL_REPO/{model-name}/ directory, then copy the other required configurations to the same directory: - -**Note:** If NGC commands fail, make sure you have access to the models you are trying to download. Some models require an active subscription. Ensure NGC is set up properly, or alternatively try using the NGC web UI to directly download the model from the links provided [here](../README.md#models-used-in-the-samples) - -```bash -ngc registry model download-version "nvidia/tao/citysemsegformer:deployable_onnx_v1.0" -# Move the folder to the model-repo directory, and the sample uses ~/.cache/model-repo by default -mv citysemsegformer_vdeployable_onnx_v1.0 $MODEL_REPO/citysemsegformer -chmod 777 $MODEL_REPO/citysemsegformer -cp -r builder/samples/ds_app/segmentation/citysemsegformer/* $MODEL_REPO/citysemsegformer/ -``` - -## Generate the deepstream application package and build it into a container image: - -Assume you've followed the [top level instructions](../../../README.md#getting-started) to set up the environment and be sure you're in the inference_builder folder, then activate your virtual environment: - -```bash -source .venv/bin/activate -``` - -### For x86 Architecture - -```bash -python builder/main.py builder/samples/ds_app/segmentation/ds_segformer.yaml \ - -o builder/samples/ds_app \ - --server-type serverless \ - -t \ -&& docker build -t deepstream-app builder/samples/ds_app -``` - -### For Tegra Architecture - -```bash -python builder/main.py builder/samples/ds_app/segmentation/ds_segformer.yaml \ - -o builder/samples/ds_app \ - --server-type serverless \ - -t \ -&& docker build \ - -t deepstream-app \ - -f builder/samples/ds_app/Dockerfile.tegra \ - builder/samples/ds_app -``` - -#### For Dgx Spark - -```bash -python builder/main.py builder/samples/ds_app/segmentation/ds_segformer.yaml \ - -o builder/samples/ds_app \ - --server-type serverless \ - -t \ -&& docker build \ - -t deepstream-app \ - -f builder/samples/ds_app/Dockerfile.dgxspark \ - builder/samples/ds_app -``` - -## Run the deepstream app with different inputs: - -**Note:** The TensorRT engine is generated during the first time run and it takes several minutes. - -**Note:** You can optionally set the `$SAMPLE_INPUT` environment variable to point to your input media directory if you want to perform inference on media files stored on your host machine. - -***Note:** To save the inference results, append the `-s result.json` option to your `docker run` command. - -```bash -# Update this with your actual samples directory path -export SAMPLE_INPUT=/path/to/your/samples/directory -``` - -**Note:** When you set `enable_display: true` under the `render_config` section of [ds_segformer.yaml](ds_segformer.yaml), you need to have a display on your host and run both commands in this order to give the container access to it. For more information about render configuration options, see the [render configuration section](../README.md#render-configuration). - -First, set the display environment variable: -```bash -export DISPLAY=:0 # or :1 depending on your system -``` - -Then, allow X server connections from any host: -```bash -xhost + -``` - -If the configuration is successful, you will see this message in the log: `access control disabled, clients can connect from any host`. - -### Run with video input - -```bash -# media-url: the path or URL to the input media. -# mime: the media type (e.g., "video/mp4" or "image/jpeg"). -docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ - -v $MODEL_REPO:/workspace/models \ - -v /tmp/.X11-unix/:/tmp/.X11-unix \ - -e DISPLAY=$DISPLAY \ - -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ - deepstream-app \ - --media-url /opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4 \ - --mime video/mp4 -``` - -### Run with RTSP input - -**Note:** Replace `rtsp://` (which is just a placeholder) with your actual RTSP stream URL. The application supports various RTSP stream formats including H.264, H.265, and MJPEG. - -```bash -# media-url: the path or URL to the input media. -# mime: the media type (e.g., "video/mp4" or "image/jpeg"). - -# Note: Replace rtsp:// with your actual RTSP stream URL - -docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ - -v $MODEL_REPO:/workspace/models \ - -v /tmp/.X11-unix/:/tmp/.X11-unix \ - -e DISPLAY=$DISPLAY \ - -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ - deepstream-app \ - --media-url rtsp:// \ - --mime video/mp4 -``` - -### Run with image input - -```bash -# media-url: the path or URL to the input media. -# mime: the media type (e.g., "video/mp4" or "image/jpeg"). - -# Note /sample_input/test_1.jpg is just a placeholder for any image present in $SAMPLE_INPUT directory - -docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ - -v $SAMPLE_INPUT:/sample_input \ - -v $MODEL_REPO:/workspace/models \ - -v /tmp/.X11-unix/:/tmp/.X11-unix \ - -e DISPLAY=$DISPLAY \ - -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ - deepstream-app \ - --media-url /sample_input/test_1.jpg \ - --mime image/jpeg -``` \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/ds_app/segmentation/citysemsegformer/labels.txt b/tools/inference_builder/builder/samples/ds_app/segmentation/citysemsegformer/labels.txt deleted file mode 100644 index ff3edb4a..00000000 --- a/tools/inference_builder/builder/samples/ds_app/segmentation/citysemsegformer/labels.txt +++ /dev/null @@ -1,20 +0,0 @@ -road -sidewalk -building -wall -fence -pole -traffic light -traffic sign -vegetation -terrain -sky -person -rider -car -truck -bus -train -motorcycle -bicycle - diff --git a/tools/inference_builder/builder/samples/ds_app/segmentation/citysemsegformer/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/segmentation/citysemsegformer/nvdsinfer_config.yaml deleted file mode 100644 index 03fc5065..00000000 --- a/tools/inference_builder/builder/samples/ds_app/segmentation/citysemsegformer/nvdsinfer_config.yaml +++ /dev/null @@ -1,35 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -property: - gie-unique-id: 6 - infer-dims: 3;1024;1820 - network-input-order: 0 - model-color-format: 1 - maintain-aspect-ratio: 1 - # std 0.226 - net-scale-factor: 0.01735207357279195 - network-mode: 2 - model-color-format: 1 - offsets: 123.675;116.28;103.53 - network-type: 2 # seg - num-detected-classes: 19 - onnx-file: citysemsegformer.onnx - model-engine-file: ./citysemsegformer.onnx_b1_gpu0_fp16.engine - workspace-size: 1048576 - parse-segmentation-func-name: NvDsInferParseCustomSegformerTAO - custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so - labelfile-path: labels.txt - diff --git a/tools/inference_builder/builder/samples/ds_app/segmentation/ds_segformer.yaml b/tools/inference_builder/builder/samples/ds_app/segmentation/ds_segformer.yaml deleted file mode 100644 index e5187f52..00000000 --- a/tools/inference_builder/builder/samples/ds_app/segmentation/ds_segformer.yaml +++ /dev/null @@ -1,66 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -name: "deepstream-app" -model_repo: "/workspace/models" - -input: -- name: media_url - data_type: TYPE_CUSTOM_LIST - dims: [ -1 ] -- name: mime - data_type: TYPE_CUSTOM_DS_MIME - dims: [ -1 ] - -output: - - name: output - data_type: TYPE_CUSTOM_DS_METADATA - dims: [ -1 ] - -models: -- name: citysemsegformer - backend: deepstream/nvinfer - max_batch_size: "$MAX_BATCH_SIZE|2" - input: - - name: images - data_type: TYPE_CUSTOM_DS_IMAGE - dims: [ -1 ] - optional: true - - name: media_url - data_type: TYPE_CUSTOM_DS_MEDIA_URL - dims: [ -1 ] - - name: mime - data_type: TYPE_CUSTOM_DS_MIME - dims: [ -1 ] - output: - - name: output - data_type: TYPE_CUSTOM_DS_METADATA - dims: [ -1 ] - parameters: - # timeout in microseconds for batching multiple inputs - batch_timeout: 33333 - infer_config_path: - - nvdsinfer_config.yaml - render_config: - enable_display: "$ENABLE_DISPLAY|true" - # enable segmentation mask visualization - seg_mask_visualization: - width: 1820 - height: 1024 - # enable_osd: true - # enable_stream: true - # rtsp_mount_point: /ds_app - # rtsp_port: 8554 - inference_timeout: "$INFERENCE_TIMEOUT|5" \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/ds_app/test_config.json b/tools/inference_builder/builder/samples/ds_app/test_config.json deleted file mode 100644 index 33ff2d6b..00000000 --- a/tools/inference_builder/builder/samples/ds_app/test_config.json +++ /dev/null @@ -1,241 +0,0 @@ -[ - { - "name": "gdino_video_inference", - "description": "Test gdino inference application with video input", - "build_args": { - "TEST_APP_NAME": "deepstream-app", - "CACHE_BUSTER": "gdino_video_test", - "APP_YAML_PATH": "./gdino/ds_gdino.yaml", - "OUTPUT_DIR": ".", - "PROCESSORS_PATH": "../tao/processors.py", - "SERVER_TYPE": "serverless" - }, - "test_config": { - "default_enable": true, - "timeout": 600, - "env": { - "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes", - "NVIDIA_DRIVER_CAPABILITIES": "compute,utility,video,graphics" - }, - "volumes": { - "/tmp/model-repo/gdino": "/workspace/models/gdino" - }, - "cmd": [ - "--media-url", - "/opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4", - "--mime", - "video/mp4", - "--text", - "car,person" - ], - "prerequisite_script": null, - "models": { - "gdino": { - "source": "NGC", - "target": "/tmp/model-repo", - "path": "nvidia/tao/grounding_dino", - "version": "grounding_dino_swin_tiny_commercial_deployable_v1.0", - "configs": "./gdino/gdino" - } - } - } - }, - { - "name": "gdino_image_inference", - "description": "Test gdino inference application with image input", - "build_args": { - "TEST_APP_NAME": "deepstream-app", - "CACHE_BUSTER": "gdino_image_test", - "APP_YAML_PATH": "./gdino/ds_gdino.yaml", - "OUTPUT_DIR": ".", - "PROCESSORS_PATH": "../tao/processors.py", - "SERVER_TYPE": "serverless" - }, - "test_config": { - "default_enable": true, - "timeout": 600, - "env": { - "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes", - "NVIDIA_DRIVER_CAPABILITIES": "compute,utility,video,graphics" - }, - "volumes": { - "/tmp/model-repo/gdino": "/workspace/models/gdino" - }, - "cmd": [ - "--media-url", - "/opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.jpg", - "--mime", - "image/jpeg", - "--text", - "car,person" - ], - "prerequisite_script": null, - "models": { - "gdino": { - "source": "NGC", - "target": "/tmp/model-repo", - "path": "nvidia/tao/grounding_dino", - "version": "grounding_dino_swin_tiny_commercial_deployable_v1.0", - "configs": "./gdino/gdino" - } - } - } - }, - { - "name": "changenet_classification_inference", - "description": "Test Visual ChangeNet classification application with image input", - "build_args": { - "TEST_APP_NAME": "deepstream-app", - "CACHE_BUSTER": "changenet_classify_test", - "APP_YAML_PATH": "./classification/ds_changenet.yaml", - "OUTPUT_DIR": ".", - "PROCESSORS_PATH": "../tao/processors.py", - "SERVER_TYPE": "serverless" - }, - "test_config": { - "default_enable": true, - "timeout": 300, - "env": { - "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes", - "NVIDIA_DRIVER_CAPABILITIES": "compute,utility,video,graphics" - }, - "volumes": { - "/tmp/model-repo/changenet-classify": "/workspace/models/changenet-classify" - }, - "cmd": [ - "--media-url", - "/workspace/test_data/pass_0.png", - "/workspace/test_data/pass_1.png", - "--mime", - "image/png", - "image/png" - ], - "prerequisite_script": null, - "models": { - "changenet-classify": { - "source": "NGC", - "target": "/tmp/model-repo", - "path": "nvidia/tao/visual_changenet_classification", - "version": "visual_changenet_nvpcb_deployable_v1.0", - "configs": "./classification/changenet-classify" - } - } - } - }, - { - "name": "rtdetr_detection_inference", - "description": "Test RT-DETR detection application with video input", - "build_args": { - "TEST_APP_NAME": "deepstream-app", - "CACHE_BUSTER": "rtdetr_detection_test", - "APP_YAML_PATH": "./detection/ds_detect.yaml", - "OUTPUT_DIR": ".", - "SERVER_TYPE": "serverless" - }, - "test_config": { - "default_enable": true, - "timeout": 600, - "env": { - "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes", - "NVIDIA_DRIVER_CAPABILITIES": "compute,utility,video,graphics", - "ENABLE_DISPLAY": "false" - }, - "volumes": { - "/tmp/model-repo/rtdetr": "/workspace/models/rtdetr" - }, - "cmd": [ - "--media-url", - "/opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4", - "--mime", - "video/mp4" - ], - "prerequisite_script": null, - "models": { - "rtdetr": { - "source": "NGC", - "target": "/tmp/model-repo", - "path": "nvidia/tao/trafficcamnet_transformer_lite", - "version": "deployable_v1.0", - "configs": "./detection/rtdetr" - } - } - } - }, - { - "name": "rtdetr_detection_inference_source_file", - "description": "Test RT-DETR detection application with video input", - "build_args": { - "TEST_APP_NAME": "deepstream-app", - "CACHE_BUSTER": "rtdetr_detection_test", - "APP_YAML_PATH": "./detection/ds_detect.yaml", - "OUTPUT_DIR": ".", - "SERVER_TYPE": "serverless" - }, - "test_config": { - "default_enable": true, - "timeout": 600, - "env": { - "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes", - "NVIDIA_DRIVER_CAPABILITIES": "compute,utility,video,graphics", - "ENABLE_DISPLAY": "false" - }, - "volumes": { - "/tmp/model-repo/rtdetr": "/workspace/models/rtdetr" - }, - "cmd": [ - "--source-config", - "/workspace/test_data/source_config.yaml" - ], - "prerequisite_script": null, - "models": { - "rtdetr": { - "source": "NGC", - "target": "/tmp/model-repo", - "path": "nvidia/tao/trafficcamnet_transformer_lite", - "version": "deployable_v1.0", - "configs": "./detection/rtdetr" - } - } - } - }, - { - "name": "citysemsegformer_segmentation_inference", - "description": "Test CitySemsegFormer semantic segmentation application with image input", - "build_args": { - "TEST_APP_NAME": "deepstream-app", - "CACHE_BUSTER": "citysemsegformer_segmentation_test", - "APP_YAML_PATH": "./segmentation/ds_segformer.yaml", - "OUTPUT_DIR": ".", - "SERVER_TYPE": "serverless" - }, - "test_config": { - "default_enable": true, - "timeout": 1000, - "env": { - "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes", - "NVIDIA_DRIVER_CAPABILITIES": "compute,utility,video,graphics", - "ENABLE_DISPLAY": "false" - }, - "volumes": { - "/tmp/model-repo/citysemsegformer": "/workspace/models/citysemsegformer" - }, - "cmd": [ - "--media-url", - "/opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.jpg", - "--mime", - "image/jpeg" - ], - "prerequisite_script": null, - "models": { - "citysemsegformer": { - "source": "NGC", - "target": "/tmp/model-repo", - "path": "nvidia/tao/citysemsegformer", - "version": "deployable_onnx_v1.0", - "configs": "./segmentation/citysemsegformer" - } - } - } - } -] - diff --git a/tools/inference_builder/builder/samples/ds_app/test_data/pass_0.png b/tools/inference_builder/builder/samples/ds_app/test_data/pass_0.png deleted file mode 100644 index fbf0437d19cca1f491438b84eb7d4a2622534084..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 93529 zcmV)BK*PU@P)y?L-D=~W+C{}CW{)qD5e-1q(N_r86rs#jI7)~@Pq z^`>sM7Gw#87$ML|YQSoc*#r_uXak`IBY}jC5FijpKmc+B7PbL{aSX zco;K(5HZaB&N-QE%wpvl8J4~A7Vs^hu{G5#_Oao2I|#*v#u9_}K+@M2oyz`=?; zEz%%QaqnIndtvCtA&$hSgQw@Yec$hgem@TSNkDfOx%fKp-M;Je1KgL@8}xeX>lb>o 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CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: LicenseRef-NvidiaProprietary -# -# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual -# property and proprietary rights in and to this material, related -# documentation and any modifications thereto. Any use, reproduction, -# disclosure or distribution of this material and related documentation -# without an express license agreement from NVIDIA CORPORATION or -# its affiliates is strictly prohibited. -#################################################################################################### - -source-list: -- uri: "file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4" - sensor-id: UniqueSensorId1 - sensor-name: UniqueSensorName1 -- uri: "file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h265.mp4" - sensor-id: UniqueSensorId2 - sensor-name: UniqueSensorName2 -source-config: - source-bin: "nvurisrcbin" - properties: - file-loop: false diff --git a/tools/inference_builder/builder/samples/dummy/Dockerfile b/tools/inference_builder/builder/samples/dummy/Dockerfile deleted file mode 100644 index 836a725e..00000000 --- a/tools/inference_builder/builder/samples/dummy/Dockerfile +++ /dev/null @@ -1,75 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - - -FROM "nvcr.io/nvidia/deepstream:9.0-triton-multiarch" AS inference_base - -RUN --mount=type=cache,target=/root/.cache/pip \ - --mount=type=cache,target=/var/cache/apt,sharing=locked \ - --mount=type=cache,target=/var/lib/apt,sharing=locked \ - apt-get update && apt-get install -y --no-install-recommends curl ca-certificates libyaml-cpp-dev pkg-config cmake - - #TODO: remove curl? - #&& apt-get purge -y --auto-remove curl - -RUN --mount=type=cache,target=/root/.cache/pip \ - pip install \ - python-multipart==0.0.9 \ - aiofiles==23.2.1 \ - aiohttp==3.11.0 \ - annotated-types==0.7.0 \ - python-magic==0.4.27 \ - torch==2.4.0 \ - numpy==1.26.4 \ - omegaconf==2.3.0 \ - fastapi==0.115.12 \ - uvicorn==0.29.0 - -# Patch JPEG Decoder -RUN if [ -e "/usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstnvcodec.so" ]; then \ - mv /usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstnvcodec.so /usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstnvcodec.so_bkp; \ - else \ - echo "File 'libgstnvcodec.so' does not exist"; \ - fi -RUN rm -rf /root/.cache/gstreamer-1.0/ -RUN mkdir /tmp/assets && chmod -R 777 /tmp/assets - -WORKDIR /workspace -ADD dummy.tgz /workspace - -RUN groupadd --gid 1000 --non-unique nvs && \ - useradd --create-home --shell /usr/sbin/nologin --uid 1000 --non-unique --gid 1000 nvs && \ - chown -R 1000.1000 /workspace && chmod o+w /workspace - -USER nvs:1000 - -## Set Environment variables -ENV LD_LIBRARY_PATH /opt/tritonserver/lib:$LD_LIBRARY_PATH -ENV NVSTREAMMUX_ADAPTIVE_BATCHING=yes - -RUN touch /workspace/start_server.sh && \ - chmod a+rx /workspace/start_server.sh && \ - cat > /workspace/start_server.sh <<-EOF - #!/usr/bin/env bash - set -eu - # SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. - # SPDX-License-Identifier: LicenseRef-NvidiaProprietary - python3 /workspace/__main__.py -# while true; do sleep 5; done -EOF - -ENTRYPOINT ["/workspace/start_server.sh"] -CMD [] diff --git a/tools/inference_builder/builder/samples/dummy/README.md b/tools/inference_builder/builder/samples/dummy/README.md deleted file mode 100644 index 8bc11a15..00000000 --- a/tools/inference_builder/builder/samples/dummy/README.md +++ /dev/null @@ -1,25 +0,0 @@ -This sample demonstrates how to build a inference pipeline with a dummy backend. The experiment is mainly for testing the basic flow. - -The sample configuration supports multiple input types including image data, image assets and video assets in one endpoint - -**Note:** Make sure you are in the root directory (`path/to/inference_builder`) to execute the commands in this README. All relative paths and commands assume you are running from the inference_builder root directory. Also ensure that your virtual environment is activated before running any commands. - -Build the dummy inference flow: - -```bash -python builder/main.py builder/samples/dummy/dummy.yaml -a builder/samples/dummy/openapi.yaml -c builder/samples/dummy/processors.py -o builder/samples/dummy --server-type fastapi -t -``` -Run the dummy inference flow: - -```bash -cd builder/samples -docker compose up --build dry-run -``` -Test the dummy inference flow: - -Go to http://localhost:8800/docs to see the swagger ui. - - - - - diff --git a/tools/inference_builder/builder/samples/dummy/dummy.yaml b/tools/inference_builder/builder/samples/dummy/dummy.yaml deleted file mode 100644 index 54f6f1b1..00000000 --- a/tools/inference_builder/builder/samples/dummy/dummy.yaml +++ /dev/null @@ -1,233 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# the name will be used to identify the microservice. -name: dummy -# the root path to the directory containing the model files. -model_repo: /workspace/.cache/model-repo/ - -# top level inference input specification -input: - # a text input typically for LLM prompts - - name: "text" - data_type: TYPE_STRING - dims: [ -1 ] - optional: true - # base64 encoded image inputs typically for computer vision tasks - - name: "images" - data_type: TYPE_CUSTOM_IMAGE_BASE64 - dims: [ -1 ] - optional: true - # a list of asset ids for image files - - name: "image_assets" - data_type: TYPE_CUSTOM_IMAGE_ASSETS - dims: [ -1 ] - optional: true - # a list of asset ids for video files - - name: "video_assets" - data_type: TYPE_CUSTOM_LONG_VIDEO_ASSETS - dims: [ -1 ] - optional: true - -# top level inference output specification -output: - - name: "output" - data_type: TYPE_FP32 - dims: [ -1, -1, -1 ] - -server: - responders: - infer: - operation: inference - requests: - InferenceRequest: > - { - {% set input_items = request.input %} - {% if input_items|length > 0 %} - {% if input_items[0] is string %} - "images": [ - {% for item in input_items %} - {{ item|tojson }}{% if not loop.last %}, {% endif %} - {% endfor %} - ] - {% if request.text is not none %} - , - "text": [ - {% for item in request.text %} - {{ item|tojson }}{% if not loop.last %}, {% endif %} - {% endfor %} - ] - {% endif %} - {% elif input_items[0] is mapping %} - {% if input_items[0].contentType.startswith("image/") %} - "image_assets": [ - {% for item in input_items %} - {{ item.id|tojson }}{% if not loop.last %}, {% endif %} - {% endfor %} - ] - {% if request.text is not none %} - , - "text": [ - {% for item in request.text %} - {{ item|tojson }}{% if not loop.last %}, {% endif %} - {% endfor %} - ] - {% endif %} - {% elif input_items[0].contentType.startswith("video/") %} - "video_assets": [ - {% for item in input_items %} - {{ item.id|tojson }}{% if not loop.last %}, {% endif %} - {% endfor %} - ] - {% if request.text is not none %} - , - "text": [ - {% for item in request.text %} - {{ item|tojson }}{% if not loop.last %}, {% endif %} - {% endfor %} - ] - {% endif %} - {% endif %} - {% endif %} - {% endif %} - } - responses: - InferenceResponse: > - { - "data": [ - {% for item in response.output %} { - "index": {{loop.index0}}, - "output": {{item | tojson}} - } {% if not loop.last %}, {% endif %} {% endfor %} ], - "usage": { "num_images": 1}, - "model": "nvidia/nvdinov2" - } - add_file: - operation: add_media_file - responses: - AddFileResponse: > - { - "data": { - "id": {{response.id|tojson}}, - "path": {{response.path|tojson}}, - "size": {{response.size|tojson}}, - "duration": {{response.duration|tojson}}, - "contentType": {{response.mime_type|tojson}} - } - } - add_live_stream: - operation: add_live_stream - requests: - AddLiveStreamRequest: > - { - "url": {{request.url|tojson}}, - "description": {{request.description|tojson}}, - "username": {{request.username|tojson}}, - "password": {{request.password|tojson}} - } - responses: - AddLiveStreamResponse: > - { - "data": { - "id": {{response.id|tojson}}, - "path": {{response.path|tojson}}, - "size": {{response.size|tojson}}, - "contentType": {{response.mime_type|tojson}}, - "description": {{response.description|tojson}} - } - } - del_file: - operation: delete_media_file - responses: - DeleteAssetResponse: > - { - "deleted": {{response.status|tojson}} - } - del_live_stream: - operation: delete_live_stream - responses: - DeleteAssetResponse: > - { - "deleted": {{response.status|tojson}} - } - list_files: - operation: list_media_files - responses: - ListFilesResponse: > - { - "data": [ - {% for item in response.assets %} { - "id": {{item.id|tojson}}, - "path": {{item.path|tojson}}, - "size": {{item.size|tojson}}, - "contentType": {{item.mime_type|tojson}} - } {% if not loop.last %}, {% endif %} {% endfor %} - ] - } - list_live_streams: - operation: list_live_streams - responses: - ListLiveStreamsResponse: > - { - "data": [ - {% for item in response.assets %} { - "id": {{item.id|tojson}}, - "path": {{item.path|tojson}}, - "size": {{item.size|tojson}}, - "contentType": {{item.mime_type|tojson}} - } {% if not loop.last %}, {% endif %} {% endfor %} - ] - } - healthy_ready: - operation: health_ready_v1_health_ready_get - -models: - - name: dummy - backend: "dummy" - max_batch_size: 1 - parameters: {} - input: - - name: images - data_type: TYPE_FP32 - dims: [3, 768, 768] - - name: input_ids - data_type: TYPE_INT32 - dims: [ -1 ] - - name: input_lengths - data_type: TYPE_INT32 - dims: [ 1 ] - output: - - name: output - data_type: TYPE_FP32 - dims: [10, 10] - preprocessors: - - kind: "custom" - name: "dummy-preprocessor" - input: ["images"] - output: ["images"] - config: - network_size: [3, 768, 768] - - kind: "custom" - name: "dummy-tokenizer" - input: ["text"] - output: ["input_ids", "input_lengths"] - -routes: { - ':["images", "text"]': 'dummy', - ':["image_assets", "text"]': 'dummy:["images", "text"]', - ':["video_assets", "text"]': 'dummy:["images", "text"]', - 'dummy:["output"]': ':' -} - diff --git a/tools/inference_builder/builder/samples/dummy/openapi.yaml b/tools/inference_builder/builder/samples/dummy/openapi.yaml deleted file mode 100644 index 342734d6..00000000 --- a/tools/inference_builder/builder/samples/dummy/openapi.yaml +++ /dev/null @@ -1,499 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -openapi: 3.1.0 -info: - title: NVIDIA INFERENCE SERVICE - description: NVIDIA INFERENCE SERVICE - version: "1.0.0" - termsOfService: https://nvidia.com/legal/terms-of-use - contact: - name: NVIDIA Support - url: https://help.nvidia.com/ - license: - name: NVIDIA AI Foundation Models Community License - url: https://docs.nvidia.com/ai-foundation-models-community-license.pdf -paths: - /v1/health/ready: - get: - tags: - - "NVIDIA METROPOLIS INFERENCE API" - summary: Health Ready - description: Provide your implementation of readiness to know when container - ready to accept traffic - operationId: health_ready_v1_health_ready_get - responses: - '200': - description: Successful Response - content: - application/json: - schema: {} - /v1/files: - post: - tags: - - Files - summary: Upload a media file that can be used as inference input. - description: Upload media files to be used as inference input. Currently, only images upto 50 MB are supported. - operationId: add_media_file - requestBody: - required: true - content: - multipart/form-data: - schema: - $ref: '#/components/schemas/AddFileRequest' - responses: - "200": - description: Successful Response. - content: - application/json: - schema: - $ref: '#/components/schemas/AddFileResponse' - "500": - description: The invocation ended with an error. - content: - application/json: - schema: - $ref: '#/components/schemas/Errors' - get: - tags: - - Files - summary: Returns a list of the uploaded files. - description: Returns a list that includes ID, file name, size, purpose, file size, and media type for the uploaded files. - operationId: list_media_files - responses: - "200": - description: Successful Response. - content: - application/json: - schema: - $ref: '#/components/schemas/ListFilesResponse' - "500": - description: The invocation ended with an error. - content: - application/json: - schema: - $ref: '#/components/schemas/Errors' - /v1/files/{file_id}: - delete: - tags: - - Files - summary: Delete a uploaded file. - description: Deletes an uploaded file based on ID. - operationId: delete_media_file - parameters: - - name: file_id - in: path - required: true - schema: - type: string - maxLength: 36 - pattern: '[0-9a-fA-F]{8}-?[0-9a-fA-F]{4}-?4[0-9a-fA-F]{3}-?[89abAB][0-9a-fA-F]{3}-?[0-9a-fA-F]{12}' - description: File having 'file_id' to be deleted. - title: File Id - description: File having 'file_id' to be deleted. - responses: - "200": - description: Successful Response. - content: - application/json: - schema: - $ref: '#/components/schemas/DeleteAssetResponse' - "500": - description: The invocation ended with an error. - content: - application/json: - schema: - $ref: '#/components/schemas/Errors' - /v1/live-streams: - post: - tags: - - Live Streams - summary: Add a live stream - description: Add a live stream as an asset - operationId: add_live_stream - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/AddLiveStreamRequest' - responses: - "200": - description: Successful Response. - content: - application/json: - schema: - $ref: '#/components/schemas/AddLiveStreamResponse' - "500": - description: The invocation ended with an error. - content: - application/json: - schema: - $ref: '#/components/schemas/Errors' - get: - tags: - - Live Streams - summary: List live streams - description: List all live streams - operationId: list_live_streams - responses: - "200": - description: Successful Response. - content: - application/json: - schema: - $ref: '#/components/schemas/ListLiveStreamsResponse' - "500": - description: The invocation ended with an error. - content: - application/json: - schema: - $ref: '#/components/schemas/Errors' - /v1/live-streams/{live_stream_id}: - delete: - tags: - - Live Streams - summary: Delete a live stream - description: Delete a live stream based on ID - operationId: delete_live_stream - parameters: - - name: live_stream_id - in: path - required: true - schema: - type: string - maxLength: 36 - pattern: '[0-9a-fA-F]{8}-?[0-9a-fA-F]{4}-?4[0-9a-fA-F]{3}-?[89abAB][0-9a-fA-F]{3}-?[0-9a-fA-F]{12}' - description: Live stream having 'live_stream_id' to be deleted. - title: Live Stream Id - description: Live stream having 'live_stream_id' to be deleted. - responses: - "200": - description: Successful Response. - content: - application/json: - schema: - $ref: '#/components/schemas/DeleteAssetResponse' - "500": - description: The invocation ended with an error. - content: - application/json: - schema: - $ref: '#/components/schemas/Errors' - /v1/inference: - post: - tags: - - "NVIDIA INFERENCE API" - summary: Inference sample - description: "Invokes inference for the input images or videos. - The input image can be Base64 encoded string in the request or it can be - specified using the file identifier of an uploaded file. " - operationId: inference - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/InferenceRequest' - required: true - responses: - "200": - description: Invocation is fulfilled. - content: - application/json: - schema: - $ref: '#/components/schemas/InferenceResponse' - application/x-ndjson: - schema: - $ref: '#/components/schemas/InferenceResponse' - "500": - description: The invocation ended with an error. - content: - application/json: - schema: - $ref: '#/components/schemas/Errors' -security: - - Token: [] - -components: - securitySchemes: - Token: - type: http - scheme: bearer - schemas: - MediaAsset: - properties: - id: - type: string - maxLength: 36 - pattern: '[0-9a-fA-F]{8}-?[0-9a-fA-F]{4}-?4[0-9a-fA-F]{3}-?[89abAB][0-9a-fA-F]{3}-?[0-9a-fA-F]{12}' - title: Id - description: The file identifier (asset_id), which can be referenced in the API endpoints. - description: - type: string - title: Description - description: Description of the asset. - path: - type: string - maxLength: 256 - pattern: ^(?:[a-zA-Z][a-zA-Z0-9+.-]*:(?://[^/?#]*)?)?(?:[A-Za-z0-9_.\-~:/?#\[\]@!$&'()*+,;=%]*|(?:[A-Za-z]:)?[\\/]?[A-Za-z0-9_.\-~:/?#\[\]@!$&'()*+,;=%\\]*)$ - title: Filename - description: Local path of the file or URI with optional protocol. - examples: - - myfile.jpg - - /tmp/assets/name.jpg - - file:///tmp/myfile.jpg - - rtsp://camera.example.com/stream - - udp://192.168.1.100:5000 - - rtsp:camera.example.com/stream - - udp:192.168.1.100:5000 - - C:\Users\username\Documents\file.jpg - - \\server\share\file.jpg - - https://example.com/image.jpg - - ftp://ftp.example.com/file.mp4 - size: - type: integer - format: int64 - title: Size - description: Size of the file in bytes. - duration: - type: integer - format: int64 - title: Duration - description: Duration of the file in nanoseconds. - contentType: - type: string - title: Content Type - description: MIME type of the file. - examples: - - image/jpeg - additionalProperties: false - type: object - required: - - id - - path - title: MediaAsset - description: Represents a media asset. - AddFileResponse: - properties: - data: - $ref: '#/components/schemas/MediaAsset' - additionalProperties: false - type: object - required: - - data - title: AddFileResponse - description: Response schema for the add file request. - AddFileRequest: - properties: - file: - type: string - maxLength: 52428800 - format: binary - title: File - description: File object (not file name) to be uploaded. - type: object - required: - - file - title: AddFileRequest - description: Schema for uploading media files. - AddLiveStreamResponse: - properties: - data: - $ref: '#/components/schemas/MediaAsset' - additionalProperties: false - type: object - required: - - data - title: AddLiveStreamResponse - description: Response schema for the add live stream request. - additionalProperties: false - AddLiveStreamRequest: - properties: - url: - type: string - title: URL - description: URL of the live stream. - description: - type: string - title: Description - description: Description of the live stream. - username: - type: string - title: Username - description: Username of the live stream. - password: - type: string - title: Password - description: Password of the live stream. - type: object - required: - - url - title: AddLiveStreamRequest - description: Schema for adding live streams. - ListFilesResponse: - properties: - data: - items: - $ref: '#/components/schemas/MediaAsset' - type: array - maxItems: 100000000000000000 - title: Data - additionalProperties: false - type: object - required: - - data - title: ListFilesResponse - description: Response schema for the list files API. - ListLiveStreamsResponse: - properties: - data: - items: - $ref: '#/components/schemas/MediaAsset' - type: array - maxItems: 100000000000000000 - title: Data - additionalProperties: false - type: object - required: - - data - title: ListLiveStreamsResponse - description: Response schema for the list live streams API. - DeleteAssetResponse: - properties: - deleted: - type: boolean - title: Deleted - description: Indicates if the file was deleted - additionalProperties: false - type: object - required: - - deleted - title: DeleteAssetResponse - description: Response schema for the delete asset request. - InferenceRequest: - additionalProperties: false - properties: - input: - type: array - items: - oneOf: - - type: string - pattern: ^.*$ - maxLength: 4294967296 - - $ref: '#/components/schemas/MediaAsset' - maxItems: 64 - title: Input - description: 'The list of images on which you want to perform inference. Images should be in form - of `data:image/{format};base64,{base64encodedimage}`.Accepted formats are `jpg`, `png` and `jpeg`. Or they - can be uploaded media assets.' - text: - type: array - items: - type: string - maxLength: 256 - title: Text - description: 'A list of text to be used for the extra prompt on the inference.' - model: - type: string - enum: [nvidia/nvdino-v2, nvidia/c-radio-p3-v3, nvidia/convnext] - title: Model - description: ID of the CV model. - example: nvidia/nvdino-v2 - type: object - title: InferenceRequest - description: List of input images for the inference request. - InferenceResponse: - type: object - properties: - data: - items: - $ref: '#/components/schemas/InferenceResult' - type: array - title: Data - description: The list of inference data generated by the model. - maxItems: 10 - model: - type: string - description: Model used to generate masks. - example: nvidia/nvdino - title: Model - usage: - $ref: '#/components/schemas/Usage' - description: Usage statistics for the masks request. - examples: - - num_images: 1 - additionalProperties: false - title: InferenceResponse - description: Response schema for the inference request. - InferenceResult: - type: object - description: Represents the inference result - additionalProperties: true - properties: - index: - type: integer - format: int32 - title: Index - description: The index of the mask in the list of masks. - minimum: 0 - maximum: 127 - required: - - index - - timestamp - title: InferenceResult - Usage: - properties: - num_images: - type: integer - format: int32 - title: Num Images - minimum: 0 - maximum: 64 - description: Number of images processed. - additionalProperties: false - required: - - num_images - title: Usage - Errors: - type: object - properties: - type: - type: string - maxLength: 128 - description: Error type - title: - type: string - maxLength: 128 - description: Error title - status: - type: integer - format: int32 - minimum: 100 - maximum: 999 - description: Error status code - detail: - type: string - maxLength: 1024 - description: Detailed information about the error - instance: - type: string - maxLength: 256 - description: Function instance used to invoke the request - required: - - type - - title - - status - - detail - - instance - title: Errors diff --git a/tools/inference_builder/builder/samples/dummy/processors.py b/tools/inference_builder/builder/samples/dummy/processors.py deleted file mode 100644 index 859c5ce8..00000000 --- a/tools/inference_builder/builder/samples/dummy/processors.py +++ /dev/null @@ -1,44 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import numpy as np -import torch - -class DummyPreprocessor: - name = "dummy-preprocessor" - def __init__(self, config): - self.network_size = config['network_size'] - - def __call__(self, *args): - if len(args) != 1: - raise ValueError("DummyPreprocessor expects exactly one argument") - input = args[0] - if len(input.shape) != 3: - raise ValueError("DummyPreprocessor expects a 3D tensor") - if isinstance(input, np.ndarray) and input.dtype != np.uint8: - raise ValueError("DummyPreprocessor expects a uint8 tensor") - if isinstance(input, torch.Tensor) and input.dtype != torch.uint8: - raise ValueError("DummyPreprocessor expects a uint8 tensor") - return np.random.randn(*self.network_size) - - -class DummyTokenizer: - name = "dummy-tokenizer" - def __init__(self, config): - pass - - def __call__(self, *args): - return np.random.randint(0, 100, size=10), np.array([10]) diff --git a/tools/inference_builder/builder/samples/dummy/test_client.py b/tools/inference_builder/builder/samples/dummy/test_client.py deleted file mode 100644 index d7412ab7..00000000 --- a/tools/inference_builder/builder/samples/dummy/test_client.py +++ /dev/null @@ -1,86 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import os -import mimetypes -import requests -from requests_toolbelt.multipart.encoder import MultipartEncoder - - -def main(host, port, image_path, video_path, text): - invoke_url = "http://" + host + ":" + port + "/v1/inference" - upload_url = "http://" + host + ":" + port + "/v1/files" - image_data =[] - video_data = [] - if image_path: - for image in image_path: - multipart_data = MultipartEncoder( - fields={ - 'file': (os.path.basename(image), open(image, 'rb'), mimetypes.guess_type(image)[0]) - } - ) - response = requests.post(upload_url, headers={"Content-Type": multipart_data.content_type}, data=multipart_data) - image_data.append(response.json()["data"]) - print(f"image_data uploaded: {image_data}") - payload = { - "input": image_data, - "text": text, - "model": "nvidia/nvdino-v2" - } - headers = { - "Content-Type": "application/json", - "accept": "application/json" - } - response = requests.post(invoke_url, headers=headers, json=payload) - print(f"Inference result from image data: {response.json()}") - - if video_path: - for video in video_path: - multipart_data = MultipartEncoder( - fields={ - 'file': (os.path.basename(video), open(video, 'rb'), mimetypes.guess_type(video)[0]) - } - ) - response = requests.post(upload_url, headers={"Content-Type": multipart_data.content_type}, data=multipart_data) - video_data.append(response.json()["data"]) - print(f"video_data uploaded: {video_data}") - - payload = { - "input": video_data, - "text": text, - "model": "nvidia/nvdino-v2" - } - headers = { - "Content-Type": "application/json", - "accept": "application/x-ndjson" - } - stream_response = requests.post(invoke_url, headers=headers, json=payload, stream=True) - for line in stream_response.iter_lines(): - if line: - print(f"Inference result from video data: {line}") - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument("--host", type=str, - help= "Server IP Address", default="127.0.0.1") - parser.add_argument("--port", type=str, - help="Server port", default="8000") - parser.add_argument("--image-path", type=str, help="Image path to send for inference", nargs='*', default=None) - parser.add_argument("--video-path", type=str, help="Video path to send for inference", nargs='*', default=None) - parser.add_argument("--text", type=str, help="Extra text to send for inference", nargs='*', default=None) - args = parser.parse_args() - main(args.host, args.port, args.image_path, args.video_path, args.text) \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/nvclip/Dockerfile b/tools/inference_builder/builder/samples/nvclip/Dockerfile deleted file mode 100644 index c1cd4ed7..00000000 --- a/tools/inference_builder/builder/samples/nvclip/Dockerfile +++ /dev/null @@ -1,75 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# Sample Dockerfile for an NVCLIP embedding model pipeline that uses -# DeepStream for hardware-accelerated video decoding and a -# TensorRT backend for inference. This is a reference for this specific -# sample, not a universal template for all deployments.em -FROM "nvcr.io/nvidia/deepstream:9.0-triton-multiarch" AS builder_base - -# Install Python dependencies required by THIS NVCLIP sample (CLIP -# model, TensorRT bindings, FastAPI server, etc.). Versions are pinned -# for reproducibility and can be adjusted for other pipelines. -RUN --mount=type=cache,target=/root/.cache/pip \ - pip install \ - requests==2.32.3 \ - python-multipart==0.0.9 \ - aiofiles==23.2.1 \ - aiohttp==3.11.0 \ - annotated-types==0.7.0 \ - python-magic==0.4.27 \ - Pillow==11.1.0 \ - open_clip_torch==3.2.0 \ - numpy==1.26.4 \ - omegaconf==2.3.0 \ - transformers==4.48.1 \ - fastapi==0.115.12 \ - uvicorn==0.29.0 \ - tensorrt==10.14.1.48.post1 --extra-index-url https://pypi.nvidia.com \ - polygraphy==0.49.24 -ENV TRANSFORMERS_CACHE=/tmp - -WORKDIR /workspace - -# Copy the inference flow package generated by Inference Builder. This -# tarball (here: nvclip.tgz) is typically produced when running the -# builder with tarball output enabled, and its name should match the -# generated pipeline artifact you want to deploy. -ADD nvclip.tgz /workspace - -RUN groupadd --gid 1000 --non-unique nvs && \ - useradd --create-home --shell /usr/sbin/nologin --uid 1000 --non-unique --gid 1000 nvs && \ - chown 1000.1000 /workspace - -USER nvs:1000 - -# Create the entrypoint script that runs the Inference Builder‑generated -# /workspace/__main__.py for this NVCLIP pipeline. Uncomment the -# while‑loop or alternative ENTRYPOINT for debugging if desired. -RUN touch /workspace/start_server.sh && \ - chmod a+rx /workspace/start_server.sh && \ - cat > /workspace/start_server.sh <<-EOF - #!/usr/bin/env bash - set -eu - - # SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. - # SPDX-License-Identifier: LicenseRef-NvidiaProprietary - python3 /workspace/__main__.py -# while true; do sleep 5; done -EOF - -ENTRYPOINT ["/workspace/start_server.sh"] -#ENTRYPOINT ["/bin/bash"] -CMD [] \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/nvclip/README.md b/tools/inference_builder/builder/samples/nvclip/README.md deleted file mode 100644 index 586a3156..00000000 --- a/tools/inference_builder/builder/samples/nvclip/README.md +++ /dev/null @@ -1,88 +0,0 @@ -# Introduction - -This sample demonstrate how to create the inference microservice for an embedding model with TensorRT backend (polygrapy) and fastapi server. - -We provide a sample Dockerfile for the example, which you can use to build a Docker image and test the microservice on any x86 system with an NVIDIA Ampere, Hopper, and Blackwell GPU. - -# Prerequisites - -**Note:** Make sure you are in the root directory (`path/to/inference_builder`) to execute the commands in this README. All relative paths and commands assume you are running from the inference_builder root directory. Also ensure that your virtual environment is activated before running any commands. - -The model used in this sample can be found from NGC: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/nvclip_vit. Make sure you have the proper access right to the models, and download the checkpoints. - -Before downloading the model file, you need to set up your model repository: - -```bash -mkdir -p ~/.cache/model-repo && chmod 777 ~/.cache/model-repo -export MODEL_REPO=~/.cache/model-repo -``` - -## Download the model file using NGC CLI: - -```bash -ngc registry model download-version "nvidian/tao-nvaie/multi_modal_foundation_models:NVCLIP_224_700M_ViTH14" --dest /tmp -chmod 666 /tmp/multi_modal_foundation_models_vNVCLIP_224_700M_ViTH14/* -``` - -Run `ls /tmp/multi_modal_foundation_models_vNVCLIP_224_700M_ViTH14 -l` and be sure we have the checkpoint file `nvclip_clipa_vit_h14_700M.ckpt`. - -**Note:** If NGC commands fail, make sure you have access to the models you are trying to download. Some models require an active subscription. Ensure NGC is set up properly, or alternatively try using the NGC web UI to directly download the model from the links provided above within the prerequisites section. - -## Create the optimized TensorRT Engine: - -Prior to using the model for accelerated inference, we need to convert the checkpoints into onnx files and generate TensorRT engines accordingly. - -First build the container image for TensorRT optimizer: - -```bash -docker build -t trt-optimizer builder/samples/nvclip/optimizer -``` - -Then generate the engine files using the trt-optimizer - -```bash -docker login nvcr.io -docker run -it --rm --gpus all \ - -v $MODEL_REPO:/workspace/checkpoints/optimized \ - -v /tmp/multi_modal_foundation_models_vNVCLIP_224_700M_ViTH14:/workspace/checkpoints/baseline \ - -e CHECKPOINT_NAME=nvclip_clipa_vit_h14_700M.ckpt \ - trt-optimizer -``` - -Run `ls $MODEL_REPO/`, If the above process runs correctly, there'll be 2 folders created under model-repo directory: -- nvclip_clipa_vit_h14_700M_vision: for vision encoder -- nvclip_clipa_vit_h14_700M_text: for text encoder - -Go to inference_builder folder, adjust the permissions of each folder and copy the model config from source tree to the model folder: - -```bash -sudo chmod 777 $MODEL_REPO/nvclip_clipa_vit_h14_700M_text $MODEL_REPO/nvclip_clipa_vit_h14_700M_vision -cp builder/samples/nvclip/optimizer/configs/* $MODEL_REPO/nvclip_clipa_vit_h14_700M_text -``` - -# Build the inference flow - -Assume you've followed the [top level instructions](../../../README.md#getting-started) to set up the environment. - -```bash -source .venv/bin/activate -python builder/main.py builder/samples/nvclip/tensorrt_nvclip.yaml -a builder/samples/nvclip/openapi.yaml -c builder/samples/nvclip/processors.py -o builder/samples/nvclip --server-type fastapi -t -``` - -# Build and run the docker image - -```bash -cd builder/samples -docker compose up --build ms-nvclip -``` - -# Test the microservice with a client - -After the server is successfully started, open a new terminal in your inference_builder folder to launch the client with a jpeg or png file. The embeddings generated by the model will be returned in the json payload. - -**important:** ** is a place holder, please replace it with the actual image path** - -```bash -source .venv/bin/activate && cd builder/samples/nvclip -./test_client.sh -``` \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/nvclip/openapi.yaml b/tools/inference_builder/builder/samples/nvclip/openapi.yaml deleted file mode 100644 index b75e748b..00000000 --- a/tools/inference_builder/builder/samples/nvclip/openapi.yaml +++ /dev/null @@ -1,487 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -openapi: 3.1.0 -info: - title: NVIDIA INFERENCE SERVICE API for nvidia/nvclip-vit-h-14 - description: NVIDIA INFERENCE SERVICE API - version: 1.0.0 - termsOfService: https://nvidia.com/legal/terms-of-use - contact: - name: NVIDIA Support - url: https://help.nvidia.com/ - license: - name: NVIDIA AI Foundation Models Community License - url: https://docs.nvidia.com/ai-foundation-models-community-license.pdf -tags: - - name: "NVIDIA NVCLIP API" - description: Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms. -paths: - "/v1/embeddings": - post: - tags: - - Embeddings - summary: Creates an embedding vector representing the input text or image. - description: "Invokes inference using the embedding parameters. If uploading - large images, this POST should be used in conjunction with the NVCF API which - allows for the upload of large assets. \nYou can find details on how to use - NVCF Asset APIs here: https://docs.api.nvidia.com/cloud-functions/reference/createasset." - operationId: create_embedding - requestBody: - content: - application/json: - schema: - "$ref": "#/components/schemas/EmbeddingsRequest" - required: true - x-nvai-meta: - name: "Create Text Embedding" - description: | - Generates an embedding vector from the provided text - using a specified model. The embedding can be returned - in either float array or base64-encoded format. - path: "create" - templates: - - title: "Synchronous requests" - requestEjs: - curl: | - curl -X POST https://integrate.api.nvidia.com/v1/embeddings \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer $NVIDIA_API_KEY" \ - -d '{ - "input": ["Image of a dog", - 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"], - "model": "nvidia/nvclip-vit-h-14", - "encoding_format": "float" - }' - python: | - from openai import OpenAI - - client = OpenAI( - api_key="$NVIDIA_API_KEY", - base_url="https://integrate.api.nvidia.com/v1" - ) - - response = client.embeddings.create( - input=[ "Image of a dog", - 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"], - model="nvidia/nvclip-vit-h-14", - encoding_format="float" - ) - - print(response.data[0].embedding) - node: | - import OpenAI from 'openai'; - - const openai = new OpenAI({ - apiKey: '$NVIDIA_API_KEY', - baseURL: 'https://integrate.api.nvidia.com/v1/', - }) - - async function main() { - const response = await openai.embeddings.create({ - input: ["The quick brown fox jumped over the lazy dog", - 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" - ], - model: "nvidia/nvclip-vit-h-14", - encoding_format: "float" - }) - - const embedding = response.data[0].embedding; - process.stdout.write(`${embedding}\n`); - } - - main(); - - response: > - { - "object":"list", - 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- "object":"embedding"}], - "usage":{"num_images":1,"prompt_tokens":77,"total_tokens":77}, - "model":"nvidia/nvclip-vit-h-14" - } - responses: - '200': - description: Invocation is fulfilled. - content: - application/json: - schema: - "$ref": "#/components/schemas/EmbeddingsResponse" - '402': - description: Payment Required. - content: - application/json: - schema: - properties: - detail: - type: string - maxLength: 256 - format: ^[a-zA-Z-]{1,64}$ - description: Contains specific information related to the error and why it occurred. - example: You have reached your limit of credits. - type: object - title: PaymentRequiredError - '422': - description: Validation failed, provided entity could not be processed. - content: - application/json: - schema: - "$ref": "#/components/schemas/Errors" - example: - type: urn:nvcf-worker-service:problem-details:unprocessable-entity - title: Unprocessable Entity - status: 422 - detail: string - instance: "/v2/nvcf/pexec/functions/bc205f8e-1740-40df-8d32-c4321763498a" - requestId: 3fa85f64-5717-4562-b3fc-2c963f66afa6 - '500': - description: The invocation ended with an error. - content: - application/json: - schema: - "$ref": "#/components/schemas/Errors" - example: - type: urn:nvcf-worker-service:problem-details:internal-server-error - title: Internal Server Error - status: 500 - detail: string - instance: "/v2/nvcf/pexec/functions/bc205f8e-1740-40df-8d32-c4321763498a" - requestId: 3fa85f64-5717-4562-b3fc-2c963f66afa6 -security: -- Token: [] -components: - securitySchemes: - Token: - type: http - scheme: bearer - schemas: - NIMAssetInfoResponse: - properties: - assetName: - type: string - title: Assetname - description: The name of the asset used by NIMs. - url: - type: string - title: Url - description: The url of the asset being used. - checksum: - type: string - title: Checksum - description: The unique identifier for asset. - type: object - required: - - assetName - - url - - checksum - title: NIMAssetInfoResponse - NIMHealthSuccessResponse: - properties: - object: - type: string - title: Object - default: health.response - message: - type: string - title: Message - type: object - required: - - message - title: NIMHealthSuccessResponse - NIMLicenseInfoResponse: - properties: - name: - type: string - title: Name - description: The name of the NIMs license. - path: - type: string - title: Path - description: The path of the NIMs license file inside the container. - sha: - type: string - title: Sha - description: The unique identifier of the license. - size: - type: integer - title: Size - description: The size of the license file in bytes. - url: - type: string - title: Url - description: The url of the NIMs license. - type: - type: string - title: Type - description: The content type of the license. - content: - type: string - title: Content - description: The content inside the NIMs license file. - type: object - required: - - name - - path - - sha - - size - - url - - type - - content - title: NIMLicenseInfoResponse - NIMMetadataResponse: - properties: - version: - type: string - title: version - description: The version of the NIM service. - modelInfo: - items: - $ref: '#/components/schemas/NIMModelInfoResponse' - type: array - title: Modelinfo - description: The list of information of models being served inside NIMs - container - default: [] - assetInfo: - items: - $ref: '#/components/schemas/NIMAssetInfoResponse' - type: array - title: Assetinfo - description: The list of different assets used by the NIMs - default: [] - licenseInfo: - allOf: - - $ref: '#/components/schemas/NIMLicenseInfoResponse' - description: The information of the license of released NIMs - type: object - required: - - version - - licenseInfo - title: NIMMetadataResponse - NIMModelInfoResponse: - properties: - shortName: - type: string - title: Shortname - description: The name of the model served by NIMs - modelUrl: - type: string - title: Modelurl - description: The URL from which the model is downloaded. - type: object - required: - - shortName - - modelUrl - title: NIMModelInfoResponse - EmbeddingsRequest: - additionalProperties: false - properties: - input: - oneOf: - - type: string - title: string - example: "This is a test." - pattern: ^.*$ - - type: array - items: - type: string - pattern: ^.*$ - maxItems: 64 - title: Input - description: 'The list of images or texts that you want to generate embeddings - for. Images should be in form of `data:image/{format};base64,{base64encodedimage}`. - If the size of an image is more than 200KB, it needs to be uploaded to - a presigned S3 bucket using NVCF Asset APIs. Once uploaded you can refer - to it using the following format: ``. Accepted formats are `jpg`, `png` and `jpeg`.' - examples: - - - The quick brown fox jumped over the lazy dog - - data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAIAAACQd1PeAAAAEElEQVR4nGK6HcwNCAAA//8DTgE8HuxwEQAAAABJRU5ErkJggg== - - - The quick brown fox jumped over the lazy dog - - data:image/png;asset_id,87b132b0-08f9-43ea-8fab-f107350a5d00 - encoding_format: - type: string - enum: - - float - - base64 - title: Encoding Format - description: "The format to return the embeddings in. Can be either `float` or `base64`." - default: float - model: - type: string - enum: [nvidia/nvclip-vit-h-14] - title: Model - description: ID of the embedding model. - examples: nvidia/nvclip-vit-h-14 - type: object - required: - - input - - model - title: EmbeddingsRequest - EmbeddingsResponse: - type: object - properties: - object: - description: 'The object type, which is always `list`.' - enum: - - list - title: Object - type: string - data: - items: - "$ref": "#/components/schemas/Embedding" - type: array - title: Data - description: The list of embeddings generated by the model. - maxItems: 128 - examples: - - - embedding: - - 0.1 - - 0.1 - - 0.1 - index: 0 - object: embedding - - embedding: - - 0.1 - - 0.1 - - 0.1 - index: 1 - object: embedding - model: - type: string - description: Model used to generate embeddings. - example: nvidia/nvclip-vit-h-14 - title: Model - const: nvidia/nvclip-vit-h-14 - usage: - allOf: - - "$ref": "#/components/schemas/Usage" - description: Usage statistics for the embeddings request. - examples: - - num_images: 1 - prompt_tokens: 80 - total_tokens: 80 - required: - - object - - data - - usage - - model - title: EmbeddingsResponse - Embedding: - type: object - description: Represents an embedding vector returned by embedding endpoint. - properties: - object: - description: 'The object type, which is always `embedding`.' - enum: - - embedding - title: Object - type: string - index: - type: integer - format: int32 - title: Index - description: The index of the embedding in the list of embeddings. - minimum: 0 - maximum: 127 - embedding: - oneOf: - - type: array - description: | - The embedding vector as a list of floats, The length of the vector depends on the model. - items: - - type: number - maxItems: 1024 - - type: string - description: | - The embedding vector as base64 string. The length of the vector depends on the model. - format: ^(?:[A-Za-z0-9+/]{4})*(?:[A-Za-z0-9+/]{4}|[A-Za-z0-9+/]{3}=|[A-Za-z0-9+/]{2}==)$ - title: Embedding - required: - - index - - object - - embedding - title: Embeddings - Usage: - properties: - num_images: - type: integer - format: int32 - title: Num Images - minimum: 0 - maximum: 64 - description: Number of images passed. - prompt_tokens: - type: integer - format: int32 - title: Prompt Tokens - description: Number of tokens in the prompt. - minimum: 77 - maximum: 4928 - total_tokens: - type: integer - format: int32 - title: Total Tokens - description: Total number of tokens used in the request. - minimum: 77 - maximum: 4928 - type: object - required: - - num_images - - prompt_tokens - - total_tokens - title: Usage - Errors: - properties: - type: - type: string - format: "^.{1, 128}$" - maxLength: 128 - description: Error type - title: - type: string - format: "^.{1, 128}$" - maxLength: 128 - description: Error title - status: - type: integer - format: int32 - minimum: 100 - maximum: 999 - description: Error status code - detail: - type: string - format: "^.{1, 1024}$" - maxLength: 1024 - description: Detailed information about the error - instance: - type: string - format: "^.{1, 256}$" - maxLength: 256 - description: Function instance used to invoke the request - requestId: - type: string - format: uuid - maxLength: 36 - description: UUID of the request - type: object - required: - - type - - title - - status - - detail - - instance - - requestId - title: InvokeError diff --git a/tools/inference_builder/builder/samples/nvclip/optimizer/CLIP.png b/tools/inference_builder/builder/samples/nvclip/optimizer/CLIP.png deleted file mode 100644 index a1b5ec9171fd7a51e36e845a02304eb837142ba1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 252444 zcmeFZc{tSV`#&tQWUY|MnuLV16|yBuNp`X;jGgR^m{4i4rU==yFH?+tPg$}X>)65+ zW{e3lmSJX|x9;xycHf`x^ZS1Od5+_Gj^p{G_c2Gz^**ojdY!Lxy*$^yqs2teMNdIN 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30fd9fd1..00000000 --- a/tools/inference_builder/builder/samples/nvclip/optimizer/Dockerfile +++ /dev/null @@ -1,47 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -FROM nvcr.io/nvidia/tritonserver:26.01-py3 - -WORKDIR /workspace/ - -# Environment variables -ENV PIP_NO_CACHE_DIR=off -ENV CUDA_MODULE_LOADING=LAZY -ENV PYTHONUNBUFFERED=1 - -RUN apt update -RUN apt-get install -y git -RUN pip install open_clip_torch==3.2.0 onnx==1.20.1 onnxscript==0.1.0 transformers==4.48.1 tokenizers>=0.21 -RUN pip install --no-cache-dir --extra-index-url https://pypi.ngc.nvidia.com polygraphy - -# Copy files into the container -COPY ./configs ./configs -COPY ./CLIP.png . -COPY ./export_to_onnx.py . -COPY ./optimize.sh . - -ENV MAX_BATCH_SIZE=64 -ENV INPUT_HEIGHT=224 -ENV INPUT_WIDTH=224 -ENV TEXT_LENGTH=77 - -# Make the script executable -RUN chmod +x /workspace/optimize.sh - - -# Set entrypoint to execute the installation script and then run optimize.sh -ENTRYPOINT ["/bin/bash", "-c", "/workspace/optimize.sh"] \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/nvclip/optimizer/configs/NVCLIP_224_700M_ViTH14.json b/tools/inference_builder/builder/samples/nvclip/optimizer/configs/NVCLIP_224_700M_ViTH14.json deleted file mode 100644 index b34a5219..00000000 --- a/tools/inference_builder/builder/samples/nvclip/optimizer/configs/NVCLIP_224_700M_ViTH14.json +++ /dev/null @@ -1,29 +0,0 @@ -{ - "embed_dim": 1024, - "init_logit_bias": -10, - "vision_cfg": { - "image_size": 224, - "layers": 32, - "width": 1280, - "head_width": 80, - "patch_size": 14, - "no_ln_pre": true, - "pool_type": "avg", - "final_ln_after_pool": true, - "pos_embed_type": "sin_cos_2d", - "patch_dropout": 0.0 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 32000, - "hf_tokenizer_name": "bert-base-uncased", - "tokenizer_kwargs": { - "strip_sep_token": true - }, - "width": 1024, - "heads": 16, - "layers": 24, - "pool_type": "last", - "no_causal_mask": true - } -} diff --git a/tools/inference_builder/builder/samples/nvclip/optimizer/export_to_onnx.py b/tools/inference_builder/builder/samples/nvclip/optimizer/export_to_onnx.py deleted file mode 100644 index 17a6d25a..00000000 --- a/tools/inference_builder/builder/samples/nvclip/optimizer/export_to_onnx.py +++ /dev/null @@ -1,81 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from pathlib import Path - -import open_clip -import torch -from PIL import Image -import os - -# Register the model config -open_clip.add_model_config("configs") - -MODEL = os.environ.get("MODEL_NAME", "NVCLIP_224_700M_ViTH14") -model, _, preprocess = open_clip.create_model_and_transforms( - MODEL, - pretrained=f"/workspace/checkpoints/baseline/{os.environ.get("CHECKPOINT_NAME")}", -) -tokenizer = open_clip.get_tokenizer(MODEL) - -text = tokenizer(["a diagram", "a dog", "a cat"]) -image = preprocess(Image.open("CLIP.png")).unsqueeze(0) - -# Export to onnx -npx = 224 -dummy_image = torch.randn(10, 3, npx, npx) -model.forward(dummy_image,text) # Original CLIP result (1) - -# Vision model -torch.onnx.export(model, (dummy_image, None), - f"/workspace/checkpoints/baseline/nvclip_clipa_vit_h14_700M_vision.onnx", - export_params=True, - input_names=["IMAGE"], - output_names=["LOGITS_PER_IMAGE"], - opset_version=19, - dynamic_axes={ - "IMAGE": { - 0: "image_batch_size", - }, - "LOGITS_PER_IMAGE": { - 0: "image_batch_size", - 1: "text_batch_size", - }, - }, - verbose=True, - dynamo=False -) - - -# Text model -torch.onnx.export(model, (None, text), - f"/workspace/checkpoints/baseline/nvclip_clipa_vit_h14_700M_text.onnx", - export_params=True, - input_names=["TEXT"], - output_names=["LOGITS_PER_TEXT"], - opset_version=19, - dynamic_axes={ - "TEXT": { - 0: "text_batch_size", - }, - "LOGITS_PER_TEXT": { - 0: "text_batch_size", - 1: "image_batch_size", - }, - }, - verbose=True, - dynamo=False -) diff --git a/tools/inference_builder/builder/samples/nvclip/optimizer/optimize.sh b/tools/inference_builder/builder/samples/nvclip/optimizer/optimize.sh deleted file mode 100644 index f036f49f..00000000 --- a/tools/inference_builder/builder/samples/nvclip/optimizer/optimize.sh +++ /dev/null @@ -1,31 +0,0 @@ -#!/usr/bin/env bash - -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -set -xe - -python3 export_to_onnx.py - -mkdir -p /workspace/checkpoints/optimized/nvclip_clipa_vit_h14_700M_vision -/usr/src/tensorrt/bin/trtexec --onnx=/workspace/checkpoints/baseline/nvclip_clipa_vit_h14_700M_vision.onnx --saveEngine=/workspace/checkpoints/optimized/nvclip_clipa_vit_h14_700M_vision/model.plan --optShapes=IMAGE:${MAX_BATCH_SIZE}x3x${INPUT_HEIGHT}x${INPUT_WIDTH} \ - --minShapes=IMAGE:1x3x${INPUT_HEIGHT}x${INPUT_WIDTH} --maxShapes=IMAGE:${MAX_BATCH_SIZE}x3x${INPUT_HEIGHT}x${INPUT_WIDTH} --fp16 - -mkdir -p /workspace/checkpoints/optimized/nvclip_clipa_vit_h14_700M_text -/usr/src/tensorrt/bin/trtexec --onnx=/workspace/checkpoints/baseline/nvclip_clipa_vit_h14_700M_text.onnx --saveEngine=/workspace/checkpoints/optimized/nvclip_clipa_vit_h14_700M_text/model.plan --optShapes=TEXT:${MAX_BATCH_SIZE}x${TEXT_LENGTH} \ - --minShapes=TEXT:1x${TEXT_LENGTH} --maxShapes=TEXT:${MAX_BATCH_SIZE}x${TEXT_LENGTH} --fp16 - - diff --git a/tools/inference_builder/builder/samples/nvclip/processors.py b/tools/inference_builder/builder/samples/nvclip/processors.py deleted file mode 100644 index ab1deca8..00000000 --- a/tools/inference_builder/builder/samples/nvclip/processors.py +++ /dev/null @@ -1,69 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import numpy as np -import io -from PIL import Image -from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize, InterpolationMode - -class OpenclipTokenizer: - name = "openclip-tokenizer" - def __init__(self, config): - import open_clip - open_clip.add_model_config(config["model_home"]) - self._tokenizer = open_clip.get_tokenizer("NVCLIP_224_700M_ViTH14") - - def __call__(self, *args): - return self._tokenizer(args[0]) - -class VisionPreprocessor: - name = "nvclip-vision-preprocessor" - def __init__(self, config): - self._transform = Compose( - [ - Resize((224, 224), interpolation=InterpolationMode.BICUBIC), - CenterCrop(224), - lambda image: image.convert('RGB'), - ToTensor(), - Normalize( - (0.48145466, 0.4578275, 0.40821073), - (0.26862954, 0.26130258, 0.27577711), - ), - ] - ) - - def __call__(self, *args): - return self._transform(Image.open(io.BytesIO(args[0]))) - -class NvClipPostProcessor: - name = "nvclip-postprocessor" - def __init__(self, config): - self._config = config - - def __call__(self, *args): - text = args[0].tolist() - images = args[1].tolist() - indices = args[2].tolist() - total_tokens = sum(len(s) for s in text) - num_images = len(images) - embeddings = [] - for index in indices: - if index == "text": - embeddings.append(text.pop(0)) - elif index == "image": - embeddings.append(images.pop(0)) - return np.array(embeddings), np.array(total_tokens), np.array(num_images) - - diff --git a/tools/inference_builder/builder/samples/nvclip/tensorrt_nvclip.yaml b/tools/inference_builder/builder/samples/nvclip/tensorrt_nvclip.yaml deleted file mode 100644 index 8c7463ca..00000000 --- a/tools/inference_builder/builder/samples/nvclip/tensorrt_nvclip.yaml +++ /dev/null @@ -1,135 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -name: nvclip -model_repo: /workspace/model-repo/ - -# top level inference input specification -input: - - name: "text" - data_type: TYPE_STRING - dims: [ -1 ] - optional: true - - name: "image" - data_type: TYPE_CUSTOM_BINARY_BASE64 - dims: [ -1 ] - optional: true - - name: "indices" - data_type: TYPE_STRING - dims: [ -1 ] - optional: true - -# top level inference output specification -output: - - name: "embeddings" - data_type: TYPE_FP32 - dims: [-1, -1] - - name: "total_tokens" - data_type: TYPE_INT32 - dims: [ 1 ] - - name: "num_images" - data_type: TYPE_INT32 - dims: [ 1 ] - -server: - responders: - infer: - operation: create_embedding - requests: - # root template for generating input json from the request - EmbeddingsRequest: > - { - "text": [ - {% set text_items = request.input | reject('startswith', 'data:image') | list %} - {% for item in text_items %} - {{ item|tojson }}{% if not loop.last %}, {% endif %} - {% endfor %} - ], - "image": [ - {% set image_items = request.input | select('startswith', 'data:image') | map('replace', 'data:image\/[a-zA-Z0-9.+-]+;base64,', '') | list %} - {% for item in image_items %} - {{ item|tojson }}{% if not loop.last %}, {% endif %} - {% endfor %} - ], - "indices": [ - {% for item in request.input %} - "{{ 'image' if item.startswith('data:image') else 'text' }}"{% if not loop.last %}, {% endif %} - {% endfor %} - ] - } - responses: - EmbeddingsResponse: > - { - "data": [{% for item in response.embeddings %} {"index": {{ loop.index }}, "embedding": {{ item|tojson }}, "object": "embedding"}{% if not loop.last %}, {% endif %}{% endfor %}], - "usage": { "num_images": {{ response.num_images }}, "prompt_tokens": {{ response.total_tokens }}, "total_tokens": {{ response.total_tokens }}}, - "model": "nvidia/nvclip-vit-h-14", - "object": "list" - } - -# list of model specifications for inference -models: - - name: nvclip_clipa_vit_h14_700M_text - backend: "polygraphy" - max_batch_size: 64 - input: - - name: "TEXT" - data_type: TYPE_INT64 - dims: [-1, -1] - output: - - name: "LOGITS_PER_TEXT" - data_type: TYPE_FP32 - dims: [-1, -1] - parameters: - tensorrt_engine: "model.plan" - preprocessors: - - kind: "custom" - name: "openclip-tokenizer" - input: ["text"] - output: ["TEXT"] - - name: nvclip_clipa_vit_h14_700M_vision - backend: "polygraphy" - max_batch_size: 64 - input: - - name: "IMAGE" - data_type: TYPE_FP32 - dims: [-1, 3, -1, -1] - output: - - name: "LOGITS_PER_IMAGE" - data_type: TYPE_FP32 - dims: [-1, -1] - parameters: - tensorrt_engine: "model.plan" - preprocessors: - - kind: "custom" - name: "nvclip-vision-preprocessor" - input: ["image"] - output: ["IMAGE"] -postprocessors: - - kind: "custom" - name: "nvclip-postprocessor" - input: ["LOGITS_PER_TEXT", "LOGITS_PER_IMAGE", "indices"] - output: ["embeddings", "total_tokens", "num_images"] -# route map -# A route is defined as -# Source and destination should be defined as -# Model name is optional, so <:["TENSOR_NAME1", "TENSOR_NAME2", ...]> is valid -# Tensor list is optional too and is valid -routes: { - ':["text"]': 'nvclip_clipa_vit_h14_700M_text', - ':["image"]': 'nvclip_clipa_vit_h14_700M_vision', - ':["indices"]': ':', - 'nvclip_clipa_vit_h14_700M_text:["LOGITS_PER_TEXT"]': ':', - 'nvclip_clipa_vit_h14_700M_vision:["LOGITS_PER_IMAGE"]': ':' -} diff --git a/tools/inference_builder/builder/samples/nvclip/test_client.sh b/tools/inference_builder/builder/samples/nvclip/test_client.sh deleted file mode 100644 index 638afcee..00000000 --- a/tools/inference_builder/builder/samples/nvclip/test_client.sh +++ /dev/null @@ -1,32 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -image=$1 -image_b64=$( base64 $image ) - -url="http://localhost:8800/v1/embeddings" -echo '{ - "input": ["Image of a dog", - "data:image/png;base64,'${image_b64}'" - ], - "model": "nvidia/nvclip-vit-h-14", - "encoding_format": "float" - }' > payload.json - -curl -N -X POST $url \ - -H "Content-Type: application/json" \ - -H "$accept_header" \ - -d @payload.json - diff --git a/tools/inference_builder/builder/samples/qwen/Dockerfile b/tools/inference_builder/builder/samples/qwen/Dockerfile deleted file mode 100644 index 22c0d8ef..00000000 --- a/tools/inference_builder/builder/samples/qwen/Dockerfile +++ /dev/null @@ -1,83 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Sample Dockerfile for a Qwen multimodal pipeline using a TensorRT-LLM -# release image combined with DeepStream components for video input and -# hardware-accelerated decoding. -# -# Stage 1: pull in DeepStream libraries, binaries, and sample assets. -FROM "nvcr.io/nvidia/deepstream:9.0-triton-multiarch" AS ds_stage - -FROM nvcr.io/nvidia/tensorrt-llm/release:1.1.0 AS inference_base - -ENV NVIDIA_DRIVER_CAPABILITIES $NVIDIA_DRIVER_CAPABILITIES,video - -# Install GStreamer plugins and DeepStream runtime dependencies needed -# for hardware-accelerated decoding and RTSP support. -RUN apt update && apt install -y --no-install-recommends gstreamer1.0-plugins-good gstreamer1.0-plugins-bad \ - libyaml-cpp-dev libgstrtspserver-1.0-0 libjsoncpp25 libgles2 pkg-config - -COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/lib /opt/nvidia/deepstream/deepstream-9.0/lib -COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/bin /opt/nvidia/deepstream/deepstream-9.0/bin -COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/*.sh /opt/nvidia/deepstream/deepstream-9.0/ -COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/*.txt /opt/nvidia/deepstream/deepstream-9.0/ -COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/README* /opt/nvidia/deepstream/deepstream-9.0/ -COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/*.pdf /opt/nvidia/deepstream/deepstream-9.0/ -COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/service-maker /opt/nvidia/deepstream/deepstream-9.0/service-maker -COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/samples/streams/* /opt/nvidia/deepstream/deepstream-9.0/samples/streams/ - -RUN /opt/nvidia/deepstream/deepstream-9.0/install.sh -RUN ln -sf deepstream-9.0 /opt/nvidia/deepstream/deepstream || true - -# Install Python dependencies required by THIS Qwen sample. These -# versions are pinned for reproducibility and can be adjusted for -# other pipelines or environments. -RUN --mount=type=cache,target=/root/.cache/pip pip install qwen-vl-utils[decord]==0.0.8 \ - python-multipart==0.0.9 \ - aiofiles==23.2.1 \ - aiohttp==3.11.0 \ - annotated-types==0.7.0 \ - python-magic==0.4.27 \ - numpy==1.26.4 \ - omegaconf==2.3.0 - -WORKDIR /workspace - -# Copy the inference flow package generated by Inference Builder. -# This tarball (here: qwen.tgz) is typically produced when running the -# builder with tarball output enabled, and its name should match the -# generated pipeline artifact you want to deploy. -ADD qwen.tgz /workspace - -## Set Environment variables required for DeepStream+TensorRT-LLM runtime. -ENV LD_LIBRARY_PATH /opt/tritonserver/lib:/opt/nvidia/deepstream/deepstream/lib:/usr/local/tensorrt/lib/:/opt/tritonserver/backends/tensorrtllm:$LD_LIBRARY_PATH -ENV NVSTREAMMUX_ADAPTIVE_BATCHING=yes -ENV HF_HOME=/tmp - -# Create the entrypoint script that runs the Inference Builder-generated -# /workspace/__main__.py for this Qwen pipeline. -RUN touch /workspace/start_server.sh && \ - chmod a+rx /workspace/start_server.sh && \ - cat > /workspace/start_server.sh <<-EOF - #!/usr/bin/env bash - set -eu - # SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. - # SPDX-License-Identifier: LicenseRef-NvidiaProprietary - python3 /workspace/__main__.py -# while true; do sleep 5; done -EOF - -ENTRYPOINT ["/workspace/start_server.sh"] -CMD [] diff --git a/tools/inference_builder/builder/samples/qwen/README.md b/tools/inference_builder/builder/samples/qwen/README.md deleted file mode 100644 index 58875de4..00000000 --- a/tools/inference_builder/builder/samples/qwen/README.md +++ /dev/null @@ -1,129 +0,0 @@ -## Introduction - -This example demonstrates how to build an inference pipeline and for the Qwen family of VLM models using the Inference Builder tool. Following models have been tested: -- Qwen/Qwen2.5-VL-3B-Instruct -- Qwen/Qwen2.5-VL-7B-Instruct -- Qwen/Qwen2.5-VL-32B-Instruct - -Three configurations are provided, allowing you to choose based on your specific software and hardware environment: - -1. pytorch_qwen.yaml: leveraging the transformer APIs and fits all the models -2. trtllm_qwen.yaml: leveraging TensorRT LLM APIs for better performance -3. trtllm_nvdec_qwen.yaml: leveraging h/w decoder and TensorRT LLM for the best performance - -We provide a sample Dockerfile for the example, which you can use to build a Docker image and test the microservice on any x86 system. - -**⚠️ System Requirements:** This model requires significant GPU resources and is optimized for high-performance systems. We recommend testing on H100 or B200 GPUs for optimal performance. Docker build may fail on less powerful hardware. - -## Prerequisites - -**Note:** Make sure you are in the root directory (`path/to/inference_builder`) to execute the commands in this README. All relative paths and commands assume you are running from the inference_builder root directory. Also ensure that your virtual environment is activated before running any commands. - -Before downloading the model file, you need to set up your model repository: - -```bash -mkdir -p ~/.cache/model-repo && chmod 777 ~/.cache/model-repo -export MODEL_REPO=~/.cache/model-repo -``` - -The model checkpoints can be downloaded from huggingface. First, install git-lfs for large model files: - -```bash -sudo apt install git-lfs -git lfs install -``` - -Then download the model: - -```bash -git clone https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct $MODEL_REPO/Qwen2.5-VL -``` - -Assume you've followed the [top level instructions](../../../README.md#getting-started) to set up the environment and be sure you're in the inference_builder folder, then activate your virtual environment: - -```bash -source .venv/bin/activate -``` - -## Build and Test the Qwen2.5-VL-7B-Instruct Inference Microservice - -### Generate the Inference Pipeline - -For Pytorch backend with transformers API, use the command below: - -```bash -python builder/main.py builder/samples/qwen/pytorch_qwen.yaml --api-spec builder/samples/qwen/openapi.yaml \ --o builder/samples/qwen/ -c builder/samples/qwen/processors.py -t -``` - -For TensorRT-LLM backend with S/W decoder, use the command below: - -```bash -python builder/main.py builder/samples/qwen/trtllm_qwen.yaml --api-spec builder/samples/qwen/openapi.yaml \ --o builder/samples/qwen/ -c builder/samples/qwen/processors.py -t -``` - -If you want to acheive the best performance with Deepstream and TensoRT-LLM, please use following command: - -```bash -# Enable hardware decoder -python builder/main.py builder/samples/qwen/trtllm_nvdec_qwen.yaml --api-spec builder/samples/qwen/openapi.yaml \ --o builder/samples/qwen/ -c builder/samples/qwen/processors.py -t -``` - -### Build and Start the Inference Microservice: - -```bash -cd builder/samples && docker compose up ms-qwen --build -``` - -The build may take a while—images are pulled from NGC and speed depends on your network. ⌛ - -### Test the Inference Microservice with a client - -Wait for the server to start, then open a new terminal in your inference_builder folder. The sample includes a test OpenAI client. For image input, run the client with the path to an image file. - - -```bash -source .venv/bin/activate && cd builder/samples/qwen -python client.py --images # replace placeholder with an actual file -``` - -For video input, you need to first upload a test video file. - -**⚠️ Important:** **replace the placeholder in below command with an actual file path in your system**. - -```bash -export VIDEO_FILE= -curl -X "POST" \ - "http://localhost:8800/v1/files" \ - -H "accept: application/json" \ - -H "Content-Type: multipart/form-data" \ - -F "file=@$VIDEO_FILE;type=video/mp4" -``` - -You'll get a response 200 with a json body which includes the id and path of the uploaded asset like below: - -{ - "data": { - "id": "577a9f11-2b24-4db8-82c8-2601e0c2b6e4", - "path": "/tmp/assets/577a9f11-2b24-4db8-82c8-2601e0c2b6e4/its_1920_30s.mp4", - "size": 3472221, - "duration": 30000000000, - "contentType": "video/mp4" - } -} - -Run the client.py with the video path from your response if the inference pipeline is built from trtllm_qwen.yaml or pytorch_qwen.yaml without H/W decoder: - -```bash -# Please use the path from your file upload response -python client.py --videos /tmp/assets/577a9f11-2b24-4db8-82c8-2601e0c2b6e4/its_1920_30s.mp4 -``` - -OR run the client.py with the returned asset id if the inference pipeline is built from trtllm_nvdec_qwen.yaml with H/W decoder enabled: - -```bash -# Please replace 577a9f11-2b24-4db8-82c8-2601e0c2b6e4 with the id returned from your file upload response -python client.py --videos 577a9f11-2b24-4db8-82c8-2601e0c2b6e4?frames=8 -``` \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/qwen/client.py b/tools/inference_builder/builder/samples/qwen/client.py deleted file mode 100644 index 4c0a780b..00000000 --- a/tools/inference_builder/builder/samples/qwen/client.py +++ /dev/null @@ -1,137 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import time -import argparse -from openai import OpenAI -import base64 -import mimetypes -from concurrent.futures import ThreadPoolExecutor, as_completed -from urllib.parse import urlparse, parse_qs - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument("--images", type=str, help="Image paths to send for inference", default=None, nargs="*") - parser.add_argument("--videos", type=str, help="Video paths to send for inference", default=None, nargs="*") - parser.add_argument("--endpoint", type=str, help="Endpoint to send for inference", default="http://0.0.0.0:8800/v1", nargs="?") - parser.add_argument("-p", "--parallel", type=int, help="Number of parallel requests to send", default=1) - args = parser.parse_args() - - messages = [] - if args.images: - for image in args.images: - with open(image, "rb") as f: - data = base64.b64encode(f.read()).decode() - mime_type = mimetypes.guess_type(image)[0] - messages.append({ - "role": "user", - "content": [ - { - "type": "text", - "text": "Please describe the image in detail." - }, - { - "type": "image", - "image": f"data:{mime_type};base64,{data}" - } - ] - }) - elif args.videos: - for video in args.videos: - # Parse video path to check for ?nframes=x query parameter - # Only extract nframes if video starts with "/" (local file path) - if video.startswith('/') and '?' in video: - video_path, query_string = video.split('?', 1) - query_params = parse_qs(query_string) - - video_content = { - "type": "video", - "video": video_path - } - - # Add nframes parameter if present - if 'nframes' in query_params: - try: - nframes_value = int(query_params['nframes'][0]) - video_content["nframes"] = nframes_value - except (ValueError, IndexError): - pass # Ignore invalid nframes values - else: - video_content = { - "type": "video", - "video": video - } - - messages.append({ - "role": "user", - "content": [ - { - "type": "text", - "text": "Please describe the video in detail." - }, - video_content - ] - }) - else: - raise ValueError("No images or videos provided") - - client = OpenAI(base_url=args.endpoint, api_key="not-used") - - def make_request(request_id): - """Make a single request to the API""" - start_time = time.time() - chat_response = client.chat.completions.create( - model="nvidia/cosmos", - messages=messages, - max_tokens=512, - stream=False - ) - infer_time = time.time() - start_time - return request_id, infer_time, chat_response.choices[0].message - - # Send requests in parallel - overall_start_time = time.time() - if args.parallel == 1: - # Single request mode - request_id, infer_time, assistant_message = make_request(0) - print(f"Inference time: {infer_time:.4f} seconds") - print(assistant_message) - else: - # Parallel requests mode - with ThreadPoolExecutor(max_workers=args.parallel) as executor: - futures = [executor.submit(make_request, i) for i in range(args.parallel)] - - results = [] - for future in as_completed(futures): - request_id, infer_time, assistant_message = future.result() - results.append((request_id, infer_time, assistant_message)) - print(f"Request {request_id} completed in {infer_time:.4f} seconds") - - # Sort results by request_id for consistent output - results.sort(key=lambda x: x[0]) - - overall_time = time.time() - overall_start_time - avg_time = sum(r[1] for r in results) / len(results) - print(f"\n--- Summary ---") - print(f"Total requests: {args.parallel}") - print(f"Overall time: {overall_time:.4f} seconds") - print(f"Average inference time: {avg_time:.4f} seconds") - print(f"Throughput: {args.parallel / overall_time:.2f} requests/second") - - # Print all responses - print(f"\n--- Responses ---") - for req_id, _, assistant_message in results: - print(f"\nResponse (Request {req_id}):") - print(assistant_message) diff --git a/tools/inference_builder/builder/samples/qwen/openapi.yaml b/tools/inference_builder/builder/samples/qwen/openapi.yaml deleted file mode 100644 index 8f444d5f..00000000 --- a/tools/inference_builder/builder/samples/qwen/openapi.yaml +++ /dev/null @@ -1,1367 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -openapi: 3.1.0 -info: - title: NVIDIA NIM for VLMs - summary: Accelerated VLM inference for NVIDIA GPUs. - version: 1.1.2 -paths: - /v1/health/ready: - get: - tags: - - Health - summary: Health Ready - description: Provide your implementation of readiness to know when container ready to accept traffic - operationId: health_ready_v1_health_ready_get - responses: - '200': - description: Successful Response - content: - application/json: - schema: {} - /v1/chat/completions: - post: - summary: OpenAI-compatible chat endpoint - operationId: create_chat_completion_v1_chat_completions_post - parameters: - - name: content-type - in: header - required: false - schema: - anyOf: - - type: string - - type: 'null' - title: Content-Type - requestBody: - required: true - content: - application/json: - schema: - $ref: '#/components/schemas/NIMLLMChatCompletionRequest' - responses: - '200': - description: Successful Response - content: - application/json: - schema: - anyOf: - - $ref: '#/components/schemas/NIMLLMChatCompletionResponse' - - $ref: '#/components/schemas/NIMLLMChatCompletionStreamResponse' - title: Response Create Chat Completion V1 Chat Completions Post - '400': - description: Received an invalid request possibly containing unsupported - or out-of-range parameter values. - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '404': - description: The requested model does not exist. - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal Server Error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '415': - description: Unsupported Media Type - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '422': - description: Validation Error - content: - application/json: - schema: - $ref: '#/components/schemas/HTTPValidationError' - /v1/files: - post: - tags: - - Files - summary: Upload a media file that can be used as inference input. - description: Upload media files to be used as inference input. Currently, only images upto 50 MB are supported. - operationId: add_media_file - requestBody: - required: true - content: - multipart/form-data: - schema: - $ref: '#/components/schemas/AddFileRequest' - responses: - "200": - description: Successful Response. - content: - application/json: - schema: - $ref: '#/components/schemas/AddFileResponse' - "500": - description: The invocation ended with an error. - content: - application/json: - schema: - $ref: '#/components/schemas/Errors' - get: - tags: - - Files - summary: Returns a list of the uploaded files. - description: Returns a list that includes ID, file name, size, purpose, file size, and media type for the uploaded files. - operationId: list_media_files - responses: - "200": - description: Successful Response. - content: - application/json: - schema: - $ref: '#/components/schemas/ListFilesResponse' - "500": - description: The invocation ended with an error. - content: - application/json: - schema: - $ref: '#/components/schemas/Errors' - /v1/files/{file_id}: - delete: - tags: - - Files - summary: Delete a uploaded file. - description: Deletes an uploaded file based on ID. - operationId: delete_media_file - parameters: - - name: file_id - in: path - required: true - schema: - type: string - maxLength: 36 - pattern: '[0-9a-fA-F]{8}-?[0-9a-fA-F]{4}-?4[0-9a-fA-F]{3}-?[89abAB][0-9a-fA-F]{3}-?[0-9a-fA-F]{12}' - description: File having 'file_id' to be deleted. - title: File Id - description: File having 'file_id' to be deleted. - responses: - "200": - description: Successful Response. - content: - application/json: - schema: - $ref: '#/components/schemas/DeleteFileResponse' - "500": - description: The invocation ended with an error. - content: - application/json: - schema: - $ref: '#/components/schemas/Errors' -components: - schemas: - BaseModel: - properties: {} - type: object - title: BaseModel - ChatCompletionLogProb: - properties: - token: - type: string - title: Token - logprob: - type: number - title: Logprob - default: -9999.0 - bytes: - anyOf: - - items: - type: integer - type: array - - type: 'null' - title: Bytes - additionalProperties: false - type: object - required: - - token - title: ChatCompletionLogProb - ChatCompletionLogProbs: - properties: - content: - anyOf: - - items: - $ref: '#/components/schemas/ChatCompletionLogProbsContent' - type: array - - type: 'null' - title: Content - additionalProperties: false - type: object - title: ChatCompletionLogProbs - ChatCompletionLogProbsContent: - properties: - token: - type: string - title: Token - logprob: - type: number - title: Logprob - default: -9999.0 - bytes: - anyOf: - - items: - type: integer - type: array - - type: 'null' - title: Bytes - top_logprobs: - items: - $ref: '#/components/schemas/ChatCompletionLogProb' - type: array - title: Top Logprobs - additionalProperties: false - type: object - required: - - token - title: ChatCompletionLogProbsContent - ChatCompletionNamedFunction: - properties: - name: - type: string - title: Name - additionalProperties: false - type: object - required: - - name - title: ChatCompletionNamedFunction - ChatCompletionNamedToolChoiceParam: - properties: - function: - $ref: '#/components/schemas/ChatCompletionNamedFunction' - type: - type: string - const: function - title: Type - default: function - additionalProperties: false - type: object - required: - - function - title: ChatCompletionNamedToolChoiceParam - ChatCompletionResponseChoice: - properties: - index: - type: integer - title: Index - message: - $ref: '#/components/schemas/ChatMessage' - logprobs: - anyOf: - - $ref: '#/components/schemas/ChatCompletionLogProbs' - - type: 'null' - finish_reason: - anyOf: - - type: string - enum: - - stop - - length - - tool_calls - - type: 'null' - title: Finish Reason - default: stop - stop_reason: - anyOf: - - type: integer - - type: string - - type: 'null' - title: Stop Reason - additionalProperties: false - type: object - required: - - index - - message - title: ChatCompletionResponseChoice - ChatCompletionResponseStreamChoice: - properties: - index: - type: integer - title: Index - delta: - $ref: '#/components/schemas/DeltaMessage' - logprobs: - anyOf: - - $ref: '#/components/schemas/ChatCompletionLogProbs' - - type: 'null' - finish_reason: - anyOf: - - type: string - enum: - - stop - - length - - tool_calls - - type: 'null' - title: Finish Reason - stop_reason: - anyOf: - - type: integer - - type: string - - type: 'null' - title: Stop Reason - additionalProperties: false - type: object - required: - - index - - delta - title: ChatCompletionResponseStreamChoice - ChatCompletionToolsParam: - properties: - type: - type: string - const: function - title: Type - default: function - function: - $ref: '#/components/schemas/FunctionDefinition' - additionalProperties: false - type: object - required: - - function - title: ChatCompletionToolsParam - ChatMessage: - properties: - role: - type: string - title: Role - content: - anyOf: - - type: string - - type: 'null' - title: Content - tool_calls: - anyOf: - - items: - $ref: '#/components/schemas/ToolCall' - type: array - - type: 'null' - title: Tool Calls - additionalProperties: false - type: object - required: - - role - title: ChatMessage - DeltaMessage: - properties: - role: - anyOf: - - type: string - - type: 'null' - title: Role - content: - anyOf: - - type: string - - type: 'null' - title: Content - tool_calls: - anyOf: - - items: - $ref: '#/components/schemas/ToolCall' - type: array - - type: 'null' - title: Tool Calls - additionalProperties: false - type: object - title: DeltaMessage - ErrorResponse: - properties: - object: - type: string - title: Object - default: error - message: - type: string - title: Message - type: - type: string - title: Type - param: - anyOf: - - type: string - - type: 'null' - title: Param - code: - type: integer - title: Code - additionalProperties: false - type: object - required: - - message - - type - - code - title: ErrorResponse - FunctionCall: - properties: - name: - anyOf: - - type: string - - type: 'null' - title: Name - default: '' - arguments: - anyOf: - - type: string - - type: 'null' - title: Arguments - default: '' - additionalProperties: false - type: object - title: FunctionCall - FunctionDefinition: - properties: - name: - type: string - title: Name - description: - anyOf: - - type: string - - type: 'null' - title: Description - parameters: - anyOf: - - type: object - - type: 'null' - title: Parameters - additionalProperties: false - type: object - required: - - name - title: FunctionDefinition - HTTPValidationError: - properties: - detail: - items: - $ref: '#/components/schemas/ValidationError' - type: array - title: Detail - type: object - title: HTTPValidationError - ImageURL: - properties: - url: - type: string - title: Url - type: object - required: - - url - title: ImageURL - Logprob: - properties: - logprob: - type: number - title: Logprob - rank: - anyOf: - - type: integer - - type: 'null' - title: Rank - decoded_token: - anyOf: - - type: string - - type: 'null' - title: Decoded Token - type: object - required: - - logprob - title: Logprob - NIMLLMChatCompletionMessage: - properties: - role: - $ref: '#/components/schemas/Role' - description: The role of the message's author. - content: - anyOf: - - type: string - minLength: 1 - - items: - anyOf: - - $ref: '#/components/schemas/NIMVLMChatCompletionContentPartImage' - - $ref: '#/components/schemas/NIMVLMChatCompletionContentPartText' - - $ref: '#/components/schemas/NIMVLMChatCompletionContentPartVideo' - - $ref: '#/components/schemas/NIMVLMChatCompletionContentPartVideoStreaming' - type: array - title: Content - description: "The contents of the message.\n
To pass images (only\ - \ with role=`user`):\n
- When content is a string, image\ - \ can be passed together with the text with `img` HTML tags that wraps\n\ - \ an image URL (``),\n base64 encoded\ - \ image data (``),\n or an NVCF asset ID (``)\n when the container is hosted in NVCF and the payload exceeds\ - \ 200KB.\n or http link (`https:{image_link}`)\n
\ - \ - When content is a list of objects, images can be passed as objects\ - \ with type=`image_url`.\n
- In both cases, images can\ - \ be PNG, JPG or JPEG.\n
To pass videos (only with role=`user`):\n\ - \
- When content is a string, video can be passed together\ - \ with the text with `video` HTML tags that wraps\n a video URL\ - \ (`&0n`apqhR)V<5#YOppV;UCdi{4g^bNwDo@ve>* zu<)n}=kVe_CYI+$f!wC(lysUbn)Ytqo zt>XD2cs33i18WsHotT7oC8^Z5Cd=(g8jTt5Rw&uESG^=PjwJ=i^lbd20 zNg3iGuki>vq@e-XWYhlQxJ6ieNH`AR%OEnaXUf8(^Jx?fqfNq9aoCc!uGqo)>})zu zuzKzJO)6g1e$9>0(W0z$%S4heywNYOn%Y1tOUVnq$vt?!zEwiz=TfIAM@)}(8`-DZs2RXMk(HRbx%0nwEY>kebuhoPE!^ByHk)9H_ zm*It_g;tK%bb4Gth6n|B`>nQ(ET7vlnpN8B7QUe}Z3OlPEx4Tu#p#|agk^Ff^Klu< zHg{QeSpai9zaRSRm9vw%U&|#%ly@~}iVoFheJ~m0C2qbdTKvlHa}opBOelrLPgstG zbwZl4*5*QpQ+JWgjB!L_3YTbQ%pT zp$~~i&vev7Lh;E$buOCa)(FYHk)5ckZS>6s-;Xd9t%Z97ZdHiLtavC;VJA?PeV_Qh zU25aP^)Mj-P2}80H5hvI1Y~ikk-`S9W>wufpXIpSZ?1HsV=WzZEHeX?O1hZQhvkCdE8V|01J&w_w)e7o&e~2N z95E*V7WDu12iPGLUUYPtp;!v8E4L|E*mc&WVf33RY$GZ!A&OcR7Ni0_F(_BV8{~Gm z|2U$|k;GBTyZ$P?ul0<|7TA5gO>H{t7$8024_Tb?3GmA&7FJkjX}ZB&8~ZMeN|k}l zsyoKhyd-{-NGF~_prSD=R3qT4agM%MM`?57+sb9ZU#_A8woH970J0L(l*J@yFc5+n5R2_y+SkDSI9^IUas4Z%$hB~rV 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/dev/null @@ -1 +0,0 @@ -{"data": [{"index": 0, "shape": [224, 224], "bboxes": [[0, 0, 224, 224]], "probs": [0.0], "labels": [["n13054560"]], "masks": [], "objects": [], "timestamp": 29306093786}], "model": "nvidia/nvdinov2", "usage": {"num_images": 1}} \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/tao/validation/cls/expected.ILSVRC2012_val_00002167.json b/tools/inference_builder/builder/samples/tao/validation/cls/expected.ILSVRC2012_val_00002167.json deleted file mode 100644 index 841e51ae..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/cls/expected.ILSVRC2012_val_00002167.json +++ /dev/null @@ -1 +0,0 @@ -{"data": [{"index": 0, "shape": [224, 224], "bboxes": [[0, 0, 224, 224]], "probs": [0.0], "labels": [["n13052670"]], "masks": [], "objects": [], "timestamp": 100464334485}], "model": "nvidia/nvdinov2", "usage": {"num_images": 1}} \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/tao/validation/cls/labels.txt b/tools/inference_builder/builder/samples/tao/validation/cls/labels.txt deleted file mode 100644 index 8972f79f..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/cls/labels.txt +++ /dev/null @@ -1 +0,0 @@ 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 \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.generate.sh b/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.generate.sh deleted file mode 100644 index 3484ffb0..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.generate.sh +++ /dev/null @@ -1,17 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -nim_workspace=/tmp/nim/generator/cv_nim -nim_generate_manifest --profiles-file model_manifest.profile.yaml --manifest-file model_manifest.yaml --nim-workspace $nim_workspace diff --git a/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.profile.yaml b/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.profile.yaml deleted file mode 100644 index 80da5367..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.profile.yaml +++ /dev/null @@ -1,24 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - ---- -schema_version: '1.0' -model: "eevaigoeixww/dev/tao_cv" -release: 'rc1' -simple: - profiles: - - model_version: cls.nvdino_pretrain_0114_final_scale0.8.engine_b1_fp16_A6000_trt10.9 - tags: - gpu: a6000 diff --git a/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.yaml b/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.yaml deleted file mode 100644 index ceee76ac..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/cls/model_manifest.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -schema_version: '2.0' -profile_selection_criteria: auto -model: eevaigoeixww/dev/tao_cv -release: rc1 -profiles: -- id: 425dcb3ef646e497d5a50bbd1eb1169c9f33f4253f36669bb428ef250e2447cd - tags: - gpu: a6000 - workspace: - files: - classifier.onnx_b1_gpu0_fp16.engine: - uri: ngc://eevaigoeixww/dev/tao_cv:cls.nvdino_pretrain_0114_final_scale0.8.engine_b1_fp16_A6000_trt10.9?file=classifier.onnx_b1_gpu0_fp16.engine - checksum: blake3:27621bbfdbba1538a1ee6b5ae527b233e841fd405887c8ac9ea46c6d509c5adb - labels.txt: - uri: ngc://eevaigoeixww/dev/tao_cv:cls.nvdino_pretrain_0114_final_scale0.8.engine_b1_fp16_A6000_trt10.9?file=labels.txt - checksum: blake3:7065fb33f25f05e562a0ac12acf084a95c2892ede4bec8b2af0b8c2df6f27172 - nvdsinfer_config.yaml: - uri: ngc://eevaigoeixww/dev/tao_cv:cls.nvdino_pretrain_0114_final_scale0.8.engine_b1_fp16_A6000_trt10.9?file=nvdsinfer_config.yaml - checksum: blake3:c406a73ece6aae6700606a937fb5b0ff38ed1ba0690c47fcd1182ffc772e9a10 diff --git a/tools/inference_builder/builder/samples/tao/validation/cls/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/tao/validation/cls/nvdsinfer_config.yaml deleted file mode 100644 index 86793ad4..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/cls/nvdsinfer_config.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -class-attrs-all: - pre-cluster-threshold: 0 - topk: 2 -property: - cluster-mode: 4 - gie-unique-id: 1 - infer-dims: 3;224;224 - maintain-aspect-ratio: 1 - net-scale-factor: 0.01508295626 # 1 / (255 * 0.26) - offsets: 122.7709;116.746;104.09373 # 255 * mean - network-mode: 2 # 0 FP32, 2 FP16 - network-type: 1 # classification - onnx-file: classifier.onnx - model-engine-file: ./classifier.onnx_b1_gpu0_fp16.engine - output-blob-names: output - workspace-size: 1048576 - classifier-threshold: 0 - labelfile-path: ./labels.txt diff --git a/tools/inference_builder/builder/samples/tao/validation/docker-compose.yaml b/tools/inference_builder/builder/samples/tao/validation/docker-compose.yaml deleted file mode 100644 index d310821e..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/docker-compose.yaml +++ /dev/null @@ -1,28 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -version: '3.8' - -services: - validation-client: - build: - context: . # Context is the validation directory - dockerfile: Dockerfile.validation - image: validation-client - # ipc: host - network_mode: "host" - environment: - - NIM_MODEL_NAME=${NIM_MODEL_NAME:-rtdetr} - - TEST_HOST=${TEST_HOST:-http://127.0.0.1:8800} diff --git a/tools/inference_builder/builder/samples/tao/validation/gdino/expected.iva_0010_02_1280_0_181016_007207.json b/tools/inference_builder/builder/samples/tao/validation/gdino/expected.iva_0010_02_1280_0_181016_007207.json deleted file mode 100644 index 33424e29..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/gdino/expected.iva_0010_02_1280_0_181016_007207.json +++ /dev/null @@ -1 +0,0 @@ -{"data": [{"index": 0, "shape": [1, 1], "bboxes": [[0.4875030517578125, 0.3655242919921875, 0.5281219482421875, 0.4118194580078125], [0.317352294921875, 0.472137451171875, 0.401885986328125, 0.546417236328125], [0.255279541015625, 0.3712921142578125, 0.301361083984375, 0.4045867919921875], [0.623138427734375, 0.483489990234375, 0.688385009765625, 0.560455322265625], [0.211151123046875, 0.366546630859375, 0.238800048828125, 0.402008056640625], [0.543182373046875, 0.45965576171875, 0.615020751953125, 0.54425048828125], [0.6658248901367188, 0.3303680419921875, 0.6906204223632812, 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a/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.generate.sh b/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.generate.sh deleted file mode 100644 index 3484ffb0..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.generate.sh +++ /dev/null @@ -1,17 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -nim_workspace=/tmp/nim/generator/cv_nim -nim_generate_manifest --profiles-file model_manifest.profile.yaml --manifest-file model_manifest.yaml --nim-workspace $nim_workspace diff --git a/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.profile.yaml b/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.profile.yaml deleted file mode 100644 index 60694f86..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.profile.yaml +++ /dev/null @@ -1,24 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - ---- -schema_version: '1.0' -model: "eevaigoeixww/dev/tao_cv" -release: 'rc1' -simple: - profiles: - - model_version: gdino.engine_b1_fp16_A6000_trt10.9 - tags: - gpu: a6000 diff --git a/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.yaml b/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.yaml deleted file mode 100644 index 1d102d72..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/gdino/model_manifest.yaml +++ /dev/null @@ -1,35 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -schema_version: '2.0' -profile_selection_criteria: auto -model: eevaigoeixww/dev/tao_cv -release: rc1 -profiles: -- id: 425dcb3ef646e497d5a50bbd1eb1169c9f33f4253f36669bb428ef250e2447cd - tags: - gpu: a6000 - workspace: - files: - grounding_dino_swin_tiny_commercial_deployable.onnx_b1_gpu0_fp16.engine: - uri: ngc://eevaigoeixww/dev/tao_cv:gdino.engine_b1_fp16_A6000_trt10.9?file=grounding_dino_swin_tiny_commercial_deployable.onnx_b1_gpu0_fp16.engine - checksum: blake3:092963900dd0b08fcdce6ab783fb7ab011051c51b92173a642dc82714af8d606 - nvdsinfer_config.yaml: - uri: ngc://eevaigoeixww/dev/tao_cv:gdino.engine_b1_fp16_A6000_trt10.9?file=nvdsinfer_config.yaml - checksum: blake3:a96518b01c399c0b22064ac73758b0555041badf26b11f20e29218e161f86c2c - nvdspreprocess_config.yaml: - uri: ngc://eevaigoeixww/dev/tao_cv:gdino.engine_b1_fp16_A6000_trt10.9?file=nvdspreprocess_config.yaml - checksum: blake3:a261def4031c9a7f068ecf144c4285d26b0767b9e143b83f48ea92b75e383041 diff --git a/tools/inference_builder/builder/samples/tao/validation/gdino/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/tao/validation/gdino/nvdsinfer_config.yaml deleted file mode 100644 index 54f07c11..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/gdino/nvdsinfer_config.yaml +++ /dev/null @@ -1,36 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -class-attrs-all: - topk: 300 - pre-cluster-threshold: 0.4 -property: - gie-unique-id: 1 - net-scale-factor: 0.0078431372549 - model-engine-file: ./grounding_dino_swin_tiny_commercial_deployable.onnx_b1_gpu0_fp16.engine - onnx-file: ./grounding_dino_swin_tiny_commercial_deployable.onnx - # 0=FP32, 1=INT8, 2=FP16 mode - network-mode: 2 - # 0 detection; 1 cls; 2 seg; 3 instance seg; 100 no pp - network-type: 100 - offsets: 127.5;127.5;127.5 - output-tensor-meta: 1 - input-tensor-from-meta: 1 - infer-dims: 3;544;960 - custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so - # process-mode: 1 - # num-detected-classes: 256 - # batch-size: 1 - diff --git a/tools/inference_builder/builder/samples/tao/validation/gdino/nvdspreprocess_config.yaml b/tools/inference_builder/builder/samples/tao/validation/gdino/nvdspreprocess_config.yaml deleted file mode 100644 index 9d097d42..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/gdino/nvdspreprocess_config.yaml +++ /dev/null @@ -1,64 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# The values in the config file are overridden by values set through GObject properties. - -property: - tensor-name: inputs - processing-width: 960 - processing-height: 544 - network-input-shape: 1;3;544;960 - # Color format (0=RGB, 1=BGR, 2=GRAY) - network-color-format: 0 - - # Network input order (0=NCHW, 1=NHWC, 2=CUSTOM) - network-input-order: 0 - # Tensor data type (0=FP32, 1=UINT8, 2=INT8, 3=UINT32, 4=INT32, 5=FP16) - tensor-data-type: 0 - # Maintain aspect ratio while scaling - maintain-aspect-ratio: 1 - # Pad symmetrically with maintain-aspect-ratio enabled - symmetric-padding: 1 - # Memory type (0=NVBUF_MEM_DEFAULT, 1=NVBUF_MEM_CUDA_PINNED, 2=NVBUF_MEM_CUDA_DEVICE, 3=NVBUF_MEM_CUDA_UNIFIED) - scaling-pool-memory-type: 0 - # Compute hardware (0=Default, 1=GPU, 2=VIC) - scaling-pool-compute-hw: 0 - # Scaling interpolation method - # 0=Nearest, 1=Bilinear, 2=Algo1, 3=Algo2, 4=Algo3, 5=Algo4, 6=Default - scaling-filter: 0 - scaling-buf-pool-size: 6 - tensor-buf-pool-size: 6 - # List of component gie-id for which tensor is prepared - target-unique-ids: 1 - # Process type (0=process on objects, 1=process on frames) - process-on-frame: 1 - # Uniquely identify the metadata generated by this element - unique-id: 5 - # Custom library configuration - custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/gst-plugins/libcustom2d_preprocess.so - custom-tensor-preparation-function: CustomTensorPreparation - # Enable the component - enable: 1 -user-configs: - # Network scaling factor - pixel-normalization-factor: 0.007843137 - # Channel offsets - offsets: 127.5;127.5;127.5 -group: - - src-ids: -1 - custom-input-transformation-function: CustomAsyncTransformation - process-on-roi: 0 - diff --git a/tools/inference_builder/builder/samples/tao/validation/mgdino/expected.iva_0010_02_1280_0_181016_007207.json b/tools/inference_builder/builder/samples/tao/validation/mgdino/expected.iva_0010_02_1280_0_181016_007207.json deleted file mode 100644 index 2211607c..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/mgdino/expected.iva_0010_02_1280_0_181016_007207.json +++ /dev/null @@ -1 +0,0 @@ -{"data": [{"index": 0, "shape": [136, 240], "bboxes": [[76.01543426513672, 64.22148275375366, 96.24960422515869, 74.30086851119995], [117.1091365814209, 49.73912835121155, 126.87460899353027, 55.97859215736389], 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a/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.generate.sh b/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.generate.sh deleted file mode 100644 index e877c8a7..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.generate.sh +++ /dev/null @@ -1,2 +0,0 @@ -nim_workspace=/tmp/nim/generator/cv_nim -nim_generate_manifest --profiles-file model_manifest.profile.yaml --manifest-file model_manifest.yaml --nim-workspace $nim_workspace diff --git a/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.profile.yaml b/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.profile.yaml deleted file mode 100644 index a493addc..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.profile.yaml +++ /dev/null @@ -1,24 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - ---- -schema_version: '1.0' -model: "eevaigoeixww/dev/tao_cv" -release: 'rc1' -simple: - profiles: - - model_version: mgdino.engine_b1_fp16_A6000_trt10.9 - tags: - gpu: a6000 diff --git a/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.yaml b/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.yaml deleted file mode 100644 index e07aa2f8..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/mgdino/model_manifest.yaml +++ /dev/null @@ -1,35 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -schema_version: '2.0' -profile_selection_criteria: auto -model: eevaigoeixww/dev/tao_cv -release: rc1 -profiles: -- id: 425dcb3ef646e497d5a50bbd1eb1169c9f33f4253f36669bb428ef250e2447cd - tags: - gpu: a6000 - workspace: - files: - model_epoch_049.onnx_b1_gpu0_fp16.engine: - uri: ngc://eevaigoeixww/dev/tao_cv:mgdino.engine_b1_fp16_A6000_trt10.9?file=model_epoch_049.onnx_b1_gpu0_fp16.engine - checksum: blake3:947483a55c860eddea37cf1d930c6d92ab4a21d33d7d1dbc4c75e2aaa53e188c - nvdsinfer_config.yaml: - uri: ngc://eevaigoeixww/dev/tao_cv:mgdino.engine_b1_fp16_A6000_trt10.9?file=nvdsinfer_config.yaml - checksum: blake3:f957d513dff19d30f911d6c83416e61588cb1f652984fc9cd164a4eb72eedad3 - nvdspreprocess_config.yaml: - uri: ngc://eevaigoeixww/dev/tao_cv:mgdino.engine_b1_fp16_A6000_trt10.9?file=nvdspreprocess_config.yaml - checksum: blake3:a261def4031c9a7f068ecf144c4285d26b0767b9e143b83f48ea92b75e383041 diff --git a/tools/inference_builder/builder/samples/tao/validation/mgdino/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/tao/validation/mgdino/nvdsinfer_config.yaml deleted file mode 100644 index aa6c12b4..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/mgdino/nvdsinfer_config.yaml +++ /dev/null @@ -1,37 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -class-attrs-all: - topk: 300 - pre-cluster-threshold: 0.4 -property: - gie-unique-id: 1 - net-scale-factor: 0.0078431372549 - model-engine-file: ./model_epoch_049.onnx_b1_gpu0_fp16.engine - onnx-file: ./model_epoch_049.onnx - # 0=FP32, 1=INT8, 2=FP16 mode - network-mode: 2 - # 0 detection; 1 cls; 2 seg; 3 instance seg; 100 no pp - network-type: 100 - offsets: 127.5;127.5;127.5 - output-tensor-meta: 1 - input-tensor-from-meta: 1 - infer-dims: 3;544;960 - custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so - segmentation-threshold: 0.5 - # process-mode: 1 - # num-detected-classes: 256 - # batch-size: 1 - diff --git a/tools/inference_builder/builder/samples/tao/validation/mgdino/nvdspreprocess_config.yaml b/tools/inference_builder/builder/samples/tao/validation/mgdino/nvdspreprocess_config.yaml deleted file mode 100644 index 195f72ef..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/mgdino/nvdspreprocess_config.yaml +++ /dev/null @@ -1,63 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# The values in the config file are overridden by values set through GObject properties. - -property: - tensor-name: inputs - processing-width: 960 - processing-height: 544 - network-input-shape: 1;3;544;960 - # Color format (0=RGB, 1=BGR, 2=GRAY) - network-color-format: 0 - # Network input order (0=NCHW, 1=NHWC, 2=CUSTOM) - network-input-order: 0 - # Tensor data type (0=FP32, 1=UINT8, 2=INT8, 3=UINT32, 4=INT32, 5=FP16) - tensor-data-type: 0 - # Maintain aspect ratio while scaling - maintain-aspect-ratio: 1 - # Pad symmetrically with maintain-aspect-ratio enabled - symmetric-padding: 1 - # Memory type (0=NVBUF_MEM_DEFAULT, 1=NVBUF_MEM_CUDA_PINNED, 2=NVBUF_MEM_CUDA_DEVICE, 3=NVBUF_MEM_CUDA_UNIFIED) - scaling-pool-memory-type: 0 - # Compute hardware (0=Default, 1=GPU, 2=VIC) - scaling-pool-compute-hw: 0 - # Scaling interpolation method - # 0=Nearest, 1=Bilinear, 2=Algo1, 3=Algo2, 4=Algo3, 5=Algo4, 6=Default - scaling-filter: 0 - scaling-buf-pool-size: 6 - tensor-buf-pool-size: 6 - # List of component gie-id for which tensor is prepared - target-unique-ids: 1 - # Process type (0=process on objects, 1=process on frames) - process-on-frame: 1 - # Uniquely identify the metadata generated by this element - unique-id: 5 - # Custom library configuration - custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/gst-plugins/libcustom2d_preprocess.so - 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-39 -40 -41 -42 -43 -44 -46 -47 -48 -49 -50 -51 -52 -53 -54 -55 -56 -57 -58 -59 -60 -61 -62 -63 -64 -65 -67 -70 -72 -73 -74 -75 -76 -77 -78 -79 -80 -81 -82 -84 -85 -86 -87 -88 -89 -90 diff --git a/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.generate.sh b/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.generate.sh deleted file mode 100644 index 3484ffb0..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.generate.sh +++ /dev/null @@ -1,17 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -nim_workspace=/tmp/nim/generator/cv_nim -nim_generate_manifest --profiles-file model_manifest.profile.yaml --manifest-file model_manifest.yaml --nim-workspace $nim_workspace diff --git a/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.profile.yaml b/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.profile.yaml deleted file mode 100644 index 90cf3907..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.profile.yaml +++ /dev/null @@ -1,24 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - ---- -schema_version: '1.0' -model: "eevaigoeixww/dev/tao_cv" -release: 'rc1' -simple: - profiles: - - model_version: rtdetr.engine-b1_fp16_A6000_trt10.9 - tags: - gpu: a6000 diff --git a/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.yaml b/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.yaml deleted file mode 100644 index 4d65502d..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/rtdetr/model_manifest.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -schema_version: '2.0' -profile_selection_criteria: auto -model: eevaigoeixww/dev/tao_cv -release: rc1 -profiles: -- id: 425dcb3ef646e497d5a50bbd1eb1169c9f33f4253f36669bb428ef250e2447cd - tags: - gpu: a6000 - workspace: - files: - labels.txt: - uri: ngc://eevaigoeixww/dev/tao_cv:rtdetr.engine-b1_fp16_A6000_trt10.9?file=labels.txt - checksum: blake3:e50ee4f9ef6d7a17e8446eccc46748be8602629b9a4f729e3f959c80c7d6939a - model.onnx_b1_gpu0_fp16.engine: - uri: ngc://eevaigoeixww/dev/tao_cv:rtdetr.engine-b1_fp16_A6000_trt10.9?file=model.onnx_b1_gpu0_fp16.engine - checksum: blake3:a800796eaf366aa3eccb5c4151258c754ea27ddd2dfeb30844b0072bd9a641e1 - nvdsinfer_config.yaml: - uri: ngc://eevaigoeixww/dev/tao_cv:rtdetr.engine-b1_fp16_A6000_trt10.9?file=nvdsinfer_config.yaml - checksum: blake3:126064d8b7353208c0dfaf24ab9cea823bc80f772da44f6add8730840095a6f3 diff --git a/tools/inference_builder/builder/samples/tao/validation/rtdetr/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/tao/validation/rtdetr/nvdsinfer_config.yaml deleted file mode 100644 index 7b42b5b9..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/rtdetr/nvdsinfer_config.yaml +++ /dev/null @@ -1,35 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -class-attrs-all: - pre-cluster-threshold: 0.5 -property: - gie-unique-id: 1 - cluster-mode: 4 - data-format: channels_first - infer-dims: 3;640;640 - maintain-aspect-ratio: 1 - model-color-format: 0 # RGB - net-scale-factor: 0.00392156863 # 1 / (255 x 1) - network-mode: 2 # 0 FP32, 2 FP16 - network-type: 0 - num-detected-classes: 80 - onnx-file: model.onnx - model-engine-file: ./model.onnx_b1_gpu0_fp16.engine - labelfile-path: ./labels.txt - parse-bbox-func-name: NvDsInferParseCustomDDETRTAO - custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so - workspace-size: 1048576 - # scaling-filter: 2 # 2 cubic diff --git a/tools/inference_builder/builder/samples/tao/validation/run_validation.sh b/tools/inference_builder/builder/samples/tao/validation/run_validation.sh deleted file mode 100644 index 27267dc2..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/run_validation.sh +++ /dev/null @@ -1,30 +0,0 @@ -#!/bin/bash - -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -set -e # Exit on any error -# Validate NIM_MODEL_NAME -valid_models=("rtdetr" "cls" "seg" "gdino" "mgdino") -if [[ ! " ${valid_models[@]} " =~ " ${NIM_MODEL_NAME} " ]]; then - echo "Error: NIM_MODEL_NAME must be one of: ${valid_models[*]}" - exit 1 -fi - -# Change to the appropriate test directory -cd "/app/validation/${NIM_MODEL_NAME}/.tmp" || exit 1 -echo $(pwd) -# Run the test -python test_runner.py diff --git a/tools/inference_builder/builder/samples/tao/validation/seg/expected.image_0.json b/tools/inference_builder/builder/samples/tao/validation/seg/expected.image_0.json deleted file mode 100644 index 878c383b..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/seg/expected.image_0.json +++ /dev/null @@ -1 +0,0 @@ -{"data": [{"index": 0, "shape": [224, 224], "bboxes": [], "probs": [], "labels": [], "masks": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 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a/tools/inference_builder/builder/samples/tao/validation/seg/model_manifest.generate.sh +++ /dev/null @@ -1,17 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -nim_workspace=/tmp/nim/generator/cv_nim -nim_generate_manifest --profiles-file model_manifest.profile.yaml --manifest-file model_manifest.yaml --nim-workspace $nim_workspace diff --git a/tools/inference_builder/builder/samples/tao/validation/seg/model_manifest.profile.yaml b/tools/inference_builder/builder/samples/tao/validation/seg/model_manifest.profile.yaml deleted file mode 100644 index ffde05a2..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/seg/model_manifest.profile.yaml +++ /dev/null @@ -1,24 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - ---- -schema_version: '1.0' -model: "eevaigoeixww/dev/tao_cv" -release: 'rc1' -simple: - profiles: - - model_version: seg.cradiov2_b.engine_b1_fp16_A6000_tao2_trt10.9 - tags: - gpu: a6000 diff --git a/tools/inference_builder/builder/samples/tao/validation/seg/model_manifest.yaml b/tools/inference_builder/builder/samples/tao/validation/seg/model_manifest.yaml deleted file mode 100644 index 31398c30..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/seg/model_manifest.yaml +++ /dev/null @@ -1,31 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -schema_version: '2.0' -profile_selection_criteria: auto -model: eevaigoeixww/dev/tao_cv -release: rc1 -profiles: -- id: 425dcb3ef646e497d5a50bbd1eb1169c9f33f4253f36669bb428ef250e2447cd - tags: - gpu: a6000 - workspace: - files: - nvdsinfer_config.yaml: - uri: ngc://eevaigoeixww/dev/tao_cv:seg.cradiov2_b.engine_b1_fp16_A6000_tao2_trt10.9?file=nvdsinfer_config.yaml - checksum: blake3:8e479d4f7fc8ac37b0b690040213f4cde8ac7c279ba120fa316e36d9d40ab41e - segformer.onnx_b1_gpu0_fp16.engine: - uri: ngc://eevaigoeixww/dev/tao_cv:seg.cradiov2_b.engine_b1_fp16_A6000_tao2_trt10.9?file=segformer.onnx_b1_gpu0_fp16.engine - checksum: blake3:13ce94093ac0f2365ccacc2ccc7fbf6be1aba981a2f3eed0422606346db6af3d diff --git a/tools/inference_builder/builder/samples/tao/validation/seg/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/tao/validation/seg/nvdsinfer_config.yaml deleted file mode 100644 index fbfb7172..00000000 --- a/tools/inference_builder/builder/samples/tao/validation/seg/nvdsinfer_config.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -property: - gie-unique-id: 6 - process-mode: 1 - infer-dims: 3;224;224 - maintain-aspect-ratio: 1 - net-scale-factor: 0.007843137255 # 1 / (0.5 x 255) - offsets: 127.5;127.5;127.5 # 0.5 x 255 - network-mode: 2 - network-type: 2 # seg - num-detected-classes: 2 - onnx-file: segformer.onnx - model-engine-file: ./segformer.onnx_b1_gpu0_fp16.engine - output-blob-names: output - # output-tensor-meta: 1 - segmentation-output-order: 1 - workspace-size: 1048576 - parse-segmentation-func-name: NvDsInferParseCustomSegformerTAO2 - custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so diff --git a/tools/inference_builder/builder/samples/vllm/Dockerfile b/tools/inference_builder/builder/samples/vllm/Dockerfile deleted file mode 100644 index 27d932c1..00000000 --- a/tools/inference_builder/builder/samples/vllm/Dockerfile +++ /dev/null @@ -1,88 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: LicenseRef-NvidiaProprietary -# -# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual -# property and proprietary rights in and to this material, related -# documentation and any modifications thereto. Any use, reproduction, -# disclosure or distribution of this material and related documentation -# without an express license agreement from NVIDIA CORPORATION or -# its affiliates is strictly prohibited. - -# Sample Dockerfile for a vLLM-based vision-language pipeline using a -# Triton-enabled DeepStream base image for accelerated video decoding. -# This is specific to the vllm sample in this repository. -# Stage 1: pull in DeepStream libraries, binaries, and sample assets. -FROM "nvcr.io/nvidia/deepstream:9.0-triton-multiarch" AS ds_stage - -FROM nvcr.io/nvidia/vllm:26.02-py3 AS inference_base - -ENV NVIDIA_DRIVER_CAPABILITIES $NVIDIA_DRIVER_CAPABILITIES,video - -# Install GStreamer plugins and DeepStream runtime dependencies needed -# for hardware-accelerated decoding and RTSP support. -RUN apt update && apt install -y --no-install-recommends gstreamer1.0-plugins-good gstreamer1.0-plugins-bad \ - libyaml-cpp-dev libgstrtspserver-1.0-0 libjsoncpp25 libgles2 pkg-config - -COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/lib /opt/nvidia/deepstream/deepstream-9.0/lib -COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/bin /opt/nvidia/deepstream/deepstream-9.0/bin -COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/*.sh /opt/nvidia/deepstream/deepstream-9.0/ -COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/*.txt /opt/nvidia/deepstream/deepstream-9.0/ -COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/README* /opt/nvidia/deepstream/deepstream-9.0/ -COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/*.pdf /opt/nvidia/deepstream/deepstream-9.0/ -COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/service-maker /opt/nvidia/deepstream/deepstream-9.0/service-maker -COPY --from=ds_stage /opt/nvidia/deepstream/deepstream-9.0/samples/streams/* /opt/nvidia/deepstream/deepstream-9.0/samples/streams/ - -# Copy CUDA NPP and nvjpeg libraries required by DeepStream GStreamer -# plugins (nvvideoconvert, nvdsgst_image, etc.) that are absent from -# the vLLM base image. -COPY --from=ds_stage /usr/local/cuda-13.1/targets/x86_64-linux/lib/libnpp*.so* /usr/local/lib/ -COPY --from=ds_stage /usr/local/cuda-13.1/targets/x86_64-linux/lib/libnvjpeg*.so* /usr/local/lib/ -RUN ldconfig - -RUN /opt/nvidia/deepstream/deepstream-9.0/install.sh -RUN ln -sf deepstream-9.0 /opt/nvidia/deepstream/deepstream || true - -# Install Python dependencies required by THIS Qwen sample. These -# versions are pinned for reproducibility and can be adjusted for -# other pipelines or environments. -RUN --mount=type=cache,target=/root/.cache/pip pip install qwen-vl-utils[decord]==0.0.8 \ - python-multipart==0.0.9 \ - aiofiles==23.2.1 \ - aiohttp==3.11.0 \ - annotated-types==0.7.0 \ - python-magic==0.4.27 \ - numpy==1.26.4 \ - omegaconf==2.3.0 - -WORKDIR /workspace - -# Copy the inference flow package generated by Inference Builder. -# This tarball (here: vllm.tgz) is typically produced when running the -# builder with tarball output enabled, and its name should match the -# generated pipeline artifact you want to deploy. -ADD vllm.tgz /workspace - -## Set Environment variables required for DeepStream+vLLM runtime. -# Ensure DeepStream libraries are on the library path and enable -# adaptive batching and appropriate CUDA architectures. -ENV LD_LIBRARY_PATH /opt/nvidia/deepstream/deepstream/lib -ENV NVSTREAMMUX_ADAPTIVE_BATCHING yes -ENV NVIDIA_DRIVER_CAPABILITIES=$NVIDIA_DRIVER_CAPABILITIES,video -ENV TORCH_CUDA_ARCH_LIST "7.5 8.0 8.6 9.0 10.0" -ENV CUDA_CACHE_DISABLE 0 - -# Create the entrypoint script that runs the Inference Builder-generated -# /workspace/__main__.py for this vLLM pipeline. -RUN touch /workspace/start_server.sh && \ - chmod a+rx /workspace/start_server.sh && \ - cat > /workspace/start_server.sh <<-EOF - #!/usr/bin/env bash - set -eu - # SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. - # SPDX-License-Identifier: LicenseRef-NvidiaProprietary - python3 /workspace/__main__.py -# while true; do sleep 5; done -EOF - -ENTRYPOINT ["/workspace/start_server.sh"] -CMD [] diff --git a/tools/inference_builder/builder/samples/vllm/README.md b/tools/inference_builder/builder/samples/vllm/README.md deleted file mode 100644 index de346aba..00000000 --- a/tools/inference_builder/builder/samples/vllm/README.md +++ /dev/null @@ -1,131 +0,0 @@ -## Introduction - -This example demonstrates how to build an inference pipeline with Nvidia Deepstream MediaExtractor and vLLM Inference backends - -## Prerequisites - -The sample models including `Cosmos-Reason2-2B` and `Qwen3-VL-2B-Instruct` can all be downloaded from huggingface (Be sure to have git-lfs installed): - -```bash -git clone https://huggingface.co/nvidia/Cosmos-Reason2-2B ~/.cache/model-repo/Cosmos -``` - -or - -```bash -git clone https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct ~/.cache/model-repo/Qwen3-VL -``` - -## Generate the Inference Package - - -### Cosmos-Reason2-2B - -```bash -python builder/main.py builder/samples/vllm/vllm_nvdec_cosmos.yaml --api-spec builder/samples/vllm/openapi.yaml \ --o builder/samples/vllm/ -c builder/samples/qwen/processors.py -t -``` - -### Qwen3-VL-2B-Instruct - -```bash -python builder/main.py builder/samples/vllm/vllm_nvdec_qwen3_vl.yaml --api-spec builder/samples/vllm/openapi.yaml \ --o builder/samples/vllm/ -c builder/samples/qwen/processors.py -t -``` - -## Build and Start the Inference Microservice: - -The sample folder already contains all the ingredients for building the microservice, all you need is to run the command: - -```bash -cd builder/samples -docker compose up ms-vllm --build -``` - -## Test the model with a client - -There is an OpenAI client included in the sample for testing, and for image input, you can directly run the client with image path: - -```bash -cd builder/samples/qwen -python client.py --images -``` - -For video input, you need to first upload a test video file. - -**⚠️ Important:** **replace the placeholder in below command with an actual file path in your system**. - -```bash -export VIDEO_FILE= -curl -X "POST" \ - "http://localhost:8800/v1/files" \ - -H "accept: application/json" \ - -H "Content-Type: multipart/form-data" \ - -F "file=@$VIDEO_FILE;type=video/mp4" -``` - -You'll get a response 200 with a json body: -{ - "data": { - "id": "577a9f11-2b24-4db8-82c8-2601e0c2b6e4", - "path": "/tmp/assets/577a9f11-2b24-4db8-82c8-2601e0c2b6e4/its_1920_30s.mp4", - "size": 3472221, - "duration": 30000000000, - "contentType": "video/mp4" - } -} - -Similarly, for live stream input, you need to first add a live stream as an asset. - -**⚠️ Important:** **replace the placeholder in below command with an actual RTSP address**. - -```bash -export LIVE_STREAM= -curl -X "POST" \ - "http://localhost:8800/v1/streams" \ - -H "accept: application/json" \ - -H "Content-Type: application/json" \ - -d "{\"url\": \"$LIVE_STREAM\", \"name\": \"my-stream\"}" -``` - -Run the client.py with the returned asset id and specify the frames per chunk and number of chunks to extract: - -```bash -cd builder/samples/qwen -python client.py --videos "577a9f11-2b24-4db8-82c8-2601e0c2b6e4?frames=8&chunks=1" -``` - -You can also optionally specify the frame interval and scaling parameters: - -```bash -# Extract 1 chunk with 8 frames at the interval of 0.5s, and resize it to 640x260, then do summarization -python client.py --videos "577a9f11-2b24-4db8-82c8-2601e0c2b6e4?frames=8&chunks=1&interval=500000000&scaling=640x360" -``` - -Or use raw json payload with curl to get the result of all the chunks using "x-ndjson": - -```bash -curl -X 'POST' \ - 'http://localhost:8800/v1/chat/completions' \ - -H 'accept: application/x-ndjson' \ - -H 'Content-Type: application/json' \ - -d '{ - "model": "qwen3_vl", - "messages": [ - { - "role": "user", - "content": [ - { - "type": "text", - "text": "Please describe the video in detail." - }, - { - "type": "video", - "video": "577a9f11-2b24-4db8-82c8-2601e0c2b6e4?frames=8" - } - ] - } - ], - "max_tokens": 200 -}' -``` diff --git a/tools/inference_builder/builder/samples/vllm/openapi.yaml b/tools/inference_builder/builder/samples/vllm/openapi.yaml deleted file mode 100644 index 8f444d5f..00000000 --- a/tools/inference_builder/builder/samples/vllm/openapi.yaml +++ /dev/null @@ -1,1367 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -openapi: 3.1.0 -info: - title: NVIDIA NIM for VLMs - summary: Accelerated VLM inference for NVIDIA GPUs. - version: 1.1.2 -paths: - /v1/health/ready: - get: - tags: - - Health - summary: Health Ready - description: Provide your implementation of readiness to know when container ready to accept traffic - operationId: health_ready_v1_health_ready_get - responses: - '200': - description: Successful Response - content: - application/json: - schema: {} - /v1/chat/completions: - post: - summary: OpenAI-compatible chat endpoint - operationId: create_chat_completion_v1_chat_completions_post - parameters: - - name: content-type - in: header - required: false - schema: - anyOf: - - type: string - - type: 'null' - title: Content-Type - requestBody: - required: true - content: - application/json: - schema: - $ref: '#/components/schemas/NIMLLMChatCompletionRequest' - responses: - '200': - description: Successful Response - content: - application/json: - schema: - anyOf: - - $ref: '#/components/schemas/NIMLLMChatCompletionResponse' - - $ref: '#/components/schemas/NIMLLMChatCompletionStreamResponse' - title: Response Create Chat Completion V1 Chat Completions Post - '400': - description: Received an invalid request possibly containing unsupported - or out-of-range parameter values. - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '404': - description: The requested model does not exist. - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal Server Error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '415': - description: Unsupported Media Type - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '422': - description: Validation Error - content: - application/json: - schema: - $ref: '#/components/schemas/HTTPValidationError' - /v1/files: - post: - tags: - - Files - summary: Upload a media file that can be used as inference input. - description: Upload media files to be used as inference input. Currently, only images upto 50 MB are supported. - operationId: add_media_file - requestBody: - required: true - content: - multipart/form-data: - schema: - $ref: '#/components/schemas/AddFileRequest' - responses: - "200": - description: Successful Response. - content: - application/json: - schema: - $ref: '#/components/schemas/AddFileResponse' - "500": - description: The invocation ended with an error. - content: - application/json: - schema: - $ref: '#/components/schemas/Errors' - get: - tags: - - Files - summary: Returns a list of the uploaded files. - description: Returns a list that includes ID, file name, size, purpose, file size, and media type for the uploaded files. - operationId: list_media_files - responses: - "200": - description: Successful Response. - content: - application/json: - schema: - $ref: '#/components/schemas/ListFilesResponse' - "500": - description: The invocation ended with an error. - content: - application/json: - schema: - $ref: '#/components/schemas/Errors' - /v1/files/{file_id}: - delete: - tags: - - Files - summary: Delete a uploaded file. - description: Deletes an uploaded file based on ID. - operationId: delete_media_file - parameters: - - name: file_id - in: path - required: true - schema: - type: string - maxLength: 36 - pattern: '[0-9a-fA-F]{8}-?[0-9a-fA-F]{4}-?4[0-9a-fA-F]{3}-?[89abAB][0-9a-fA-F]{3}-?[0-9a-fA-F]{12}' - description: File having 'file_id' to be deleted. - title: File Id - description: File having 'file_id' to be deleted. - responses: - "200": - description: Successful Response. - content: - application/json: - schema: - $ref: '#/components/schemas/DeleteFileResponse' - "500": - description: The invocation ended with an error. - content: - application/json: - schema: - $ref: '#/components/schemas/Errors' -components: - schemas: - BaseModel: - properties: {} - type: object - title: BaseModel - ChatCompletionLogProb: - properties: - token: - type: string - title: Token - logprob: - type: number - title: Logprob - default: -9999.0 - bytes: - anyOf: - - items: - type: integer - type: array - - type: 'null' - title: Bytes - additionalProperties: false - type: object - required: - - token - title: ChatCompletionLogProb - ChatCompletionLogProbs: - properties: - content: - anyOf: - - items: - $ref: '#/components/schemas/ChatCompletionLogProbsContent' - type: array - - type: 'null' - title: Content - additionalProperties: false - type: object - title: ChatCompletionLogProbs - ChatCompletionLogProbsContent: - properties: - token: - type: string - title: Token - logprob: - type: number - title: Logprob - default: -9999.0 - bytes: - anyOf: - - items: - type: integer - type: array - - type: 'null' - title: Bytes - top_logprobs: - items: - $ref: '#/components/schemas/ChatCompletionLogProb' - type: array - title: Top Logprobs - additionalProperties: false - type: object - required: - - token - title: ChatCompletionLogProbsContent - ChatCompletionNamedFunction: - properties: - name: - type: string - title: Name - additionalProperties: false - type: object - required: - - name - title: ChatCompletionNamedFunction - ChatCompletionNamedToolChoiceParam: - properties: - function: - $ref: '#/components/schemas/ChatCompletionNamedFunction' - type: - type: string - const: function - title: Type - default: function - additionalProperties: false - type: object - required: - - function - title: ChatCompletionNamedToolChoiceParam - ChatCompletionResponseChoice: - properties: - index: - type: integer - title: Index - message: - $ref: '#/components/schemas/ChatMessage' - logprobs: - anyOf: - - $ref: '#/components/schemas/ChatCompletionLogProbs' - - type: 'null' - finish_reason: - anyOf: - - type: string - enum: - - stop - - length - - tool_calls - - type: 'null' - title: Finish Reason - default: stop - stop_reason: - anyOf: - - type: integer - - type: string - - type: 'null' - title: Stop Reason - additionalProperties: false - type: object - required: - - index - - message - title: ChatCompletionResponseChoice - ChatCompletionResponseStreamChoice: - properties: - index: - type: integer - title: Index - delta: - $ref: '#/components/schemas/DeltaMessage' - logprobs: - anyOf: - - $ref: '#/components/schemas/ChatCompletionLogProbs' - - type: 'null' - finish_reason: - anyOf: - - type: string - enum: - - stop - - length - - tool_calls - - type: 'null' - title: Finish Reason - stop_reason: - anyOf: - - type: integer - - type: string - - type: 'null' - title: Stop Reason - additionalProperties: false - type: object - required: - - index - - delta - title: ChatCompletionResponseStreamChoice - ChatCompletionToolsParam: - properties: - type: - type: string - const: function - title: Type - default: function - function: - $ref: '#/components/schemas/FunctionDefinition' - additionalProperties: false - type: object - required: - - function - title: ChatCompletionToolsParam - ChatMessage: - properties: - role: - type: string - title: Role - content: - anyOf: - - type: string - - type: 'null' - title: Content - tool_calls: - anyOf: - - items: - $ref: '#/components/schemas/ToolCall' - type: array - - type: 'null' - title: Tool Calls - additionalProperties: false - type: object - required: - - role - title: ChatMessage - DeltaMessage: - properties: - role: - anyOf: - - type: string - - type: 'null' - title: Role - content: - anyOf: - - type: string - - type: 'null' - title: Content - tool_calls: - anyOf: - - items: - $ref: '#/components/schemas/ToolCall' - type: array - - type: 'null' - title: Tool Calls - additionalProperties: false - type: object - title: DeltaMessage - ErrorResponse: - properties: - object: - type: string - title: Object - default: error - message: - type: string - title: Message - type: - type: string - title: Type - param: - anyOf: - - type: string - - type: 'null' - title: Param - code: - type: integer - title: Code - additionalProperties: false - type: object - required: - - message - - type - - code - title: ErrorResponse - FunctionCall: - properties: - name: - anyOf: - - type: string - - type: 'null' - title: Name - default: '' - arguments: - anyOf: - - type: string - - type: 'null' - title: Arguments - default: '' - additionalProperties: false - type: object - title: FunctionCall - FunctionDefinition: - properties: - name: - type: string - title: Name - description: - anyOf: - - type: string - - type: 'null' - title: Description - parameters: - anyOf: - - type: object - - type: 'null' - title: Parameters - additionalProperties: false - type: object - required: - - name - title: FunctionDefinition - HTTPValidationError: - properties: - detail: - items: - $ref: '#/components/schemas/ValidationError' - type: array - title: Detail - type: object - title: HTTPValidationError - ImageURL: - properties: - url: - type: string - title: Url - type: object - required: - - url - title: ImageURL - Logprob: - properties: - logprob: - type: number - title: Logprob - rank: - anyOf: - - type: integer - - type: 'null' - title: Rank - decoded_token: - anyOf: - - type: string - - type: 'null' - title: Decoded Token - type: object - required: - - logprob - title: Logprob - NIMLLMChatCompletionMessage: - properties: - role: - $ref: '#/components/schemas/Role' - description: The role of the message's author. - content: - anyOf: - - type: string - minLength: 1 - - items: - anyOf: - - $ref: '#/components/schemas/NIMVLMChatCompletionContentPartImage' - - $ref: '#/components/schemas/NIMVLMChatCompletionContentPartText' - - $ref: '#/components/schemas/NIMVLMChatCompletionContentPartVideo' - - $ref: '#/components/schemas/NIMVLMChatCompletionContentPartVideoStreaming' - type: array - title: Content - description: "The contents of the message.\n
To pass images (only\ - \ with role=`user`):\n
- When content is a string, image\ - \ can be passed together with the text with `img` HTML tags that wraps\n\ - \ an image URL (``),\n base64 encoded\ - \ image data (``),\n or an NVCF asset ID (``)\n when the container is hosted in NVCF and the payload exceeds\ - \ 200KB.\n or http link (`https:{image_link}`)\n
\ - \ - When content is a list of objects, images can be passed as objects\ - \ with type=`image_url`.\n
- In both cases, images can\ - \ be PNG, JPG or JPEG.\n
To pass videos (only with role=`user`):\n\ - \
- When content is a string, video can be passed together\ - \ with the text with `video` HTML tags that wraps\n a video URL\ - \ (`

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All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import argparse -import requests, base64 -import time -import numpy as np -import os -import io -from PIL import Image - -API_KEY_REQUIRED_IF_EXECUTING_OUTSIDE_NGC="shfklsjlfjsljgl" - - -def main(host , port, files, out_format): - if not files: - print("Need the file path for inference") - return - - # invoke_url = "http://localhost:8001/inference" - invoke_url = "http://" + host + ":" + port + "/v1/infer" - - file_exts = [] - b64_images = [] - for file in files: - file_exts.append(os.path.splitext(file)[1][1:]) - with open(file, "rb") as f: - b64_images.append(base64.b64encode(f.read()).decode()) - -# assert len(image_b64) < 180_000, \ -# "To upload larger images, use the assets API (see docs)" - - headers = { - "Authorization": "Bearer $API_KEY_REQUIRED_IF_EXECUTING_OUTSIDE_NGC", - "Accept": "application/json", - # "NVCF-ASSET-DIR": "some-temp-dir", - # "NVCF-FUNCTION-ASSET-IDS": "udjflsjo-jfoisjof-lsdfjofdj" - } - - payload = { - "input": [f"data:image/{e};base64,{i}" for e, i in zip(file_exts, b64_images)], - "model": "nvidia/changenet" - } - start_time = time.time() - response = requests.post(invoke_url, headers=headers, json=payload) - infer_time = time.time() - start_time - print(response) - print(infer_time) - - if response.status_code == 200: - output = response.json() - print(f"Usage: num_images= {output['usage']['num_images']}") - mask = base64.b64decode(output["data"]) - image_buffer = io.BytesIO(mask) - image = Image.open(image_buffer) - image.show() - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - - parser.add_argument("--host", type=str, - help= "Server IP Address", default="0.0.0.0") - parser.add_argument("--port", type=str, - help="Server port", default="8000") - parser.add_argument("--file", type=str, help="File to send for inference", nargs='*', default=None) - parser.add_argument("--format", type=str, help="Output embedding format, integer or base64", - default="integer") - - args = parser.parse_args() - main(args.host, args.port, args.file, args.format) diff --git a/tools/inference_builder/builder/samples/changenet/openapi.yaml b/tools/inference_builder/builder/samples/changenet/openapi.yaml deleted file mode 100644 index 8f35e32c..00000000 --- a/tools/inference_builder/builder/samples/changenet/openapi.yaml +++ /dev/null @@ -1,248 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -openapi: 3.1.0 -info: - title: NVIDIA INFERENCE SERVICE API for nvidia/changenet - description: NVIDIA INFERENCE SERVICE API - version: 1.0.0 - termsOfService: https://nvidia.com/legal/terms-of-use - contact: - name: NVIDIA Support - url: https://help.nvidia.com/ - license: - name: NVIDIA AI Foundation Models Community License - url: https://docs.nvidia.com/ai-foundation-models-community-license.pdf -tags: - - name: "NVIDIA CHANGENET API" - description: Get a segmentation representation for the difference between two pictures. - - name: "Health" - description: Health check endpoints -paths: - "/v1/health/ready": - get: - tags: - - Health - summary: Health Ready - description: Provide your implementation of readiness to know when container ready to accept traffic - operationId: health_ready_v1_health_ready_get - responses: - '200': - description: Successful Response - content: - application/json: - schema: {} - "/v1/infer": - post: - tags: - - "NVIDIA ChangeNet API" - summary: Change detection on images. - description: "Invokes inference for the input images." - operationId: inference - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/DetectionRequest' - required: true - responses: - "200": - description: Invocation is fulfilled. - content: - application/json: - schema: - $ref: '#/components/schemas/DetectionResponse' - "202": - description: Result is pending. Client should poll using the requestId. - content: - application/json: - example: {} - schema: {} - headers: - NVCF-REQID: - description: requestId required for pooling. - schema: - type: string - format: uuid - maxLength: 36 - NVCF-STATUS: - description: Invocation status. - schema: - type: string - format: ^[a-zA-Z-]{1,64}$ - maxLength: 64 - "402": - description: Payment Required. - content: - application/json: - schema: - properties: - detail: - type: string - maxLength: 256 - format: ^[a-zA-Z-]{1,64}$ - description: Contains specific information related to the error and why it occurred. - example: You have reached your limit of credits. - type: object - title: PaymentRequiredError - "422": - description: Validation failed, provided entity could not be processed. - content: - application/json: - schema: - $ref: '#/components/schemas/Errors' - example: - type: urn:nvcf-worker-service:problem-details:unprocessable-entity - title: Unprocessable Entity - status: 422 - detail: string - instance: /v2/nvcf/pexec/functions/9446dad0-ff4f-46d1-89f4-726e113cfca0 - requestId: 3fa85f64-5717-4562-b3fc-2c963f66afa6 - "500": - description: The invocation ended with an error. - content: - application/json: - schema: - $ref: '#/components/schemas/Errors' - example: - type: urn:nvcf-worker-service:problem-details:internal-server-error - title: Internal Server Error - status: 500 - detail: string - instance: /v2/nvcf/pexec/functions/9446dad0-ff4f-46d1-89f4-726e113cfca0 - requestId: 3fa85f64-5717-4562-b3fc-2c963f66afa6 -security: - - Token: [] -components: - securitySchemes: - Token: - type: http - scheme: bearer - schemas: - DetectionRequest: - additionalProperties: false - properties: - input: - type: array - items: - type: string - pattern: ^.*$ - maxLength: 4294967296 - maxItems: 2 - minItems: 2 - title: Input - description: 'The two of images that you want to detect change - for. Images should be in form of `data:image/{format};base64,{base64encodedimage}`. - For NVCF deployment, if the size of an image is more than 200KB, it needs to be uploaded to - a presigned S3 bucket using NVCF Asset APIs. Once uploaded you can refer - to it using the following format: ``. Accepted formats are `jpg`, `png` and `jpeg`.' - model: - type: string - enum: [nvidia/changenet] - title: Model - description: ID of the mask model. - examples: - - nvidia/changenet - type: object - required: - - input - - model - title: DetectionRequest - description: Input images for the inference request. - DetectionResponse: - type: object - properties: - data: - type: string - description: base64 encoded image for change representation - pattern: ^.*$ - maxLength: 4294967296 - model: - type: string - description: Model used to generate masks. - example: nvidia/changenet - title: Model - const: nvidia/changenet - usage: - $ref: '#/components/schemas/Usage' - description: Usage statistics for the masks request. - examples: - - num_images: 1 - additionalProperties: false - required: - - data - - usage - - model - title: DetectionResponse - description: Response schema for the inference request. - Usage: - properties: - num_images: - type: integer - format: int32 - title: Num Images - minimum: 0 - maximum: 64 - description: Number of images processed. - additionalProperties: false - type: object - required: - - num_images - title: Usage - description: Usage information for the inference request. - Errors: - properties: - type: - type: string - format: "^.{1, 128}$" - maxLength: 128 - description: Error type - title: - type: string - format: "^.{1, 128}$" - maxLength: 128 - description: Error title - status: - type: integer - format: int32 - minimum: 100 - maximum: 999 - description: Error status code - detail: - type: string - format: "^.{1, 1024}$" - maxLength: 1024 - description: Detailed information about the error - instance: - type: string - format: "^.{1, 256}$" - maxLength: 256 - description: Function instance used to invoke the request - requestId: - type: string - format: uuid - maxLength: 36 - description: UUID of the request - type: object - required: - - type - - title - - status - - detail - - instance - - requestId - title: InvokeError diff --git a/tools/inference_builder/builder/samples/changenet/payloads/payloads.jsonl b/tools/inference_builder/builder/samples/changenet/payloads/payloads.jsonl deleted file mode 100644 index 76efd9cd..00000000 --- a/tools/inference_builder/builder/samples/changenet/payloads/payloads.jsonl +++ /dev/null @@ -1 +0,0 @@ -{"input": 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", 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"], "model": "nvidia/changenet"} diff --git a/tools/inference_builder/builder/samples/changenet/processors.py b/tools/inference_builder/builder/samples/changenet/processors.py deleted file mode 100644 index e0b1104c..00000000 --- a/tools/inference_builder/builder/samples/changenet/processors.py +++ /dev/null @@ -1,107 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import numpy as np -import cvcuda -import torch -from PIL import Image -from io import BytesIO -import base64 - -class PreprocessorCvcuda: - """This module converts the input RGB frame into the format expected by the model.""" - name = "changenet-normalizer" - def __init__(self, config): - self.device_id = config["device_id"] - self.network_size = config['network_size'] - self.mean_tensor = torch.Tensor([0.5, 0.5, 0.5]) - self.mean_tensor = self.mean_tensor.reshape(1, 1, 1, 3).cuda(self.device_id) - self.mean_tensor = cvcuda.as_tensor(self.mean_tensor, "NHWC") - self.stddev_tensor = torch.Tensor([0.5, 0.5, 0.5]) - self.stddev_tensor = self.stddev_tensor.reshape(1, 1, 1, 3).cuda(self.device_id) - self.stddev_tensor = cvcuda.as_tensor(self.stddev_tensor, "NHWC") - - def __call__(self, *args): - images = [arg for arg in args] - frame_nhwc = torch.stack(images) - resized = cvcuda.resize( - cvcuda.as_tensor(frame_nhwc, "NHWC"), - ( - frame_nhwc.shape[0], - self.network_size[1], - self.network_size[0], - frame_nhwc.shape[3], - ), - cvcuda.Interp.LINEAR, - ) - - # Convert to floating point range 0-1. - normalized = cvcuda.convertto(resized, np.float32, scale=1 / 255) - - # Normalize with mean and std-dev. - normalized = cvcuda.normalize( - normalized, - base=self.mean_tensor, - scale=self.stddev_tensor, - flags=cvcuda.NormalizeFlags.SCALE_IS_STDDEV, - ) - - # Convert it to NCHW layout and return it. - normalized = cvcuda.reformat(normalized, "NCHW") - result = torch.as_tensor(normalized.cuda(), device=f"cuda:{self.device_id}") - result = list(torch.unbind(result, dim=0)) - - return tuple(result) - -class PostprocessorCvcuda: - name = "changenet-masking" - def __init__(self, config): - self.color_map = { - "0": torch.ByteTensor([255, 255, 255]), - "1": torch.ByteTensor([255, 165, 0]), - "2": torch.ByteTensor([230, 30, 100]), - "3": torch.ByteTensor([70, 140, 0]), - "4": torch.ByteTensor([218, 112, 214]), - "5": torch.ByteTensor([0, 170, 240]), - "6": torch.ByteTensor([127, 235, 170]), - "7": torch.ByteTensor([230, 80, 0]), - "8": torch.ByteTensor([205, 220, 57]), - "9": torch.ByteTensor([218, 165, 32]), - } - self.network_size = config['network_size'] - self.n_class = config['n_class'] - - def get_color_coded_img(self, vis, num_class=10): - color_coded = torch.ones( - (self.network_size[1], self.network_size[0], 3), - dtype=torch.uint8, - ) - for idx in range(num_class): - filtered_indices = vis == idx - color_coded[filtered_indices] = self.color_map[str(idx)] - return color_coded - - def __call__(self, *args): - if not (isinstance(args[0], torch.Tensor) or isinstance(args[0], np.ndarray)): - raise Exception(f"Unsupported type {type(args[0])}") - output = args[0] if isinstance(args[0], torch.Tensor) else torch.from_numpy(args[0]) - pred = torch.argmax(output, dim=0, keepdim=True) - pred = pred.squeeze(0) - image_tensor = self.get_color_coded_img(pred, self.n_class) - buffer = BytesIO() - image = Image.fromarray(image_tensor.numpy()) - image.save(buffer, format="JPEG") - return np.array([base64.b64encode(buffer.getvalue())]) diff --git a/tools/inference_builder/builder/samples/changenet/test_0.jpg b/tools/inference_builder/builder/samples/changenet/test_0.jpg deleted file mode 100644 index f99d78af18e3d81bdc3842cd3cd3ebd1fcb89506..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 32942 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file mode 100644 index 5748c00d..00000000 --- a/tools/inference_builder/builder/samples/changenet/test_config.json +++ /dev/null @@ -1,58 +0,0 @@ -[ - { - "name": "changenet_inference", - "description": "Test ChangeNet inference for change detection on images", - "build_args": { - "TEST_APP_NAME": "changenet", - "CACHE_BUSTER": "changenet_test", - "APP_YAML_PATH": "trt_changenet.yaml", - "OUTPUT_DIR": ".", - "PROCESSORS_PATH": "processors.py", - "SERVER_TYPE": "fastapi", - "OPENAPI_SPEC": "openapi.yaml" - }, - "test_config": { - "default_enable": true, - "timeout": 600, - "env": { - "NVIDIA_DRIVER_CAPABILITIES": "compute,utility,video,graphics", - "MODEL_REPO": "/workspace/model_repo", - "HTTP_PORT": "8003" - }, - "volumes": { - "/tmp/model-repo/visual_changenet": "/workspace/model_repo/visual_changenet/1" - }, - "test_requests": [ - { - "payload_path": "payloads/payloads.jsonl", - "method": "POST", - "headers": { - "Content-Type": "application/json" - } - } - ], - "endpoint": "/v1/infer", - "error_detection": { - "enabled": true, - "patterns": [ - "ERROR", - "Exception:", - "Traceback", - "Fatal error", - "CRITICAL" - ] - }, - "prerequisite_script": null, - "models": { - "visual_changenet": { - "source": "NGC", - "target": "/tmp/model-repo", - "path": "nvidia/tao/visual_changenet_segmentation_landsatscd", - "version": "deployable_v1.2", - "post_script": "docker run --rm --gpus all -v $(pwd):/changenet nvcr.io/nvidia/tensorrt:25.12-py3 trtexec --onnx=/changenet/changenet_768.onnx --saveEngine=/changenet/model.plan --fp16" - } - } - } - } -] - diff --git a/tools/inference_builder/builder/samples/changenet/trt_changenet.yaml b/tools/inference_builder/builder/samples/changenet/trt_changenet.yaml deleted file mode 100644 index c1efecd1..00000000 --- a/tools/inference_builder/builder/samples/changenet/trt_changenet.yaml +++ /dev/null @@ -1,109 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -name: "changenet" -model_repo: /workspace/model_repo/ - -input: - - name: "reference_image" - data_type: TYPE_CUSTOM_IMAGE_BASE64 - dims: [ -1 ] - optional: false - - - name: "test_image" - data_type: TYPE_CUSTOM_IMAGE_BASE64 - dims: [ -1 ] - optional: false - -output: - - name: "output" - data_type: TYPE_STRING - dims: [ -1 ] - -server: - responders: - infer: - operation: inference - requests: - DetectionRequest: > - { - "reference_image": [{{ request.input[0]|tojson }}], - "test_image": [{{ request.input[1]|tojson }}] - } - responses: - DetectionResponse: > - { - "data": {{ response.output[0]|tojson }}, - "usage": { "num_images": 1}, - "model": "nvidia/changenet" - } - healthy_ready: - operation: health_ready_v1_health_ready_get - -models: -- name: visual_changenet - backend: "triton/tensorrt" - max_batch_size: 1 - parameters: - FORCE_CPU_ONLY_INPUT_TENSORS: "no" - input: - - name: input0 - data_type: TYPE_FP32 - dims: [3, 768, 768] - - name: input1 - data_type: TYPE_FP32 - dims: [3, 768, 768] - output: - - name: output_final - data_type: TYPE_FP32 - dims: [10,768, 768] - - name: output0 - data_type: TYPE_FP32 - dims: [10, 48, 48] - - name: output1 - data_type: TYPE_FP32 - dims: [10, 48, 48] - - name: output2 - data_type: TYPE_FP32 - dims: [10, 96, 96] - - name: output3 - data_type: TYPE_FP32 - dims: [10, 192, 192] - preprocessors: - - kind: "custom" - name: "changenet-normalizer" - input: ["reference_image", "test_image"] - output: ["input0", "input1"] - config: - network_size: [768, 768] - postprocessors: - - kind: "custom" - name: "changenet-masking" - input: ["output_final"] - output: ["output_final"] - config: - network_size: [768, 768] - n_class: 10 - -# route map -# A route is defined as -# Source and destination should be defined as -# Model name is optional, so <:["TENSOR_NAME1", "TENSOR_NAME2", ...]> is valid -# Tensor list is optional too and is valid -routes: { - ':["reference_image", "test_image"]': 'visual_changenet', - 'visual_changenet:["output_final"]': ':["output"]', -} diff --git a/tools/inference_builder/builder/samples/cradio/Dockerfile b/tools/inference_builder/builder/samples/cradio/Dockerfile deleted file mode 100644 index de47c315..00000000 --- a/tools/inference_builder/builder/samples/cradio/Dockerfile +++ /dev/null @@ -1,102 +0,0 @@ -# Dockerfile for ds-cradio: PeopleNet Transformer + C-RADIOv3-H pipeline -# Uses DeepStream/nvinfer for detection and polygraphy (TensorRT) for embeddings - -FROM "nvcr.io/nvidia/deepstream:9.0-triton-multiarch" AS inference_base - -# Install Python dependencies required by this pipeline. -RUN --mount=type=cache,target=/root/.cache/pip \ - pip install \ - torch==2.7.0 \ - numpy==1.26.4 \ - omegaconf==2.3.0 \ - transformers==4.54.1 \ - opencv-python - -# Build TensorRT OSS to provide customized plugins (e.g. for PeopleNet -# Transformer ONNX model) and the trtexec tool. -ARG TRT_VERSION_MAJOR=10 -ARG TRT_VERSION_MINOR=14 -ARG TRT_VERSION_PATCH=1 -ARG TRT_VERSION_BUILD=48 - -ARG TRT_VERSION_MAJOR_MINOR=$TRT_VERSION_MAJOR.$TRT_VERSION_MINOR -ARG TRT_VERSION_MAJOR_MINOR_PATCH=$TRT_VERSION_MAJOR.$TRT_VERSION_MINOR.$TRT_VERSION_PATCH -ARG TRT_VERSION_FULL=$TRT_VERSION_MAJOR_MINOR_PATCH.$TRT_VERSION_BUILD - -ARG CUDA_VERSION_MAJOR=13 -ARG CUDA_VERSION_MINOR=1 -ARG CUDA_VERSION_PATCH=80 -ARG CUDA_VERSION_BUILD=36836380_0 -ARG CUDA_VERSION_MAJOR_MINOR=$CUDA_VERSION_MAJOR.$CUDA_VERSION_MINOR -ARG CUDA_VERSION_FULL=$CUDA_VERSION_MAJOR_MINOR.$CUDA_VERSION_PATCH.$CUDA_VERSION_BUILD -ARG CUDNN_VERSION=9.17.0.29 - -ENV TRT_VERSION=$TRT_VERSION_FULL+cuda$CUDA_VERSION_FULL - -WORKDIR /tmp -ENV TRT_TAG="release/$TRT_VERSION_MAJOR_MINOR" -RUN mkdir trt_oss_src && \ - cd trt_oss_src && \ - echo "$PWD Building TRT OSS..." && \ - git clone -b $TRT_TAG https://github.com/nvidia/TensorRT TensorRT && \ - cd TensorRT && \ - git submodule update --init --recursive && \ - mkdir -p build && cd build && \ - cmake .. \ - -DGPU_ARCHS="80;86;90;100;120" \ - -DTRT_LIB_DIR=/usr/lib/x86_64-linux-gnu \ - -DTRT_OUT_DIR=/tmp/out \ - -DCUDA_VERSION=$CUDA_VERSION_MAJOR_MINOR \ - -DCUDNN_VERSION=$CUDNN_VERSION \ - && for i in {1..50}; do make -j 12 && break; done -RUN cp /tmp/out/libnvinfer_plugin.so.$TRT_VERSION_MAJOR_MINOR_PATCH /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.$TRT_VERSION_MAJOR_MINOR_PATCH && \ - cp /tmp/out/libnvinfer_plugin_static.a /usr/lib/x86_64-linux-gnu/libnvinfer_plugin_static.a && \ - cp /tmp/out/libnvonnxparser.so.$TRT_VERSION_MAJOR_MINOR_PATCH /usr/lib/x86_64-linux-gnu/libnvonnxparser.so.$TRT_VERSION_MAJOR_MINOR_PATCH && \ - cp /tmp/out/trtexec /usr/local/bin/ && \ - cd ../../../ && \ - rm -rf trt_oss_src - -# Build DeepStream TAO app post-processors (provides NvDsInferParseCustomDDETRTAO -# for PeopleNet Transformer). -ENV DS_TAO_APPS_REPO=https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git -ENV DS_TAO_APPS_TAG="master" -ARG CACHE_BUSTER=unique_value_to_bypass_docker_cache -RUN mkdir ds_tao_apps_src && \ - cd ds_tao_apps_src && \ - echo "$PWD Pulling DS TAO apps repo..." && \ - git clone -b $DS_TAO_APPS_TAG $DS_TAO_APPS_REPO ds_tao_apps && \ - cd ds_tao_apps/post_processor && \ - echo "$PWD Building DS TAO apps post processing..." && \ - CUDA_MODULE_LOADING=LAZY CUDA_VER=$CUDA_VERSION_MAJOR.$CUDA_VERSION_MINOR make && \ - [ -f libnvds_infercustomparser_tao.so ] && echo "$PWD post processing build successful" || echo "$PWD post processing build failed" && \ - cp libnvds_infercustomparser_tao.so /opt/nvidia/deepstream/deepstream/lib && \ - cd ../../../ && \ - rm -rf ds_tao_apps_src - -# Install Python dependencies for the polygraphy backend -RUN --mount=type=cache,target=/root/.cache/pip \ - pip install --extra-index-url https://pypi.nvidia.com \ - Pillow==11.1.0 \ - timm \ - torchvision \ - einops \ - onnx \ - onnxscript \ - tensorrt==10.14.1.48.post1 \ - polygraphy==0.49.24 - -WORKDIR /workspace - -# Copy the generated inference pipeline -ADD ds-cradio.tgz /workspace - -# Copy the ONNX export script (for model preparation inside container) -COPY export_onnx.py /workspace/export_onnx.py - -# Set environment variables required for DeepStream runtime -ENV GST_PLUGIN_PATH=/opt/nvidia/deepstream/deepstream/lib/gst-plugins -ENV LD_LIBRARY_PATH=/opt/nvidia/deepstream/deepstream/lib:$LD_LIBRARY_PATH -ENV NVSTREAMMUX_ADAPTIVE_BATCHING=yes - -ENTRYPOINT ["python3", "/workspace/__main__.py"] -CMD [] diff --git a/tools/inference_builder/builder/samples/cradio/README.md b/tools/inference_builder/builder/samples/cradio/README.md deleted file mode 100644 index 11b097f2..00000000 --- a/tools/inference_builder/builder/samples/cradio/README.md +++ /dev/null @@ -1,255 +0,0 @@ -# PeopleNet Transformer + C-RADIOv3-H Pipeline - -A two-stage serverless inference pipeline that detects people/objects in images using PeopleNet Transformer (DeepStream/nvinfer), then generates per-detection embeddings using C-RADIOv3-H (TensorRT/polygraphy). The pipeline runs as a CLI application (serverless, no HTTP server). - -We provide a sample Dockerfile for the example, which you can use to build a Docker image and run inference on any x86 system with an NVIDIA Ampere, Hopper, or Blackwell GPU. - -## Pipeline Structure - -``` -Input (image path + MIME) - | - v - +--------------+ - | PeopleNet | DeepStream/nvinfer backend - | Transformer | - Decodes image (DS_IMAGE) - | | - Runs detection (DS_METADATA) - | | - C++ parser: NvDsInferParseCustomDDETRTAO - +--------------+ - | | - | +---> detections (direct to output) - | {shape, bboxes, probs, labels, ...} - v - BboxCropPostprocessor - - Crops each bbox from decoded frame - - Resizes to 256x256 (bicubic) - - CLIP mean/std normalization - - Returns N [3, 256, 256] crops (batched by model operator) - | - v - +--------------+ - | C-RADIOv3-H | polygraphy/TensorRT backend - | | - Batch up to 16 crops - +--------------+ - | - v - Output: - - detections: {shape, bboxes, probs, labels, seg_maps, objects, timestamp} - - summary: [N, 3840] per-detection embeddings - - spatial_features: [N, 256, 1280] per-detection spatial tokens -``` - -### Data Flow - -``` -route: input[media_url, mime] --> peoplenet -route: peoplenet[pixel_values] --> cradio_v3_h -route: peoplenet[detections] --> output -route: cradio_v3_h[summary, spatial_features] --> output -``` - -### Custom Processors - -- **BboxCropPostprocessor** (postprocessor on peoplenet): Crops detected bounding boxes from the decoded frame, resizes/normalizes for C-RADIO input, and returns a list of `[3, 256, 256]` tensors. The model operator batches them into `[N, 3, 256, 256]` for the TRT engine. - -# Prerequisites - -**Note:** Make sure you are in the root directory (`path/to/inference_builder`) to execute the commands in this README. All relative paths and commands assume you are running from the inference_builder root directory. Also ensure that your virtual environment is activated before running any commands. - -Refer to the [top-level README](../../../README.md#getting-started) for base setup (virtual environment, protobuf, etc.). - -Before downloading the model files, set up your model repository: - -```bash -mkdir -p ~/.cache/model-repo && chmod 777 ~/.cache/model-repo -export MODEL_REPO=~/.cache/model-repo -``` - -# Download Models - -## PeopleNet Transformer (NGC) - -The PeopleNet Transformer model is available from NGC: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplenet_transformer - -Download using the NGC CLI and move the files into the expected model directory: - -```bash -ngc registry model download-version \ - nvidia/tao/peoplenet_transformer:deployable_v1.1 \ - --dest /tmp -mv /tmp/peoplenet_transformer_vdeployable_v1.1 $MODEL_REPO/peoplenet -``` - -Run `ls $MODEL_REPO/peoplenet/` and verify you have `resnet50_peoplenet_transformer_op17.onnx`. - -Copy the nvinfer config and labels file to the model directory: - -```bash -cp builder/samples/ds_app/detection/peoplenet_transformer/nvdsinfer_config.yaml \ - builder/samples/ds_app/detection/peoplenet_transformer/labels.txt \ - $MODEL_REPO/peoplenet/ -``` - -**Note:** The ONNX model uses custom TensorRT plugins. The Dockerfile builds TRT OSS from source to provide these plugins. The TensorRT engine is built automatically by DeepStream on first run. - -**Note:** If NGC commands fail, make sure you have access to the models you are trying to download. Some models require an active subscription. Ensure NGC is set up properly, or alternatively try using the NGC web UI to directly download the model. - -## C-RADIOv3-H (HuggingFace) - -Make sure you have the HuggingFace CLI installed and are logged in: - -```bash -pip install huggingface_hub -hf auth login -``` - -Or set the `HF_TOKEN` environment variable: - -```bash -export HF_TOKEN= -``` - -Then download the model: - -```bash -git lfs install -git clone https://huggingface.co/nvidia/C-RADIOv3-H $MODEL_REPO/cradio_v3_h -``` - -**Note:** ONNX export and TRT engine build for this model happen after the Docker image is built, since they require the GPU and container environment. See [Export C-RADIOv3-H ONNX & Build TRT Engine](#export-c-radiov3-h-onnx--build-trt-engine) below. - -# Build the Inference Pipeline - -```bash -source .venv/bin/activate -python builder/main.py builder/samples/cradio/ds_cradio.yaml \ - -c builder/samples/cradio/processors.py \ - -o builder/samples/cradio \ - --server-type serverless -t -``` - -The `-t` flag creates `ds-cradio.tgz` in the output directory (from the `name` in `ds_cradio.yaml`). The Dockerfile expects this tarball. - -# Build the Docker Image - -```bash -docker build -f builder/samples/cradio/Dockerfile -t cradio:latest builder/samples/cradio -``` - -# Export C-RADIOv3-H ONNX & Build TRT Engine - -## Export to ONNX - -Run the export script inside the container (requires GPU): - -```bash -docker run --rm --gpus all \ - -v $MODEL_REPO:/models \ - -v $MODEL_REPO/cradio_v3_h:/root/.cache/huggingface/modules/transformers_modules/cradio_v3_h \ - --entrypoint python3 \ - cradio:latest /workspace/export_onnx.py \ - --output-dir /models/cradio_v3_h \ - --resolution 256 \ - --model-path /models/cradio_v3_h -``` - -## Build TensorRT Engine - -Build the TensorRT engine with polygraphy: - -```bash -docker run --rm --gpus all \ - -v $MODEL_REPO:/models \ - --entrypoint polygraphy \ - cradio:latest convert /models/cradio_v3_h/model.onnx \ - --fp16 \ - --trt-min-shapes pixel_values:[1,3,256,256] \ - --trt-opt-shapes pixel_values:[8,3,256,256] \ - --trt-max-shapes pixel_values:[16,3,256,256] \ - -o /models/cradio_v3_h/model.plan -``` - -Run `ls $MODEL_REPO/cradio_v3_h/model.plan` to verify the engine was created. - -## Automated Script - -Alternatively, `prepare_engine.sh` automates both steps (skips if outputs already exist): - -```bash -bash builder/samples/cradio/prepare_engine.sh cradio:latest $MODEL_REPO -``` - -This is the same script used by the CI test via `prerequisite_script` in `test_config.json`. - -# Run Inference - -Run inference on a single image (output to stdout): - -```bash -docker run --rm --gpus all \ - -v $MODEL_REPO:/models \ - -e MODEL_REPO=/models \ - cradio:latest \ - --media-url --mime image/jpeg -s stdout -``` - -To save the output to a file: - -```bash -docker run --rm --gpus all \ - -v $MODEL_REPO:/models \ - -e MODEL_REPO=/models \ - cradio:latest \ - --media-url --mime image/jpeg -s /models/output.json -``` - -**Important:** Replace `` with an actual image path accessible inside the container (e.g., mount a volume or use a path already in the image such as `/opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.jpg`). - -## Expected Output Format - -```json -{ - "detections": { - "shape": [544, 960], - "bboxes": [[x1, y1, x2, y2], ...], - "probs": [0.977, 0.953, ...], - "labels": [["Person"], ["Person"], ...], - "seg_maps": [], - "objects": [], - "timestamp": 1234567890 - }, - "summary": [[...3840 floats...], ...], - "spatial_features": [[[...1280 floats...] x 256], ...] -} -``` - -- `detections`: PeopleNet bounding boxes, labels, and probabilities -- `summary`: `[N, 3840]` per-detection embeddings (N = number of detections, max 16) -- `spatial_features`: `[N, 256, 1280]` per-detection spatial tokens -- When no objects are detected, N=1 with a dummy zero-crop embedding - -# Model Repository Layout - -``` -$MODEL_REPO/ - peoplenet/ - resnet50_peoplenet_transformer_op17.onnx - nvdsinfer_config.yaml - labels.txt - cradio_v3_h/ - model.plan # TensorRT engine (built from ONNX) - model.onnx # Exported ONNX model - ... # HuggingFace model files (used for ONNX export) -``` - -# Project Files - -| File | Description | -|------|-------------| -| `ds_cradio.yaml` | Pipeline configuration (models, routes, I/O types) | -| `processors.py` | Custom postprocessor (BboxCropPostprocessor) | -| `Dockerfile` | Docker build (DeepStream base + TRT OSS + TAO parser + pipeline) | -| `export_onnx.py` | C-RADIOv3-H ONNX export script | -| `prepare_engine.sh` | Automated ONNX export + TRT engine build (used by CI) | -| `nvdsinfer_config.yaml` | PeopleNet nvinfer runtime config (copy from `ds_app/detection/peoplenet_transformer/` to `$MODEL_REPO/peoplenet/`) | -| `test_config.json` | Test configuration for CI | diff --git a/tools/inference_builder/builder/samples/cradio/ds_cradio.yaml b/tools/inference_builder/builder/samples/cradio/ds_cradio.yaml deleted file mode 100644 index 4a235135..00000000 --- a/tools/inference_builder/builder/samples/cradio/ds_cradio.yaml +++ /dev/null @@ -1,85 +0,0 @@ -name: ds-cradio -model_repo: /models - -# Top-level input: image URL/path + MIME type. -input: - - name: "media_url" - data_type: TYPE_STRING - dims: [ -1 ] - - name: "mime" - data_type: TYPE_STRING - dims: [ -1 ] - -# Top-level output: detections + per-detection embeddings -output: - - name: "detections" - data_type: TYPE_CUSTOM_OBJECT - dims: [ -1 ] - - name: "summary" - data_type: TYPE_FP32 - dims: [-1, -1] - - name: "spatial_features" - data_type: TYPE_FP32 - dims: [-1, -1, -1] - -models: - # Stage 1: PeopleNet Transformer (object detection via DeepStream) - - name: peoplenet - backend: "deepstream/nvinfer" - max_batch_size: 1 - input: - - name: "image_bytes" - data_type: TYPE_CUSTOM_DS_IMAGE - dims: [ -1 ] - - name: "media_url" - data_type: TYPE_CUSTOM_DS_MEDIA_URL - dims: [ -1 ] - - name: "mime" - data_type: TYPE_CUSTOM_DS_MIME - dims: [1] - output: - - name: "detections" - data_type: TYPE_CUSTOM_DS_METADATA - dims: [ -1 ] - - name: "decoded_frames" - data_type: TYPE_CUSTOM_DS_IMAGE - dims: [-1, 3] - parameters: - infer_config_path: - - nvdsinfer_config.yaml - inference_timeout: 10 - postprocessors: - # After DS inference: crop each detected bbox from decoded_frames, - # resize+normalize for C-RADIO, and return a stacked [N,3,256,256] - # tensor. Because peoplenet is on the implicit path the tensor - # flows directly to cradio via kwargs (no stack_tensors_in_dict). - - kind: "custom" - name: "bbox-crop-postprocessor" - input: ["detections", "decoded_frames"] - output: ["pixel_values", "detections"] - config: - max_crops: 16 # must match cradio_v3_h max_batch_size below - - # Stage 2: C-RADIOv3-H (per-crop embedding via TensorRT) - - name: cradio_v3_h - backend: "polygraphy" - max_batch_size: 16 - input: - - name: "pixel_values" - data_type: TYPE_FP32 - dims: [3, 256, 256] - output: - - name: "summary" - data_type: TYPE_FP32 - dims: [-1, -1] - - name: "spatial_features" - data_type: TYPE_FP32 - dims: [-1, -1, -1] - parameters: - tensorrt_engine: "model.plan" - -routes: - ':[\"media_url\", \"mime\"]': 'peoplenet' - 'peoplenet:[\"pixel_values\"]': 'cradio_v3_h' - 'peoplenet:[\"detections\"]': ':' - 'cradio_v3_h:[\"summary\", \"spatial_features\"]': ':' diff --git a/tools/inference_builder/builder/samples/cradio/export_onnx.py b/tools/inference_builder/builder/samples/cradio/export_onnx.py deleted file mode 100644 index 57dd58a1..00000000 --- a/tools/inference_builder/builder/samples/cradio/export_onnx.py +++ /dev/null @@ -1,128 +0,0 @@ -#!/usr/bin/env python3 -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -""" -Export C-RADIOv3-H model from HuggingFace to ONNX format. - -Usage: - python export_onnx.py --output-dir /models/cradio_v3_h --resolution 256 - -After export, build a TensorRT engine: - polygraphy convert /models/cradio_v3_h/model.onnx \ - --fp16 \ - -o /models/cradio_v3_h/model.plan -""" - -import argparse -import os -import sys -import torch - - -def validate_directory_path(dir_path): - """ - Validate directory path to prevent OS access violations. - Args: - dir_path: Directory path to validate - Returns: - str: Validated and sanitized directory path - Raises: - ValueError: If path is invalid or potentially dangerous - """ - if not dir_path: - raise ValueError("Directory path cannot be empty") - - try: - abs_path = os.path.abspath(dir_path) - resolved_path = os.path.realpath(abs_path) - except (OSError, ValueError) as e: - raise ValueError(f"Invalid directory path: {e}") - - if ".." in os.path.normpath(dir_path): - raise ValueError("Path traversal detected in directory path") - - invalid_chars = ['<', '>', ':', '"', '|', '?', '*'] - if any(char in dir_path for char in invalid_chars): - raise ValueError("Invalid characters in directory path") - - system_dirs = ['/etc', '/sys', '/proc', '/dev', '/boot', '/usr/bin', '/usr/sbin'] - for sys_dir in system_dirs: - if resolved_path.startswith(sys_dir): - raise ValueError(f"Access to system directory {sys_dir} is not allowed") - - return resolved_path - - -def export_onnx(output_dir: str, resolution: int = 256, model_path: str = None): - from transformers import AutoModel - - output_dir = validate_directory_path(output_dir) - if model_path is not None: - model_path = validate_directory_path(model_path) - if not isinstance(resolution, int) or resolution <= 0: - raise ValueError(f"Resolution must be a positive integer, got: {resolution}") - - os.makedirs(output_dir, exist_ok=True) - onnx_path = os.path.join(output_dir, "model.onnx") - - source = model_path or "nvidia/C-RADIOv3-H" - print(f"Loading C-RADIOv3-H model from {source}...") - # trust_remote_code=True is required by the C-RADIOv3-H architecture; - # model_path is sanitized by validate_directory_path() above. - model = AutoModel.from_pretrained( - source, trust_remote_code=True # nosec - required by model, path is validated - ) - model.train(False).cuda() - - dummy_input = torch.randn(1, 3, resolution, resolution, device="cuda") - - print(f"Exporting to ONNX: {onnx_path}") - torch.onnx.export( - model, - dummy_input, - onnx_path, - input_names=["pixel_values"], - output_names=["summary", "spatial_features"], - dynamic_axes={ - "pixel_values": {0: "batch_size"}, - "summary": {0: "batch_size"}, - "spatial_features": {0: "batch_size"}, - }, - opset_version=17, - ) - print(f"ONNX model saved to {onnx_path}") - print( - f"\nNext step - build TensorRT engine:\n" - f" polygraphy convert {onnx_path} --fp16 -o {os.path.join(output_dir, 'model.plan')}" - ) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Export C-RADIOv3-H to ONNX") - parser.add_argument( - "--output-dir", - default="/models/cradio_v3_h", - help="Directory for output ONNX and TensorRT engine files", - ) - parser.add_argument( - "--resolution", - type=int, - default=256, - help="Input image resolution (default: 256)", - ) - parser.add_argument( - "--model-path", - default=None, - help="Local path to the model directory (skips HF download)", - ) - try: - args = parser.parse_args() - except Exception as e: - print(f"Error: Argument parsing failed: {str(e)}") - sys.exit(1) - - try: - export_onnx(args.output_dir, args.resolution, args.model_path) - except ValueError as e: - print(f"Security validation failed: {e}") - sys.exit(1) diff --git a/tools/inference_builder/builder/samples/cradio/prepare_engine.sh b/tools/inference_builder/builder/samples/cradio/prepare_engine.sh deleted file mode 100644 index e774b933..00000000 --- a/tools/inference_builder/builder/samples/cradio/prepare_engine.sh +++ /dev/null @@ -1,61 +0,0 @@ -#!/bin/bash -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Prepare C-RADIOv3-H TensorRT engine: export ONNX and build engine. -# Usage: prepare_engine.sh -# -# Requires: model files at /cradio_v3_h (from HuggingFace) -# Produces: /cradio_v3_h/model.plan (TensorRT FP16 engine) - -set -e - -IMAGE="$1" -MODEL_REPO="${2:-/tmp/model-repo}" -MODEL_DIR="$MODEL_REPO/cradio_v3_h" -RESOLUTION=256 - -if [ -z "$IMAGE" ]; then - echo "Usage: $0 [model_repo]" - exit 1 -fi - -# Skip if engine already exists -if [ -f "$MODEL_DIR/model.plan" ]; then - echo "TensorRT engine already exists at $MODEL_DIR/model.plan, skipping" - exit 0 -fi - -# Check that model files exist -if [ ! -d "$MODEL_DIR" ]; then - echo "Error: Model directory not found: $MODEL_DIR" - exit 1 -fi - -echo "=== Step 1: Export ONNX ===" -if [ ! -f "$MODEL_DIR/model.onnx" ]; then - docker run --rm --gpus all \ - -v "$MODEL_REPO:/models" \ - -v "$MODEL_DIR:/root/.cache/huggingface/modules/transformers_modules/cradio_v3_h" \ - --entrypoint python3 \ - "$IMAGE" /workspace/export_onnx.py \ - --output-dir /models/cradio_v3_h \ - --resolution "$RESOLUTION" \ - --model-path /models/cradio_v3_h - echo "ONNX export complete: $MODEL_DIR/model.onnx" -else - echo "ONNX model already exists at $MODEL_DIR/model.onnx, skipping export" -fi - -echo "=== Step 2: Build TensorRT Engine ===" -docker run --rm --gpus all \ - -v "$MODEL_REPO:/models" \ - --entrypoint polygraphy \ - "$IMAGE" convert /models/cradio_v3_h/model.onnx \ - --fp16 \ - --trt-min-shapes "pixel_values:[1,3,${RESOLUTION},${RESOLUTION}]" \ - --trt-opt-shapes "pixel_values:[8,3,${RESOLUTION},${RESOLUTION}]" \ - --trt-max-shapes "pixel_values:[16,3,${RESOLUTION},${RESOLUTION}]" \ - -o /models/cradio_v3_h/model.plan - -echo "TensorRT engine built: $MODEL_DIR/model.plan" diff --git a/tools/inference_builder/builder/samples/cradio/processors.py b/tools/inference_builder/builder/samples/cradio/processors.py deleted file mode 100644 index 533aff9f..00000000 --- a/tools/inference_builder/builder/samples/cradio/processors.py +++ /dev/null @@ -1,97 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -import torch -import torch.nn.functional as F - - -class BboxCropPostprocessor: - """Postprocessor on PeopleNet: crop detected bboxes for C-RADIO. - - Runs after DeepStream inference on peoplenet. Receives the - DS_METADATA ``detections`` dict and the DS_IMAGE - ``decoded_frames`` (RGB uint8 HWC tensor extracted from the - DeepStream pipeline). - - For each detected bounding box the processor: - 1. Crops the bbox region from ``decoded_frames``. - 2. Resizes to 256x256 (bicubic). - 3. Normalises with CLIP mean/std. - - Returns: - * ``pixel_values`` — list of N ``[3, 256, 256]`` float32 tensors - * ``detections`` — the original detection dict (passthrough) - - The list of crops is passed to the cradio ModelOperator, which - batches them via ``stack_tensors_in_dict`` so the TRT engine - receives ``[N, 3, 256, 256]``. - - If no bboxes are detected a single dummy crop is produced so the - downstream model always receives a valid batch. - """ - name = "bbox-crop-postprocessor" - - def __init__(self, config): - # must not exceed cradio max_batch_size - self._max_crops = int(config.get("max_crops", 16)) - self._mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).view(3, 1, 1) - self._std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).view(3, 1, 1) - - def __call__(self, detections, decoded_frames): - # decoded_frames is an RGB uint8 HWC tensor from DS_IMAGE output - if decoded_frames is None: - raise ValueError("decoded_frames is None") - if not isinstance(decoded_frames, torch.Tensor): - try: - decoded_frames = torch.as_tensor(decoded_frames) - except (TypeError, ValueError) as e: - raise ValueError(f"decoded_frames could not be converted to tensor: {e}") from e - if decoded_frames.ndim != 3: - raise ValueError( - f"decoded_frames must be 3D (HWC or CHW), got ndim={decoded_frames.ndim}, shape={decoded_frames.shape}" - ) - if decoded_frames.ndim == 3 and decoded_frames.shape[0] == 3: - decoded_frames = decoded_frames.permute(1, 2, 0) # CHW → HWC - # Expect HWC: (height, width, 3) - if decoded_frames.shape[2] != 3: - raise ValueError( - f"decoded_frames must have 3 channels (RGB), got shape={decoded_frames.shape}" - ) - frame_h, frame_w = decoded_frames.shape[0], decoded_frames.shape[1] - # -- Crop each bbox --------------------------------------------------- - bboxes = detections.get("bboxes", []) - meta_shape = detections.get("shape", [frame_h, frame_w]) - scale_y = frame_h / meta_shape[0] if meta_shape[0] > 0 else 1.0 - scale_x = frame_w / meta_shape[1] if meta_shape[1] > 0 else 1.0 - - crops = [] - for bbox in bboxes[: self._max_crops]: - left, top, right, bottom = bbox - left = int(left * scale_x) - top = int(top * scale_y) - right = int(right * scale_x) - bottom = int(bottom * scale_y) - - left = max(0, min(left, frame_w - 1)) - top = max(0, min(top, frame_h - 1)) - right = max(left + 1, min(right, frame_w)) - bottom = max(top + 1, min(bottom, frame_h)) - - crop = decoded_frames[top:bottom, left:right, :] # HWC - crop = crop.permute(2, 0, 1).float() / 255.0 # CHW [0,1] - crop = F.interpolate( - crop.unsqueeze(0), size=(256, 256), - mode="bicubic", align_corners=False, - ).squeeze(0) # [3, 256, 256] - - mean = self._mean.to(crop.device) - std = self._std.to(crop.device) - crop = (crop - mean) / std - crops.append(crop) - - if len(crops) == 0: - crops = [torch.zeros(3, 256, 256)] - - # Return list of [3, 256, 256] crops — the model operator will - # batch them via stack_tensors_in_dict into [N, 3, 256, 256]. - return (crops, detections) diff --git a/tools/inference_builder/builder/samples/cradio/test_config.json b/tools/inference_builder/builder/samples/cradio/test_config.json deleted file mode 100644 index 4296b97c..00000000 --- a/tools/inference_builder/builder/samples/cradio/test_config.json +++ /dev/null @@ -1,47 +0,0 @@ -[ - { - "name": "cradio_peoplenet_inference", - "description": "Test PeopleNet Transformer + C-RADIOv3-H sequential multi-model pipeline", - "build_args": { - "TEST_APP_NAME": "ds-cradio", - "CACHE_BUSTER": "cradio_peoplenet_test", - "APP_YAML_PATH": "ds_cradio.yaml", - "OUTPUT_DIR": ".", - "PROCESSORS_PATH": "processors.py", - "SERVER_TYPE": "serverless" - }, - "test_config": { - "default_enable": false, - "timeout": 1000, - "env": { - "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes", - "NVIDIA_DRIVER_CAPABILITIES": "compute,utility,video,graphics", - "MODEL_REPO": "/models" - }, - "volumes": { - "/tmp/model-repo": "/models" - }, - "cmd": [ - "--media-url", - "/opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.jpg", - "--mime", - "image/jpeg" - ], - "prerequisite_script": "bash prepare_engine.sh {image_name} /tmp/model-repo", - "models": { - "peoplenet": { - "source": "NGC", - "target": "/tmp/model-repo", - "path": "nvidia/tao/peoplenet_transformer", - "version": "deployable_v1.1", - "configs": "../ds_app/detection/peoplenet_transformer" - }, - "cradio_v3_h": { - "source": "HF", - "target": "/tmp/model-repo", - "path": "nvidia/C-RADIOv3-H" - } - } - } - } -] diff --git a/tools/inference_builder/builder/samples/docker-compose.yml b/tools/inference_builder/builder/samples/docker-compose.yml deleted file mode 100644 index 01271415..00000000 --- a/tools/inference_builder/builder/samples/docker-compose.yml +++ /dev/null @@ -1,155 +0,0 @@ -################################################################################ -# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: LicenseRef-NvidiaProprietary -# -# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual -# property and proprietary rights in and to this material, related -# documentation and any modifications thereto. Any use, reproduction, -# disclosure or distribution of this material and related documentation -# without an express license agreement from NVIDIA CORPORATION or -# its affiliates is strictly prohibited. -################################################################################ - -services: - ms-nvclip: - build: - context: nvclip - target: builder_base - dockerfile: Dockerfile - image: ms-nvclip - volumes: - - '~/.cache/model-repo/:/workspace/model-repo' - ipc: host - # init: true - ports: - - "8800-8803:8000-8003" - deploy: - resources: - reservations: - devices: - - driver: nvidia - #count: 1 - device_ids: ['0'] - capabilities: [gpu] - environment: - NGC_API_KEY: "" -# DEBUG: 1 - ms-changenet: - build: - context: changenet - target: builder_base - dockerfile: Dockerfile - image: ms-changenet - volumes: - - '~/.cache/model-repo/visual_changenet:/workspace/model_repo/visual_changenet/1' - ipc: host - # init: true - ports: - - "8800-8803:8000-8003" - deploy: - resources: - reservations: - devices: - - driver: nvidia - #count: 1 - device_ids: ['0'] - capabilities: [gpu] - environment: - HTTP_PORT: 8003 - DEBUG: 0 - tao-cv: - user: nvs:1000 - build: - context: tao - target: cv_tao_base - dockerfile: Dockerfile - image: tao-cv - # entrypoint: ["/bin/bash"] - # # Keep container running - # command: ["-c", "sleep infinity"] - volumes: - - '~/.cache/model-repo/:/workspace/.cache/model-repo' - ipc: host - # init: true - ports: - - "8800-8803:8000-8003" - deploy: - resources: - reservations: - devices: - - driver: nvidia - #count: 1 - device_ids: ['0'] - capabilities: [gpu] - environment: - DEBUG: 0 - TAO_MODEL_NAME: rtdetr - dry-run: - user: nvs:1000 - build: - context: dummy - target: inference_base - dockerfile: Dockerfile - image: dry-run - volumes: - - '~/.cache:/workspace/.cache' - ipc: host - # init: true - ports: - - "8800-8803:8000-8003" - deploy: - resources: - reservations: - devices: - - driver: nvidia - #count: 1 - device_ids: ['0'] - capabilities: [gpu] - environment: - DEBUG: 1 - ms-qwen: - build: - context: qwen - target: inference_base - dockerfile: Dockerfile - image: ms-qwen - volumes: - - '~/.cache/model-repo:/workspace/model-repo' - ipc: host - # init: true - ports: - - "8800-8803:8000-8003" - deploy: - resources: - reservations: - devices: - - driver: nvidia - #count: 1 - device_ids: ['0'] - capabilities: [gpu] - environment: - DEBUG: 0 - ms-vllm: - build: - context: vllm - target: inference_base - dockerfile: Dockerfile - image: ms-vllm - volumes: - - '~/.cache/model-repo:/workspace/model-repo' - ipc: host - # init: true - ports: - - "8800-8803:8000-8003" - deploy: - resources: - reservations: - devices: - - driver: nvidia - device_ids: ['0'] - #count: 1 - capabilities: [gpu] - environment: - DEBUG: 0 - # number of codec instances based on the hardware specification - N_CODEC_INSTANCES: 7 diff --git a/tools/inference_builder/builder/samples/ds_app/Dockerfile b/tools/inference_builder/builder/samples/ds_app/Dockerfile deleted file mode 100644 index 43363aba..00000000 --- a/tools/inference_builder/builder/samples/ds_app/Dockerfile +++ /dev/null @@ -1,130 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# Sample Dockerfile for a DeepStream TAO ds_app-style pipeline using -# a Triton-enabled DeepStream base image. -# -# Target platform: x86_64 (amd64) -# Intended for standard datacenter/workstation GPUs (e.g. A100, H100, B200, -# RTX series) running Linux on x86_64. Uses pre-built PyTorch wheels from -# PyTorch's CUDA index and links against x86_64 system libraries. -FROM "nvcr.io/nvidia/deepstream:9.0-triton-multiarch" AS inference_base - -# Install Python dependencies required by THIS ds_app sample. These -# versions are pinned for reproducibility and can be adjusted for -# other pipelines or environments. -RUN --mount=type=cache,target=/root/.cache/pip \ - pip install \ - --extra-index-url https://download.pytorch.org/whl/cu130 \ - torch==2.9.0+cu130 \ - numpy==1.26.4 \ - omegaconf==2.3.0 \ - transformers==4.54.1 \ - opencv-python - -# Build TensorRT OSS to provide customized plugins (e.g. for ONNX) and -# the trtexec tool. The version and GPU architectures are controlled -# by the ARGs below; update them if you target different CUDA/TRT -# versions or GPU architectures. -ARG TRT_VERSION_MAJOR=10 -ARG TRT_VERSION_MINOR=14 -ARG TRT_VERSION_PATCH=1 -ARG TRT_VERSION_BUILD=48 - -ARG TRT_VERSION_MAJOR_MINOR=$TRT_VERSION_MAJOR.$TRT_VERSION_MINOR -ARG TRT_VERSION_MAJOR_MINOR_PATCH=$TRT_VERSION_MAJOR.$TRT_VERSION_MINOR.$TRT_VERSION_PATCH -ARG TRT_VERSION_FULL=$TRT_VERSION_MAJOR_MINOR_PATCH.$TRT_VERSION_BUILD - -ARG CUDA_VERSION_MAJOR=13 -ARG CUDA_VERSION_MINOR=1 -ARG CUDA_VERSION_PATCH=80 -ARG CUDA_VERSION_BUILD=36836380_0 -ARG CUDA_VERSION_MAJOR_MINOR=$CUDA_VERSION_MAJOR.$CUDA_VERSION_MINOR -ARG CUDA_VERSION_FULL=$CUDA_VERSION_MAJOR_MINOR.$CUDA_VERSION_PATCH.$CUDA_VERSION_BUILD -ARG CUDNN_VERSION=9.17.0.29 - -ENV TRT_VERSION=$TRT_VERSION_FULL+cuda$CUDA_VERSION_FULL - -WORKDIR /tmp -ENV TRT_TAG="release/$TRT_VERSION_MAJOR_MINOR" -RUN mkdir trt_oss_src && \ - cd trt_oss_src && \ - echo "$PWD Building TRT OSS..." && \ - git clone -b $TRT_TAG https://github.com/nvidia/TensorRT TensorRT && \ - cd TensorRT && \ - git submodule update --init --recursive && \ - mkdir -p build && cd build && \ - cmake .. \ - -DGPU_ARCHS="80;86;90;100;120" \ - -DTRT_LIB_DIR=/usr/lib/x86_64-linux-gnu \ - -DTRT_OUT_DIR=/tmp/out \ - -DCUDA_VERSION=$CUDA_VERSION_MAJOR_MINOR \ - -DCUDNN_VERSION=$CUDNN_VERSION \ - && for i in {1..50}; do make -j 12 && break; done -RUN cp /tmp/out/libnvinfer_plugin.so.$TRT_VERSION_MAJOR_MINOR_PATCH /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.$TRT_VERSION_MAJOR_MINOR_PATCH && \ - cp /tmp/out/libnvinfer_plugin_static.a /usr/lib/x86_64-linux-gnu/libnvinfer_plugin_static.a && \ - cp /tmp/out/libnvonnxparser.so.$TRT_VERSION_MAJOR_MINOR_PATCH /usr/lib/x86_64-linux-gnu/libnvonnxparser.so.$TRT_VERSION_MAJOR_MINOR_PATCH && \ - cp /tmp/out/trtexec /usr/local/bin/ && \ - cd ../../../ && \ - rm -rf trt_oss_src - - -# Build DeepStream TAO app post-processors. This must come AFTER the -# DeepStream SDK is installed in the base image. The resulting -# libnvds_infercustomparser_tao.so is copied into the DeepStream lib -# directory for use by ds_app pipelines. Adjust the repo/tag or CUDA -# version if you use different DS/TAO releases. -ENV DS_TAO_APPS_REPO=https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git -ENV DS_TAO_APPS_TAG="master" -ENV DS_SRC_PATH=/opt/nvidia/deepstream/deepstream -ARG CACHE_BUSTER=unique_value_to_bypass_docker_cache -RUN mkdir ds_tao_apps_src && \ - cd ds_tao_apps_src && \ - echo "$PWD Pulling DS TAO apps repo..." && \ - git clone -b $DS_TAO_APPS_TAG $DS_TAO_APPS_REPO ds_tao_apps && \ - cd ds_tao_apps/post_processor && \ - echo "$PWD Building DS TAO apps post processing..." && \ - CUDA_MODULE_LOADING=LAZY CUDA_VER=$CUDA_VERSION_MAJOR.$CUDA_VERSION_MINOR make DS_SRC_PATH=$DS_SRC_PATH && \ - [ -f libnvds_infercustomparser_tao.so ] && echo "$PWD post processing build successful" || echo "$PWD post processing build failed" && \ - cp libnvds_infercustomparser_tao.so /opt/nvidia/deepstream/deepstream/lib && \ - cd ../../../ && \ - rm -rf ds_tao_apps_src - -WORKDIR /workspace - -# Copy the inference flow package generated by Inference Builder. -# This tarball (here: deepstream-app.tgz) is typically produced when -# running the builder with tarball output enabled, and its name should -# match the generated pipeline artifact you want to deploy. -ADD deepstream-app.tgz /workspace - -# Copy test data for CI/regression testing of this sample pipeline. -# This is optional and specific to the ds_app sample setup. -COPY test_data/* /workspace/test_data/ - -## Set Environment variables required for DeepStream runtime. -# Make sure DeepStream GStreamer plugins and libraries are discoverable. -ENV GST_PLUGIN_PATH=/opt/nvidia/deepstream/deepstream/lib/gst-plugins -ENV LD_LIBRARY_PATH=/opt/nvidia/deepstream/deepstream/lib:$LD_LIBRARY_PATH -# Enable adaptive batching in nvstreammux for better GPU utilization. -ENV NVSTREAMMUX_ADAPTIVE_BATCHING=yes -# Optional: enable detailed latency measurement if needed. -#ENV NVDS_ENABLE_LATENCY_MEASUREMENT=1 -#ENV NVDS_ENABLE_COMPONENT_LATENCY_MEASUREMENT=1 - -ENTRYPOINT ["python3", "/workspace/__main__.py"] -#ENTRYPOINT ["/bin/bash"] -CMD [] diff --git a/tools/inference_builder/builder/samples/ds_app/Dockerfile.dgxspark b/tools/inference_builder/builder/samples/ds_app/Dockerfile.dgxspark deleted file mode 100644 index 267c98d4..00000000 --- a/tools/inference_builder/builder/samples/ds_app/Dockerfile.dgxspark +++ /dev/null @@ -1,149 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# Sample Dockerfile for a DeepStream TAO ds_app-style pipeline on DGX -# with Spark, using a Triton-enabled DeepStream base image and a PyTorch -# source image for copying Python packages. -# -# Target platform: aarch64 / NVIDIA server-grade ARM64 (GB300, DGX Spark) -# Intended for NVIDIA ARM64 server systems such as GB300 NVL and DGX Spark -# that run the arm-sbsa DeepStream variant. Like the Tegra variant, PyTorch -# packages are copied from the NGC pytorch image. Unlike Tegra, this targets -# high-performance datacenter ARM64 hardware (SBSA) rather than embedded -# Jetson devices. -FROM "nvcr.io/nvidia/pytorch:25.08-py3" AS pytorch_src - -FROM "nvcr.io/nvidia/deepstream:9.0-triton-sbsa-dgx-spark" AS inference_base - -RUN --mount=type=cache,target=/root/.cache/pip \ - --mount=type=cache,target=/var/cache/apt,sharing=locked \ - --mount=type=cache,target=/var/lib/apt,sharing=locked \ - apt-get update && apt-get install -y --no-install-recommends libyaml-cpp-dev pkg-config cmake - -RUN --mount=type=bind,from=pytorch_src,source=/usr/local/lib/python3.12/dist-packages,target=/tmp/pytorch_src \ - cp -r /tmp/pytorch_src/{torch,functorch,torchgen,torch-*,torchvision*,flash_attn*,tensorrt*} \ - /usr/local/lib/python3.12/dist-packages/ && \ - cp -r /tmp/pytorch_src/triton /usr/local/lib/python3.12/dist-packages/ && \ - sed -i '/self.capability = target.arch/a\ if self.capability == 121: self.capability = 120' /usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py -COPY --from=pytorch_src /usr/local/lib/libnvpl* /usr/local/lib/ - -RUN --mount=type=cache,target=/root/.cache/pip \ - pip install \ - jinja2==3.1.6 \ - numpy==1.26.4 \ - omegaconf==2.3.0 \ - networkx==3.5 \ - sympy==1.14.0 \ - pillow==11.3 \ - transformers==4.54.1 \ - opencv-python - -# Build TensorRT OSS to provide customized plugins (e.g. for ONNX) and -# the trtexec tool. The version and GPU architectures are controlled -# by the ARGs below; update them if you target different CUDA/TRT -# versions or GPU architectures. -ARG TRT_VERSION_MAJOR=10 -ARG TRT_VERSION_MINOR=13 -ARG TRT_VERSION_PATCH=0 -ARG TRT_VERSION_BUILD=0 - -ARG TRT_VERSION_MAJOR_MINOR=$TRT_VERSION_MAJOR.$TRT_VERSION_MINOR -ARG TRT_VERSION_MAJOR_MINOR_PATCH=$TRT_VERSION_MAJOR.$TRT_VERSION_MINOR.$TRT_VERSION_PATCH -ARG TRT_VERSION_FULL=$TRT_VERSION_MAJOR_MINOR_PATCH.$TRT_VERSION_BUILD - -ARG CUDA_VERSION_MAJOR=13 -ARG CUDA_VERSION_MINOR=0 -ARG CUDA_VERSION_PATCH=0 -ARG CUDA_VERSION_BUILD=1 -ARG CUDA_VERSION_MAJOR_MINOR=$CUDA_VERSION_MAJOR.$CUDA_VERSION_MINOR -ARG CUDA_VERSION_FULL=$CUDA_VERSION_MAJOR_MINOR.$CUDA_VERSION_PATCH.$CUDA_VERSION_BUILD -ARG CUDNN_VERSION=9.12.0.46-1 - -ENV TRT_VERSION=$TRT_VERSION_FULL+cuda$CUDA_VERSION_FULL - -WORKDIR /tmp -ENV TRT_TAG="release/$TRT_VERSION_MAJOR_MINOR" -RUN mkdir trt_oss_src && \ - cd trt_oss_src && \ - echo "$PWD Building TRT OSS..." && \ - git clone -b $TRT_TAG https://github.com/nvidia/TensorRT TensorRT && \ - cd TensorRT && \ - git submodule update --init --recursive && \ - mkdir -p build && cd build && \ - cmake .. \ - -DGPU_ARCHS="110;120;121" \ - -DTRT_LIB_DIR=/usr/lib/aarch64-linux-gnu \ - -DTRT_OUT_DIR=/tmp/out \ - -DCUDA_VERSION=$CUDA_VERSION_MAJOR_MINOR \ - -DCUDNN_VERSION=$CUDNN_VERSION \ - -DCMAKE_C_COMPILER=/usr/bin/gcc \ - && for i in {1..50}; do make -j 12 && break; done -RUN echo "Listing /tmp/out:" && ls -l /tmp/out/libnvinfer_plugin.so.* -RUN cp /tmp/out/libnvinfer_plugin.so.$TRT_VERSION_MAJOR_MINOR_PATCH /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so.$TRT_VERSION_MAJOR_MINOR_PATCH && \ - cp /tmp/out/libnvinfer_plugin_static.a /usr/lib/aarch64-linux-gnu/libnvinfer_plugin_static.a && \ - cp /tmp/out/libnvonnxparser.so.$TRT_VERSION_MAJOR_MINOR_PATCH /usr/lib/aarch64-linux-gnu/libnvonnxparser.so.$TRT_VERSION_MAJOR_MINOR_PATCH && \ - cp /tmp/out/trtexec /usr/local/bin/ && \ - cd ../../../ && \ - rm -rf trt_oss_src - - -# Build DeepStream TAO app post-processors. This must come AFTER the -# DeepStream SDK is installed in the base image. The resulting -# libnvds_infercustomparser_tao.so is copied into the DeepStream lib -# directory for use by ds_app pipelines. Adjust the repo/tag or CUDA -# version if you use different DS/TAO releases. -ENV DS_TAO_APPS_REPO=https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git -ENV DS_TAO_APPS_TAG="master" -ENV DS_SRC_PATH=/opt/nvidia/deepstream/deepstream -ARG CACHE_BUSTER=unique_value_to_bypass_docker_cache -RUN mkdir ds_tao_apps_src && \ - cd ds_tao_apps_src && \ - echo "$PWD Pulling DS TAO apps repo..." && \ - git clone -b $DS_TAO_APPS_TAG $DS_TAO_APPS_REPO ds_tao_apps && \ - cd ds_tao_apps/post_processor && \ - echo "$PWD Building DS TAO apps post processing..." && \ - CUDA_MODULE_LOADING=LAZY CUDA_VER=$CUDA_VERSION_MAJOR_MINOR make DS_SRC_PATH=$DS_SRC_PATH && \ - [ -f libnvds_infercustomparser_tao.so ] && echo "$PWD post processing build successful" || echo "$PWD post processing build falied" && \ - cp libnvds_infercustomparser_tao.so /opt/nvidia/deepstream/deepstream/lib && \ - cd ../../../ && \ - rm -rf ds_tao_apps_src - -RUN rm -rf ~/.cache/gstreamer-1.0 - -WORKDIR /workspace - -# Copy the inference flow package generated by Inference Builder. -# This tarball (here: deepstream-app.tgz) is typically produced when -# running the builder with tarball output enabled, and its name should -# match the generated pipeline artifact you want to deploy. -ADD deepstream-app.tgz /workspace - -# Copy test data for CI/regression testing of this sample pipeline. -# This is optional and specific to the ds_app sample setup. -COPY test_data/* /workspace/test_data/ - -## Set Environment variables required for DeepStream runtime. -# Make sure DeepStream GStreamer plugins and libraries are discoverable. -ENV GST_PLUGIN_PATH=/opt/nvidia/deepstream/deepstream/lib/gst-plugins -ENV LD_LIBRARY_PATH=/opt/nvidia/deepstream/deepstream/lib:$LD_LIBRARY_PATH -# Enable adaptive batching in nvstreammux for better GPU utilization. -ENV NVSTREAMMUX_ADAPTIVE_BATCHING=yes -# Optional: enable detailed latency measurement if needed. -#ENV NVDS_ENABLE_LATENCY_MEASUREMENT=1 -#ENV NVDS_ENABLE_COMPONENT_LATENCY_MEASUREMENT=1 - -ENTRYPOINT ["python3", "/workspace/__main__.py"] -#ENTRYPOINT ["/bin/bash"] -CMD [] diff --git a/tools/inference_builder/builder/samples/ds_app/Dockerfile.tegra b/tools/inference_builder/builder/samples/ds_app/Dockerfile.tegra deleted file mode 100644 index a1d593c1..00000000 --- a/tools/inference_builder/builder/samples/ds_app/Dockerfile.tegra +++ /dev/null @@ -1,147 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# Sample Dockerfile for a DeepStream TAO ds_app-style pipeline on Tegra -# (Jetson), using a Triton-enabled DeepStream base image and a PyTorch -# source image for copying Python packages for aarch64. -# -# Target platform: aarch64 / NVIDIA Tegra (Jetson) -# Intended for Jetson devices (e.g. Orin, AGX Orin, Thor) running JetPack / -# Tegra Linux. PyTorch and related packages are copied from the NGC -# pytorch image rather than installed via pip, since Tegra-compatible -# wheels are not available on the standard PyTorch index. -FROM "nvcr.io/nvidia/pytorch:25.08-py3" AS pytorch_src - -# Pull the base image from Deepstream Triton multiarch image. -FROM "nvcr.io/nvidia/deepstream:9.0-triton-multiarch" AS inference_base - -RUN --mount=type=cache,target=/root/.cache/pip \ - --mount=type=cache,target=/var/cache/apt,sharing=locked \ - --mount=type=cache,target=/var/lib/apt,sharing=locked \ - apt-get update && apt-get install -y --no-install-recommends libyaml-cpp-dev pkg-config cmake - -RUN --mount=type=bind,from=pytorch_src,source=/usr/local/lib/python3.12/dist-packages,target=/tmp/pytorch_src \ - cp -r /tmp/pytorch_src/{torch,functorch,torchgen,torch-*,torchvision*,flash_attn*,tensorrt*} \ - /usr/local/lib/python3.12/dist-packages/ && \ - cp -r /tmp/pytorch_src/triton /usr/local/lib/python3.12/dist-packages/ && \ - sed -i '/self.capability = target.arch/a\ if self.capability == 121: self.capability = 120' /usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py -COPY --from=pytorch_src /usr/local/lib/libnvpl* /usr/local/lib/ - -RUN --mount=type=cache,target=/root/.cache/pip \ - pip install \ - jinja2==3.1.6 \ - numpy==1.26.4 \ - omegaconf==2.3.0 \ - networkx==3.5 \ - sympy==1.14.0 \ - pillow==11.3 \ - transformers==4.54.1 \ - opencv-python - -# Build TensorRT OSS to provide customized plugins (e.g. for ONNX) and -# the trtexec tool. The version and GPU architectures are controlled -# by the ARGs below; update them if you target different CUDA/TRT -# versions or GPU architectures. -ARG TRT_VERSION_MAJOR=10 -ARG TRT_VERSION_MINOR=13 -ARG TRT_VERSION_PATCH=0 -ARG TRT_VERSION_BUILD=0 - -ARG TRT_VERSION_MAJOR_MINOR=$TRT_VERSION_MAJOR.$TRT_VERSION_MINOR -ARG TRT_VERSION_MAJOR_MINOR_PATCH=$TRT_VERSION_MAJOR.$TRT_VERSION_MINOR.$TRT_VERSION_PATCH -ARG TRT_VERSION_FULL=$TRT_VERSION_MAJOR_MINOR_PATCH.$TRT_VERSION_BUILD - -ARG CUDA_VERSION_MAJOR=13 -ARG CUDA_VERSION_MINOR=0 -ARG CUDA_VERSION_PATCH=0 -ARG CUDA_VERSION_BUILD=1 -ARG CUDA_VERSION_MAJOR_MINOR=$CUDA_VERSION_MAJOR.$CUDA_VERSION_MINOR -ARG CUDA_VERSION_FULL=$CUDA_VERSION_MAJOR_MINOR.$CUDA_VERSION_PATCH.$CUDA_VERSION_BUILD -ARG CUDNN_VERSION=9.12.0.46-1 - -ENV TRT_VERSION=$TRT_VERSION_FULL+cuda$CUDA_VERSION_FULL - -WORKDIR /tmp -ENV TRT_TAG="release/$TRT_VERSION_MAJOR_MINOR" -RUN mkdir trt_oss_src && \ - cd trt_oss_src && \ - echo "$PWD Building TRT OSS..." && \ - git clone -b $TRT_TAG https://github.com/nvidia/TensorRT TensorRT && \ - cd TensorRT && \ - git submodule update --init --recursive && \ - mkdir -p build && cd build && \ - cmake .. \ - -DGPU_ARCHS="110;120;121" \ - -DTRT_LIB_DIR=/usr/lib/aarch64-linux-gnu \ - -DTRT_OUT_DIR=/tmp/out \ - -DCUDA_VERSION=$CUDA_VERSION_MAJOR_MINOR \ - -DCUDNN_VERSION=$CUDNN_VERSION \ - -DCMAKE_C_COMPILER=/usr/bin/gcc \ - && for i in {1..50}; do make -j 12 && break; done -RUN echo "Listing /tmp/out:" && ls -l /tmp/out/libnvinfer_plugin.so.* -RUN cp /tmp/out/libnvinfer_plugin.so.$TRT_VERSION_MAJOR_MINOR_PATCH /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so.$TRT_VERSION_MAJOR_MINOR_PATCH && \ - cp /tmp/out/libnvinfer_plugin_static.a /usr/lib/aarch64-linux-gnu/libnvinfer_plugin_static.a && \ - cp /tmp/out/libnvonnxparser.so.$TRT_VERSION_MAJOR_MINOR_PATCH /usr/lib/aarch64-linux-gnu/libnvonnxparser.so.$TRT_VERSION_MAJOR_MINOR_PATCH && \ - cp /tmp/out/trtexec /usr/local/bin/ && \ - cd ../../../ && \ - rm -rf trt_oss_src - - -# Build DeepStream TAO app post-processors. This must come AFTER the -# DeepStream SDK is installed in the base image. The resulting -# libnvds_infercustomparser_tao.so is copied into the DeepStream lib -# directory for use by ds_app pipelines. Adjust the repo/tag or CUDA -# version if you use different DS/TAO releases. -ENV DS_TAO_APPS_REPO=https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git -ENV DS_TAO_APPS_TAG="master" -ENV DS_SRC_PATH=/opt/nvidia/deepstream/deepstream -ARG CACHE_BUSTER=unique_value_to_bypass_docker_cache -RUN mkdir ds_tao_apps_src && \ - cd ds_tao_apps_src && \ - echo "$PWD Pulling DS TAO apps repo..." && \ - git clone -b $DS_TAO_APPS_TAG $DS_TAO_APPS_REPO ds_tao_apps && \ - cd ds_tao_apps/post_processor && \ - echo "$PWD Building DS TAO apps post processing..." && \ - CUDA_MODULE_LOADING=LAZY CUDA_VER=$CUDA_VERSION_MAJOR_MINOR make DS_SRC_PATH=$DS_SRC_PATH && \ - [ -f libnvds_infercustomparser_tao.so ] && echo "$PWD post processing build successful" || echo "$PWD post processing build falied" && \ - cp libnvds_infercustomparser_tao.so /opt/nvidia/deepstream/deepstream/lib && \ - cd ../../../ && \ - rm -rf ds_tao_apps_src - -WORKDIR /workspace - -# Copy the inference flow package generated by Inference Builder. -# This tarball (here: deepstream-app.tgz) is typically produced when -# running the builder with tarball output enabled, and its name should -# match the generated pipeline artifact you want to deploy. -ADD deepstream-app.tgz /workspace - -# Copy test data for CI/regression testing of this sample pipeline. -# This is optional and specific to the ds_app sample setup. -COPY test_data/* /workspace/test_data/ - -## Set Environment variables required for DeepStream runtime. -# Make sure DeepStream GStreamer plugins and libraries are discoverable. -ENV GST_PLUGIN_PATH=/opt/nvidia/deepstream/deepstream/lib/gst-plugins -ENV LD_LIBRARY_PATH=/opt/nvidia/deepstream/deepstream/lib:$LD_LIBRARY_PATH -# Enable adaptive batching in nvstreammux for better GPU utilization. -ENV NVSTREAMMUX_ADAPTIVE_BATCHING=yes -# Optional: enable detailed latency measurement if needed. -#ENV NVDS_ENABLE_LATENCY_MEASUREMENT=1 -#ENV NVDS_ENABLE_COMPONENT_LATENCY_MEASUREMENT=1 - -ENTRYPOINT ["python3", "/workspace/__main__.py"] -#ENTRYPOINT ["/bin/bash"] -CMD [] diff --git a/tools/inference_builder/builder/samples/ds_app/README.md b/tools/inference_builder/builder/samples/ds_app/README.md deleted file mode 100644 index ff66dedc..00000000 --- a/tools/inference_builder/builder/samples/ds_app/README.md +++ /dev/null @@ -1,251 +0,0 @@ -# DeepStream Application Samples - -This directory contains sample configurations demonstrating how to build DeepStream applications using Inference Builder. - -While the samples support Ampere, Hopper, and Blackwell architectures, the model and the backend set the real hardware requirements. - -**Platform Support:** All ds_app samples are compatible with both x86-64 systems and NVIDIA Jetson Thor device. - -## Models used in the samples - -All the models can be downloaded from NGC and certain models need active subscription. - -If you don't have NGC CLI installed, please download and install it from [this page](https://org.ngc.nvidia.com/setup/installers/cli). - -### Image Classification -- **PCB Classification**: [PCB Classification Model](https://catalog.ngc.nvidia.com/orgs/nvaie/models/pcbclassification) - -### Visual Change Detection -- **Visual Changenet Classification**: [Visual Changenet Classification Model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/visual_changenet_classification) - -### Semantic Segmentation -- **CitySemSegFormer**: [CitySemSegFormer Model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer) - -### Instance Segmentation -- **Mask2Fomer**: [Masked-attention Mask Transformer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/mask2former) - -### Grounding Dino -- **Grounding DINO**: [Grounding DINO Model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/grounding_dino) -- **Mask Grounding DINO**: [Mask Grounding DINO Model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/mask_grounding_dino) - -### RT-DETR Detector -- **RT-DETR**: [TrafficCamNet Lite](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/trafficcamnet_transformer_lite) -- **RT-DETR**: [PeopleNet Transformer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplenet_transformer) - -## Getting Started - -Each sample includes its own README with specific instructions for building and running the DeepStream application. Please refer to the individual sample directories for detailed setup and usage instructions. - -The provided samples can serve as references for creating inference pipelines across a wide range of TAO computer vision models. Each model falls into one of the following categories, with corresponding instructions available: - -- classification -- detection -- segmentation -- GDINO (Open vocabulary detection and segmentation) - -Required model files including the onnx, nvinfer configuration, preprocess configuration can be found from NGC model repository or exported through TAO Finetune Microservice. - -## Configurable Parameters for the DeepStream Backend - -You can enable these features under the [`parameters section of the configuration file`](#complete-configuration-example). - -### Inference Configuration - -The core inference configuration that specifies the model and inference parameters. - -```yaml -parameters: - infer_config_path: - - nvdsinfer_config.yaml # Path to the nvinfer configuration file -``` - -### Preprocess Configuration - -Used to define preprocessing operations that run before inference. This is required when non-image tensors must be injected into the pipeline (e.g., for models that require additional metadata or custom tensor inputs). Refer to [nvdspreprocess plugin documentation](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvdspreprocess.html) for more details. - -```yaml -parameters: - preprocess_config_path: - - nvdspreprocess_config.yaml # Path to the nvdspreprocess configuration file -``` - -### Tracker Configuration - -Used for object tracking along with inference using nvtracker. This enables multi-object tracking across frames. - -```yaml -tracker_config: - ll_lib_file: /opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so # Path to the tracker library - ll_config_file: /opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/config_tracker_IOU.yml # Path to tracker configuration file - width: 1920 # Tracker width - height: 1088 # Tracker height - display_tracking_id: true # Display tracking id in object text -``` - -### Analytics Configuration - -Used for object activity analysis such as line-crossing, ROI enetering/exiting, crowding and direction detection. - -```yaml -analytics_config: - config_file_path: /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-nvdsanalytics-test/config_nvdsanalytics.txt # Path to the nvdsanalytics configuration file - gpu_id: 0 # GPU core id for analytics running -``` - - -### Message Broker Configuration - -Used to send inference data over the cloud using nvmsgbroker and nvmsgconverter. Supports various message brokers like Kafka, MQTT, etc. - -> **Note:** To use message broker functionality, you need to set up a broker service first and then update the parameters below accordingly. - -```yaml -msgbroker_config: - msgbroker_proto_lib_path: /opt/nvidia/deepstream/deepstream/lib/libnvds_kafka_proto.so # Path to the message broker protocol library - msgbroker_conn_str: localhost:9092 # Message broker connection string (host:port) - msgbroker_topic: ds_app # Topic name for publishing inference results - msgconv_config_path: /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-test5/configs/dstest5_msgconv_sample_config.txt # Path to message converter configuration - msgconv_payload_type: 2 # Payload type for message conversion (0=DeepStream JSON, 1=DeepStream Minimal JSON, 2=DeepStream Protobuf) - msgconv_msg2p_new_api: false # Use new API which supports publishing multiple payloads using NvDsFrameMeta - msgconv_frame_interval: 1 # Frame interval at which payload is generated (every Nth frame) - msgconv_msg2p_lib: /opt/nvidia/deepstream/deepstream/lib/libnvds_msgconv.so # Path to the message converter library -``` - -### Render Configuration - -To visually see the inference output, you have two options: - -#### 1. Display on Host Machine Terminal - -```yaml -render_config: - enable_display: true # Enable display output on host machine - enable_osd: true # Enable on-screen display with bounding boxes and metadata using nvdsosd -``` - -#### 2. Stream to RTSP Port - -```yaml -render_config: - enable_stream: true # Enable RTSP streaming output - enable_osd: true # Enable on-screen display with bounding boxes and metadata using nvdsosd - rtsp_mount_point: /ds_app # RTSP mount point for the stream - rtsp_port: 8554 # RTSP server port number -``` - -This will start RTSP streaming at `rtsp://localhost:8554/ds_app` - -### Performance Measurement Configuration - -Used to monitor the performance of the inference pipeline and individual components. - -```yaml -perf_config: - enable_fps_logs: true # Enable FPS logging for each source on the host machine terminal - enable_latency_logs: true # Enable element-specific latency logging on host machine terminal -``` - ->**Note:** For latency measurements to work properly, make sure to export these environment variables: - -```bash -export NVDS_ENABLE_LATENCY_MEASUREMENT=1 -export NVDS_ENABLE_COMPONENT_LATENCY_MEASUREMENT=1 -``` - -### KITTI Data Configuration - -When enabled, this configuration dumps inference and tracking data in KITTI format to the specified directories. - -```yaml -kitti_output_path: - infer: /workspace/models/infer-kitti-dump/ # Directory for inference KITTI data - tracker: /workspace/models/tracker-kitti-dump/ # Directory for tracking KITTI data -``` - -> **Note:** Make sure to create the directories mentioned above before running the application to ensure proper KITTI data dumping. - -### Resolution Configuration - -When specified in the configuration, the input stream will be resized to the specified dimensions before processing. - -```yaml -resize_video: [height, width] # Format: [height, width] -resize_video: [1080, 1920] # Example: Resize to 1080p (1920x1080) -``` - -### Timeout Configuration - -Inference timeout ensures the inference process completes within a specified time limit. The timeout value is specified in seconds. - -```yaml -parameters: - inference_timeout: 5 # Timeout in seconds for inference operations -``` - -Batch timeout specifies the time in microseconds to wait for batched buffers to be sent out after the first buffer is available. - -- **Default value:** `1000 * max_batch_size` microseconds -- **Set to -1:** Wait infinitely for batch formation - -> **Warning:** If your `max_batch_size` in the models section is greater than the number of streams you're running, do not set the timeout to -1. This will cause the pipeline to wait infinitely for batch formation and become unresponsive. In such scenarios, set an appropriate timeout value. - -```yaml -parameters: - batch_timeout: 33000 # Timeout in microseconds for batching -``` - -### [Complete Configuration Example](#complete-configuration-example) - -Here's a complete example showing all the features enabled: - -```yaml -parameters: - # Core inference configuration - infer_config_path: - - nvdsinfer_config.yaml # Path to nvinfer configuration file - - # Resolution configuration - resize_video: [1080, 1920] # Resize input stream to 1080p (1920x1080) - - # Object tracking configuration - tracker_config: - ll_lib_file: /opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so # Tracker library path - ll_config_file: /opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/config_tracker_IOU.yml # Tracker config path - - # Message broker configuration for cloud integration - # Note: To use message broker functionality, you need to set up a broker service first and then update the parameters below accordingly. - msgbroker_config: - msgbroker_proto_lib_path: /opt/nvidia/deepstream/deepstream/lib/libnvds_kafka_proto.so # Protocol library - msgbroker_conn_str: localhost:9092 # Broker connection string - msgbroker_topic: ds_app # Topic for publishing results - msgconv_config_path: /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-test5/configs/dstest5_msgconv_sample_config.txt # Message converter config - msgconv_payload_type: 2 # Payload type for message conversion (0=DeepStream JSON, 1=DeepStream Minimal JSON, 2=DeepStream Protobuf) - msgconv_msg2p_new_api: false # Use new API which supports publishing multiple payloads using NvDsFrameMeta - msgconv_frame_interval: 1 # Frame interval at which payload is generated (every Nth frame) - msgconv_msg2p_lib: /opt/nvidia/deepstream/deepstream/lib/libnvds_msgconv.so # Path to the message converter library - - # Rendering and display configuration - # Choose your output method: enable_display (local display), enable_stream (RTSP streaming), or both - render_config: - enable_display: true # Enable display output - enable_osd: true # Enable on-screen display with metadata - enable_stream: true # Enable RTSP streaming - rtsp_mount_point: /ds_app # RTSP mount point - rtsp_port: 8554 # RTSP server port - - # Performance monitoring configuration - perf_config: - enable_fps_logs: true # Enable FPS logging - enable_latency_logs: true # Enable latency logging - - # KITTI dump configuration - kitti_output_path: - infer: /workspace/models/infer-kitti-dump/ # Directory for inference KITTI data - tracker: /workspace/models/tracker-kitti-dump/ # Directory for tracking KITTI data - - # Inference timeout configuration - inference_timeout: 5 # Timeout in seconds for inference operations - - # Batch timeout configuration - batch_timeout: 33000 # Timeout in microseconds to wait for batching operation -``` diff --git a/tools/inference_builder/builder/samples/ds_app/classification/README.md b/tools/inference_builder/builder/samples/ds_app/classification/README.md deleted file mode 100644 index 83f75a8b..00000000 --- a/tools/inference_builder/builder/samples/ds_app/classification/README.md +++ /dev/null @@ -1,201 +0,0 @@ -## Introduction - -This sample demonstrates how to build a deepstream application with Inference Builder using classification models: -1. changenet-classify: visual_changenet_nvpcb_deployable_v1.0 from https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/visual_changenet_classification -2. pcbclassification: deployable_v1.1 from https://catalog.ngc.nvidia.com/orgs/nvaie/models/pcbclassification - -## Prerequisites - -**Note:** Make sure you are in the root directory (`path/to/inference_builder`) to execute the commands in this README. All relative paths and commands assume you are running from the inference_builder root directory. Also ensure that your virtual environment is activated before running any commands. - -Model files are loaded from '/workspace/models/{MODEL_NAME}' within the container, thus the volume must be correctly mapped from the host. -You need to export MODEL_REPO environment variable to the path where you want to store the model files. - -```bash -mkdir ~/.cache/model-repo/ -sudo chmod -R 777 ~/.cache/model-repo/ -export MODEL_REPO=~/.cache/model-repo -``` - -You need first download the model files from the NGC catalog and put them in the $MODEL_REPO/{model-name}/ directory, then copy the other required configurations to the same directory: - -**Note:** If NGC commands fail, make sure you have access to the models you are trying to download. Some models require an active subscription. Ensure NGC is set up properly, or alternatively try using the NGC web UI to directly download the model from the links provided [here](../README.md#models-used-in-the-samples) - -### For pcbclassification sample - -Please use `pcbclassification` as the directory: - -```bash -ngc registry model download-version "nvaie/pcbclassification:deployable_v1.1" -# Move the folder to the model-repo directory, and the sample uses ~/.cache/model-repo by default -mv pcbclassification_vdeployable_v1.1 $MODEL_REPO/pcbclassification -chmod 777 $MODEL_REPO/pcbclassification -cp -r builder/samples/ds_app/classification/pcbclassification/* $MODEL_REPO/pcbclassification/ -``` - -### For changenet-classify sample - -Please use `changenet-classify` as the directory: - -```bash -ngc registry model download-version "nvidia/tao/visual_changenet_classification:visual_changenet_nvpcb_deployable_v1.0" -mv visual_changenet_classification_vvisual_changenet_nvpcb_deployable_v1.0 $MODEL_REPO/changenet-classify/ -# Move the folder to the model-repo directory, and the sample uses ~/.cache/model-repo by default -chmod 777 $MODEL_REPO/changenet-classify -cp -r builder/samples/ds_app/classification/changenet-classify/* $MODEL_REPO/changenet-classify/ -``` - -## Generate the DeepStream Application Package and Build Container Image - -Assume you've followed the [top level instructions](../../../README.md#getting-started) to set up the environment and be sure you're in the inference_builder folder, then activate your virtual environment: - -```bash -source .venv/bin/activate -``` - -### For pcbclassification sample - -Please use ds_pcb.yaml as the configuration: - -#### For x86 Architecture - -```bash -python builder/main.py builder/samples/ds_app/classification/ds_pcb.yaml \ - -o builder/samples/ds_app \ - --server-type serverless \ - -t \ -&& docker build -t deepstream-app builder/samples/ds_app -``` - -#### For Tegra Architecture - -```bash -python builder/main.py builder/samples/ds_app/classification/ds_pcb.yaml \ - -o builder/samples/ds_app \ - --server-type serverless \ - -t \ -&& docker build -t deepstream-app \ - -f builder/samples/ds_app/Dockerfile.tegra \ - builder/samples/ds_app -``` - -#### For Dgx Spark - -```bash -python builder/main.py builder/samples/ds_app/classification/ds_pcb.yaml \ - -o builder/samples/ds_app \ - --server-type serverless \ - -t \ -&& docker build \ - -t deepstream-app \ - -f builder/samples/ds_app/Dockerfile.dgxspark \ - builder/samples/ds_app -``` - -### For changenet-classify sample - -Please use ds_changenet.yaml as the configuration: - -#### For x86 Architecture - -```bash -python builder/main.py builder/samples/ds_app/classification/ds_changenet.yaml \ - -o builder/samples/ds_app \ - --server-type serverless \ - -t \ -&& docker build -t deepstream-app builder/samples/ds_app -``` - -#### For Tegra Architecture - -```bash -python builder/main.py builder/samples/ds_app/classification/ds_changenet.yaml \ - -o builder/samples/ds_app \ - --server-type serverless \ - -t \ -&& docker build \ - -t deepstream-app \ - -f builder/samples/ds_app/Dockerfile.tegra \ - builder/samples/ds_app -``` - -#### For Dgx Spark - -```bash -python builder/main.py builder/samples/ds_app/classification/ds_changenet.yaml \ - -o builder/samples/ds_app \ - --server-type serverless \ - -t \ -&& docker build \ - -t deepstream-app \ - -f builder/samples/ds_app/Dockerfile.dgxspark \ - builder/samples/ds_app -``` - -## Run the deepstream app with image inputs: - -**Note:** The TensorRT engine is generated during the first time run and it takes several minutes. - -**Note:** You can optionally set the `$SAMPLE_INPUT` environment variable to point to your input media directory if you want to perform inference on media files stored on your host machine. - -**Note:** To save the inference results, append the `-s result.json` option to your `docker run` command. - -```bash -# Update this with your actual samples directory path -export SAMPLE_INPUT=/path/to/your/samples/directory -``` - -### For pcbclassification sample - -```bash -# media-url: the path or URL to the input media. -# mime: the media type (e.g., "video/mp4" or "image/jpeg"). -export SAMPLE_INPUT=$(realpath builder/samples/ds_app/classification/sample-inputs/) -docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ - -v $SAMPLE_INPUT:/sample_input \ - -v $MODEL_REPO:/workspace/models \ - -v /tmp/.X11-unix/:/tmp/.X11-unix \ - -e DISPLAY=$DISPLAY \ - -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ - deepstream-app \ - --media-url /sample_input/IMG_0002_C71.png \ - --mime image/png -``` - -### For changenet-classify sample - -**Note:** For changenet classification model, it requires two images as input. Sample test images (`pass_0.png` and `pass_1.png`) are embedded in the Docker image at `/workspace/test_data/` for testing purposes. - -**Using embedded test images (recommended for testing):** - -```bash -docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ - -v $MODEL_REPO:/workspace/models \ - -v /tmp/.X11-unix/:/tmp/.X11-unix \ - -e DISPLAY=$DISPLAY \ - -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ - deepstream-app \ - --media-url /workspace/test_data/pass_0.png /workspace/test_data/pass_1.png \ - --mime image/png image/png -``` - -**Using your own images from host:** - -```bash -# media-url: the path or URL to the input media. -# mime: the media type (e.g., "video/mp4" or "image/jpeg"). -export SAMPLE_INPUT=/path/to/your/images -docker run --rm --network=host --gpus all --privileged --runtime=nvidia \ - -v $SAMPLE_INPUT:/sample_input \ - -v $MODEL_REPO:/workspace/models \ - -v /tmp/.X11-unix/:/tmp/.X11-unix \ - -e DISPLAY=$DISPLAY \ - -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ - deepstream-app \ - --media-url /sample_input/ /sample_input/ \ - --mime image/png image/png -``` - -For classification samples, the output is a list of labels defined in labels.txt. e.g, for pcbclassification model, the output label will be "missing" if there is a part missing from the input image. - -**Note:** You may see `pybind11::handle::dec_ref()` related errors after the inference is completed. This is a known issue and will be fixed in the next release. \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/labels.txt b/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/labels.txt deleted file mode 100644 index 8be0ce5e..00000000 --- a/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/labels.txt +++ /dev/null @@ -1,2 +0,0 @@ -notdefect -defect diff --git a/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdsinfer_config.yaml deleted file mode 100644 index e7a51da0..00000000 --- a/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdsinfer_config.yaml +++ /dev/null @@ -1,29 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -property: - gie-unique-id: 1 - net-scale-factor: 0.00392156862745098 - onnx-file: changenet-classify.onnx - model-engine-file: changenet-classify.onnx_b2_gpu0_fp16.engine - network-mode: 2 - network-type: 1 - offsets: 127.5;127.5;127.5 - num-detected-classes: 2 - input-tensor-from-meta: 1 - infer-dims: 3;512;128 - classifier-threshold: 0 - labelfile-path: labels.txt - custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so diff --git a/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdspreprocess_config_0.yaml b/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdspreprocess_config_0.yaml deleted file mode 100644 index 1d3d860f..00000000 --- a/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdspreprocess_config_0.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -property: - tensor-name: input_1 - processing-width: 128 - processing-height: 512 - network-input-shape: 2;3;512;128 - network-color-format: 0 - network-input-order: 0 - tensor-data-type: 0 - maintain-aspect-ratio: 1 - symmetric-padding: 1 - scaling-pool-memory-type: 0 - scaling-pool-compute-hw: 0 - scaling-filter: 0 - scaling-buf-pool-size: 6 - tensor-buf-pool-size: 6 - target-unique-ids: 1 - process-on-frame: 1 - unique-id: 5 - custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/gst-plugins/libcustom2d_preprocess.so - custom-tensor-preparation-function: CustomTensorPreparation - enable: 1 - -user-configs: - pixel-normalization-factor: 0.007843137 - offsets: 127.5;127.5;127.5 - -group: - - src-ids: 0;2;4;6;8;10;12;14 - custom-input-transformation-function: CustomAsyncTransformation - process-on-roi: 0 - diff --git a/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdspreprocess_config_1.yaml b/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdspreprocess_config_1.yaml deleted file mode 100644 index c700c9b6..00000000 --- a/tools/inference_builder/builder/samples/ds_app/classification/changenet-classify/nvdspreprocess_config_1.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -property: - tensor-name: input_2 - processing-width: 128 - processing-height: 512 - network-input-shape: 2;3;512;128 - network-color-format: 0 - network-input-order: 0 - tensor-data-type: 0 - maintain-aspect-ratio: 1 - symmetric-padding: 1 - scaling-pool-memory-type: 0 - scaling-pool-compute-hw: 0 - scaling-filter: 0 - scaling-buf-pool-size: 6 - tensor-buf-pool-size: 6 - target-unique-ids: 1 - process-on-frame: 1 - unique-id: 5 - custom-lib-path: /opt/nvidia/deepstream/deepstream/lib/gst-plugins/libcustom2d_preprocess.so - custom-tensor-preparation-function: CustomTensorPreparation - enable: 1 - -user-configs: - pixel-normalization-factor: 0.007843137 - offsets: 127.5;127.5;127.5 - -group: - - src-ids: 1;3;5;7;9;11;13;15 - custom-input-transformation-function: CustomAsyncTransformation - process-on-roi: 0 - diff --git a/tools/inference_builder/builder/samples/ds_app/classification/ds_changenet.yaml b/tools/inference_builder/builder/samples/ds_app/classification/ds_changenet.yaml deleted file mode 100644 index c43b2d04..00000000 --- a/tools/inference_builder/builder/samples/ds_app/classification/ds_changenet.yaml +++ /dev/null @@ -1,59 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -name: "deepstream-app" -model_repo: "/workspace/models" - -input: -- name: media_url - data_type: TYPE_CUSTOM_LIST - dims: [ -1 ] -- name: mime - data_type: TYPE_CUSTOM_DS_MIME - dims: [ -1 ] - -output: - - name: output - data_type: TYPE_CUSTOM_DS_METADATA - dims: [ -1 ] - -models: -- name: changenet-classify - backend: deepstream/nvinfer - max_batch_size: 2 - input: - - name: images - data_type: TYPE_CUSTOM_DS_IMAGE - dims: [-1, -1, 3] - optional: true - - name: media_url - data_type: TYPE_CUSTOM_DS_MEDIA_URL - dims: [ -1 ] - - name: mime - data_type: TYPE_CUSTOM_DS_MIME - dims: [ -1 ] - optional: false - output: - - name: output - data_type: TYPE_CUSTOM_DS_METADATA - dims: [ -1 ] - parameters: - infer_config_path: - - nvdsinfer_config.yaml - preprocess_config_path: - - nvdspreprocess_config_0.yaml - - nvdspreprocess_config_1.yaml - inference_timeout: "$INFERENCE_TIMEOUT|3" diff --git a/tools/inference_builder/builder/samples/ds_app/classification/ds_pcb.yaml b/tools/inference_builder/builder/samples/ds_app/classification/ds_pcb.yaml deleted file mode 100644 index caf2e051..00000000 --- a/tools/inference_builder/builder/samples/ds_app/classification/ds_pcb.yaml +++ /dev/null @@ -1,55 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -name: "deepstream-app" -model_repo: "/workspace/models" - -input: -- name: media_url - data_type: TYPE_CUSTOM_LIST - dims: [ -1 ] -- name: mime - data_type: TYPE_CUSTOM_DS_MIME - dims: [ -1 ] - -output: - - name: output - data_type: TYPE_CUSTOM_DS_METADATA - dims: [ -1 ] - -models: -- name: pcbclassification - backend: deepstream/nvinfer - max_batch_size: 1 - input: - - name: images - data_type: TYPE_CUSTOM_DS_IMAGE - dims: [-1, -1, 3] - optional: true - - name: media_url - data_type: TYPE_CUSTOM_DS_MEDIA_URL - dims: [ -1 ] - - name: mime - data_type: TYPE_CUSTOM_DS_MIME - dims: [ -1 ] - output: - - name: output - data_type: TYPE_CUSTOM_DS_METADATA - dims: [ -1 ] - parameters: - infer_config_path: - - nvdsinfer_config.yaml - inference_timeout: 5 \ No newline at end of file diff --git a/tools/inference_builder/builder/samples/ds_app/classification/pcbclassification/labels.txt b/tools/inference_builder/builder/samples/ds_app/classification/pcbclassification/labels.txt deleted file mode 100644 index 33e45d56..00000000 --- a/tools/inference_builder/builder/samples/ds_app/classification/pcbclassification/labels.txt +++ /dev/null @@ -1 +0,0 @@ -missing diff --git a/tools/inference_builder/builder/samples/ds_app/classification/pcbclassification/nvdsinfer_config.yaml b/tools/inference_builder/builder/samples/ds_app/classification/pcbclassification/nvdsinfer_config.yaml deleted file mode 100644 index 6ed69a4a..00000000 --- a/tools/inference_builder/builder/samples/ds_app/classification/pcbclassification/nvdsinfer_config.yaml +++ /dev/null @@ -1,30 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -property: - gie-unique-id: 1 - infer-dims: 3;224;224 - maintain-aspect-ratio: 1 - # 0.226 - net-scale-factor: 0.0173520735728 - network-mode: 2 - network-type: 1 # classification - onnx-file: pcbclassification.onnx - model-engine-file: ./pcbclassification.onnx_b1_gpu0_fp16.engine - workspace-size: 1048576 - classifier-threshold: 0 - scaling-filter: 1 - scaling-compute-hw: 1 - 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z(9bJsnWSDVpvFHE^VQwHqs9CW-k^`0f7&GgP1Ia&xG%%V={l%YwkxAdP%8YZOSJz0 z5A1(sUH6Iolu;y$dC?L^S4~X1PHMny5*2pCX;&-Y@8#^MaQ^@azM`hvf4a@~$Lea9 zS^=kzN}|4!tIct!{<`CHt~Evh^zv_7iT*F{(_;0f5Mx$FX;t5GUkNmFw6Zp(mr^lp yO4?};^8IOx{4w=Es+ffwmMXGf50_UDvY8x{tc$bZU-{lCDdROFK5Ie=AOG2JfIMda diff --git a/tools/inference_builder/builder/tests/frame_sampling/app.yaml b/tools/inference_builder/builder/tests/frame_sampling/app.yaml deleted file mode 100644 index 2fba2217..00000000 --- a/tools/inference_builder/builder/tests/frame_sampling/app.yaml +++ /dev/null @@ -1,42 +0,0 @@ -name: "test-app" -model_repo: "/workspace/models" - -input: - - name: "video_files" - data_type: TYPE_CUSTOM_VIDEO_CHUNK_ASSETS - dims: [ -1 ] - optional: true - - name: "video_streams" - data_type: TYPE_CUSTOM_LONG_VIDEO_ASSETS - dims: [ -1 ] - optional: true - -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: media_frames - data_type: TYPE_FP32 - dims: [ -1, -1, -1, -1] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] - preprocessors: - - kind: "custom" - name: "frame-picker" - input: ["video_streams"] - output: ["media_frames"] - config: { "num_frames": 10 } - - kind: "custom" - name: "frame-picker" - input: ["video_files"] - output: ["media_frames"] - config: { "num_frames": 10 } - -routes: { - ':["video_files"]': 'dummy', - ':["video_streams"]': 'dummy', - 'dummy:["output"]': ':' -} \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/frame_sampling/processors.py b/tools/inference_builder/builder/tests/frame_sampling/processors.py deleted file mode 100644 index 07d5b12e..00000000 --- a/tools/inference_builder/builder/tests/frame_sampling/processors.py +++ /dev/null @@ -1,36 +0,0 @@ -import torch -import logging - -logger = logging.getLogger(__name__) - -class FramePickerProcessor: - name = "frame-picker" - - def __init__(self, config): - self.num_frames = config['num_frames'] - # Eagerly initialize CUDA to avoid GIL deadlock with GStreamer threads - torch.cuda.init() - - def __call__(self, *args, **kwargs): - if len(args) != 1: - raise ValueError( - "FramePickerProcessor expects exactly one argument" - ) - frame_list = args[0] - if len(frame_list) != self.num_frames: - raise ValueError( - f"FramePickerProcessor expects a list of " - f"{self.num_frames} frames, while got {len(frame_list)}" - ) - torch_tensors = [ - torch.utils.dlpack.from_dlpack(frame.tensor) - for frame in frame_list - ] - - # Log frame dimensions for verification - if torch_tensors: - # Tensor shape is typically (H, W, C) from MediaExtractor - h, w = torch_tensors[0].shape[0], torch_tensors[0].shape[1] - logger.info(f"FramePickerProcessor: received {len(torch_tensors)} frames with dimensions {w}x{h}") - - return torch.stack(torch_tensors) diff --git a/tools/inference_builder/builder/tests/negative/df_1.yaml b/tools/inference_builder/builder/tests/negative/df_1.yaml deleted file mode 100644 index 824be9c9..00000000 --- a/tools/inference_builder/builder/tests/negative/df_1.yaml +++ /dev/null @@ -1,50 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -input: - - name: "base64" - data_type: TYPE_CUSTOM_BINARY_BASE64 - dims: [ -1 ] - -output: - - name: "output" - data_type: TYPE_FP32 - dims: [ 1 ] - -server: - responders: - healthy_ready: - operation: health_ready_v1_health_ready_get - infer: - operation: inference - requests: - InferenceRequest: > - { - "base64": ["hello"] - } - responses: - InferenceResponse: > - { - "data": {{ response.outputs | tojson }} - } - -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: inputs - data_type: TYPE_STRING - dims: [ -1] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] - -routes: { - ':["base64"]': 'dummy:["inputs"]', - 'dummy:["output"]': ':' -} diff --git a/tools/inference_builder/builder/tests/negative/df_2.yaml b/tools/inference_builder/builder/tests/negative/df_2.yaml deleted file mode 100644 index 76db5efe..00000000 --- a/tools/inference_builder/builder/tests/negative/df_2.yaml +++ /dev/null @@ -1,50 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -input: - - name: "base64" - data_type: TYPE_CUSTOM_BINARY_BASE64 - dims: [ -1 ] - -output: - - name: "output" - data_type: TYPE_FP32 - dims: [ 1 ] - -server: - responders: - healthy_ready: - operation: health_ready_v1_health_ready_get - infer: - operation: inference - requests: - InferenceRequest: > - { - "base64": [1, 2] - } - responses: - InferenceResponse: > - { - "data": {{ response.outputs | tojson }} - } - -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: inputs - data_type: TYPE_STRING - dims: [ -1] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] - -routes: { - ':["base64"]': 'dummy:["inputs"]', - 'dummy:["output"]': ':' -} diff --git a/tools/inference_builder/builder/tests/negative/df_3 copy.yaml b/tools/inference_builder/builder/tests/negative/df_3 copy.yaml deleted file mode 100644 index 4b3d5cf7..00000000 --- a/tools/inference_builder/builder/tests/negative/df_3 copy.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -input: - - name: "inputs" - data_type: TYPE_STRING - dims: [ -1 ] - -output: - - name: "output" - data_type: TYPE_FP32 - dims: [ 1 ] - -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: inputs - data_type: TYPE_STRING - dims: [ -1] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] - postprocessors: - - kind: "custom" - name: "pp-output-2" - input: ["output"] - output: ["output_1", "output_2"] \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/df_3.yaml b/tools/inference_builder/builder/tests/negative/df_3.yaml deleted file mode 100644 index a83bc7e8..00000000 --- a/tools/inference_builder/builder/tests/negative/df_3.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -input: - - name: "video" - data_type: TYPE_CUSTOM_LONG_VIDEO_ASSETS - dims: [ -1 ] - optional: true - - name: "image" - data_type: TYPE_CUSTOM_IMAGE_ASSETS - dims: [ -1 ] - optional: true - - name: "video_chunk" - data_type: TYPE_CUSTOM_VIDEO_CHUNK_ASSETS - dims: [ -1 ] - optional: true - -output: - - name: "output" - data_type: TYPE_FP32 - dims: [ 1 ] - -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: inputs - data_type: TYPE_STRING - dims: [ -1 ] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] - -routes: { - ':["video"]': 'dummy:["inputs"]', - ':["image"]': 'dummy:["inputs"]', - ':["video_chunk"]': 'dummy:["inputs"]', - 'dummy:["output"]': ':' -} \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/image.yaml b/tools/inference_builder/builder/tests/negative/image.yaml deleted file mode 100644 index d9a4f0e6..00000000 --- a/tools/inference_builder/builder/tests/negative/image.yaml +++ /dev/null @@ -1,29 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -input: - - name: "image" - data_type: TYPE_CUSTOM_IMAGE_BASE64 - dims: [ -1 ] - optional: true - -output: - - name: "output" - data_type: TYPE_FP32 - dims: [ 1 ] - -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: image - data_type: TYPE_UINT8 - dims: [ -1, -1, -1, -1 ] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] diff --git a/tools/inference_builder/builder/tests/negative/mo_1.yaml b/tools/inference_builder/builder/tests/negative/mo_1.yaml deleted file mode 100644 index 4b3d5cf7..00000000 --- a/tools/inference_builder/builder/tests/negative/mo_1.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -input: - - name: "inputs" - data_type: TYPE_STRING - dims: [ -1 ] - -output: - - name: "output" - data_type: TYPE_FP32 - dims: [ 1 ] - -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: inputs - data_type: TYPE_STRING - dims: [ -1] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] - postprocessors: - - kind: "custom" - name: "pp-output-2" - input: ["output"] - output: ["output_1", "output_2"] \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/mo_2.yaml b/tools/inference_builder/builder/tests/negative/mo_2.yaml deleted file mode 100644 index f20dc58a..00000000 --- a/tools/inference_builder/builder/tests/negative/mo_2.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -server: - responders: - healthy_ready: - operation: health_ready_v1_health_ready_get - infer: - operation: inference - requests: - InferenceRequest: > - { - "inputs_not_found": ["hello"] - } - responses: - InferenceResponse: > - { - "output": {{ response.outputs | tojson }} - } -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: inputs - data_type: TYPE_STRING - dims: [ -1] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/model.yaml b/tools/inference_builder/builder/tests/negative/model.yaml deleted file mode 100644 index ce03f7ca..00000000 --- a/tools/inference_builder/builder/tests/negative/model.yaml +++ /dev/null @@ -1,28 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -input: - - name: "inputs" - data_type: TYPE_STRING - dims: [ -1 ] - -output: - - name: "output" - data_type: TYPE_FP32 - dims: [ 1 ] - -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: inputs - data_type: TYPE_STRING - dims: [ -1] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] diff --git a/tools/inference_builder/builder/tests/negative/payloads/payloads.jsonl b/tools/inference_builder/builder/tests/negative/payloads/payloads.jsonl deleted file mode 100644 index 63702345..00000000 --- a/tools/inference_builder/builder/tests/negative/payloads/payloads.jsonl +++ /dev/null @@ -1 +0,0 @@ -{"text": ["hello"]} diff --git a/tools/inference_builder/builder/tests/negative/pp_1.yaml b/tools/inference_builder/builder/tests/negative/pp_1.yaml deleted file mode 100644 index 698a2950..00000000 --- a/tools/inference_builder/builder/tests/negative/pp_1.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -input: - - name: "inputs" - data_type: TYPE_STRING - dims: [ -1 ] - -output: - - name: "output" - data_type: TYPE_FP32 - dims: [ 1 ] - -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: inputs - data_type: TYPE_STRING - dims: [ -1] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] - postprocessors: - - kind: "custom" - name: "pp-output-2" - input: ["output"] - output: ["output"] \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/pp_2.yaml b/tools/inference_builder/builder/tests/negative/pp_2.yaml deleted file mode 100644 index 2c1d58cb..00000000 --- a/tools/inference_builder/builder/tests/negative/pp_2.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -input: - - name: "inputs" - data_type: TYPE_STRING - dims: [ -1 ] - -output: - - name: "output" - data_type: TYPE_FP32 - dims: [ 1 ] - -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: inputs - data_type: TYPE_STRING - dims: [ -1] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] - preprocessors: - - kind: "custom" - name: "pp-output-2" - input: ["inputs"] - output: ["inputs"] \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/pp_3.yaml b/tools/inference_builder/builder/tests/negative/pp_3.yaml deleted file mode 100644 index fa869b99..00000000 --- a/tools/inference_builder/builder/tests/negative/pp_3.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -input: - - name: "inputs" - data_type: TYPE_STRING - dims: [ -1 ] - -output: - - name: "output" - data_type: TYPE_FP32 - dims: [ 1 ] - -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: inputs - data_type: TYPE_STRING - dims: [ -1] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] - preprocessors: - - kind: "custom" - name: "pp-nonexist" - input: ["inputs"] - output: ["inputs"] \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/processors.py b/tools/inference_builder/builder/tests/negative/processors.py deleted file mode 100644 index 9a35c1ab..00000000 --- a/tools/inference_builder/builder/tests/negative/processors.py +++ /dev/null @@ -1,32 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -class InvalidProcessor1: - name = "pp-output-2" - - def __init__(self, config): - pass - - def __call__(self, *args, **kwargs): - return 0, 1 - -class InvalidProcessor2: - name = "pp-output-int" - - def __init__(self, config): - pass - - def __call__(self, *args, **kwargs): - return 0 diff --git a/tools/inference_builder/builder/tests/negative/route_1.yaml b/tools/inference_builder/builder/tests/negative/route_1.yaml deleted file mode 100644 index 465caf3e..00000000 --- a/tools/inference_builder/builder/tests/negative/route_1.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -input: - - name: "inputs" - data_type: TYPE_STRING - dims: [ -1 ] - -output: - - name: "output" - data_type: TYPE_FP32 - dims: [ 1 ] - -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: inputs - data_type: TYPE_STRING - dims: [ -1] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] - -routes: { - ':["inputs"]:': 'dummy::', - 'dummy:["output"]': ':' -} \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/route_2.yaml b/tools/inference_builder/builder/tests/negative/route_2.yaml deleted file mode 100644 index 4123eb75..00000000 --- a/tools/inference_builder/builder/tests/negative/route_2.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -input: - - name: "inputs" - data_type: TYPE_STRING - dims: [ -1 ] - -output: - - name: "output" - data_type: TYPE_FP32 - dims: [ 1 ] - -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: inputs - data_type: TYPE_STRING - dims: [ -1] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] - -routes: { - ':["inputs"]': 'dummy:', - 'dummy:"output"': ':' -} \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/route_3.yaml b/tools/inference_builder/builder/tests/negative/route_3.yaml deleted file mode 100644 index 8b18d7b8..00000000 --- a/tools/inference_builder/builder/tests/negative/route_3.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -input: - - name: "inputs" - data_type: TYPE_STRING - dims: [ -1 ] - -output: - - name: "output" - data_type: TYPE_FP32 - dims: [ 1 ] - -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: inputs - data_type: TYPE_STRING - dims: [ -1] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] - -routes: { - ':["inputs"]': 'dummy:"inputs"', - 'dummy:["output"]': ':' -} \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/route_4.yaml b/tools/inference_builder/builder/tests/negative/route_4.yaml deleted file mode 100644 index 71029f9b..00000000 --- a/tools/inference_builder/builder/tests/negative/route_4.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -input: - - name: "inputs" - data_type: TYPE_STRING - dims: [ -1 ] - -output: - - name: "output" - data_type: TYPE_FP32 - dims: [ 1 ] - -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: inputs - data_type: TYPE_STRING - dims: [ -1] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] - -routes: { - ':["inputs"]': 'dummy2', - 'dummy:["output"]': ':' -} \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/route_5.yaml b/tools/inference_builder/builder/tests/negative/route_5.yaml deleted file mode 100644 index 803c66c6..00000000 --- a/tools/inference_builder/builder/tests/negative/route_5.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -input: - - name: "inputs" - data_type: TYPE_STRING - dims: [ -1 ] - -output: - - name: "output" - data_type: TYPE_FP32 - dims: [ 1 ] - -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: inputs - data_type: TYPE_STRING - dims: [ -1] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] - -routes: { - ':["inputs"]': 'dummy', - 'dummy2:["output"]': ':' -} \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/negative/video.yaml b/tools/inference_builder/builder/tests/negative/video.yaml deleted file mode 100644 index c0987670..00000000 --- a/tools/inference_builder/builder/tests/negative/video.yaml +++ /dev/null @@ -1,29 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -input: - - name: "video_chunk" - data_type: TYPE_CUSTOM_VIDEO_CHUNK_ASSETS - dims: [ -1 ] - optional: true - -output: - - name: "output" - data_type: TYPE_FP32 - dims: [ 1 ] - -models: -- name: dummy - backend: dummy - max_batch_size: 1 - input: - - name: video_chunk - data_type: TYPE_UINT8 - dims: [ -1, -1, -1, -1 ] - output: - - name: output - data_type: TYPE_FP32 - dims: [ 1 ] diff --git a/tools/inference_builder/builder/tests/output_types/app.yaml b/tools/inference_builder/builder/tests/output_types/app.yaml deleted file mode 100644 index 9759e4c5..00000000 --- a/tools/inference_builder/builder/tests/output_types/app.yaml +++ /dev/null @@ -1,110 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 - -name: "test-app" -model_repo: "/workspace/models" - -# Input specification for testing various input types -input: - - name: "text" - data_type: TYPE_STRING - dims: [ -1 ] - optional: true - -# Output specification with multiple numpy array types -output: - - name: "outputs" - data_type: TYPE_STRING - dims: [ -1 ] - -server: - responders: - healthy_ready: - operation: health_ready_v1_health_ready_get - infer: - operation: inference - requests: - InferenceRequest: > - { - {% if request.text is not none %} - "text": [ - {% for item in request.text %} - {{ item|tojson }}{% if not loop.last %}, {% endif %} - {% endfor %} - ] - {% endif %} - } - responses: - InferenceResponse: > - { - "data": {{ response.outputs | tojson }} - } - -models: - - name: output_types_dummy - backend: "dummy" - max_batch_size: 1 - parameters: {} - input: - - name: text - data_type: TYPE_STRING - dims: [ -1 ] - output: - - name: int8_output - data_type: TYPE_INT8 - dims: [5, 4] - - name: int16_output - data_type: TYPE_INT16 - dims: [5, 4] - - name: int32_output - data_type: TYPE_INT32 - dims: [5, 4] - - name: int64_output - data_type: TYPE_INT64 - dims: [5, 4] - - name: uint8_output - data_type: TYPE_UINT8 - dims: [5, 4] - - name: uint16_output - data_type: TYPE_UINT16 - dims: [5, 4] - - name: uint32_output - data_type: TYPE_UINT32 - dims: [5, 4] - - name: uint64_output - data_type: TYPE_UINT64 - dims: [5, 4] - - name: fp16_output - data_type: TYPE_FP16 - dims: [5, 4] - - name: fp32_output - data_type: TYPE_FP32 - dims: [5, 4] - - name: fp64_output - data_type: TYPE_FP64 - dims: [5, 4] - - name: bool_output - data_type: TYPE_BOOL - dims: [5, 4] - - name: string_output - data_type: TYPE_STRING - dims: [5] - postprocessors: - - kind: "custom" - name: "output-types-validator" - input: - - int8_output - - int16_output - - int32_output - - int64_output - - uint8_output - - uint16_output - - uint32_output - - uint64_output - - fp16_output - - fp32_output - - fp64_output - - bool_output - - string_output - output: - - outputs diff --git a/tools/inference_builder/builder/tests/output_types/payloads/payloads.jsonl b/tools/inference_builder/builder/tests/output_types/payloads/payloads.jsonl deleted file mode 100644 index 79d79b3d..00000000 --- a/tools/inference_builder/builder/tests/output_types/payloads/payloads.jsonl +++ /dev/null @@ -1,4 +0,0 @@ -{"text": ["hello-1"]} -{"text": ["hello-2"]} -{"text": ["hello-3"]} - diff --git a/tools/inference_builder/builder/tests/output_types/processors.py b/tools/inference_builder/builder/tests/output_types/processors.py deleted file mode 100644 index dc977f08..00000000 --- a/tools/inference_builder/builder/tests/output_types/processors.py +++ /dev/null @@ -1,68 +0,0 @@ -from numpy import int64, uint, uint8 -import numpy as np - - -class Validator: - name = "output-types-validator" - - def __init__(self, config): - pass - - def __call__(self, *args): - int8_outputs = args[0] - int16_outputs = args[1] - int32_outputs = args[2] - int64_outputs = args[3] - uint8_outputs = args[4] - uint16_outputs = args[5] - uint32_outputs = args[6] - uint64_outputs = args[7] - fp16_outputs = args[8] - fp32_outputs = args[9] - fp64_outputs = args[10] - bool_outputs = args[11] - string_outputs = args[12] - - # Type assertions - assert isinstance(int8_outputs, np.ndarray), f"int8_outputs should be ndarray, got {type(int8_outputs)}" - assert int8_outputs.dtype == np.int8, f"int8_outputs should have dtype int8, got {int8_outputs.dtype}" - - assert isinstance(int16_outputs, np.ndarray), f"int16_outputs should be ndarray, got {type(int16_outputs)}" - assert int16_outputs.dtype == np.int16, f"int16_outputs should have dtype int16, got {int16_outputs.dtype}" - - assert isinstance(int32_outputs, np.ndarray), f"int32_outputs should be ndarray, got {type(int32_outputs)}" - assert int32_outputs.dtype == np.int32, f"int32_outputs should have dtype int32, got {int32_outputs.dtype}" - - assert isinstance(int64_outputs, np.ndarray), f"int64_outputs should be ndarray, got {type(int64_outputs)}" - assert int64_outputs.dtype == np.int64, f"int64_outputs should have dtype int64, got {int64_outputs.dtype}" - - assert isinstance(uint8_outputs, np.ndarray), f"uint8_outputs should be ndarray, got {type(uint8_outputs)}" - assert uint8_outputs.dtype == np.uint8, f"uint8_outputs should have dtype uint8, got {uint8_outputs.dtype}" - - assert isinstance(uint16_outputs, np.ndarray), f"uint16_outputs should be ndarray, got {type(uint16_outputs)}" - assert uint16_outputs.dtype == np.uint16, f"uint16_outputs should have dtype uint16, got {uint16_outputs.dtype}" - - assert isinstance(uint32_outputs, np.ndarray), f"uint32_outputs should be ndarray, got {type(uint32_outputs)}" - assert uint32_outputs.dtype == np.uint32, f"uint32_outputs should have dtype uint32, got {uint32_outputs.dtype}" - - assert isinstance(uint64_outputs, np.ndarray), f"uint64_outputs should be ndarray, got {type(uint64_outputs)}" - assert uint64_outputs.dtype == np.uint64, f"uint64_outputs should have dtype uint64, got {uint64_outputs.dtype}" - - assert isinstance(fp16_outputs, np.ndarray), f"fp16_outputs should be ndarray, got {type(fp16_outputs)}" - assert fp16_outputs.dtype == np.float16, f"fp16_outputs should have dtype float16, got {fp16_outputs.dtype}" - - assert isinstance(fp32_outputs, np.ndarray), f"fp32_outputs should be ndarray, got {type(fp32_outputs)}" - assert fp32_outputs.dtype == np.float32, f"fp32_outputs should have dtype float32, got {fp32_outputs.dtype}" - - assert isinstance(fp64_outputs, np.ndarray), f"fp64_outputs should be ndarray, got {type(fp64_outputs)}" - assert fp64_outputs.dtype == np.float64, f"fp64_outputs should have dtype float64, got {fp64_outputs.dtype}" - - assert isinstance(bool_outputs, np.ndarray), f"bool_outputs should be ndarray, got {type(bool_outputs)}" - assert bool_outputs.dtype == np.bool_, f"bool_outputs should have dtype bool_, got {bool_outputs.dtype}" - - assert isinstance(string_outputs, np.ndarray), f"string_outputs should be ndarray, got {type(string_outputs)}" - # String arrays can be np.bytes_ (|S*), np.str_ (U*), or np.object_ - assert np.issubdtype(string_outputs.dtype, np.bytes_) or np.issubdtype(string_outputs.dtype, np.str_) or string_outputs.dtype == np.object_, \ - f"string_outputs should have string-like dtype, got {string_outputs.dtype}" - - return [], diff --git a/tools/inference_builder/builder/tests/run_tests.sh b/tools/inference_builder/builder/tests/run_tests.sh deleted file mode 100644 index 8ecb80e4..00000000 --- a/tools/inference_builder/builder/tests/run_tests.sh +++ /dev/null @@ -1,194 +0,0 @@ -#!/bin/bash - -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# Test script wrapper for Docker container builds -# This script provides easy-to-use commands for testing Docker builds - -set -e - -SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -TEST_SCRIPT="$SCRIPT_DIR/test_docker_builds.py" - -# Colors for output -RED='\033[0;31m' -GREEN='\033[0;32m' -YELLOW='\033[1;33m' -BLUE='\033[0;34m' -NC='\033[0m' # No Color - -# Function to print colored output -print_info() { - echo -e "${BLUE}[INFO]${NC} $1" -} - -print_success() { - echo -e "${GREEN}[SUCCESS]${NC} $1" -} - -print_warning() { - echo -e "${YELLOW}[WARNING]${NC} $1" -} - -print_error() { - echo -e "${RED}[ERROR]${NC} $1" -} - -# Function to show usage -show_usage() { - cat << EOF -Usage: $0 [OPTIONS] - -Run Docker build tests using a configuration file (defaults to test_configs.json). - -Options: - --no-cleanup Don't cleanup Docker images after testing - --output FILE Save test report to specified file - -c, --config FILE Use configuration file (optional, defaults to test_configs.json) - --log-dir DIR Directory to save container logs (default: logs) - -t, --test-case NAME Run only test cases matching this name (partial match). Use '*' for all. - --gpus DEVICES GPU devices to use (default: 'all'). Examples: 'all', 'device=0', 'device=0,1' - -Examples: - $0 - $0 -c my_config.json --output results.json - $0 --no-cleanup --output full_results.json --log-dir test_logs - $0 -t "Frame Sampling" - $0 -t "*" - $0 --test-case "FastAPI" - $0 --gpus "device=0" - $0 --gpus "device=0,1" -t "*" - -EOF -} - -# Function to check prerequisites -check_prerequisites() { - print_info "Checking prerequisites..." - - # Check if Docker is installed and running - if ! command -v docker &> /dev/null; then - print_error "Docker is not installed or not in PATH" - exit 1 - fi - - if ! docker info &> /dev/null; then - print_error "Docker is not running or you don't have permissions" - exit 1 - fi - - # Check if Python script exists - if [ ! -f "$TEST_SCRIPT" ]; then - print_error "Test script not found: $TEST_SCRIPT" - exit 1 - fi - - # Check if Python script is executable - if [ ! -x "$TEST_SCRIPT" ]; then - print_warning "Making test script executable..." - chmod +x "$TEST_SCRIPT" - fi - - print_success "Prerequisites check passed" -} - -# Parse command line arguments -NO_CLEANUP="false" -OUTPUT_FILE="" -CONFIG_FILE="" -LOG_DIR="logs" -TEST_CASE="" -GPUS="all" - -while [[ $# -gt 0 ]]; do - case $1 in - -h|--help) - show_usage - exit 0 - ;; - --no-cleanup) - NO_CLEANUP="true" - shift - ;; - --output) - OUTPUT_FILE="$2" - shift 2 - ;; - -c|--config) - CONFIG_FILE="$2" - shift 2 - ;; - --log-dir) - LOG_DIR="$2" - shift 2 - ;; - -t|--test-case) - TEST_CASE="$2" - shift 2 - ;; - --gpus) - GPUS="$2" - shift 2 - ;; - *) - print_error "Unknown option: $1" - show_usage - exit 1 - ;; - esac -done - -# Change to script directory -cd "$SCRIPT_DIR" - -# Check prerequisites -check_prerequisites - -# Determine configuration file (default to test_configs.json) -if [ -z "$CONFIG_FILE" ]; then - CONFIG_FILE="test_configs.json" - print_info "No configuration file provided, using default: $CONFIG_FILE" -fi - -if [ ! -f "$CONFIG_FILE" ]; then - print_error "Configuration file not found: $CONFIG_FILE" - exit 1 -fi - -print_info "Running tests with configuration: $CONFIG_FILE" - -args=("--dockerfile" "Dockerfile" "--base-dir" "." "--config-file" "$CONFIG_FILE" "--log-dir" "$LOG_DIR") - -if [ "$NO_CLEANUP" = "true" ]; then - args+=("--no-cleanup") -fi - -if [ -n "$OUTPUT_FILE" ]; then - args+=("--output" "$OUTPUT_FILE") -fi - -if [ -n "$TEST_CASE" ]; then - args+=("--test-case" "$TEST_CASE") -fi - -if [ -n "$GPUS" ]; then - args+=("--gpus" "$GPUS") -fi - -python3 "$TEST_SCRIPT" "${args[@]}" - -print_success "Test execution completed" \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/setup_rtsp_server.sh b/tools/inference_builder/builder/tests/setup_rtsp_server.sh deleted file mode 100644 index 6f359e5b..00000000 --- a/tools/inference_builder/builder/tests/setup_rtsp_server.sh +++ /dev/null @@ -1,376 +0,0 @@ -#!/bin/bash - -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# RTSP Server Setup Script for Testing -# This script sets up an RTSP server using cvlc to stream MP4 videos for testing -# Supports both MP4 files directly and JPEG images (transcoded to MP4) - -set -e - -# Colors for output -RED='\033[0;31m' -GREEN='\033[0;32m' -YELLOW='\033[1;33m' -BLUE='\033[0;34m' -NC='\033[0m' # No Color - -# Function to print colored output -print_info() { - echo -e "${BLUE}[INFO]${NC} $1" -} - -print_success() { - echo -e "${GREEN}[SUCCESS]${NC} $1" -} - -print_warning() { - echo -e "${YELLOW}[WARNING]${NC} $1" -} - -print_error() { - echo -e "${RED}[ERROR]${NC} $1" -} - -# Default configuration -RTSP_PORT=8554 -RTSP_MOUNT_POINT="video-stream" -INPUT_FILE="" -CACHE_TIME=1500 -FRAME_RATE=1 -DURATION=5 -TEMP_DIR="/tmp/rtsp_server" -SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -PID_FILE="${SCRIPT_DIR}/logs/rtsp_server.pid" -LOG_FILE="${SCRIPT_DIR}/logs/rtsp_server.log" - -# Function to show usage -show_usage() { - cat << EOF -Usage: $0 [OPTIONS] - -Options: - -p, --port PORT RTSP port (default: 8554) - -m, --mount MOUNT_POINT RTSP mount point (default: video-stream) - -c, --cache CACHE_TIME Network caching time in ms (default: 1500) - -f, --fps FRAME_RATE Frame rate for JPEG transcoding (default: 1) - -d, --duration DURATION Duration per frame in seconds for JPEG (default: 5) - --daemon Run as daemon in background - -k, --kill Kill existing RTSP server - -s, --status Check RTSP server status - -h, --help Show this help message - -Examples: - $0 sample_video.mp4 - $0 sample_image.jpg - $0 -p 8555 -m test-stream sample_image.jpg - $0 --daemon -f 2 -d 3 sample_image.jpg - $0 --kill - $0 --status - -Environment Variables: - RTSP_PORT Default RTSP port - RTSP_MOUNT_POINT Default mount point - INPUT_FILE Default input file path - FRAME_RATE Default frame rate for JPEG transcoding - DURATION Default duration per frame for JPEG - -EOF -} - -# Function to check prerequisites -check_prerequisites() { - print_info "Checking prerequisites..." - - # Check if cvlc is installed - if ! command -v cvlc &> /dev/null; then - print_error "cvlc (VLC command line) is not installed" - print_info "Install VLC: sudo apt-get install vlc" - exit 1 - fi - - # Check if ffmpeg is installed (for JPEG transcoding) - if ! command -v ffmpeg &> /dev/null; then - print_error "ffmpeg is not installed" - print_info "Install ffmpeg: sudo apt-get install ffmpeg" - exit 1 - fi - - # Check if port is available - if netstat -tuln 2>/dev/null | grep -q ":$RTSP_PORT "; then - print_warning "Port $RTSP_PORT is already in use" - print_info "Use --kill to stop existing server or specify different port" - fi - - # Create temp directory - mkdir -p "$TEMP_DIR" - - print_success "Prerequisites check passed" -} - -# Function to transcode JPEG to MP4 -transcode_jpeg_to_mp4() { - local jpeg_file="$1" - local output_mp4="$2" - local frame_rate="$3" - local duration="$4" - - print_info "Transcoding JPEG to MP4..." - print_info "Input: $jpeg_file" - print_info "Output: $output_mp4" - print_info "Frame rate: $frame_rate fps" - print_info "Duration per frame: $duration seconds" - - # Create a video from JPEG image with specified frame rate and duration - ffmpeg -loop 1 -i "$jpeg_file" -c:v libx264 -tune stillimage -crf 18 -preset ultrafast \ - -r "$frame_rate" -t "$duration" -y "$output_mp4" > /dev/null 2>&1 - - if [ $? -eq 0 ] && [ -f "$output_mp4" ]; then - print_success "JPEG transcoded to MP4 successfully" - return 0 - else - print_error "Failed to transcode JPEG to MP4" - return 1 - fi -} - -# Function to get file type -get_file_type() { - local file="$1" - local file_info=$(file "$file" 2>/dev/null) - - if echo "$file_info" | grep -q "JPEG\|JFIF"; then - echo "jpeg" - elif echo "$file_info" | grep -q "MP4\|MPEG\|AVI\|MOV\|WebM\|video"; then - echo "video" - else - echo "unknown" - fi -} - -# Function to kill existing RTSP server -kill_rtsp_server() { - if [ -f "$PID_FILE" ]; then - local pid=$(cat "$PID_FILE") - if kill -0 "$pid" 2>/dev/null; then - print_info "Killing existing RTSP server (PID: $pid)" - kill "$pid" - sleep 2 - if kill -0 "$pid" 2>/dev/null; then - print_warning "Server still running, force killing..." - kill -9 "$pid" - fi - print_success "RTSP server stopped" - else - print_warning "PID file exists but process not running" - fi - rm -f "$PID_FILE" - else - print_info "No RTSP server PID file found" - fi -} - -# Function to check server status -check_status() { - if [ -f "$PID_FILE" ]; then - local pid=$(cat "$PID_FILE") - if kill -0 "$pid" 2>/dev/null; then - print_success "RTSP server is running (PID: $pid)" - print_info "RTSP URL: rtsp://localhost:$RTSP_PORT/$RTSP_MOUNT_POINT" - return 0 - else - print_warning "PID file exists but process not running" - rm -f "$PID_FILE" - return 1 - fi - else - print_info "RTSP server is not running" - return 1 - fi -} - -# Function to cleanup temp files -cleanup_temp_files() { - if [ -d "$TEMP_DIR" ]; then - rm -rf "$TEMP_DIR" - print_info "Cleaned up temporary files" - fi -} - -# Function to start RTSP server -start_rtsp_server() { - local input_file="$1" - local daemon_mode="$2" - - # Validate input file - if [ ! -f "$input_file" ]; then - print_error "Input file not found: $input_file" - exit 1 - fi - - # Get file type - local file_type=$(get_file_type "$input_file") - local video_file="$input_file" - - if [ "$file_type" = "jpeg" ]; then - print_info "JPEG file detected, transcoding to MP4..." - local output_mp4="$TEMP_DIR/transcoded_$(basename "$input_file" .jpg).mp4" - - if ! transcode_jpeg_to_mp4 "$input_file" "$output_mp4" "$FRAME_RATE" "$DURATION"; then - exit 1 - fi - - video_file="$output_mp4" - print_info "Using transcoded MP4: $video_file" - elif [ "$file_type" = "video" ]; then - print_info "Video file detected, using directly" - else - print_warning "Unknown file type: $input_file" - print_info "Attempting to use as video file..." - fi - - # Kill existing server - kill_rtsp_server - - # Create cvlc command for video streaming - # Use loop to continuously stream the video - # Add headless flags for Docker/CI environments - local cvlc_cmd="vlc-wrapper -I dummy --no-video-title-show --no-xlib --loop \"$video_file\" \":sout=#gather:rtp{sdp=rtsp://:$RTSP_PORT/$RTSP_MOUNT_POINT}\" :network-caching=$CACHE_TIME :sout-all :sout-keep" - - print_info "Starting RTSP server..." - print_info "Input file: $input_file" - print_info "Video file: $video_file" - print_info "RTSP URL: rtsp://localhost:$RTSP_PORT/$RTSP_MOUNT_POINT" - print_info "Command: $cvlc_cmd" - - if [ "$daemon_mode" = "true" ]; then - # Run as daemon - print_info "Running in daemon mode" - nohup bash -c "$cvlc_cmd" > "$LOG_FILE" 2>&1 & - local pid=$! - echo "$pid" > "$PID_FILE" - sleep 2 - - if kill -0 "$pid" 2>/dev/null; then - print_success "RTSP server started in background (PID: $pid)" - print_info "Log file: $LOG_FILE" - print_info "PID file: $PID_FILE" - else - print_error "Failed to start RTSP server" - if [ -f "$LOG_FILE" ]; then - print_error "Log file contents:" - cat "$LOG_FILE" - fi - exit 1 - fi - else - # Run in foreground - print_info "Running in foreground mode (Ctrl+C to stop)" - print_info "RTSP server will be available at: rtsp://localhost:$RTSP_PORT/$RTSP_MOUNT_POINT" - echo "$$" > "$PID_FILE" - - # Set up cleanup on exit - trap cleanup_temp_files EXIT - - exec bash -c "$cvlc_cmd" - fi -} - -# Parse command line arguments -DAEMON_MODE="false" -KILL_SERVER="false" -CHECK_STATUS="false" - -while [[ $# -gt 0 ]]; do - case $1 in - -p|--port) - RTSP_PORT="$2" - shift 2 - ;; - -m|--mount) - RTSP_MOUNT_POINT="$2" - shift 2 - ;; - -c|--cache) - CACHE_TIME="$2" - shift 2 - ;; - -f|--fps) - FRAME_RATE="$2" - shift 2 - ;; - -d|--duration) - DURATION="$2" - shift 2 - ;; - --daemon) - DAEMON_MODE="true" - shift - ;; - -k|--kill) - KILL_SERVER="true" - shift - ;; - -s|--status) - CHECK_STATUS="true" - shift - ;; - -h|--help) - show_usage - exit 0 - ;; - -*) - print_error "Unknown option: $1" - show_usage - exit 1 - ;; - *) - if [ -z "$INPUT_FILE" ]; then - INPUT_FILE="$1" - else - print_error "Multiple input files specified" - exit 1 - fi - shift - ;; - esac -done - -# Handle special commands -if [ "$KILL_SERVER" = "true" ]; then - kill_rtsp_server - cleanup_temp_files - exit 0 -fi - -if [ "$CHECK_STATUS" = "true" ]; then - check_status - exit $? -fi - -# Check if input file is provided -if [ -z "$INPUT_FILE" ]; then - print_error "Input file (JPEG or MP4) is required" - show_usage - exit 1 -fi - -# Check prerequisites -check_prerequisites - -# Start RTSP server -start_rtsp_server "$INPUT_FILE" "$DAEMON_MODE" \ No newline at end of file diff --git a/tools/inference_builder/builder/tests/test_configs.json b/tools/inference_builder/builder/tests/test_configs.json deleted file mode 100644 index a3e697a0..00000000 --- a/tools/inference_builder/builder/tests/test_configs.json +++ /dev/null @@ -1,504 +0,0 @@ -[ - { - "name": "frame_sampling_on_files", - "description": "Test with default TensorRT and CUDA versions", - "build_args": { - "TEST_APP_NAME": "frame_sampling", - "CACHE_BUSTER": "default_test" - }, - "test_config": { - "default_enable": true, - "timeout": 30, - "env": { - "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes" - }, - "volumes": { - "frame_sampling/models": "/workspace/models" - }, - "cmd": [ - "--video-files", "file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h265.mp4?frames=10&start=0&duration=10000000000" - ], - "prerequisite_script": null - } - }, - { - "name": "frame_sampling_on_streams", - "description": "Test with default TensorRT and CUDA versions", - "build_args": { - "TEST_APP_NAME": "frame_sampling", - "CACHE_BUSTER": "default_test" - }, - "test_config": { - "default_enable": false, - "timeout": 30, - "env": { - "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes" - }, - "volumes": { - "frame_sampling/models": "/workspace/models" - }, - "cmd": [ - "--video-streams", "rtsp://localhost:8554/test?frames=10&interval=100000000&chunks=3&scale=640x360" - ], - "prerequisite_script": "./setup_rtsp_server.sh frame.jpg -m test -f 30 --daemon" - } - }, - { - "name": "asset_upload_test", - "description": "Test asset upload", - "build_args": { - "TEST_APP_NAME": "asset_management", - "CACHE_BUSTER": "asset_management", - "SERVER_TYPE": "fastapi", - "OPENAPI_SPEC": "builder/samples/dummy/openapi.yaml" - }, - "test_config": { - "default_enable": true, - "timeout": 5, - "env": { - "MODEL_REPO": "/workspace/model-repo" - }, - "volumes": { - "asset_management/models": "/workspace/models" - }, - "ports": { - "8000": "8000" - }, - "test_requests": [ - { - "payload_path": "asset_management/payloads/assets.jsonl", - "method": "POST", - "headers": { - "Content-Type": "multipart/form-data" - } - }, - { - "method": "GET", - "headers": { - "Content-Type": "application/json" - } - } - ], - "endpoint": "/v1/files" - } - }, - { - "name": "Output Types Compatibility Test", - "description": "Test numpy array output type compatibility with simplified dummy backend", - "build_args": { - "TEST_APP_NAME": "output_types", - "CACHE_BUSTER": "output_types_simplified", - "SERVER_TYPE": "fastapi", - "OPENAPI_SPEC": "builder/samples/dummy/openapi.yaml" - }, - - "test_config": { - "default_enable": true, - "timeout": 10, - "volumes": { - "output_types/models": "/workspace/models" - }, - "env": { - "DEBUG": "0" - }, - "cmd": [] - } - }, - { - "name": "fastapi_concurrency_test", - "description": "Build concurrency app and run threaded client", - "build_args": { - "TEST_APP_NAME": "concurrency", - "CACHE_BUSTER": "concurrency_fastapi", - "SERVER_TYPE": "fastapi", - "OPENAPI_SPEC": "builder/samples/dummy/openapi.yaml" - }, - "test_config": { - "default_enable": true, - "timeout": 30, - "volumes": { - "concurrency/models": "/workspace/models" - }, - "cmd": [] - } - }, - { - "name": "negative_invalid_route_config_1", - "description": "Negative test: Invalid route configuration should fail with proper error", - "build_args": { - "TEST_APP_NAME": "negative", - "CACHE_BUSTER": "negative_route_test", - "APP_YAML_PATH": "./negative/route_1.yaml" - }, - "test_config": { - "default_enable": true, - "timeout": 5, - "expected_error": "ERR_ROUTE_001", - "volumes": { - "concurrency/models": "/workspace/models" - }, - "cmd": [ "--inputs", "string" ] - } - }, - { - "name": "negative_invalid_route_config_2", - "description": "Negative test: Invalid route configuration should fail with proper error", - "build_args": { - "TEST_APP_NAME": "negative", - "CACHE_BUSTER": "negative_route_test", - "APP_YAML_PATH": "./negative/route_2.yaml" - }, - "test_config": { - "default_enable": true, - "timeout": 5, - "expected_error": "ERR_ROUTE_001", - "volumes": { - "concurrency/models": "/workspace/models" - }, - "cmd": [ "--inputs", "string" ] - } - }, - { - "name": "negative_invalid_route_config_3", - "description": "Negative test: Invalid route configuration should fail with proper error", - "build_args": { - "TEST_APP_NAME": "negative", - "CACHE_BUSTER": "negative_route_test", - "APP_YAML_PATH": "./negative/route_3.yaml" - }, - "test_config": { - "default_enable": true, - "timeout": 5, - "expected_error": "ERR_ROUTE_001", - "volumes": { - "concurrency/models": "/workspace/models" - }, - "cmd": [ "--inputs", "string" ] - } - }, - { - "name": "negative_invalid_route_config_4", - "description": "Negative test: Invalid route configuration should fail with proper error", - "build_args": { - "TEST_APP_NAME": "negative", - "CACHE_BUSTER": "negative_route_test", - "APP_YAML_PATH": "./negative/route_4.yaml" - }, - "test_config": { - "default_enable": true, - "timeout": 5, - "expected_error": "ERR_ROUTE_002", - "volumes": { - "concurrency/models": "/workspace/models" - }, - "cmd": [ "--inputs", "string" ] - } - }, - { - "name": "negative_invalid_route_config_5", - "description": "Negative test: Invalid route configuration should fail with proper error", - "build_args": { - "TEST_APP_NAME": "negative", - "CACHE_BUSTER": "negative_route_test", - "APP_YAML_PATH": "./negative/route_5.yaml" - }, - "test_config": { - "default_enable": true, - "timeout": 5, - "expected_error": "ERR_ROUTE_002", - "volumes": { - "concurrency/models": "/workspace/models" - }, - "cmd": [ "--inputs", "string" ] - } - }, - { - "name": "negative_invalid_model_repo", - "description": "Negative test: Invalid model repository", - "build_args": { - "TEST_APP_NAME": "negative", - "CACHE_BUSTER": "negative_model_test", - "APP_YAML_PATH": "./negative/model.yaml" - }, - "test_config": { - "default_enable": true, - "timeout": 5, - "expected_error": "ERR_SYS_001", - "volumes": {}, - "cmd": [ "--inputs", "string" ] - } - }, - { - "name": "negative_invalid_processor_1", - "description": "Negative test: Invalid post-processor", - "build_args": { - "TEST_APP_NAME": "negative", - "CACHE_BUSTER": "negative_pp_test", - "APP_YAML_PATH": "./negative/pp_1.yaml" - }, - "test_config": { - "default_enable": true, - "timeout":10, - "expected_error": "ERR_PROC_001", - "volumes": { - "concurrency/models": "/workspace/models" - }, - "cmd": [ "--inputs", "string" ] - } - }, - { - "name": "negative_invalid_processor_2", - "description": "Negative test: Invalid post-processor", - "build_args": { - "TEST_APP_NAME": "negative", - "CACHE_BUSTER": "negative_pp_test", - "APP_YAML_PATH": "./negative/pp_2.yaml" - }, - "test_config": { - "default_enable": true, - "timeout": 10, - "expected_error": "ERR_PROC_001", - "volumes": { - "concurrency/models": "/workspace/models" - }, - "cmd": [ "--inputs", "string" ] - } - }, - { - "name": "negative_invalid_processor_3", - "description": "Negative test: Invalid post-processor", - "build_args": { - "TEST_APP_NAME": "negative", - "CACHE_BUSTER": "negative_pp_test", - "APP_YAML_PATH": "./negative/pp_3.yaml" - }, - "test_config": { - "default_enable": true, - "timeout": 10, - "expected_error": "ERR_PROC_004", - "volumes": { - "concurrency/models": "/workspace/models" - }, - "cmd": [ "--inputs", "string" ] - } - }, - { - "name": "negative_dataflow_test_1", - "description": "Negative test: Invalid base64 input", - "build_args": { - "TEST_APP_NAME": "negative", - "CACHE_BUSTER": "negative_df_test", - "APP_YAML_PATH": "./negative/df_1.yaml", - "SERVER_TYPE": "fastapi", - "OPENAPI_SPEC": "builder/samples/dummy/openapi.yaml" - }, - - "test_config": { - "default_enable": true, - "timeout": 5, - "volumes": { - "output_types/models": "/workspace/models" - }, - "expected_error": "ERR_DF_002", - "cmd": [] - } - }, - { - "name": "negative_dataflow_test_2", - "description": "Negative test: Invalid base64 input", - "build_args": { - "TEST_APP_NAME": "negative", - "CACHE_BUSTER": "negative_df_test", - "APP_YAML_PATH": "./negative/df_2.yaml", - "SERVER_TYPE": "fastapi", - "OPENAPI_SPEC": "builder/samples/dummy/openapi.yaml" - }, - - "test_config": { - "default_enable": true, - "timeout": 5, - "volumes": { - "output_types/models": "/workspace/models" - }, - "expected_error": "ERR_DF_002", - "cmd": [] - } - }, - { - "name": "negative_dataflow_test_3", - "description": "Negative test: Invalid asset", - "build_args": { - "TEST_APP_NAME": "negative", - "CACHE_BUSTER": "negative_df_test", - "APP_YAML_PATH": "./negative/df_3.yaml" - }, - - "test_config": { - "default_enable": true, - "timeout": 10, - "volumes": { - "output_types/models": "/workspace/models" - }, - "expected_error": "ERR_DF_003", - "cmd": [ "--video", "hello" ] - } - }, - { - "name": "negative_dataflow_test_4", - "description": "Negative test: Invalid video frame sampling parameters", - "build_args": { - "TEST_APP_NAME": "negative", - "CACHE_BUSTER": "negative_df_test", - "APP_YAML_PATH": "./negative/video.yaml" - }, - - "test_config": { - "default_enable": true, - "timeout": 10, - "volumes": { - "output_types/models": "/workspace/models" - }, - "expected_error": "ERR_VIDEO_001", - "cmd": [ - "--video-chunk", - "/opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h265.mp4?frames=10&duration=10000000000", - "/opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h265.mp4?frames=10&duration=20000000000" - ] - } - }, - { - "name": "negative_dataflow_test_5", - "description": "Negative test: Invalid video frame sampling parameters", - "build_args": { - "TEST_APP_NAME": "negative", - "CACHE_BUSTER": "negative_df_test", - "APP_YAML_PATH": "./negative/video.yaml" - }, - - "test_config": { - "default_enable": true, - "timeout": 10, - "volumes": { - "output_types/models": "/workspace/models" - }, - "expected_error": "ERR_VIDEO_001", - "cmd": [ - "--video-chunk", - "/opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h265.mp4?frames=10&duration=10000000000", - "/opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h265.mp4?frames=1&duration=10000000000" - ] - } - }, - { - "name": "negative_dataflow_test_6", - "description": "Negative test: Invalid base64 string", - "build_args": { - "TEST_APP_NAME": "negative", - "CACHE_BUSTER": "negative_df_test", - "APP_YAML_PATH": "./negative/image.yaml" - }, - - "test_config": { - "default_enable": true, - "timeout": 10, - "volumes": { - "output_types/models": "/workspace/models" - }, - "expected_error": "ERR_DF_002", - "cmd": [ "--image", "hello" ] - } - }, - { - "name": "negative_model_output_incomplete", - "description": "Negative test: Invalid model output", - "build_args": { - "TEST_APP_NAME": "negative", - "CACHE_BUSTER": "negative_mo_test", - "APP_YAML_PATH": "./negative/mo_1.yaml" - }, - "test_config": { - "default_enable": true, - "timeout": 10, - "expected_error": "ERR_MO_002", - "volumes": { - "concurrency/models": "/workspace/models" - }, - "cmd": [ "--inputs", "string" ] - } - }, - { - "name": "negative_model_input_incomplete", - "description": "Negative test: Invalid base64 input", - "build_args": { - "TEST_APP_NAME": "negative", - "CACHE_BUSTER": "negative_df_test", - "APP_YAML_PATH": "./negative/mo_2.yaml", - "SERVER_TYPE": "fastapi", - "OPENAPI_SPEC": "builder/samples/dummy/openapi.yaml" - }, - - "test_config": { - "default_enable": true, - "timeout": 5, - "volumes": { - "output_types/models": "/workspace/models" - }, - "expected_error": "ERR_DF_006", - "cmd": [] - } - }, - { - "name": "live_stream_graceful_deletion_test", - "description": "Test graceful deletion of RTSP stream during processing", - "build_args": { - "TEST_APP_NAME": "asset_management", - "CACHE_BUSTER": "live_stream_deletion", - "SERVER_TYPE": "fastapi", - "OPENAPI_SPEC": "builder/tests/asset_management/openapi.yaml" - }, - "test_config": { - "default_enable": false, - "timeout": 120, - "env": { - "NVSTREAMMUX_ADAPTIVE_BATCHING": "yes", - "DEBUG": "0", - "HTTP_PORT": "8000" - }, - "volumes": { - "asset_management/models": "/workspace/models" - }, - "ports": { - "8000": "8000" - }, - "assets": [ - { - "assetId": "00000000-0000-4000-8000-000000000001", - "path": "rtsp://localhost:8554/test", - "fileName": "", - "mimeType": "", - "duration": 0, - "username": "", - "password": "", - "description": "Test RTSP stream for graceful deletion", - "size": 0, - "live": true - } - ], - "test_requests": [ - { - "scheduled_time": 10, - "method": "POST", - "endpoint": "/v1/inference", - "payload_path": "asset_management/payloads/start_stream.jsonl", - "async": true - }, - { - "scheduled_time": 15, - "method": "DELETE", - "endpoint": "/v1/live-streams/00000000-0000-4000-8000-000000000001" - } - ], - "prerequisite_script": "./setup_rtsp_server.sh frame.jpg -m test -f 30 --daemon" - } - } -] diff --git a/tools/inference_builder/builder/tests/test_docker_builds.py b/tools/inference_builder/builder/tests/test_docker_builds.py deleted file mode 100644 index fe2d6014..00000000 --- a/tools/inference_builder/builder/tests/test_docker_builds.py +++ /dev/null @@ -1,2971 +0,0 @@ -#!/usr/bin/env python3 - -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -""" -Test script for Docker container builds with different arguments. -This script tests the Dockerfile in the tests directory with various configurations. - -Security Features: -- Input validation for app names and script commands to prevent command injection -- File path validation to prevent directory traversal attacks -- Safe subprocess calls using argument lists instead of shell string interpolation -- Logging of all executed commands for audit trails -""" - -import subprocess -import sys -import os -import json -import time -import argparse -from pathlib import Path -import shutil -import socket -from typing import Dict, List, Optional, Tuple -import logging -import re -import urllib.request -import urllib.error -from huggingface_hub import snapshot_download - -# Set up logging -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') -logger = logging.getLogger(__name__) - - -def validate_app_name(app_name: str) -> bool: - """Validate app name to prevent command injection.""" - # Allow only alphanumeric characters, underscores, and hyphens - return bool(re.match(r'^[a-zA-Z0-9_-]+$', app_name)) - - -def validate_script_command(script_command: str) -> bool: - """Validate script command using a flexible approach that allows legitimate arguments.""" - if not isinstance(script_command, str) or not script_command.strip(): - return False - - script_command = script_command.strip() - - # Split command into parts for analysis - try: - import shlex - parts = shlex.split(script_command) - except ValueError: - # Invalid shell syntax - return False - - if not parts: - return False - - # Check the main command/script - main_command = parts[0] - - # Allow specific known scripts - allowed_script_names = [ - "setup_rtsp_server.sh", - "./setup_rtsp_server.sh", - "prepare_engine.sh", - "./prepare_engine.sh", - ] - - allowed_shell_commands = ["bash", "sh"] - - # Validate main command - if main_command not in allowed_script_names and main_command not in allowed_shell_commands: - return False - - # If it's a shell command, check the script being executed - if main_command in allowed_shell_commands and len(parts) > 1: - script_name = parts[1] - if script_name not in allowed_script_names: - return False - - # Validate all arguments - for arg in parts[1:]: - if not validate_script_arg(arg): - return False - - # Check for dangerous patterns in the full command - dangerous_patterns = [ - r'[;&|`]', # Shell metacharacters (but allow some like spaces) - r'\$\(', # Command substitution - r'`', # Backticks - r'>>?', # Redirections - r'\|\|', # OR operator - r'&&', # AND operator - r'<', # Input redirection - r'\x00', # Null bytes - ] - - for pattern in dangerous_patterns: - if re.search(pattern, script_command): - return False - - return True - - -def validate_script_arg(arg: str) -> bool: - """Validate individual script argument.""" - if not isinstance(arg, str): - return False - - # Check for null bytes and control characters - if '\x00' in arg or any(ord(c) < 32 for c in arg if c not in ['\t']): - return False - - # Allow common script arguments - dangerous_chars = ['`', '$', ';', '|', '>', '<', '(', ')'] - if any(char in arg for char in dangerous_chars): - return False - - # Allow legitimate flags and file arguments - if arg.startswith('-'): - # Allow common flag patterns - if not re.match(r'^-{1,2}[a-zA-Z0-9][a-zA-Z0-9_-]*$', arg): - return False - - # Prevent excessively long arguments - if len(arg) > 1024: - return False - - return True - - -def validate_safe_path(path: str) -> bool: - """Validate file path to prevent directory traversal and command injection.""" - if not path or not isinstance(path, str): - return False - - # Check for double slashes - if '//' in path: - return False - - # Check for absolute paths that could access system directories - if os.path.isabs(path): - system_dirs = ['/etc', '/sys', '/proc', '/dev', '/boot', '/usr/bin', '/usr/sbin', '/root'] - for sys_dir in system_dirs: - if path.startswith(sys_dir): - return False - - # Check for invalid characters - invalid_chars = ['<', '>', ':', '"', '|', '?', '*', '\\'] - if any(char in path for char in invalid_chars): - return False - - return True - -def validate_config_file_path(config_file: str) -> bool: - """Validate configuration file path for security.""" - if not validate_safe_path(config_file): - return False - - # Ensure the filename ends with .json - filename = os.path.basename(config_file) - if not filename.lower().endswith('.json'): - return False - - return True - -def validate_dockerfile_path(dockerfile: str) -> bool: - """Validate Dockerfile path for security.""" - if not validate_safe_path(dockerfile): - return False - - # Ensure the filename is Dockerfile or has .dockerfile extension - filename = os.path.basename(dockerfile) - if not (filename.lower() == 'dockerfile' or filename.lower().endswith('.dockerfile')): - return False - - return True - -def validate_log_directory(log_dir: str) -> bool: - """Validate log directory path for security.""" - if not validate_safe_path(log_dir): - return False - - # Prevent access to system directories - if log_dir.startswith('/') and not log_dir.startswith('./'): - return False - - return True - -def validate_gitlab_token(token: str) -> bool: - """Validate GitLab token format.""" - if not token: - return True # Empty token is allowed - - # Basic validation for GitLab token format - if not isinstance(token, str) or len(token) < 10: - return False - - # Check for suspicious patterns - suspicious_patterns = [' bool: - """Validate docker argument to prevent command injection while allowing legitimate flags.""" - if not isinstance(arg, str): - return False - - # Check for null bytes and control characters - if '\x00' in arg or any(ord(c) < 32 for c in arg if c not in ['\t', '\n', '\r']): - return False - - # Check for dangerous characters and patterns (but allow legitimate usage) - dangerous_patterns = [ - r'[;|`$()]', # Shell metacharacters (removed & for now) - r'\$\(', # Command substitution - r'`', # Backticks - r'>>?', # Redirections - r'\|\|', # OR operator - r'&&', # AND operator for command chaining - r'[\x00-\x08\x0B\x0C\x0E-\x1F\x7F]', # Control characters - ] - - for pattern in dangerous_patterns: - if re.search(pattern, arg): - return False - - # Check for dangerous & usage (but allow in URL query parameters) - if '&' in arg: - # Allow & in URL-like contexts (after ?) - if '?' not in arg: - # & without ? suggests command chaining, not URL parameter - return False - # Check for command chaining patterns even with ? - if ' &' in arg or '& ' in arg or arg.endswith('&'): - return False - - # Additional dangerous characters for docker contexts (be selective) - # Note: Removed '?' to allow URL query parameters, '*' and '!' for legitimate use - dangerous_chars = ['[', ']', '{', '}', '\\'] - if any(char in arg for char in dangerous_chars): - return False - - # Check for wildcard patterns that could be dangerous in specific contexts - if '*' in arg and any(pattern in arg for pattern in ['*.*', '*.sh', '*.py', '*/', '/*']): - return False - - # Allow legitimate command-line flags but prevent injection attempts - if arg.strip().startswith('-'): - # Allow common legitimate patterns - legitimate_flag_patterns = [ - r'^--[a-zA-Z0-9][a-zA-Z0-9_-]*$', # --flag-name - r'^--[a-zA-Z0-9][a-zA-Z0-9_-]*=.*$', # --flag=value - r'^-[a-zA-Z0-9]$', # -f - r'^-[a-zA-Z0-9][a-zA-Z0-9]*$', # -abc - ] - - # Check if it matches any legitimate pattern - is_legitimate = any(re.match(pattern, arg) for pattern in legitimate_flag_patterns) - - if not is_legitimate: - return False - - # Additional checks for flag values (after =) - if '=' in arg: - flag_value = arg.split('=', 1)[1] - # Check flag value for dangerous patterns - if any(char in flag_value for char in ['`', '$', ';', '&', '|', '(', ')']): - return False - - # Prevent excessively long arguments (potential DoS) - if len(arg) > 8192: # Reasonable limit for docker arguments - return False - - # Check for potential escape sequences in non-flag arguments - if not arg.startswith('-') and '\\' in arg: - if any(seq in arg for seq in ['\\n', '\\r', '\\t', '\\x', '\\u']): - return False - - return True - - -def validate_env_var_name(name: str) -> bool: - """Validate environment variable name.""" - if not isinstance(name, str) or not name: - return False - - # Environment variable names should be alphanumeric + underscore, starting with letter or underscore - return bool(re.match(r'^[a-zA-Z_][a-zA-Z0-9_]*$', name)) - - -def validate_env_var_value(value: str) -> bool: - """Validate environment variable value.""" - if not isinstance(value, str): - return False - - # Check for null bytes - if '\x00' in value: - return False - - # Check for dangerous shell characters - dangerous_chars = ['`', '$', ';', '&', '|', '(', ')', '>', '<'] - if any(char in value for char in dangerous_chars): - return False - - return True - - -def validate_http_method(method: str) -> bool: - """Validate HTTP method.""" - if not isinstance(method, str): - return False - - # Allow common HTTP methods - allowed_methods = ['GET', 'POST', 'PUT', 'PATCH', 'DELETE', 'HEAD', 'OPTIONS'] - return method.upper() in allowed_methods - - -def validate_http_header_name(name: str) -> bool: - """Validate HTTP header name.""" - if not isinstance(name, str) or not name: - return False - - # Header names should be alphanumeric with hyphens - # Follow RFC 7230 field-name = token - if not re.match(r'^[a-zA-Z0-9!#$%&\'*+\-.^_`|~]+$', name): - return False - - # Prevent excessively long header names - if len(name) > 256: - return False - - return True - - -def validate_http_header_value(value: str) -> bool: - """Validate HTTP header value.""" - if not isinstance(value, str): - return False - - # Check for null bytes and control characters (except tab and space) - if '\x00' in value or any(ord(c) < 32 for c in value if c not in ['\t', ' ']): - return False - - # Check for newlines that could cause header injection - if '\n' in value or '\r' in value: - return False - - # Prevent excessively long header values - if len(value) > 8192: - return False - - return True - - -def validate_volume_path(path: str) -> bool: - """Validate volume mount path to prevent injection attacks. - - Note: Allows legitimate relative paths with .. for test configs. - """ - if not isinstance(path, str) or not path: - return False - - # Check for null bytes and control characters - if '\x00' in path or any(ord(c) < 32 for c in path if c not in ['\t']): - return False - - # Check for dangerous path patterns (but allow relative paths) - # Allow: ../tao/, ./models, ../../shared - # Reject: /../../../etc/passwd (absolute path traversal attacks) - - # Check for double slashes (suspicious) - if '//' in path: - return False - - # Check for Windows-style backslashes (not needed in Docker contexts) - if '\\' in path: - return False - - # Check for absolute path traversal attacks (starting with / and going up) - # This would be trying to escape from an absolute path to system directories - if path.startswith('/') and '/../' in path: - # Check if trying to access system directories - system_dirs = ['/../etc', '/../root', '/../sys', '/../proc', '/../dev'] - if any(path.startswith(sys_dir) for sys_dir in system_dirs): - return False - - # Check for dangerous shell characters and command injection attempts - # Allow ~ for home directory expansion at the start of path - dangerous_chars = ['`', '$', ';', '&', '|', '(', ')', '>', '<', '*', '?', '!', '[', ']', '{', '}'] - if any(char in path for char in dangerous_chars): - return False - - # Allow ~ only at the start of path (for home directory expansion) - if '~' in path and not path.startswith('~'): - return False - - # Check for spaces at beginning/end (could be injection attempts) - if path.startswith(' ') or path.endswith(' '): - return False - - # Check for argument injection (starting with dash) - if path.startswith('-'): - return False - - # Ensure path doesn't contain colon (except for Windows drive letters or container paths) - colon_count = path.count(':') - if colon_count > 1: # Allow one colon for Windows drive letters or container paths - return False - if colon_count == 1: - # Allow Windows drive letters (C:) or absolute container paths (/app:) - if not (re.match(r'^[A-Za-z]:', path) or ':' in path[1:]): - return False - - # Additional check: ensure reasonable path length to prevent buffer overflow attacks - if len(path) > 4096: # Most systems limit paths to 4096 characters - return False - - return True - - -def validate_build_arg_name(name: str) -> bool: - """Validate Docker build argument name to prevent injection.""" - if not isinstance(name, str) or not name: - return False - - # Build arg names should be alphanumeric + underscore, no dashes at start - if name.startswith('-'): - return False - - # Allow standard environment variable naming convention - return bool(re.match(r'^[a-zA-Z_][a-zA-Z0-9_]*$', name)) - - -def validate_build_arg_value(value: str) -> bool: - """Validate Docker build argument value to prevent injection.""" - if not isinstance(value, str): - return False - - # Check for null bytes - if '\x00' in value: - return False - - # Check for dangerous shell characters that could cause issues - dangerous_chars = ['`', '$', ';', '&', '|', '(', ')', '>', '<', '\n', '\r'] - if any(char in value for char in dangerous_chars): - return False - - # Check for argument injection attempts - if value.strip().startswith('-'): - return False - - return True - - -def validate_image_name(image_name: str) -> bool: - """Validate Docker image name to prevent injection.""" - if not isinstance(image_name, str) or not image_name: - return False - - # Check for null bytes - if '\x00' in image_name: - return False - - # Check for dangerous characters - dangerous_chars = ['`', '$', ';', '&', '|', '(', ')', '>', '<', ' ', '\n', '\r'] - if any(char in image_name for char in dangerous_chars): - return False - - # Basic docker image name validation (simplified) - # Allow alphanumeric, hyphens, underscores, slashes, colons, dots - if not re.match(r'^[a-zA-Z0-9._/-]+(?::[a-zA-Z0-9._-]+)?$', image_name): - return False - - return True - - -def validate_test_config(test_config: dict) -> Tuple[bool, str]: - """Validate test configuration to prevent command injection.""" - if not isinstance(test_config, dict): - return False, "Test config must be a dictionary" - - # Validate environment variables - if "env" in test_config: - if not isinstance(test_config["env"], dict): - return False, "env must be a dictionary" - - for key, value in test_config["env"].items(): - if not validate_env_var_name(key): - return False, f"Invalid environment variable name: {key}" - if not validate_env_var_value(str(value)): - return False, f"Invalid environment variable value for {key}: {value}" - - # Validate volume mounts - if "volumes" in test_config: - if not isinstance(test_config["volumes"], dict): - return False, "volumes must be a dictionary" - - for host_path, container_path in test_config["volumes"].items(): - if not validate_volume_path(host_path): - return False, f"Invalid host path in volume mount: {host_path}" - if not validate_volume_path(container_path): - return False, f"Invalid container path in volume mount: {container_path}" - - # Validate command arguments - if "cmd" in test_config: - if not isinstance(test_config["cmd"], list): - return False, "cmd must be a list" - - for arg in test_config["cmd"]: - if not validate_docker_arg(str(arg)): - return False, f"Invalid command argument: {arg}" - - # Validate timeout - if "timeout" in test_config: - if not isinstance(test_config["timeout"], (int, float)) or test_config["timeout"] <= 0: - return False, "timeout must be a positive number" - if test_config["timeout"] > 3600: # Max 1 hour - return False, "timeout cannot exceed 3600 seconds" - - # Validate prerequisite script - if "prerequisite_script" in test_config: - script = test_config["prerequisite_script"] - if script and not validate_script_command(script): - return False, f"Invalid prerequisite script: {script}" - - # Validate auto_validation configuration - if "auto_validation" in test_config: - if not isinstance(test_config["auto_validation"], str): - return False, "auto_validation must be a string path" - if not validate_safe_path(test_config["auto_validation"]): - return False, f"Invalid auto_validation path: {test_config['auto_validation']}" - # Validate test_requests (must be a list of objects with optional payload_path, method, headers) - if "test_requests" in test_config: - test_requests = test_config["test_requests"] - if not isinstance(test_requests, list): - return False, "test_requests must be a list" - - for item in test_requests: - # Must be dict format with payload_path (optional), method (optional), and headers (optional) - if not isinstance(item, dict): - return False, "test_requests items must be dictionaries" - - # Validate payload_path if present (optional for GET requests without body) - if "payload_path" in item: - payload_path = item["payload_path"] - if not isinstance(payload_path, str): - return False, "test_requests 'payload_path' must be a string" - if not validate_safe_path(payload_path): - return False, f"Invalid test_requests payload_path: {payload_path}" - - # Validate method if specified (optional, defaults to POST) - if "method" in item: - method = item["method"] - if not validate_http_method(method): - return False, f"Invalid HTTP method in payload_config: {method}" - - # Validate headers if specified (optional) - if "headers" in item: - headers = item["headers"] - if not isinstance(headers, dict): - return False, "payload_config headers must be a dictionary" - for header_name, header_value in headers.items(): - if not validate_http_header_name(header_name): - return False, f"Invalid HTTP header name in payload_config: {header_name}" - if not validate_http_header_value(str(header_value)): - return False, f"Invalid HTTP header value in payload_config for {header_name}: {header_value}" - - # Validate scheduled_time if specified (optional, for sequential execution) - if "scheduled_time" in item: - if not isinstance(item["scheduled_time"], (int, float)) or item["scheduled_time"] < 0: - return False, "test_requests 'scheduled_time' must be a non-negative number" - - # Validate async if specified (optional, for non-blocking requests) - if "async" in item: - if not isinstance(item["async"], bool): - return False, "test_requests 'async' must be a boolean" - - # Validate endpoint - if "endpoint" in test_config: - endpoint = test_config["endpoint"] - if not isinstance(endpoint, str): - return False, "endpoint must be a string" - # Basic validation for endpoint path format - if not endpoint.startswith("/"): - return False, "endpoint must start with /" - # Check for dangerous characters - if any(char in endpoint for char in ['<', '>', '"', '{', '}', '|', '\\', '^', '`', ' ']): - return False, f"Invalid characters in endpoint: {endpoint}" - - # Validate expected_error for negative tests - if "expected_error" in test_config: - expected_error = test_config["expected_error"] - if not isinstance(expected_error, str): - return False, "expected_error must be a string" - if not expected_error: - return False, "expected_error cannot be empty" - # Basic validation - should be an error code pattern (e.g., ERR_ROUTE_001) - if len(expected_error) > 100: - return False, "expected_error is too long (max 100 characters)" - - return True, "" - - -class DockerBuildTester: - def __init__(self, dockerfile_path: str, base_dir: str, log_dir: Optional[str] = None): - self.dockerfile_path = Path(dockerfile_path) - self.base_dir = Path(base_dir) - self.log_dir = Path(log_dir) if log_dir else Path("logs") - self.test_results = [] - - # Create log directory if it doesn't exist - self.log_dir.mkdir(exist_ok=True) - - def download_models(self, models_config: Dict, config_dir: Path) -> Tuple[bool, str]: - """Download models from NGC based on the models configuration. - - Args: - models_config: Dictionary with model configurations - config_dir: Directory containing the test config (for resolving relative paths) - - Returns: - Tuple of (success, message) - """ - if not models_config: - return True, "No models to download" - - for model_name, model_info in models_config.items(): - try: - source = model_info.get("source", "NGC") - - if source == "HF": - # Handle Hugging Face model download - target_dir = Path(model_info["target"]).expanduser() - model_path = model_info["path"] # e.g., "Qwen/Qwen2.5-VL-3B-Instruct" - - # Final destination - final_model_dir = target_dir / model_name - - # Check if model already exists - if final_model_dir.exists(): - logger.info(f"✅ Model '{model_name}' already exists at {final_model_dir}, skipping download") - continue - - logger.info(f"📥 Downloading model '{model_name}' from Hugging Face...") - logger.info(f" HF path: {model_path}") - logger.info(f" Target: {final_model_dir}") - - # Create target directory - target_dir.mkdir(parents=True, exist_ok=True) - - # Download using huggingface_hub - hf_token = os.environ.get("HF_TOKEN") - try: - snapshot_download( - model_path, - local_dir=str(final_model_dir), - token=hf_token, - ) - except Exception as e: - error_msg = f"Failed to download model '{model_name}' from HF: {e}" - logger.error(f"❌ {error_msg}") - return False, error_msg - - # Execute post-script if specified (for HF models) - post_script = model_info.get("post_script") - if post_script: - logger.info(f"🔧 Executing post-script for '{model_name}': {post_script}") - try: - script_result = subprocess.run( - post_script, - shell=True, - capture_output=True, - text=True, - cwd=str(final_model_dir), - timeout=600 - ) - if script_result.returncode == 0: - logger.info(f"✅ Post-script executed successfully for '{model_name}'") - if script_result.stdout.strip(): - logger.info(f" Output: {script_result.stdout.strip()}") - else: - error_msg = ( - f"Post-script failed for '{model_name}' " - f"(exit code {script_result.returncode}): " - f"{script_result.stderr.strip()}" - ) - logger.error(f"❌ {error_msg}") - return False, error_msg - except subprocess.TimeoutExpired: - error_msg = f"Post-script timed out for '{model_name}' after 600 seconds" - logger.error(f"❌ {error_msg}") - return False, error_msg - except Exception as e: - error_msg = f"Failed to execute post-script for '{model_name}': {str(e)}" - logger.error(f"❌ {error_msg}") - return False, error_msg - - logger.info(f"✅ Successfully downloaded model '{model_name}' from Hugging Face") - continue - - if source != "NGC": - logger.warning(f"⚠️ Unsupported model source '{source}' for {model_name}, skipping") - continue - - # NGC model download logic - target_dir = Path(model_info["target"]).expanduser() - model_path = model_info["path"] - version = model_info["version"] - configs_path = model_info.get("configs", "") - - # Construct the full NGC path - ngc_path = f"{model_path}:{version}" - - # Determine the downloaded folder name (NGC naming convention) - # e.g., "nvidia/tao/grounding_dino:grounding_dino_swin_tiny_commercial_deployable_v1.0" - # becomes "grounding_dino_vgrounding_dino_swin_tiny_commercial_deployable_v1.0" - model_base_name = model_path.split('/')[-1] # e.g., "grounding_dino" - downloaded_folder = f"{model_base_name}_v{version}" - - # Final destination - final_model_dir = target_dir / model_name - - # Check if model already exists - if final_model_dir.exists(): - logger.info(f"✅ Model '{model_name}' already exists at {final_model_dir}, skipping download") - continue - - logger.info(f"📥 Downloading model '{model_name}' from NGC...") - logger.info(f" NGC path: {ngc_path}") - logger.info(f" Target: {final_model_dir}") - - # Create target directory - target_dir.mkdir(parents=True, exist_ok=True) - - # Download from NGC - download_cmd = ["ngc", "registry", "model", "download-version", ngc_path] - logger.info(f" Running: {' '.join(download_cmd)}") - - result = subprocess.run( - download_cmd, - capture_output=True, - text=True, - timeout=600 # 10 minute timeout for download - ) - - if result.returncode != 0: - error_msg = f"Failed to download model '{model_name}': {result.stderr}" - logger.error(f"❌ {error_msg}") - return False, error_msg - - # Move the downloaded folder to the target location - downloaded_path = Path(downloaded_folder) - if not downloaded_path.exists(): - error_msg = f"Downloaded folder not found: {downloaded_path}" - logger.error(f"❌ {error_msg}") - return False, error_msg - - logger.info(f" Moving {downloaded_path} to {final_model_dir}") - shutil.move(str(downloaded_path), str(final_model_dir)) - - # Set permissions - try: - os.chmod(final_model_dir, 0o777) - logger.info(f" Set permissions: chmod 777 {final_model_dir}") - except Exception as e: - logger.warning(f"⚠️ Failed to set permissions on {final_model_dir}: {e}") - - # Copy config files if specified - if configs_path: - # Resolve configs_path relative to test config directory - source_configs = (config_dir / configs_path).resolve() - if source_configs.exists(): - logger.info(f" Copying configs from {source_configs} to {final_model_dir}") - # Copy all files from source_configs to final_model_dir - for item in source_configs.iterdir(): - dest = final_model_dir / item.name - if item.is_file(): - shutil.copy2(str(item), str(dest)) - elif item.is_dir(): - shutil.copytree(str(item), str(dest), dirs_exist_ok=True) - logger.info(f" ✅ Copied config files") - else: - logger.warning(f"⚠️ Config path not found: {source_configs}") - - # Execute post-script if specified - post_script = model_info.get("post_script") - if post_script: - logger.info(f"🔧 Executing post-script for '{model_name}': {post_script}") - try: - script_result = subprocess.run( - post_script, - shell=True, - capture_output=True, - text=True, - cwd=str(final_model_dir), - timeout=600 - ) - if script_result.returncode == 0: - logger.info(f"✅ Post-script executed successfully for '{model_name}'") - if script_result.stdout.strip(): - logger.info(f" Output: {script_result.stdout.strip()}") - else: - error_msg = ( - f"Post-script failed for '{model_name}' " - f"(exit code {script_result.returncode}): " - f"{script_result.stderr.strip()}" - ) - logger.error(f"❌ {error_msg}") - return False, error_msg - except subprocess.TimeoutExpired: - error_msg = f"Post-script timed out for '{model_name}' after 600 seconds" - logger.error(f"❌ {error_msg}") - return False, error_msg - except Exception as e: - error_msg = f"Failed to execute post-script for '{model_name}': {str(e)}" - logger.error(f"❌ {error_msg}") - return False, error_msg - - logger.info(f"✅ Successfully downloaded and setup model '{model_name}'") - - except KeyError as e: - error_msg = f"Missing required field in model config for '{model_name}': {e}" - logger.error(f"❌ {error_msg}") - return False, error_msg - except subprocess.TimeoutExpired: - error_msg = f"Model download timed out for '{model_name}'" - logger.error(f"❌ {error_msg}") - return False, error_msg - except Exception as e: - error_msg = f"Exception during model download for '{model_name}': {str(e)}" - logger.error(f"❌ {error_msg}") - return False, error_msg - - return True, "All models downloaded successfully" - - def get_service_host(self) -> str: - """ - Determine the appropriate host to use for connecting to Docker - containers. In CI environments (like GitLab CI with - Docker-in-Docker), 127.0.0.1 won't work because each container has - its own localhost. Use Docker gateway IP instead. - """ - # Check if running in CI environment - if os.environ.get('CI') or os.environ.get('GITLAB_CI'): - logger.info( - "🔍 CI environment detected, using Docker gateway IP " - "for service connectivity" - ) - # Try to get docker gateway IP from bridge network - try: - result = subprocess.run( - [ - "docker", "network", "inspect", "bridge", "-f", - "{{range .IPAM.Config}}{{.Gateway}}{{end}}" - ], - capture_output=True, - text=True, - timeout=5 - ) - if result.returncode == 0 and result.stdout.strip(): - gateway_ip = result.stdout.strip() - logger.info(f"📡 Using Docker gateway IP: {gateway_ip}") - return gateway_ip - except Exception as e: - logger.warning(f"⚠️ Failed to get Docker gateway IP: {e}") - - # Fallback to default Docker bridge gateway - logger.info( - "📡 Using default Docker bridge gateway: 172.17.0.1" - ) - return "172.17.0.1" - - # Local development environment - use localhost - logger.info("📡 Using localhost for service connectivity") - return "127.0.0.1" - - def generate_inference_code(self, build_args: Dict[str, str], test_config: Dict = None) -> Tuple[bool, str]: - """Generate inference code (codegen) without building or testing Docker images. - - This runs the same pre-build code generation step used by build_image(), - including optional OPENAPI_SPEC staging, but skips Docker build. - - Supports flexible path configuration via build_args: - - APP_YAML_PATH: Custom path to app.yaml (overrides {TEST_APP_NAME}/app.yaml) - - OUTPUT_DIR: Custom output directory (overrides {TEST_APP_NAME}) - - PROCESSORS_PATH: Custom processors.py path (overrides auto-detection) - - Args: - build_args: Build arguments for code generation - test_config: Optional test configuration. If present and contains 'auto_validation', - the validation directory will be passed to code generation via -v flag. - """ - try: - # Validate all build arguments to prevent command injection - for key, value in build_args.items(): - if not validate_build_arg_name(key): - error_msg = f"Invalid build arg name: {key}. Only alphanumeric characters and underscores allowed." - logger.error(f"❌ {error_msg}") - return False, error_msg - if not validate_build_arg_value(str(value)): - error_msg = f"Invalid build arg value for {key}: {value}. Value contains dangerous characters." - logger.error(f"❌ {error_msg}") - return False, error_msg - - # Determine parameters for code generation - test_app_name = build_args.get("TEST_APP_NAME", "frame_sampling") - if not validate_app_name(test_app_name): - error_msg = ( - f"Invalid app name: {test_app_name}. Only alphanumeric characters, underscores, and hyphens are allowed." - ) - logger.error(f"❌ {error_msg}") - return False, error_msg - - server_type = build_args.get("SERVER_TYPE", "serverless") - openapi_spec = build_args.get("OPENAPI_SPEC") - - # Support flexible paths via build_args - app_yaml_path = build_args.get("APP_YAML_PATH", f"{test_app_name}/app.yaml") - output_dir = build_args.get("OUTPUT_DIR", test_app_name) - processors_path_arg = build_args.get("PROCESSORS_PATH", "") - - # Find main.py relative to this script (in builder/main.py) - main_py_path = Path(__file__).parent.parent / "main.py" - if not main_py_path.exists(): - error_msg = f"Cannot find main.py at {main_py_path}" - logger.error(f"❌ {error_msg}") - return False, error_msg - - pre_build_command = [ - "python", str(main_py_path), app_yaml_path, - "-o", output_dir - ] - - # Add processors.py if specified or auto-detect - if processors_path_arg: - processors_path = Path(processors_path_arg) - if processors_path.exists(): - pre_build_command.extend(["-c", processors_path_arg]) - else: - logger.warning(f"⚠️ Specified PROCESSORS_PATH not found: {processors_path_arg}") - else: - # Auto-detect in output directory - processors_path = Path(f"{test_app_name}/processors.py") - if processors_path.exists(): - pre_build_command.extend(["-c", f"{test_app_name}/processors.py"]) - - # Add validation directory if specified in test_config - if test_config and "auto_validation" in test_config: - validation_folder = test_config["auto_validation"] - # Resolve path relative to config directory if it's relative - config_dir = test_config.get("_config_dir") - if config_dir: - validation_path = (Path(config_dir) / validation_folder).resolve() - else: - validation_path = Path(validation_folder).resolve() - - if validation_path.exists(): - pre_build_command.extend(["--validation-dir", str(validation_path), "--no-docker"]) - logger.info(f"📁 Adding validation directory for build: {validation_path}") - else: - logger.warning(f"⚠️ Auto validation folder not found: {validation_path}") - - pre_build_command.extend(["--server-type", server_type, "-t"]) - - if openapi_spec: - # Resolve provided spec relative to project root, copy into local app folder to avoid unsafe paths - project_root = Path(__file__).resolve().parents[2] - resolved_spec = None - candidates = [] - spec_path = Path(openapi_spec) - if spec_path.is_absolute(): - candidates.append(spec_path) - else: - candidates.append((project_root / openapi_spec).resolve()) - candidates.append((project_root / "builder" / openapi_spec).resolve()) - - for cand in candidates: - try: - if cand.exists() and str(cand).startswith(str(project_root)): - resolved_spec = cand - break - except Exception: - continue - - if not resolved_spec: - error_msg = f"Invalid OPENAPI_SPEC path: {openapi_spec}" - logger.error(f"❌ {error_msg}") - return False, error_msg - - local_spec_path = Path(output_dir) / "openapi.yaml" - - # Only copy if source and destination are different - if resolved_spec.resolve() != local_spec_path.resolve(): - try: - shutil.copyfile(str(resolved_spec), str(local_spec_path)) - logger.info(f"📄 Staged OPENAPI_SPEC: {resolved_spec} -> {local_spec_path}") - except Exception as e: - error_msg = f"Failed to stage OPENAPI_SPEC: {e}" - logger.error(f"❌ {error_msg}") - return False, error_msg - else: - logger.info(f"📄 OPENAPI_SPEC already in output directory: {local_spec_path}") - - pre_build_command[3:3] = ["-a", str(local_spec_path)] - - logger.info(f"🔧 Executing codegen command: {' '.join(pre_build_command)}") - pre_build_result = subprocess.run( - pre_build_command, - capture_output=True, - text=True, - timeout=600 - ) - - if pre_build_result.returncode != 0: - error_msg = f"Code generation failed: {pre_build_result.stderr}" - logger.error(f"❌ {error_msg}") - return False, error_msg - - logger.info("✅ Code generation completed successfully") - if pre_build_result.stdout: - logger.info(f"Codegen output: {pre_build_result.stdout}") - return True, pre_build_result.stdout - - except subprocess.TimeoutExpired: - error_msg = "Code generation timed out" - logger.error(f"❌ {error_msg}") - return False, error_msg - except Exception as e: - error_msg = f"Exception during code generation: {str(e)}" - logger.error(f"❌ {error_msg}") - return False, error_msg - - def build_image(self, build_args: Dict[str, str], image_name: str, dockerfile: Optional[str] = None, base_dir: Optional[str] = None, test_config: Dict = None) -> Tuple[bool, str]: - """Build Docker image with given arguments. - - Supports flexible path configuration via build_args: - - APP_YAML_PATH: Custom path to app.yaml (overrides {TEST_APP_NAME}/app.yaml) - - OUTPUT_DIR: Custom output directory (overrides {TEST_APP_NAME}) - - PROCESSORS_PATH: Custom processors.py path (overrides auto-detection) - - Args: - build_args: Build arguments for the Docker image - image_name: Name for the Docker image - dockerfile: Optional path to Dockerfile (overrides self.dockerfile_path) - base_dir: Optional Docker build context directory (overrides self.base_dir) - test_config: Optional test configuration. If present and contains 'auto_validation', - the validation directory will be passed to code generation via -v flag. - """ - try: - # Use provided paths or fall back to instance defaults - dockerfile_path = Path(dockerfile) if dockerfile else self.dockerfile_path - build_context = Path(base_dir) if base_dir else self.base_dir - # Validate image name to prevent command injection - if not validate_image_name(image_name): - error_msg = f"Invalid image name: {image_name}. Image name contains invalid characters." - logger.error(f"❌ {error_msg}") - return False, error_msg - - # Validate all build arguments to prevent command injection - for key, value in build_args.items(): - if not validate_build_arg_name(key): - error_msg = f"Invalid build arg name: {key}. Only alphanumeric characters and underscores allowed." - logger.error(f"❌ {error_msg}") - return False, error_msg - if not validate_build_arg_value(str(value)): - error_msg = f"Invalid build arg value for {key}: {value}. Value contains dangerous characters." - logger.error(f"❌ {error_msg}") - return False, error_msg - - # Execute pre-build command using TEST_APP_NAME - test_app_name = build_args.get("TEST_APP_NAME", "frame_sampling") - - # Validate app name to prevent command injection - if not validate_app_name(test_app_name): - error_msg = f"Invalid app name: {test_app_name}. Only alphanumeric characters, underscores, and hyphens are allowed." - logger.error(f"❌ {error_msg}") - return False, error_msg - - # Determine server type from build args (fallback to 'serverless') - server_type = build_args.get("SERVER_TYPE", "serverless") - - # Optional: OpenAPI spec path (to pass -a) - openapi_spec = build_args.get("OPENAPI_SPEC") - - # Require OpenAPI spec for non-serverless builds - if server_type != "serverless" and not openapi_spec: - error_msg = ( - "OPENAPI_SPEC is required in build_args when SERVER_TYPE is not 'serverless'" - ) - logger.error(f"❌ {error_msg}") - return False, error_msg - - # Support flexible paths via build_args - app_yaml_path = build_args.get("APP_YAML_PATH", f"{test_app_name}/app.yaml") - output_dir = build_args.get("OUTPUT_DIR", test_app_name) - processors_path_arg = build_args.get("PROCESSORS_PATH", "") - - # Find main.py relative to this script (in builder/main.py) - main_py_path = Path(__file__).parent.parent / "main.py" - if not main_py_path.exists(): - error_msg = f"Cannot find main.py at {main_py_path}" - logger.error(f"❌ {error_msg}") - return False, error_msg - - # Use safer subprocess call without shell=True - pre_build_command = [ - "python", str(main_py_path), app_yaml_path, - "-o", output_dir - ] - - # Add processors.py if specified or auto-detect - if processors_path_arg: - processors_path = Path(processors_path_arg) - if processors_path.exists(): - pre_build_command.extend(["-c", processors_path_arg]) - else: - logger.warning(f"⚠️ Specified PROCESSORS_PATH not found: {processors_path_arg}") - else: - # Auto-detect in test_app_name directory - processors_path = Path(f"{test_app_name}/processors.py") - if processors_path.exists(): - pre_build_command.extend(["-c", f"{test_app_name}/processors.py"]) - - # Add validation directory if specified in test_config - if test_config and "auto_validation" in test_config: - validation_folder = test_config["auto_validation"] - # Resolve path relative to config directory if it's relative - config_dir = test_config.get("_config_dir") - if config_dir: - validation_path = (Path(config_dir) / validation_folder).resolve() - else: - validation_path = Path(validation_folder).resolve() - - if validation_path.exists(): - pre_build_command.extend(["--validation-dir", str(validation_path), "--no-docker"]) - logger.info(f"📁 Adding validation directory for build: {validation_path}") - else: - logger.warning(f"⚠️ Auto validation folder not found: {validation_path}") - - pre_build_command.extend(["--server-type", server_type, "-t"]) - - if openapi_spec: - # Resolve provided spec relative to project root, copy into local app folder to avoid unsafe paths - project_root = Path(__file__).resolve().parents[2] - resolved_spec = None - candidates = [] - spec_path = Path(openapi_spec) - if spec_path.is_absolute(): - candidates.append(spec_path) - else: - candidates.append((project_root / openapi_spec).resolve()) - candidates.append((project_root / "builder" / openapi_spec).resolve()) - - for cand in candidates: - try: - if cand.exists() and str(cand).startswith(str(project_root)): - resolved_spec = cand - break - except Exception: - continue - - if not resolved_spec: - error_msg = f"Invalid OPENAPI_SPEC path: {openapi_spec}" - logger.error(f"❌ {error_msg}") - return False, error_msg - - local_spec_path = Path(output_dir) / "openapi.yaml" - - # Only copy if source and destination are different - if resolved_spec.resolve() != local_spec_path.resolve(): - try: - shutil.copyfile(str(resolved_spec), str(local_spec_path)) - logger.info(f"📄 Staged OPENAPI_SPEC: {resolved_spec} -> {local_spec_path}") - except Exception as e: - error_msg = f"Failed to stage OPENAPI_SPEC: {e}" - logger.error(f"❌ {error_msg}") - return False, error_msg - else: - logger.info(f"📄 OPENAPI_SPEC already in output directory: {local_spec_path}") - - pre_build_command[3:3] = ["-a", str(local_spec_path)] - - logger.info(f"🔧 Executing pre-build command: {' '.join(pre_build_command)}") - pre_build_result = subprocess.run( - pre_build_command, - capture_output=True, - text=True, - timeout=600 # 10 minute timeout for pre-build - ) - - if pre_build_result.returncode != 0: - error_msg = f"Pre-build command failed: {pre_build_result.stderr}" - logger.error(f"❌ {error_msg}") - return False, error_msg - - logger.info("✅ Pre-build command completed successfully") - if pre_build_result.stdout: - logger.info(f"Pre-build output: {pre_build_result.stdout}") - - cmd = [ - "docker", "build", - "-f", str(dockerfile_path), - "-t", image_name - ] - - # Add build arguments - for key, value in build_args.items(): - cmd.extend(["--build-arg", f"{key}={value}"]) - - # Add context directory - cmd.append(str(build_context)) - - logger.info(f"Building image: {image_name}") - logger.info(f"Command: {' '.join(cmd)}") - - result = subprocess.run( - cmd, - capture_output=True, - text=True, - timeout=3600 # 1 hour timeout - ) - - if result.returncode == 0: - logger.info(f"✅ Successfully built image: {image_name}") - return True, result.stdout - else: - logger.error(f"❌ Failed to build image: {image_name}") - logger.error(f"Error: {result.stderr}") - return False, result.stderr - - except subprocess.TimeoutExpired: - error_msg = f"Build timed out for image: {image_name}" - logger.error(f"❌ {error_msg}") - return False, error_msg - except Exception as e: - error_msg = f"Exception during build: {str(e)}" - logger.error(f"❌ {error_msg}") - return False, error_msg - - def run_prerequisite_script(self, script_command: str, test_id: int, image_name: str = "", cwd: str = None) -> Tuple[bool, str]: - """Run a prerequisite script before testing the docker container. - - Supports {image_name} placeholder in script_command, which is replaced - with the built Docker image name before execution. - """ - if not script_command: - return True, "No prerequisite script specified" - - # Substitute {image_name} placeholder - if image_name: - script_command = script_command.replace("{image_name}", image_name) - - # Validate script command to prevent command injection - if not validate_script_command(script_command): - error_msg = f"Invalid script command: {script_command}. Command contains potentially dangerous characters." - logger.error(f"❌ {error_msg}") - return False, error_msg - - log_file = self.log_dir / f"prerequisite_{test_id}.log" - - try: - logger.info(f"🔧 Running prerequisite script: {script_command}") - logger.info(f"📄 Prerequisite logs will be saved to: {log_file}") - - # Use safer command execution - split command into arguments when possible - import shlex - try: - # Attempt to parse command safely first - cmd_args = shlex.split(script_command) - if len(cmd_args) == 1 and cmd_args[0].endswith('.sh'): - # Single script file - use direct execution without shell - result = subprocess.run( - cmd_args, - capture_output=True, - text=True, - timeout=1800, - cwd=cwd, - shell=False # Safer execution without shell - ) - else: - # Complex command - use shell with additional validation - # Double-check validation before shell execution - if not validate_script_command(script_command): - raise ValueError("Command failed security validation") - - result = subprocess.run( - script_command, - shell=True, # Only when necessary and after validation - capture_output=True, - text=True, - timeout=1800, - cwd=cwd - ) - except ValueError as e: - if "failed security validation" in str(e): - raise e - # Fall back to shell execution for complex commands (with validation) - result = subprocess.run( - script_command, - shell=True, - capture_output=True, - text=True, - timeout=1800, - cwd=cwd - ) - - # Save prerequisite script logs - with open(log_file, 'w') as f: - f.write("=== Prerequisite Script Execution ===\n") - f.write(f"Command: {script_command}\n") - f.write(f"Return Code: {result.returncode}\n") - f.write(f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}\n") - f.write("\n=== STDOUT ===\n") - f.write(result.stdout) - f.write("\n=== STDERR ===\n") - f.write(result.stderr) - f.write("\n=== END LOG ===\n") - - if result.returncode == 0: - logger.info(f"✅ Prerequisite script completed successfully") - if result.stdout: - logger.info(f"Prerequisite output: {result.stdout}") - return True, result.stdout - else: - error_msg = f"Prerequisite script failed with return code {result.returncode}" - logger.error(f"❌ {error_msg}") - logger.error(f"Prerequisite stderr: {result.stderr}") - return False, error_msg - - except subprocess.TimeoutExpired: - error_msg = f"Prerequisite script timed out after 1800 seconds" - logger.error(f"❌ {error_msg}") - - # Save timeout log - with open(log_file, 'w') as f: - f.write("=== Prerequisite Script Execution ===\n") - f.write(f"Command: {script_command}\n") - f.write(f"Status: TIMEOUT\n") - f.write(f"Timeout: 1800 seconds\n") - f.write(f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}\n") - f.write("\n=== ERROR ===\n") - f.write(error_msg) - f.write("\n=== END LOG ===\n") - - return False, error_msg - except Exception as e: - error_msg = f"Exception during prerequisite script execution: {str(e)}" - logger.error(f"❌ {error_msg}") - - # Save exception log - with open(log_file, 'w') as f: - f.write("=== Prerequisite Script Execution ===\n") - f.write(f"Command: {script_command}\n") - f.write(f"Status: EXCEPTION\n") - f.write(f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}\n") - f.write("\n=== ERROR ===\n") - f.write(error_msg) - f.write("\n=== END LOG ===\n") - - return False, error_msg - - def test_image(self, image_name: str, test_config: Dict, test_id: int, gpus: str = "all") -> Tuple[bool, str, str]: - """Test the built image by running it and capture logs. - - Args: - image_name: Name of the Docker image to test - test_config: Test configuration dictionary - test_id: Unique test identifier - gpus: GPU devices to use (default: 'all') - """ - log_file = self.log_dir / f"test_{test_id}_{image_name.replace(':', '_')}.log" - - # Validate image name to prevent command injection - if not validate_image_name(image_name): - error_msg = f"Invalid image name: {image_name}. Image name contains invalid characters." - logger.error(f"❌ {error_msg}") - return False, error_msg, "" - - # Validate test configuration to prevent command injection - config_valid, config_error = validate_test_config(test_config) - if not config_valid: - error_msg = f"Invalid test configuration: {config_error}" - logger.error(f"❌ {error_msg}") - return False, error_msg, "" - - # Get timeout from test config, default to 10 seconds - timeout = test_config.get("timeout", 10) - - try: - # Check if image exists - result = subprocess.run( - ["docker", "image", "inspect", image_name], - capture_output=True, - text=True - ) - - if result.returncode != 0: - return False, f"Image {image_name} not found", "" - - # Run prerequisite script if specified - prerequisite_script = test_config.get("prerequisite_script") - if prerequisite_script: - config_dir = str(Path(test_config.get("_config_dir", ".")).resolve()) - prerequisite_success, prerequisite_output = self.run_prerequisite_script( - prerequisite_script, test_id, image_name=image_name, cwd=config_dir - ) - if not prerequisite_success: - return False, f"Prerequisite script failed: {prerequisite_output}", "" - - # Detect server type to choose test strategy - server_type = test_config.get("SERVER_TYPE", "serverless") - is_serverless = server_type == "serverless" - - # Prepare assets if specified - asset_ids = [] - asset_temp_dir = None - if "assets" in test_config: - import tempfile - - assets_config = test_config["assets"] - logger.info(f"📦 Preparing {len(assets_config)} asset(s)...") - - # Create temp directory for asset info.json files - asset_temp_dir = Path(tempfile.mkdtemp(prefix=f"test_assets_{test_id}_")) - - for idx, asset_data in enumerate(assets_config): - # Use the provided assetId - asset_id = asset_data["assetId"] - asset_ids.append(asset_id) - - # Create asset directory structure - asset_dir = asset_temp_dir / asset_id - asset_dir.mkdir(parents=True) - - # Write asset data to info.json inside the directory - info_file = asset_dir / "info.json" - with info_file.open("w") as f: - json.dump(asset_data, f, indent=2) - - logger.info(f"✅ Created asset {idx}: {asset_id} ({asset_data['path']})") - - # Run the container with test configuration - # Use host network for serverless; use port mapping for non-serverless to avoid port conflicts - # Use --ipc=host to share host's /dev/shm with the container; - # Docker's default 64MB /dev/shm is insufficient for PyTorch/TensorRT-LLM - # multiprocessing which shares tensors via shared memory, causing SIGBUS - # (Bus error: nonexistent physical address) when /dev/shm is exhausted. - cmd = ["docker", "run", "--ipc=host", "--gpus", gpus] - - # Add environment variables if specified - if "env" in test_config: - for key, value in test_config["env"].items(): - # Double-check validation to prevent injection - if not validate_env_var_name(key) or not validate_env_var_value(str(value)): - error_msg = f"Invalid environment variable: {key}={value}" - logger.error(f"❌ {error_msg}") - return False, error_msg, "" - cmd.extend(["-e", f"{key}={value}"]) - - # Get the test config directory for resolving relative paths - config_dir = Path(test_config.get("_config_dir", ".")).resolve() - - # Automatically add ERROR_EXPORT_PATH for error file collection - # Use unique error file names for different test cases - error_export_dir = Path("/tmp/error") - error_export_dir.mkdir(parents=True, exist_ok=True) - error_filename = f"inference_errors_{test_id}_{int(time.time())}.json" - error_export_file = error_export_dir / error_filename - container_error_path = f"/tmp/error/{error_filename}" - cmd.extend(["-e", f"ERROR_EXPORT_PATH={container_error_path}"]) - cmd.extend(["-v", f"{error_export_dir}:{error_export_dir}"]) - logger.info(f"📄 Error export: {error_export_file} -> {container_error_path}") - - # Add volume mounts if specified - if "volumes" in test_config: - logger.info(f"📁 Processing volumes: {test_config['volumes']}") - - for host_path, container_path in test_config["volumes"].items(): - # Double-check validation to prevent injection - if not validate_volume_path(host_path) or not validate_volume_path(container_path): - error_msg = f"Invalid volume path: {host_path}:{container_path}" - logger.error(f"❌ {error_msg}") - return False, error_msg, "" - - # Resolve host path to absolute path - # Priority: 1) Expand ~ for home directory, 2) Resolve relative to config dir, 3) Use as-is if absolute - expanded_path = os.path.expanduser(host_path) - - if not os.path.isabs(expanded_path): - # Relative path - resolve relative to test config directory - abs_host_path = str((config_dir / expanded_path).resolve()) - logger.info(f"📁 Resolved relative path: {host_path} -> {abs_host_path} (relative to config dir)") - else: - # Already absolute path - abs_host_path = expanded_path - logger.info(f"📁 Using absolute path: {host_path} -> {abs_host_path}") - - # Verify the resolved path exists (optional warning, not error) - if not os.path.exists(abs_host_path): - logger.warning(f"⚠️ Volume path does not exist yet: {abs_host_path}") - logger.warning(f"⚠️ This is OK if the path will be created by model download or other setup") - - volume_arg = f"{abs_host_path}:{container_path}" - cmd.extend(["-v", volume_arg]) - logger.info(f"📁 Adding volume mount: {volume_arg}") - else: - logger.info("📁 No additional volumes specified in test config") - - # Give the container a name for logging/cleanup purposes - expected_error = test_config.get("expected_error") - container_name = f"test-{test_id}-{int(time.time())}" - cmd.extend(["--name", container_name]) - logger.info(f"🏷️ Container name: {container_name}") - - # For FastAPI (or any non-serverless), run detached on host network (DinD friendly) - if not is_serverless: - cmd.insert(2, "--network=host") - cmd.insert(2, "-d") # run detached - else: - cmd.insert(2, "--network=host") - - # For serverless non-negative tests, save inference results for validation - result_export_file = None - if is_serverless and not expected_error: - result_export_dir = Path("/tmp/result") - result_export_dir.mkdir(parents=True, exist_ok=True) - result_filename = f"result_{test_id}_{int(time.time())}.json" - result_export_file = result_export_dir / result_filename - container_result_path = f"/tmp/result/{result_filename}" - cmd.extend(["-v", f"{result_export_dir}:{result_export_dir}"]) - logger.info(f"📄 Result export: {result_export_file} -> {container_result_path}") - - cmd.append(image_name) - - # Add command arguments if specified (serverless only) - if server_type == "serverless" and "cmd" in test_config: - cmd_args = test_config["cmd"] - - # Double-check validation for each command argument - for arg in cmd_args: - if not validate_docker_arg(str(arg)): - error_msg = f"Invalid command argument: {arg}" - logger.error(f"❌ {error_msg}") - return False, error_msg, "" - cmd.extend(cmd_args) - - # Append -s flag for serverless non-negative tests - if result_export_file: - cmd.extend(["-s", container_result_path]) - - logger.info(f"Testing image: {image_name}") - logger.info(f"Command: {' '.join(cmd)}") - logger.info(f"Timeout: {timeout} seconds") - logger.info(f"Logs will be saved to: {log_file}") - - # Log the complete command for debugging - logger.info("🔍 Complete docker run command:") - logger.info(f" {' '.join(cmd)}") - - if is_serverless: - # For serverless with assets, we need to start detached, copy assets, then wait - if asset_temp_dir: - # Insert -d flag to run detached - cmd.insert(2, "-d") - logger.info("🔍 Starting container in detached mode to copy assets...") - result = subprocess.run( - cmd, - capture_output=True, - text=True, - timeout=30 - ) - if result.returncode != 0: - logger.error(f"❌ Failed to start container: {result.stderr}") - return False, f"Container start failed: {result.stderr}", "" - - # Container ID is in stdout - container_id = result.stdout.strip() - logger.info(f"✅ Container started: {container_id[:12]}") - - # Create /tmp/assets directory in container - logger.info("📁 Creating /tmp/assets directory in container...") - mkdir_result = subprocess.run( - ["docker", "exec", container_name, "mkdir", "-p", "/tmp/assets"], - capture_output=True, - text=True, - timeout=10 - ) - if mkdir_result.returncode != 0: - logger.error(f"❌ Failed to create /tmp/assets: {mkdir_result.stderr}") - subprocess.run(["docker", "stop", container_name], capture_output=True, timeout=10) - subprocess.run(["docker", "rm", container_name], capture_output=True, timeout=10) - return False, f"Failed to create /tmp/assets: {mkdir_result.stderr}", "" - - # Copy assets into container - for idx, asset_id in enumerate(asset_ids): - src_path = asset_temp_dir / asset_id - dest_path = f"{container_name}:/tmp/assets/{asset_id}" - - logger.info(f"📦 Copying asset {idx} to container: {asset_id}") - copy_result = subprocess.run( - ["docker", "cp", str(src_path), dest_path], - capture_output=True, - text=True, - timeout=30 - ) - if copy_result.returncode != 0: - logger.error(f"❌ Failed to copy asset: {copy_result.stderr}") - subprocess.run(["docker", "stop", container_name], capture_output=True, timeout=10) - subprocess.run(["docker", "rm", container_name], capture_output=True, timeout=10) - return False, f"Asset copy failed: {copy_result.stderr}", "" - logger.info(f"✅ Asset {idx} copied successfully") - - # Now wait for container to complete - logger.info("⏳ Waiting for container to complete...") - wait_result = subprocess.run( - ["docker", "wait", container_name], - capture_output=True, - text=True, - timeout=timeout - ) - - # Get logs - logs_result = subprocess.run( - ["docker", "logs", container_name], - capture_output=True, - text=True, - timeout=30 - ) - stdout_output = logs_result.stdout - stderr_output = logs_result.stderr - - # Clean up the temp asset directory - import shutil as shutil_module - shutil_module.rmtree(asset_temp_dir, ignore_errors=True) - logger.info("🧹 Cleaned up temporary asset directory") - else: - # Run container normally and capture output - logger.info("🔍 Running container and capturing output...") - result = subprocess.run( - cmd, - capture_output=True, - text=True, - timeout=timeout - ) - stdout_output = result.stdout - stderr_output = result.stderr - - # Clean up the container - subprocess.run(["docker", "rm", container_name], capture_output=True, text=True, timeout=10) - - # Check if error export file was created - error_copy_failed = False - error_copy_msg = "" - if error_export_file.exists(): - file_size = error_export_file.stat().st_size - logger.info(f"📄 Found error export file: {error_export_file} (size: {file_size} bytes)") - else: - error_copy_failed = True - error_copy_msg = f"Error export file not found: {error_export_file}" - logger.warning(f"⚠️ {error_copy_msg}") - else: - # FastAPI-like server flow: start container detached, poll readiness, run client, then stop and collect logs - logger.info("🚀 Starting server container in detached mode") - start_proc = subprocess.run( - cmd, - capture_output=True, - text=True - ) - if start_proc.returncode != 0: - error_msg = f"Failed to start server container: {start_proc.stderr}" - logger.error(f"❌ {error_msg}") - return False, error_msg, "" - container_id = start_proc.stdout.strip() - logger.info(f"🆔 Container ID: {container_id}") - - # Copy assets into container if specified - if asset_temp_dir: - # Create /tmp/assets directory in container - logger.info("📁 Creating /tmp/assets directory in container...") - mkdir_result = subprocess.run( - ["docker", "exec", container_name, "mkdir", "-p", "/tmp/assets"], - capture_output=True, - text=True, - timeout=10 - ) - if mkdir_result.returncode != 0: - logger.error(f"❌ Failed to create /tmp/assets: {mkdir_result.stderr}") - subprocess.run(["docker", "stop", container_name], capture_output=True, timeout=10) - subprocess.run(["docker", "rm", container_name], capture_output=True, timeout=10) - return False, f"Failed to create /tmp/assets: {mkdir_result.stderr}", "" - - for idx, asset_id in enumerate(asset_ids): - src_path = asset_temp_dir / asset_id - dest_path = f"{container_name}:/tmp/assets/{asset_id}" - - logger.info(f"📦 Copying asset {idx} to container: {asset_id}") - copy_result = subprocess.run( - ["docker", "cp", str(src_path), dest_path], - capture_output=True, - text=True, - timeout=30 - ) - if copy_result.returncode != 0: - logger.error(f"❌ Failed to copy asset: {copy_result.stderr}") - subprocess.run(["docker", "stop", container_name], capture_output=True, timeout=10) - subprocess.run(["docker", "rm", container_name], capture_output=True, timeout=10) - return False, f"Asset copy failed: {copy_result.stderr}", "" - logger.info(f"✅ Asset {idx} copied successfully") - - # Clean up the temp asset directory - import shutil as shutil_module - shutil_module.rmtree(asset_temp_dir, ignore_errors=True) - logger.info("🧹 Cleaned up temporary asset directory") - - # Readiness probe - ready = False - http_error_status = None # Track if we got an HTTP error - ready_deadline = time.time() + max(1, timeout) - service_host = self.get_service_host() - # Get HTTP port from test config env, default to 8000 - http_port = test_config.get("env", {}).get("HTTP_PORT", "8000") - health_url = f"http://{service_host}:{http_port}/v1/health/ready" - logger.info(f"🔎 Probing readiness: {health_url}") - while time.time() < ready_deadline: - try: - with urllib.request.urlopen(health_url, timeout=2) as resp: - if resp.status == 200: - ready = True - break - else: - logger.info(f"Health probe returned non-200 status: {resp.status}") - except urllib.error.HTTPError as e: - http_error_status = e.code - logger.error(f"❌ Health probe HTTPError: status={e.code} - server returned error, stopping retry") - break # No retry on HTTP errors - server is responding but not healthy - except urllib.error.URLError as e: - if isinstance(e.reason, socket.timeout): - logger.warning("Health probe request timed out") - else: - logger.info(f"Health probe URLError: {e.reason}, retrying...") - except (TimeoutError, socket.timeout): - logger.warning("Health probe request timed out, retrying...") - time.sleep(2) - - client_stdout = "" - client_stderr = "" - client_rc = 1 - - if not ready: - if http_error_status: - logger.error(f"❌ Server health check failed with HTTP error {http_error_status}") - else: - logger.error("❌ Server did not become ready within timeout") - # Collect logs to see what went wrong during initialization - logger.info("📋 Collecting container logs for failed readiness...") - logs_proc = subprocess.run(["docker", "logs", container_id], capture_output=True, text=True) - if logs_proc.stdout: - logger.info("🔍 Container initialization logs:") - for line in logs_proc.stdout.split('\n')[-20:]: # Show last 20 lines - if line.strip(): - logger.info(f" {line}") - if logs_proc.stderr: - logger.error("🔍 Container error logs:") - for line in logs_proc.stderr.split('\n')[-10:]: # Show last 10 error lines - if line.strip(): - logger.error(f" {line}") - else: - # Check if auto_validation is specified - run directly on host - auto_validation_path = test_config.get("auto_validation") - if auto_validation_path: - logger.info("✅ Server is ready. Running validation script on host...") - - try: - # Resolve validation folder path relative to config directory - config_dir = Path(test_config.get("_config_dir", ".")).resolve() - validation_folder = (config_dir / auto_validation_path).resolve() - - # Fixed script name is test_runner.py (generated during build via -v flag) - # It's located in the validation folder subdirectories (e.g., gdino/.tmp/test_runner.py) - # Determine which subdirectory based on TAO_MODEL_NAME - tao_model_name = test_config.get("env", {}).get("TAO_MODEL_NAME", "") - if not tao_model_name: - logger.error("❌ TAO_MODEL_NAME not specified in test_config.env") - client_rc = 1 - client_stdout = "" - client_stderr = "TAO_MODEL_NAME not specified" - else: - validation_script_path = validation_folder / ".tmp" / "test_runner.py" - - if not validation_script_path.exists(): - logger.error(f"❌ Validation script not found: {validation_script_path}") - client_rc = 1 - client_stdout = "" - client_stderr = f"Validation script not found: {validation_script_path}" - else: - logger.info(f"🔧 Running validation script: {validation_script_path}") - - # Set up environment variables for validation script - validation_env = os.environ.copy() - if "env" in test_config: - validation_env.update({k: str(v) for k, v in test_config["env"].items()}) - - # Add service host for validation script to connect to server - # The validation script expects TEST_HOST environment variable - # Use HTTP_PORT from test config env, default to 8000 - validation_env["TEST_HOST"] = f"http://{service_host}:{http_port}" - - # Run validation script with Python - validation_proc = subprocess.run( - ["python", str(validation_script_path)], - capture_output=True, - text=True, - timeout=max(60, timeout), - cwd=str(validation_script_path.parent), - env=validation_env - ) - client_rc = validation_proc.returncode - client_stdout = validation_proc.stdout - client_stderr = validation_proc.stderr - - if client_rc == 0: - logger.info("✅ Validation script completed successfully") - if client_stdout: - logger.info(f"Validation output:\n{client_stdout}") - else: - logger.error(f"❌ Validation script failed with return code {client_rc}") - if client_stderr: - logger.error(f"Validation stderr:\n{client_stderr}") - if client_stdout: - logger.error(f"Validation stdout:\n{client_stdout}") - except subprocess.TimeoutExpired: - logger.error("❌ Validation script timed out") - client_rc = 1 - client_stdout = "" - client_stderr = "Validation script timed out" - except Exception as e: - logger.error(f"❌ Exception running validation script: {e}") - client_rc = 1 - client_stdout = "" - client_stderr = str(e) - else: - logger.info("✅ Server is ready. Launching concurrent curl requests...") - # Read NDJSON files and launch curl for each line - # Get test_requests from test_config, with a default fallback based on app name - test_app_name = test_config.get("TEST_APP_NAME") or None - default_test_requests = [{"payload_path": f"{test_app_name}/payloads/payloads.jsonl"}] - test_requests_list = test_config.get("test_requests", default_test_requests) - - # Ensure test_requests is a list - if not isinstance(test_requests_list, list): - logger.error(f"❌ test_requests must be a list, got: {type(test_requests_list)}") - client_rc = 1 - client_stdout = "" - client_stderr = "test_requests must be a list" - else: - # Check if any request has scheduled_time (for sequential execution) - has_scheduled = any("scheduled_time" in item for item in test_requests_list) - - # Resolve payloads_path relative to config directory - config_dir = Path(test_config.get("_config_dir", ".")).resolve() - - # Get endpoint from test_config, default to /v1/inference - endpoint = test_config.get("endpoint", "/v1/inference") - logger.info(f"🎯 Using endpoint: {endpoint}") - - if has_scheduled: - # Sequential execution with timing - logger.info("⏰ Using sequential execution with scheduled times") - - # Sort requests by scheduled_time - sorted_requests = sorted(test_requests_list, key=lambda x: x.get("scheduled_time", 0)) - - start_time = time.time() - all_succeeded = True - async_processes = [] # Track async processes for later status check - - for request_item in sorted_requests: - scheduled_time = request_item.get("scheduled_time", 0) - elapsed = time.time() - start_time - - # Wait until scheduled time - if scheduled_time > elapsed: - wait_time = scheduled_time - elapsed - logger.info(f"⏳ Waiting {wait_time:.1f}s until scheduled time {scheduled_time}s...") - time.sleep(wait_time) - - # Extract request config - http_method = request_item.get("method", "POST").upper() - http_headers = request_item.get("headers", {"Content-Type": "application/json"}) - request_endpoint = request_item.get("endpoint", endpoint) - - # Get payload - payload_data = None - if "payload_path" in request_item: - payload_path = (config_dir / request_item["payload_path"]).resolve() - if payload_path.exists(): - with payload_path.open("r") as f: - first_line = f.readline().strip() - if first_line: - payload_data = first_line - else: - logger.error(f"❌ Payload file not found: {payload_path}") - all_succeeded = False - continue - - # Build curl command - url = f"http://{service_host}:{http_port}{request_endpoint}" - curl_cmd = ["curl", "-sS", "-X", http_method, "-w", "\\n%{http_code}"] - - for header_name, header_value in http_headers.items(): - curl_cmd.extend(["-H", f"{header_name}: {header_value}"]) - - if payload_data and http_method in ["POST", "PUT", "PATCH", "DELETE"]: - curl_cmd.extend(["-d", payload_data]) - - curl_cmd.append(url) - - logger.info(f"🌐 [{scheduled_time}s] {http_method} {request_endpoint}") - if payload_data: - logger.info(f"📦 Payload: {payload_data[:200]}{'...' if len(payload_data) > 200 else ''}") - - # Check if this is an async request - is_async = request_item.get("async", False) - - if is_async: - # Launch async request without waiting for response - logger.info(f"🚀 Launching async request") - try: - proc = subprocess.Popen( - curl_cmd, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - text=True - ) - async_processes.append((proc, http_method, request_endpoint)) - logger.info(f"✅ Async request launched (PID: {proc.pid})") - except Exception as e: - logger.error(f"❌ Failed to launch async request: {e}") - all_succeeded = False - else: - # Execute synchronous request - try: - result = subprocess.run(curl_cmd, capture_output=True, text=True, timeout=30) - response_output = result.stdout.strip() - - # Split response body and status code - lines = response_output.split('\n') - status_code = int(lines[-1]) if lines and lines[-1].isdigit() else 0 - response_body = '\n'.join(lines[:-1]) if len(lines) > 1 else "" - - logger.info(f"📡 Status: {status_code}") - if response_body: - logger.info(f"✅ Response: {response_body[:300]}{'...' if len(response_body) > 300 else ''}") - - if status_code < 200 or status_code >= 300: - logger.error(f"❌ Request failed with status {status_code}") - all_succeeded = False - except subprocess.TimeoutExpired: - logger.error(f"❌ Request timed out") - all_succeeded = False - except Exception as e: - logger.error(f"❌ Request failed: {e}") - all_succeeded = False - - # Wait for async processes and check their results - if async_processes: - logger.info(f"\n⏳ Waiting for {len(async_processes)} async request(s) to complete...") - for proc, method, endpoint in async_processes: - try: - stdout, stderr = proc.communicate(timeout=30) - response_output = stdout.strip() - - # Split response body and status code - lines = response_output.split('\n') - status_code = int(lines[-1]) if lines and lines[-1].isdigit() else 0 - response_body = '\n'.join(lines[:-1]) if len(lines) > 1 else "" - - logger.info(f"📡 Async {method} {endpoint} - Status: {status_code}") - if response_body: - logger.info(f" Response: {response_body[:200]}{'...' if len(response_body) > 200 else ''}") - - if status_code < 200 or status_code >= 300: - logger.error(f"❌ Async request failed with status {status_code}") - all_succeeded = False - except subprocess.TimeoutExpired: - logger.error(f"❌ Async {method} {endpoint} timed out") - proc.kill() - all_succeeded = False - except Exception as e: - logger.error(f"❌ Async {method} {endpoint} failed: {e}") - all_succeeded = False - - # Set final result - if all_succeeded: - logger.info("\n✅ All scheduled requests completed successfully") - client_rc = 0 - client_stdout = "All requests succeeded" - client_stderr = "" - else: - logger.error("\n❌ Some requests failed") - client_rc = 1 - client_stdout = "" - client_stderr = "One or more requests failed" - else: - # Parallel execution (existing logic) - logger.info("🚀 Using parallel execution") - - # Collect all payload lines from all files with their configs - # Each entry: (payload_line, method, headers) - all_payloads = [] - for request_item in test_requests_list: - # Parse test request (must be dict) - if not isinstance(request_item, dict): - logger.error(f"❌ Invalid test_requests item (must be dict): {request_item}") - continue - - # Extract config with defaults - http_method = request_item.get("method", "POST").upper() - http_headers = request_item.get("headers", {"Content-Type": "application/json"}) - - # Check if payload_path is provided - if "payload_path" not in request_item: - # No payload_path - single request with no payload (useful for GET) - logger.info(f"📄 No payload file specified") - logger.info(f" Method: {http_method}, Headers: {http_headers}") - all_payloads.append(("", http_method, http_headers)) - else: - # Path provided - load payloads from file - payload_path_str = request_item["payload_path"] - payload_path = (config_dir / payload_path_str).resolve() - logger.info(f"📄 Resolved payload path: {payload_path_str} -> {payload_path}") - logger.info(f" Method: {http_method}, Headers: {http_headers}") - - if not payload_path.exists(): - logger.error(f"❌ Payload file not found: {payload_path}") - else: - with payload_path.open("r") as f: - payload_lines = [line.strip() for line in f if line.strip()] - # Associate each payload with its method and headers - for line in payload_lines: - all_payloads.append((line, http_method, http_headers)) - logger.info(f"📋 Loaded {len(payload_lines)} payloads from {payload_path.name}") - - if not all_payloads: - logger.error(f"❌ No payloads found in any file") - client_rc = 1 - client_stdout = "" - client_stderr = "No payloads found" - else: - logger.info(f"📋 Total payloads to process: {len(all_payloads)}") - - procs: List[subprocess.Popen] = [] - for payload_line, method, headers in all_payloads: - # Check if this is a multipart/form-data upload - content_type = headers.get("Content-Type", "").lower() - is_multipart = "multipart/form-data" in content_type - - # Build curl command with method and headers from payload config - curl_cmd = ["curl", "-sS", "-X", method, "-w", "%{http_code}"] - - if is_multipart and method in ["POST", "PUT", "PATCH"] and payload_line: - # Multipart upload: parse JSON to get file field and use -F - try: - payload_data = json.loads(payload_line) - - # Add non-Content-Type headers (curl -F sets Content-Type automatically) - for header_name, header_value in headers.items(): - if header_name.lower() != "content-type": - curl_cmd.extend(["-H", f"{header_name}: {header_value}"]) - - # Add form fields with -F - for field_name, field_value in payload_data.items(): - if isinstance(field_value, str): - # Try to resolve as a file path (relative to config dir) - file_path = Path(field_value) - if not file_path.is_absolute(): - # Try relative to config dir - resolved_path = (config_dir / field_value).resolve() - else: - # Already absolute - resolved_path = file_path.resolve() - - # Check if the resolved path exists and is a file - if resolved_path.exists() and resolved_path.is_file(): - # Use @ prefix for file upload - curl_cmd.extend(["-F", f"{field_name}=@{resolved_path}"]) - logger.info(f" 📎 Uploading file: {field_name}={resolved_path}") - else: - # Not a valid file path, send as string value - curl_cmd.extend(["-F", f"{field_name}={field_value}"]) - if field_value: - logger.debug(f" 📝 Form field: {field_name}={field_value}") - else: - # Non-string value (number, bool, etc.) - curl_cmd.extend(["-F", f"{field_name}={field_value}"]) - except json.JSONDecodeError as e: - logger.error(f"❌ Failed to parse payload as JSON for multipart upload: {e}") - logger.error(f" Payload: {payload_line}") - continue - else: - # Regular JSON request - # Add headers - for header_name, header_value in headers.items(): - curl_cmd.extend(["-H", f"{header_name}: {header_value}"]) - - # Add data for methods that support body (only if payload is not empty) - if method in ["POST", "PUT", "PATCH"] and payload_line: - curl_cmd.extend(["--data", payload_line]) - - # Add URL - curl_cmd.append(f"http://{service_host}:{http_port}{endpoint}") - - procs.append(subprocess.Popen( - curl_cmd, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - text=True - )) - - outs: List[str] = [] - errs: List[str] = [] - client_rc = 0 - http_status_errors = [] - for i, p in enumerate(procs): - out, err = p.communicate(timeout=max(5, timeout)) - outs.append(out or "") - errs.append(err or "") - if p.returncode != 0: - client_rc = p.returncode - else: - # For non-serverless, check HTTP status is 200 - # curl -w "%{http_code}" appends status to stdout - if out and len(out) >= 3: - # Extract last 3 chars as HTTP status code - http_status = out[-3:] - if http_status != "200": - http_status_errors.append( - f"Request {i+1}: HTTP {http_status}" - ) - client_rc = 1 # Mark as failed - - client_stdout = "\n".join(outs) - client_stderr = "\n".join(errs) - - # Log HTTP status errors for non-serverless - if http_status_errors: - error_msg = ( - f"Non-serverless server returned non-200 status " - f"codes: {'; '.join(http_status_errors)}" - ) - logger.error("❌ %s", error_msg) - client_stderr += f"\n{error_msg}" - - # Always attempt to stop and collect logs - logger.info("🛑 Stopping server container") - - # Collect logs BEFORE stopping container (while it's still running) - logger.info("📋 Collecting container logs...") - logs_proc = subprocess.run(["docker", "logs", container_id], capture_output=True, text=True) - server_logs = logs_proc.stdout - if logs_proc.stderr: - server_logs += "\n=== STDERR ===\n" + logs_proc.stderr - - # Check if error export file was created - error_copy_failed = False - error_copy_msg = "" - if error_export_file.exists(): - file_size = error_export_file.stat().st_size - logger.info(f"📄 Found error export file: {error_export_file} (size: {file_size} bytes)") - else: - error_copy_failed = True - error_copy_msg = f"Error export file not found: {error_export_file}" - logger.warning(f"⚠️ {error_copy_msg}") - - # Now stop and remove container - subprocess.run(["docker", "stop", container_id], capture_output=True, text=True) - subprocess.run(["docker", "rm", container_id], capture_output=True, text=True) - - # Prepare a Result-like object - class Result: - def __init__(self, returncode, stdout, stderr): - self.returncode = returncode - self.stdout = stdout - self.stderr = stderr - - combined_stdout = "".join([ - "=== SERVER LOGS ===\n", server_logs or "", - "\n=== CLIENT STDOUT ===\n", client_stdout or "", - ]) - combined_stderr = client_stderr or "" - # For non-serverless flows, we care about HTTP responses and validation status, - # not the container's own exit code. Track client failure separately and - # keep Result.returncode neutral (0) so final status can branch by server type. - client_failed = not (ready and client_rc == 0) - result = Result(0, combined_stdout, combined_stderr) - - # Save logs to file - with open(log_file, 'w') as f: - f.write("=== Test Configuration ===\n") - f.write(f"Image: {image_name}\n") - f.write(f"Command: {' '.join(cmd)}\n") - f.write(f"Return Code: {result.returncode}\n") - f.write(f"Timeout: {timeout} seconds\n") - f.write("Error Detection: Enabled (via JSON export)\n") - f.write(f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}\n") - f.write("\n=== STDOUT ===\n") - f.write(result.stdout) - f.write("\n=== STDERR ===\n") - f.write(result.stderr) - f.write("\n=== END LOG ===\n") - - # Check for expected_error (negative test) using exported error JSON - expected_error = test_config.get("expected_error") - if expected_error: - # This is a negative test - fail immediately if error file not found - if error_copy_failed or error_export_file is None: - error_msg = f"Negative test FAILED: Could not find error export file: {error_copy_msg}" - logger.error(f"❌ {error_msg}") - logger.info(f"📄 Logs saved to: {log_file}") - return False, error_msg, str(log_file) - - # Try to read the exported error file - matched_error = None # Will hold the error that matches expected_error exactly - - if error_export_file.exists(): - try: - with open(error_export_file, 'r') as f: - error_data = json.load(f) - - stats = error_data.get("stats", {}) - total_errors = stats.get("total_errors", 0) - errors = error_data.get("errors", []) - collected_codes = [err.get("error_code") for err in errors] - - # Search for EXACT match of expected error code - for err in errors: - if err.get("error_code") == expected_error: - matched_error = err - break - - if matched_error is not None: - # EXACT match found - test passes - logger.info(f"✅ Negative test PASSED: Error code '{expected_error}' matches exactly") - logger.info(f" Error message: {matched_error.get('message', 'N/A')[:200]}") - logger.info(f" Component: {matched_error.get('component', 'N/A')}") - logger.info(f" Operation: {matched_error.get('operation', 'N/A')}") - logger.info(f" Total errors collected: {total_errors}") - logger.info(f"📄 Logs saved to: {log_file}") - return True, result.stdout, str(log_file) - else: - # No exact match - test fails - error_msg = f"Negative test FAILED: Expected error '{expected_error}' not found" - logger.error(f"❌ {error_msg}") - logger.error(f" Collected error codes: {collected_codes}") - logger.error(f" Total errors: {total_errors}") - logger.info(f"📄 Logs saved to: {log_file}") - return False, error_msg, str(log_file) - except json.JSONDecodeError as e: - error_msg = f"Negative test FAILED: Failed to parse error export file: {e}" - logger.error(f"❌ {error_msg}") - logger.info(f"📄 Logs saved to: {log_file}") - return False, error_msg, str(log_file) - except Exception as e: - error_msg = f"Negative test FAILED: Failed to read error export file: {e}" - logger.error(f"❌ {error_msg}") - logger.info(f"📄 Logs saved to: {log_file}") - return False, error_msg, str(log_file) - else: - # Error export file doesn't exist - error_msg = f"Negative test FAILED: Error export file not found: {error_export_file}" - logger.error(f"❌ {error_msg}") - logger.info(f"📄 Logs saved to: {log_file}") - return False, error_msg, str(log_file) - - # For serverless non-negative tests, check that the result file exists and is non-empty - if result_export_file: - if not result_export_file.exists(): - error_msg = f"Result file not found: {result_export_file}" - logger.error(f"❌ {error_msg}") - logger.info(f"📄 Logs saved to: {log_file}") - return False, error_msg, str(log_file) - file_size = result_export_file.stat().st_size - if file_size == 0: - error_msg = f"Result file is empty: {result_export_file}" - logger.error(f"❌ {error_msg}") - logger.info(f"📄 Logs saved to: {log_file}") - return False, error_msg, str(log_file) - logger.info(f"📄 Result file OK: {result_export_file} ({file_size} bytes)") - - # Check for errors using the exported error JSON file (if ERROR_EXPORT_PATH was configured) - has_errors = False - error_details = [] - - if error_export_file and error_export_file.exists(): - try: - with open(error_export_file, 'r') as f: - error_data = json.load(f) - - stats = error_data.get("stats", {}) - total_errors = stats.get("total_errors", 0) - errors = error_data.get("errors", []) - - if total_errors > 0: - has_errors = True - for err in errors: - error_details.append({ - "code": err.get("error_code", "UNKNOWN"), - "message": err.get("message", "N/A"), - "severity": err.get("severity", "unknown"), - "component": err.get("component", "N/A") - }) - except json.JSONDecodeError as e: - logger.warning(f"⚠️ Failed to parse error export file: {e}") - except Exception as e: - logger.warning(f"⚠️ Failed to read error export file: {e}") - - # Determine overall success: - # - For serverless flows, rely on container return code and error exports. - # - For non-serverless HTTP server flows, rely on HTTP/client status (client_failed) - # and error exports; ignore the container's own exit code. - if is_serverless: - rc_failed = result.returncode < 0 - else: - # client_failed is only set in non-serverless branch; default to False otherwise - rc_failed = 'client_failed' in locals() and client_failed - - if not rc_failed and not has_errors: - logger.info(f"✅ Successfully tested image: {image_name}") - logger.info(f"📄 Logs saved to: {log_file}") - return True, result.stdout, str(log_file) - elif has_errors: - error_msg = f"Test failed: {len(error_details)} error(s) detected in image: {image_name}" - logger.error(f"❌ {error_msg}") - logger.error("Errors found:") - for err in error_details[:10]: # Show first 10 errors - logger.error(f" [{err['severity'].upper()}] {err['code']}: {err['message'][:100]}") - if len(error_details) > 10: - logger.error(f" ... and {len(error_details) - 10} more errors") - logger.info(f"📄 Logs saved to: {log_file}") - logger.info(f"📄 Error export: {error_export_file}") - return False, error_msg, str(log_file) - else: - if is_serverless: - logger.error( - f"❌ Test failed with negative return code for image: {image_name}" - ) - logger.error(f"Return code: {result.returncode}") - failure_reason = f"Test failed with negative return code {result.returncode}" - else: - logger.error( - f"❌ Test failed due to client/HTTP errors for image: {image_name}" - ) - failure_reason = "Test failed due to client/HTTP errors" - logger.info(f"📄 Logs saved to: {log_file}") - return False, failure_reason, str(log_file) - - except subprocess.TimeoutExpired as timeout_ex: - error_msg = f"Test timed out after {timeout} seconds for image: {image_name}" - logger.error(f"❌ {error_msg}") - - # Try to collect container logs if this was a detached container - container_logs = "" - if server_type != "serverless": - # For non-serverless, we have a detached container that needs cleanup - try: - # Check if container_id was defined (container was started) - if 'container_id' in locals(): - logger.info(f"📋 Collecting logs from timed-out container: {container_id}") - logs_proc = subprocess.run( - ["docker", "logs", container_id], - capture_output=True, - text=True, - timeout=10 - ) - container_logs = logs_proc.stdout - if logs_proc.stderr: - container_logs += "\n=== STDERR ===\n" + logs_proc.stderr - - # Stop and remove the container - logger.info(f"🛑 Stopping timed-out container: {container_id}") - subprocess.run(["docker", "stop", container_id], capture_output=True, text=True, timeout=30) - subprocess.run(["docker", "rm", container_id], capture_output=True, text=True, timeout=10) - except Exception as cleanup_error: - logger.warning(f"⚠️ Failed to collect logs or cleanup container: {cleanup_error}") - container_logs += f"\n\n[Error during log collection: {cleanup_error}]\n" - - # Save timeout log with container logs if available - with open(log_file, 'w') as f: - f.write("=== Test Configuration ===\n") - f.write(f"Image: {image_name}\n") - f.write(f"Command: {' '.join(cmd)}\n") - f.write(f"Status: TIMEOUT\n") - f.write(f"Timeout: {timeout} seconds\n") - f.write(f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}\n") - f.write("\n=== ERROR ===\n") - f.write(error_msg) - if container_logs: - f.write("\n\n=== CONTAINER LOGS ===\n") - f.write(container_logs) - f.write("\n=== END LOG ===\n") - - return False, error_msg, str(log_file) - except Exception as e: - error_msg = f"Exception during test: {str(e)}" - logger.error(f"❌ {error_msg}") - - # Add full traceback for debugging - import traceback - full_traceback = traceback.format_exc() - logger.error(f"Traceback:\n{full_traceback}") - - # Try to collect container logs if this was a detached container - container_logs = "" - if server_type != "serverless": - # For non-serverless, we have a detached container that needs cleanup - try: - # Check if container_id was defined (container was started) - if 'container_id' in locals(): - logger.info(f"📋 Collecting logs from failed container: {container_id}") - logs_proc = subprocess.run( - ["docker", "logs", container_id], - capture_output=True, - text=True, - timeout=10 - ) - container_logs = logs_proc.stdout - if logs_proc.stderr: - container_logs += "\n=== STDERR ===\n" + logs_proc.stderr - - # Stop and remove the container - logger.info(f"🛑 Stopping failed container: {container_id}") - subprocess.run(["docker", "stop", container_id], capture_output=True, text=True, timeout=30) - subprocess.run(["docker", "rm", container_id], capture_output=True, text=True, timeout=10) - except Exception as cleanup_error: - logger.warning(f"⚠️ Failed to collect logs or cleanup container: {cleanup_error}") - container_logs += f"\n\n[Error during log collection: {cleanup_error}]\n" - - # Save exception log - with open(log_file, 'w') as f: - f.write("=== Test Configuration ===\n") - f.write(f"Image: {image_name}\n") - f.write(f"Status: EXCEPTION\n") - f.write(f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}\n") - f.write("\n=== ERROR ===\n") - f.write(error_msg) - f.write("\n\n=== TRACEBACK ===\n") - f.write(full_traceback) - if container_logs: - f.write("\n\n=== CONTAINER LOGS ===\n") - f.write(container_logs) - f.write("\n=== END LOG ===\n") - - return False, error_msg, str(log_file) - - def cleanup_prerequisite_script(self, test_config: Dict, test_id: int) -> bool: - """Clean up resources created by prerequisite scripts.""" - prerequisite_script = test_config.get("prerequisite_script") - if not prerequisite_script: - return True - - try: - # Check if the script is the RTSP server setup script - if "setup_rtsp_server.sh" in prerequisite_script: - logger.info("🧹 Cleaning up RTSP server...") - # Use secure command execution for cleanup - cleanup_command = ["./setup_rtsp_server.sh", "--kill"] - cleanup_result = subprocess.run( - cleanup_command, - shell=False, # Safer execution without shell - capture_output=True, - text=True, - timeout=30 - ) - - if cleanup_result.returncode == 0: - logger.info("✅ RTSP server cleanup completed") - return True - else: - logger.warning(f"⚠️ RTSP server cleanup failed: {cleanup_result.stderr}") - return False - - # Add more cleanup logic for other prerequisite scripts here - logger.info("🧹 No specific cleanup needed for prerequisite script") - return True - - except Exception as e: - logger.warning(f"⚠️ Exception during prerequisite cleanup: {str(e)}") - return False - - def cleanup_image(self, image_name: str) -> bool: - """Remove the test image.""" - try: - result = subprocess.run( - ["docker", "rmi", image_name], - capture_output=True, - text=True - ) - - if result.returncode == 0: - logger.info(f"🧹 Cleaned up image: {image_name}") - return True - else: - logger.warning(f"⚠️ Failed to cleanup image: {image_name}") - return False - - except Exception as e: - logger.warning(f"⚠️ Exception during cleanup: {str(e)}") - return False - - def run_test_suite(self, test_configs: List[Dict], cleanup: bool = True, gitlab_token: Optional[str] = None, force_full_flow: bool = False, gpus: str = "all") -> Dict: - """Run a suite of tests with different configurations. - - Args: - test_configs: List of test configurations - cleanup: Whether to cleanup Docker images after testing - gitlab_token: GitLab token for authentication - force_full_flow: Force full build+test flow even for disabled tests - gpus: GPU devices to use (default: 'all') - """ - results = { - "total_tests": len(test_configs), - "passed": 0, - "failed": 0, - "results": [] - } - - for i, config in enumerate(test_configs, 1): - logger.info(f"\n{'='*60}") - logger.info(f"Running test {i}/{len(test_configs)}") - logger.info(f"{'='*60}") - - # Generate unique image name - image_name = f"test-inference_builder-{i}-{int(time.time())}" - - build_args = config.get("build_args", {}).copy() - test_cfg = config.get("test_config", {}).copy() - - # Add _config_dir to test_cfg so paths can be resolved correctly - if "_config_dir" in config: - test_cfg["_config_dir"] = config["_config_dir"] - - # Note: auto_validation is passed directly to build_image via test_config - # No need to add it to build_args - - # Debug logging for flow decision - default_enable = test_cfg.get("default_enable", True) - logger.info(f"🔍 Test flow decision: default_enable={default_enable}, force_full_flow={force_full_flow}") - - # If default_enable is False, skip Docker build/test but run codegen, - # unless full flow is forced by selection (e.g., --test-case provided) - if not default_enable and not force_full_flow: - logger.info("⚙️ Test disabled via default_enable=false. Running code generation only (skipping model download)...") - codegen_success, codegen_output = self.generate_inference_code(build_args, test_cfg) - - status = "SKIPPED" - test_result = { - "test_id": i, - "config": config, - "status": status, - "build_success": False, - "test_success": False, - "build_output": codegen_output, - "test_output": "", - "log_file": "", - "image_name": image_name - } - results["results"].append(test_result) - logger.info(f"Test {i} result: {status} (codegen_only)") - # Do not increment passed/failed counters for skipped tests - continue - - # Download models if specified in test config (only for full build+test flow) - models_config = test_cfg.get("models", {}) - if models_config: - logger.info(f"📦 Checking and downloading models (full build+test flow)...") - config_dir = Path(config.get("_config_dir", ".")) - download_success, download_msg = self.download_models(models_config, config_dir) - if not download_success: - logger.error(f"❌ Model download failed: {download_msg}") - results["failed"] += 1 - test_result = { - "test_id": i, - "config": config, - "status": "FAILED", - "build_success": False, - "test_success": False, - "build_output": "", - "test_output": download_msg, - "log_file": "", - "image_name": image_name - } - results["results"].append(test_result) - logger.info(f"Test {i} result: FAILED (model download)") - continue - - # Build image with resolved dockerfile and base_dir - dockerfile_to_use = config.get("_resolved_dockerfile") - base_dir_to_use = config.get("_resolved_base_dir") - build_success, build_output = self.build_image(build_args, image_name, dockerfile_to_use, base_dir_to_use, test_cfg) - - if build_success: - # Test image - pass server type and config dir to test_config - test_config = config.get("test_config", {}).copy() - if "SERVER_TYPE" in build_args: - test_config["SERVER_TYPE"] = build_args["SERVER_TYPE"] - # Pass config directory for relative path resolution - if "_config_dir" in config: - test_config["_config_dir"] = config["_config_dir"] - if "TEST_APP_NAME" in build_args: - test_config["TEST_APP_NAME"] = build_args["TEST_APP_NAME"] - test_success, test_output, log_file = self.test_image( - image_name, - test_config, - i, - gpus=gpus - ) - - if test_success: - results["passed"] += 1 - status = "PASSED" - else: - results["failed"] += 1 - status = "FAILED" - else: - results["failed"] += 1 - test_success = False - test_output = "" - log_file = "" - status = "FAILED" - - # Cleanup (run cleanup for full-flow tests; skip if codegen-only path was taken) - if cleanup: - self.cleanup_image(image_name) - # Clean up prerequisite scripts - self.cleanup_prerequisite_script(config.get("test_config", {}), i) - - # Store result - test_result = { - "test_id": i, - "config": config, - "status": status, - "build_success": build_success, - "test_success": test_success, - "build_output": build_output, - "test_output": test_output, - "log_file": log_file, - "image_name": image_name - } - - results["results"].append(test_result) - - logger.info(f"Test {i} result: {status}") - - return results - - def generate_report(self, results: Dict, output_file: Optional[str] = None): - """Generate a test report.""" - report = { - "summary": { - "total_tests": results["total_tests"], - "passed": results["passed"], - "failed": results["failed"], - "success_rate": f"{(results['passed'] / results['total_tests'] * 100):.1f}%" if results["total_tests"] > 0 else "0%" - }, - "results": results["results"] - } - - if output_file: - with open(output_file, 'w') as f: - json.dump(report, f, indent=2) - logger.info(f"📄 Report saved to: {output_file}") - - # Print summary - logger.info(f"\n{'='*60}") - logger.info("TEST SUMMARY") - logger.info(f"{'='*60}") - logger.info(f"Total tests: {results['total_tests']}") - logger.info(f"Passed: {results['passed']}") - logger.info(f"Failed: {results['failed']}") - logger.info(f"Success rate: {report['summary']['success_rate']}") - - # Print test results with log file locations - logger.info(f"\n{'='*60}") - logger.info("TEST RESULTS") - logger.info(f"{'='*60}") - for result in results["results"]: - test_name = result["config"].get("name", f"Test {result['test_id']}") - status = result.get("status", "UNKNOWN") - log_file = result.get("log_file", "") - - if status == "PASSED": - status_icon = "✅" - elif status == "FAILED": - status_icon = "❌" - elif status == "SKIPPED": - status_icon = "⏭️" - else: - status_icon = "❓" - - if log_file: - logger.info(f"{status_icon} [{result['test_id']}] {test_name}: {log_file}") - else: - logger.info(f"{status_icon} [{result['test_id']}] {test_name}") - - return report - - -def main(): - parser = argparse.ArgumentParser(description="Test Docker builds with different arguments") - parser.add_argument("--dockerfile", default="Dockerfile", help="Path to Dockerfile") - parser.add_argument("--base-dir", default=".", help="Base directory for Docker build context") - parser.add_argument("--config-file", required=True, help="JSON file with test configurations") - parser.add_argument("--output", help="Output file for test report") - parser.add_argument("--log-dir", default="logs", help="Directory to save container logs") - parser.add_argument("--no-cleanup", action="store_true", help="Don't cleanup images after testing") - parser.add_argument("--gitlab-token", help="GitLab token for authentication") - parser.add_argument("--test-case", help="Run only the test case with this name (partial match supported). Supplying this forces full flow (build+test) even if disabled.") - parser.add_argument("--gpus", default="all", help="GPU devices to use for Docker containers (default: 'all'). Examples: 'all', 'device=0', 'device=0,1', '\"device=0,1\"'") - - # Parse arguments with security validation - try: - args = parser.parse_args() - except Exception as e: - logger.error(f"❌ Argument parsing failed: {str(e)}") - sys.exit(1) - - # Comprehensive security validation - validation_errors = [] - - # Validate dockerfile path - if not validate_dockerfile_path(args.dockerfile): - validation_errors.append(f"Invalid Dockerfile path: {args.dockerfile}") - - # Validate base directory - if not validate_safe_path(args.base_dir): - validation_errors.append(f"Invalid base directory path: {args.base_dir}") - - # Validate config file - if not validate_config_file_path(args.config_file): - validation_errors.append(f"Invalid config file path: {args.config_file}") - - # Validate output file if provided - if args.output and not validate_safe_path(args.output): - validation_errors.append(f"Invalid output file path: {args.output}") - - # Validate log directory - if not validate_log_directory(args.log_dir): - validation_errors.append(f"Invalid log directory path: {args.log_dir}") - - # Validate GitLab token if provided - if args.gitlab_token and not validate_gitlab_token(args.gitlab_token): - validation_errors.append("Invalid GitLab token format") - - # Exit if any validation errors - if validation_errors: - logger.error("❌ Security validation failed:") - for error in validation_errors: - logger.error(f" - {error}") - sys.exit(1) - - # Additional file existence checks with security - try: - # Validate config file first (required) - if not os.path.isfile(args.config_file): - logger.error(f"❌ Config file not found: {args.config_file}") - sys.exit(1) - - # Dockerfile and base-dir are optional - they'll be resolved from config file if not found - # Only warn if they're explicitly specified but don't exist - dockerfile_exists = os.path.isfile(args.dockerfile) - base_dir_exists = os.path.isdir(args.base_dir) - - if not dockerfile_exists: - logger.info(f"ℹ️ Dockerfile not found at command-line path: {args.dockerfile}") - logger.info(f"ℹ️ Will use Dockerfile from test config or config directory") - - if not base_dir_exists: - logger.info(f"ℹ️ Base directory not found at command-line path: {args.base_dir}") - logger.info(f"ℹ️ Will use base directory from test config or config directory") - - # Validate log directory path (prevent directory traversal) - log_dir_path = Path(args.log_dir).resolve() - current_dir = Path.cwd().resolve() - try: - log_dir_path.relative_to(current_dir) - except ValueError: - logger.error(f"❌ Log directory path is outside current directory: {args.log_dir}") - sys.exit(1) - - except Exception as e: - logger.error(f"❌ File validation failed: {str(e)}") - sys.exit(1) - - # Initialize tester with validated arguments - try: - tester = DockerBuildTester(args.dockerfile, args.base_dir, args.log_dir) - except Exception as e: - logger.error(f"❌ Failed to initialize DockerBuildTester: {str(e)}") - sys.exit(1) - - # Load test configurations from file with security validation - try: - with open(args.config_file, 'r') as f: - test_configs = json.load(f) - - # Validate JSON structure - if not isinstance(test_configs, list): - logger.error("❌ Config file must contain a list of test configurations") - sys.exit(1) - - # Get the directory containing the config file for resolving relative paths - config_dir = Path(args.config_file).parent.resolve() - logger.info(f"📁 Config file directory: {config_dir}") - - # Validate each test configuration for security - for i, config in enumerate(test_configs): - if not isinstance(config, dict): - logger.error(f"❌ Test configuration {i+1} must be a dictionary") - sys.exit(1) - - # Handle dockerfile specification - # Priority: 1) dockerfile in test_config, 2) Dockerfile in config dir, 3) command-line arg - if "dockerfile" in config: - # User explicitly specified dockerfile in config - dockerfile_path = (config_dir / config["dockerfile"]).resolve() - if not dockerfile_path.exists(): - logger.error(f"❌ Dockerfile specified in test config not found: {dockerfile_path}") - sys.exit(1) - config["_resolved_dockerfile"] = str(dockerfile_path) - logger.info(f"🔗 Using Dockerfile from config: {config['dockerfile']} -> {dockerfile_path}") - else: - # Check for Dockerfile in same directory as config - default_dockerfile = config_dir / "Dockerfile" - if default_dockerfile.exists(): - config["_resolved_dockerfile"] = str(default_dockerfile) - logger.info(f"🔗 Using default Dockerfile from config directory: {default_dockerfile}") - else: - # Fall back to command-line specified dockerfile - config["_resolved_dockerfile"] = args.dockerfile - logger.info(f"🔗 Using Dockerfile from command-line: {args.dockerfile}") - - # Base directory is ALWAYS the directory containing the test config JSON - # This makes test configs self-contained and portable - config["_resolved_base_dir"] = str(config_dir) - config["_config_dir"] = str(config_dir) # Store for model downloads - logger.info(f"🔗 Using base_dir (config directory): {config_dir}") - - # Validate build_args if present - if "build_args" in config: - if not isinstance(config["build_args"], dict): - logger.error(f"❌ build_args in test configuration {i+1} must be a dictionary") - sys.exit(1) - - # Resolve paths relative to config file directory - path_args = ["APP_YAML_PATH", "OUTPUT_DIR", "PROCESSORS_PATH", "OPENAPI_SPEC"] - for path_arg in path_args: - if path_arg in config["build_args"]: - original_path = config["build_args"][path_arg] - # Resolve relative to config file directory - resolved_path = (config_dir / original_path).resolve() - # Convert back to relative path from current working directory - try: - rel_path = resolved_path.relative_to(Path.cwd().resolve()) - config["build_args"][path_arg] = str(rel_path) - logger.info(f"🔗 Resolved {path_arg}: {original_path} -> {rel_path}") - except ValueError: - # If can't make relative, use absolute path - config["build_args"][path_arg] = str(resolved_path) - logger.info(f"🔗 Resolved {path_arg}: {original_path} -> {resolved_path}") - - for key, value in config["build_args"].items(): - if not validate_build_arg_name(key): - logger.error(f"❌ Invalid build arg name in test configuration {i+1}: {key}") - sys.exit(1) - if not isinstance(value, str) or not validate_build_arg_value(value): - logger.error(f"❌ Invalid build arg value in test configuration {i+1}: {value}") - sys.exit(1) - - # Validate test_config if present - if "test_config" in config: - test_config = config["test_config"] - config_valid, config_error = validate_test_config(test_config) - if not config_valid: - logger.error(f"❌ Test configuration {i+1} validation failed: {config_error}") - sys.exit(1) - - # Filter test configurations by test case name if specified - if args.test_case: - original_count = len(test_configs) - if args.test_case == "*": - logger.info("--test-case '*' specified: selecting all tests (including disabled).") - else: - test_configs = [ - config for config in test_configs - if args.test_case.lower() in config.get("name", "").lower() - ] - - if not test_configs: - logger.error(f"❌ No test cases found matching '{args.test_case}'") - logger.info("Available test cases:") - # Reload original configs to show available names - with open(args.config_file, 'r') as f: - original_configs = json.load(f) - for i, config in enumerate(original_configs, 1): - logger.info(f" {i}. {config.get('name', f'Unnamed test {i}')}") - sys.exit(1) - - logger.info(f"Filtered {original_count} test configurations to {len(test_configs)} matching '{args.test_case}'") - for i, config in enumerate(test_configs, 1): - logger.info(f" {i}. {config.get('name', f'Test {i}')}") - else: - # Include disabled tests; they will run codegen-only path unless -c/--test-case forces full flow. - logger.info("Including disabled tests (default_enable=false). They will run codegen-only unless --test-case is provided.") - - except json.JSONDecodeError as e: - logger.error(f"❌ Invalid JSON in config file: {str(e)}") - sys.exit(1) - except Exception as e: - logger.error(f"❌ Failed to load config file: {str(e)}") - sys.exit(1) - - # Run tests with error handling - try: - logger.info(f"Starting test suite with {len(test_configs)} configurations") - logger.info(f"Logs will be saved to: {args.log_dir}") - logger.info(f"Using GPU devices: {args.gpus}") - # Force full flow if --test-case provided (including "*") - force_full_flow = args.test_case is not None and len(args.test_case) > 0 - logger.info(f"🔍 Test suite settings: test_case={args.test_case}, force_full_flow={force_full_flow}") - results = tester.run_test_suite(test_configs, cleanup=not args.no_cleanup, gitlab_token=args.gitlab_token, force_full_flow=force_full_flow, gpus=args.gpus) - except Exception as e: - logger.error(f"❌ Test suite execution failed: {str(e)}") - sys.exit(1) - - # Generate report with error handling - try: - tester.generate_report(results, args.output) - except Exception as e: - logger.error(f"❌ Report generation failed: {str(e)}") - sys.exit(1) - - # Exit with appropriate code - if results["failed"] > 0: - sys.exit(1) - else: - sys.exit(0) - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/tools/inference_builder/builder/triton/__init__.py b/tools/inference_builder/builder/triton/__init__.py deleted file mode 100644 index 3159bfe6..00000000 --- a/tools/inference_builder/builder/triton/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tools/inference_builder/builder/triton/model_config_pb2.py b/tools/inference_builder/builder/triton/model_config_pb2.py deleted file mode 100644 index fb8e904e..00000000 --- a/tools/inference_builder/builder/triton/model_config_pb2.py +++ /dev/null @@ -1,195 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# -*- coding: utf-8 -*- -# Generated by the protocol buffer compiler. DO NOT EDIT! -# NO CHECKED-IN PROTOBUF GENCODE -# source: model_config.proto -# Protobuf Python Version: 5.27.3 -"""Generated protocol buffer code.""" -from google.protobuf import descriptor as _descriptor -from google.protobuf import descriptor_pool as _descriptor_pool -from google.protobuf import runtime_version as _runtime_version -from google.protobuf import symbol_database as _symbol_database -from google.protobuf.internal import builder as _builder -_runtime_version.ValidateProtobufRuntimeVersion( - _runtime_version.Domain.PUBLIC, - 5, - 27, - 3, - '', - 'model_config.proto' -) -# @@protoc_insertion_point(imports) - -_sym_db = _symbol_database.Default() - - - - -DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x12model_config.proto\x12\tinference\"\x96\x01\n\x10ModelRateLimiter\x12\x37\n\tresources\x18\x01 \x03(\x0b\x32$.inference.ModelRateLimiter.Resource\x12\x10\n\x08priority\x18\x02 \x01(\r\x1a\x37\n\x08Resource\x12\x0c\n\x04name\x18\x01 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_globals['_MODELCONFIG']._serialized_end=8368 - _globals['_MODELCONFIG_CCMODELFILENAMESENTRY']._serialized_start=8163 - _globals['_MODELCONFIG_CCMODELFILENAMESENTRY']._serialized_end=8218 - _globals['_MODELCONFIG_METRICTAGSENTRY']._serialized_start=8220 - _globals['_MODELCONFIG_METRICTAGSENTRY']._serialized_end=8269 - _globals['_MODELCONFIG_PARAMETERSENTRY']._serialized_start=8271 - _globals['_MODELCONFIG_PARAMETERSENTRY']._serialized_end=8347 -# @@protoc_insertion_point(module_scope) diff --git a/tools/inference_builder/builder/triton/utils.py b/tools/inference_builder/builder/triton/utils.py deleted file mode 100644 index 27aa8e1b..00000000 --- a/tools/inference_builder/builder/triton/utils.py +++ /dev/null @@ -1,80 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from .model_config_pb2 import DataType, ModelConfig, ModelParameter, ModelTensorReshape -from google.protobuf import text_format -from typing import Dict - -datatype_mapping = { - "TYPE_INVALID": DataType.TYPE_INVALID, - "TYPE_BOOL": DataType.TYPE_BOOL, - "TYPE_UINT8": DataType.TYPE_UINT8, - "TYPE_UINT16": DataType.TYPE_UINT16, - "TYPE_UINT32": DataType.TYPE_UINT32, - "TYPE_UINT64": DataType.TYPE_UINT64, - "TYPE_INT8": DataType.TYPE_INT8, - "TYPE_INT16": DataType.TYPE_INT16, - "TYPE_INT32": DataType.TYPE_INT32, - "TYPE_INT64": DataType.TYPE_INT64, - "TYPE_FP16": DataType.TYPE_FP16, - "TYPE_FP32": DataType.TYPE_FP32, - "TYPE_FP64": DataType.TYPE_FP64, - "TYPE_STRING": DataType.TYPE_STRING, - "TYPE_BF16": DataType.TYPE_BF16 -} - -def generate_pbtxt(model_config: Dict, backend:str): - triton_model_config = ModelConfig() - for key, value in model_config.items(): - if hasattr(triton_model_config, key): - # only pick the standard triton model configuration items to the pbtxt - if key == "input" or key == "output": - for i in value: - entry = triton_model_config.input.add() if key == "input" else triton_model_config.output.add() - for k, v in i.items(): - if hasattr(entry, k): - if k == "data_type": - setattr(entry, k, datatype_mapping[v]) - elif k == "dims": - for dim in v: - entry.dims.append(dim) - elif k == "reshape": - if len(v['shape']) == 0: - # WAR: force generating reshape - entry.reshape.shape.append(-1) - else: - for s in v['shape']: - entry.reshape.shape.append(s) - else: - setattr(entry, k, v) - elif key == "parameters": - for k, p in value.items(): - triton_model_config.parameters[k].string_value = p - elif key == "instance_group": - for i in value: - entry = triton_model_config.instance_group.add() - for k, v in i.items(): - if k == "gpus": - entry.gpus.extend(v) - else: - setattr(entry, k, v) - elif key == "model_transaction_policy": - for k, v in value.items(): - setattr(triton_model_config.model_transaction_policy, k, v) - else: - setattr(triton_model_config, key, value) - # set the triton backend - setattr(triton_model_config, "backend", backend) - return text_format.MessageToString(triton_model_config, use_short_repeated_primitives=True).replace('shape: [-1]', 'shape: [ ]') \ No newline at end of file diff --git a/tools/inference_builder/builder/utils.py b/tools/inference_builder/builder/utils.py deleted file mode 100644 index f0fd292a..00000000 --- a/tools/inference_builder/builder/utils.py +++ /dev/null @@ -1,172 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from pathlib import Path -import os -import shutil -from typing import Callable -import tarfile -import mimetypes -import base64 - -class PayloadBuilder: - """ - Builder class for constructing the payload with required and optional fields - - Example: - >>> builder = PayloadBuilder(files=["image1.jpg"], model="nvdino-v2") - >>> payload = builder.add_text(["cat,dog"]).build() - { - "input": ["data:image/jpeg;base64,/9j/4AAQSkZJRg..."], - "model": "nvidia/nvdino-v2", - "text": [["cat", "dog"]] - } - """ - @staticmethod - def prepare_image_inputs(files): - """ - Prepare base64 encoded image inputs for the payload - Args: - files: List of file paths, e.g. ["path/to/image1.jpg", "path/to/image2.png"] - Returns: - list: List of encoded image strings in format "data:mime_type;base64,..." - - Example: - >>> prepare_image_inputs(["image1.jpg", "image2.png"]) - [ - "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/4QBmRX...", - "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA7..." - ] - """ - encoded_inputs = [] - for file in files: - mime_type = mimetypes.guess_type(file)[0] - with open(file, "rb") as f: - b64_image = base64.b64encode(f.read()).decode() - encoded_inputs.append(f"data:{mime_type};base64,{b64_image}") - return encoded_inputs - - @staticmethod - def prepare_text_input(text): - """ - Prepare text input for the payload - Args: - text: List of comma-separated text strings, e.g. ["cat,dog", "person,car"] - Returns: - list: List of text arrays split by comma, or None if text is None - - Example: - >>> prepare_text_input(["cat,dog", "person,car"]) - [["cat", "dog"], ["person", "car"]] - - >>> prepare_text_input(None) - None - """ - return [t.split(",") for t in text] if text else None - - @staticmethod - def prepare_text_input_from_file(txt_file_path): - """ - Prepare text input from a .txt file - Args: - txt_file_path: Path to .txt file containing comma-separated text - Returns: - list: List of text arrays split by comma, or None if file not found/empty - - Example: - >>> prepare_text_input_from_file("labels.txt") - # For a file containing "cat,car,truck" - [["cat", "car", "truck"]] - - >>> prepare_text_input_from_file("nonexistent.txt") - None - """ - try: - with open(txt_file_path, 'r') as f: - content = f.read().strip() - return PayloadBuilder.prepare_text_input([content]) if content else None - except FileNotFoundError: - print(f"Warning: Text file not found: {txt_file_path}") - return None - except Exception as e: - print(f"Warning: Error reading text file: {e}") - return None - - @staticmethod - def prepare_model_name(model): - """ - Prepare model name for the payload - Args: - model: Base model name, e.g. "nvdino-v2" - Returns: - str: Full model name with nvidia prefix - - Example: - >>> prepare_model_name("nvdino-v2") - "nvidia/nvdino-v2" - """ - return f"nvidia/{model}" - - def __init__(self, files, model): - """Initialize with required fields""" - self.payload = { - "input": self.prepare_image_inputs(files), - "model": self.prepare_model_name(model) - } - - def add_text(self, text): - """Add optional text field if provided""" - if text: - text_input = self.prepare_text_input(text) - if text_input: - self.payload["text"] = text_input - return self - - def add_more_field(self, key, value): - """Add more field to the payload""" - if key is not None and value is not None: - self.payload[key] = value - return self - - def build(self): - """Return the constructed payload""" - return self.payload - -def get_resource_path(relative_path): - """ Get absolute path to resource, works for dev and for PyInstaller """ - base_path = Path(__file__).absolute().parent.parent - return str(Path(base_path, relative_path)) - -def copy_files(source_dir, destination_dir, filter: Callable[[str], bool]=None): - # Ensure destination directory exists - if not os.path.exists(destination_dir): - os.makedirs(destination_dir) - - # List all files in the source directory - files = os.listdir(source_dir) - - for file in files: - if filter and not filter(file): - continue - # Full path to the source file - full_file_name = os.path.join(source_dir, file) - - # Check if it's a file (not a directory) - if os.path.isfile(full_file_name): - shutil.copy(full_file_name, destination_dir) - -def create_tar_gz(output_filename, source_dir): - with tarfile.open(output_filename, "w:gz") as tar: - tar.add(source_dir, arcname=".") \ No newline at end of file diff --git a/tools/inference_builder/builder/validate.py b/tools/inference_builder/builder/validate.py deleted file mode 100644 index d05819b7..00000000 --- a/tools/inference_builder/builder/validate.py +++ /dev/null @@ -1,1012 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import json -import math -import os -import sys -import subprocess -import base64 -import importlib -from pathlib import Path -from typing import List, Dict, Union, Any -from jinja2 import Environment, FileSystemLoader -from utils import get_resource_path -import shutil -import yaml -from utils import PayloadBuilder - -# Common paths - single source of truth -OUT_DIR = ".tmp" -GENERATED_CLIENT_DIR = "generated_client" -TEST_CASES_FILE = "test_cases.yaml" - -# Setup paths and generate OpenAPI client -def check_client_exists() -> bool: - """Check if OpenAPI client exists and is importable. - - Returns: - bool: True if client exists and can be imported, False otherwise - """ - # Check if the module can be imported - try: - importlib.import_module('openapi_client') - print("Able to import OpenAPI client") - return True - except ImportError: - print("Not able to import OpenAPI client") - return False - -def setup_environment(generated_client_path: Path) -> bool: - """Setup environment for client import.""" - try: - # First check if client directory exists - if not generated_client_path.exists(): - print(f"Generated client directory not found at: {generated_client_path}") - return False - - # Add to PYTHONPATH if needed - if str(generated_client_path) not in sys.path: - sys.path.insert(0, str(generated_client_path)) # Insert at beginning of path - print(f"✓ Added to PYTHONPATH: {generated_client_path}") - - # Try importing to verify setup - importlib.import_module('openapi_client') - print("✓ Successfully imported OpenAPI client") - return True - - except ImportError as e: - print(f"Failed to import OpenAPI client: {e}") - print(f"Directory contents: {os.listdir(generated_client_path)}") - return False - except Exception as e: - print(f"Unexpected error setting up environment: {e}") - return False - - -def check_client_valid(client_dir: Path) -> bool: - """Check if existing OpenAPI client is valid. - - Args: - client_dir: Path to generated client directory - - Returns: - bool: True if client exists and is valid, False otherwise - """ - try: - # Check if directory exists and has key files - if not client_dir.exists(): - return False - - required_files = [ - "setup.py", - "openapi_client/__init__.py", - "openapi_client/api/nvidiametropolisinferenceapi_api.py", - "openapi_client/models/inference_request.py", - "openapi_client/models/inference_response.py" - ] - - for file in required_files: - if not (client_dir / file).exists(): - print(f"Missing required file: {file}") - return False - - # Try importing to validate - sys_path_modified = False - if str(client_dir) not in sys.path: - sys.path.insert(0, str(client_dir)) - sys_path_modified = True - - try: - import openapi_client - from openapi_client.models.inference_request import InferenceRequest - from openapi_client.models.inference_response import InferenceResponse - print(f"✓ Found valid existing client at: {client_dir}") - return True - except ImportError as e: - print(f"Client validation failed: {e}") - return False - finally: - if sys_path_modified: - sys.path.remove(str(client_dir)) - - except Exception as e: - print(f"Error checking client: {e}") - return False - -def generate_openapi_client(openapi_spec_path: Path, output_dir: Path, use_docker: bool = True) -> bool: - """Generate OpenAPI client using Docker or local OpenAPI Generator. - - Args: - openapi_spec_path: Path to OpenAPI specification file - output_dir: Target output directory for generated client - use_docker: Whether to use Docker for generation (default: True) - """ - try: - # Check if valid client already exists - if check_client_valid(output_dir): - print("Using existing OpenAPI client") - return True - - print("Generating new OpenAPI client...") - # Get absolute paths - abs_spec_path = openapi_spec_path.resolve() - abs_output_dir = output_dir.resolve() - - tmp_client = "tmp_generated_client" - tmp_client_path = abs_spec_path.parent / tmp_client - - if use_docker: - # Construct docker command - # docker run --rm -v "${PWD}:/local" openapitools/openapi-generator-cli generate \ - # -i /local/openapi.yaml \ - # -g python \ - # -o /local/generated_client - generator_cmd = [ - "docker", "run", "--rm", - "-v", f"{abs_spec_path.parent}:/local", - "--user", f"{os.getuid()}:{os.getgid()}", # Use current user's UID/GID - "openapitools/openapi-generator-cli", "generate", - "-i", f"/local/{abs_spec_path.name}", - "-g", "python", - "-o", f"/local/{tmp_client}" - ] - print(f"Generating OpenAPI client using Docker...") - else: - # Use local OpenAPI Generator - generator_cmd = [ - "openapi-generator-cli", "generate", - "-i", str(abs_spec_path), - "-g", "python", - "-o", str(tmp_client_path) - ] - print(f"Generating OpenAPI client using local OpenAPI Generator...") - print(f"Using OpenAPI spec: {abs_spec_path}") - print(f"Temporary directory: {tmp_client_path}") - print(f"Final output directory: {output_dir.resolve()}") - - result = subprocess.run(generator_cmd, - check=True, - capture_output=True, - text=True) - - if result.stdout: - print(result.stdout) - - # Move generated files to target directory - output_dir.mkdir(parents=True, exist_ok=True) - try: - # Use shutil.copytree to copy the contents - shutil.copytree(tmp_client_path, output_dir, dirs_exist_ok=True) - # Clean up temp directory - shutil.rmtree(tmp_client_path) - return True - except Exception as e: - print(f"Failed to move generated client: {e}") - return False - - return True - - except subprocess.CalledProcessError as e: - print(f"✗ Docker command failed: {e}") - if e.stdout: - print("stdout:", e.stdout) - if e.stderr: - print("stderr:", e.stderr) - return False - except Exception as e: - print(f"✗ Failed to generate client: {e}") - return False - finally: - # Ensure temp directory is cleaned up - if 'temp_client_dir' in locals() and tmp_client_path.exists(): - shutil.rmtree(tmp_client_path) - - - - -def prepare_image_input(image_path: Path) -> str: - """Prepare image for inference by converting to base64 format. - - Examples: - image_input = tester.prepare_image_input("path/to/image.jpg") - # image_input = "data:image/jpg;base64,/9j/4AAQSkZJRg..." - - Args: - image_path: Path to image file (jpg, jpeg, or png) - - Returns: - str: Base64 encoded image with data URI scheme - - Raises: - ValueError: If file doesn't exist or has unsupported format - IOError: If file can't be read - """ - if not os.path.exists(image_path): - raise ValueError(f"Image file not found: {image_path}") - - ext = os.path.splitext(image_path)[1][1:].lower() - if ext not in ['jpg', 'jpeg', 'png']: - raise ValueError(f"Unsupported image format: {ext}. Use jpg, jpeg or png") - - with open(image_path, "rb") as f: - image_data = f.read() - b64_image = base64.b64encode(image_data).decode() - return f"data:image/{ext};base64,{b64_image}" - - -def get_test_cases(validation_dir: Path) -> List[Dict[str, str]]: - """Discover test cases by scanning validation directory for images. - - Args: - validation_dir: Path to validation directory - - Returns: - List of test cases with image and expected response paths - """ - test_cases = [] - supported_formats = {'.jpg', '.jpeg', '.png'} - - # Find all image files - for idx, file_path in enumerate(validation_dir.iterdir()): - if file_path.suffix.lower() in supported_formats: - image_name = file_path.stem - expected_path = validation_dir / f"expected.{image_name}.json" - text_path = validation_dir / f"{image_name}.txt" - - if expected_path.exists(): - test_cases.append({ - "name": image_name + f"_{idx}", - "input": file_path.name, - "text": text_path.name if text_path.exists() else None, - "expected": expected_path.name, - }) - else: - print(f"Warning: Found image {file_path.name} but missing expected response: {expected_path.name}") - - print(f"Discovered {len(test_cases)} test cases:") - for test in test_cases: - print(f" • {test['name']}: {test['input']} → {test['expected']}") - - return test_cases - -def generate_default_test_cases(validation_dir: Path) -> List[Dict[str, str]]: - """Generate default test cases when no test cases are found in validation directory. - - Creates a test case with: - - A predefined base64 encoded image data (saved as a temporary PNG file) - - A predefined text "describe the picture" - - An empty expected result - - Args: - validation_dir: Path to validation directory - - Returns: - List of default test cases with predefined image and text - """ - # Predefined base64 encoded PNG image (1x1 transparent pixel) - # This is the same as used in lib/codec.py - predefined_image_data = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAIAAACQd1PeAAAAEElEQVR4nGK6HcwNCAAA//8DTgE8HuxwEQAAAABJRU5ErkJggg==" - - # Create the image file with predefined base64 data - image_path = validation_dir / "default_test.png" - if not image_path.exists(): - try: - # Decode the base64 data and write to file - image_data = base64.b64decode(predefined_image_data) - with open(image_path, "wb") as f: - f.write(image_data) - print(f"✓ Created default image file: {image_path}") - except Exception as e: - print(f"Warning: Failed to create default image file: {e}") - return [] - - # Create the expected result file (empty result) - expected_path = validation_dir / "expected.default_test.json" - if not expected_path.exists(): - with open(expected_path, "w") as f: - json.dump({}, f, indent=2) - print(f"✓ Created default expected result file: {expected_path}") - - # Create the text file - text_path = validation_dir / "default_test.txt" - if not text_path.exists(): - with open(text_path, "w") as f: - f.write("describe the picture") - print(f"✓ Created default text file: {text_path}") - - # Create the test case structure - test_cases = [{ - "name": "default_test_0", - "input": "default_test.png", - "text": "default_test.txt", - "expected": "expected.default_test.json" - }] - - print(f"Generated {len(test_cases)} default test cases:") - for test in test_cases: - print(f" • {test['name']}: {test['input']} → {test['expected']}") - - return test_cases - - -def get_request_template(client_dir: Path) -> Dict: - """Generate request template from OpenAPI client models. - - Args: - client_dir: Path to generated OpenAPI client directory - - Returns: - Dict containing request template - """ - try: - # Temporarily add client to path - if str(client_dir) not in sys.path: - sys.path.insert(0, str(client_dir)) - - # Import the request model - try: - from openapi_client.models.input_inner import InputInner - - # Create request using the model class - input_data = [InputInner("")] # Create Input instance first - except ImportError: - from openapi_client.models.inference_request_input_inner import InferenceRequestInputInner - input_data = [InferenceRequestInputInner("")] - from openapi_client.models.inference_request import InferenceRequest - request = InferenceRequest( - model='nvidia/nvdino-v2', # First allowed value from model_validate_enum - input=input_data - ) - - # Convert to dict for JSON serialization - template = request.to_dict() - print("Generated request template from OpenAPI client models") - return template - - except ImportError as e: - print(f"Failed to import OpenAPI client models: {e}") - raise - except Exception as e: - print(f"Failed to generate request template: {e}") - raise - finally: - # Clean up sys.path - if str(client_dir) in sys.path: - sys.path.remove(str(client_dir)) - - -def build_requests(validation_dir: Path, out_dir: Path, client_dir: Path, test_cases_abs_path: bool = False) -> bool: - """Build request payloads from template and config. - - Args: - validation_dir: Directory containing validation data - out_dir: Directory to write generated files - client_dir: Directory containing OpenAPI client - test_cases_abs_path: Whether to use absolute paths in test_cases.yaml (default: False) - """ - try: - print("\nBuilding request payloads...") - # Get request template from OpenAPI client models - template = get_request_template(client_dir) - - # Get test cases from directory scan - test_cases = get_test_cases(validation_dir) - if not test_cases: - test_cases = generate_default_test_cases(validation_dir) - - # Store request-response mapping - request_response_map = [] - - # Generate request for each test case - for test in test_cases: - request = template.copy() - image_path = validation_dir / test["input"] - request["input"] = PayloadBuilder.prepare_image_inputs([image_path]) - if test["text"]: - request["text"] = PayloadBuilder.prepare_text_input_from_file(validation_dir / test["text"]) - - # write request file - request_path = out_dir / f"request.{test['name']}.json" - with open(request_path, "w") as f: - json.dump(request, f, indent=2) - print(f"✓ Generated request for test '{test['name']}': {request_path}") - - expected_path = validation_dir / test["expected"] - - # Add to mapping with either absolute or relative paths - if test_cases_abs_path: - request_path_str = str(request_path.resolve()) - expected_path_str = str(expected_path.resolve()) - else: - # Make paths relative to validation directory - request_path_str = os.path.relpath(request_path, out_dir) - expected_path_str = os.path.relpath(expected_path, out_dir) - - # Add to mapping - request_response_map.append({ - "name": test["name"], - "request": request_path_str, - "expected": expected_path_str - }) - - # Write request-response mapping - test_cases_file_path = out_dir / TEST_CASES_FILE - with open(test_cases_file_path, "w") as f: - yaml.safe_dump(request_response_map, f, default_flow_style=False) - print(f"✓ Generated test cases file: {test_cases_file_path}") - print("✓ Successfully built all request payloads") - return True - except Exception as e: - print(f"✗ Failed to build requests: {e}") - return False - -def build_test_runner(out_dir: Path) -> bool: - """Generate test runner from template.""" - try: - print("\nBuilding test runner...") - - tpl_dir = get_resource_path("templates") - print(f"Using template directory: {tpl_dir}") - - jinja_env = Environment(loader=FileSystemLoader(tpl_dir)) - test_tpl = jinja_env.get_template("client/test_runner.jinja.py") - output_path = out_dir / "test_runner.py" - with open(output_path, 'w') as f: - validator_dir = Path(__file__).parent.resolve() - f.write(test_tpl.render(validator_dir=validator_dir)) - print(f"✓ Generated test runner: {output_path}") - return True - except Exception as e: - print(f"✗ Failed to build test runner: {e}") - return False - -def copy_test_runner_dep_tree(out_dir: Path) -> bool: - """Copy test runner dependency tree.""" - try: - print("\nCopying test runner dependency tree...") - # Copy all local dependencies - builder_dir = Path(__file__).parent - files_to_copy = { - builder_dir / "validate.py": out_dir / "validate.py", - builder_dir / "utils.py": out_dir / "utils.py", - # Add any other dependencies here - } - - for src, dest in files_to_copy.items(): - shutil.copy2(src, dest) - print(f"✓ Copied {src.name} to: {dest}") - return True - except Exception as e: - print(f"✗ Failed to copy test runner dependency tree: {e}") - return False - -def build_validation(openapi_spec_path: Path, validation_dir: Path, use_docker: bool = True, test_cases_abs_path: bool = False) -> bool: - """Generate validation components.""" - try: - print("\n=== Building Validator ===") - - # Create temp directory - out_dir = validation_dir / OUT_DIR - out_dir.mkdir(parents=True, exist_ok=True) - print(f"Created temporary directory: {out_dir}") - - # 1. Generate OpenAPI client - print("\n1. Generating OpenAPI client...") - client_dir = out_dir / GENERATED_CLIENT_DIR - if not generate_openapi_client(openapi_spec_path, client_dir, use_docker): - raise Exception("Failed to generate OpenAPI client") - - # 2. Build request payloads - print("\n2. Building request payloads...") - if not build_requests(validation_dir, out_dir, client_dir, test_cases_abs_path): - raise Exception("Failed to build requests") - - # 3. Generate test runner - print("\n3. Generating test runner...") - if not build_test_runner(out_dir): - raise Exception("Failed to build test runner") - - # 4. Copy test runner deps like validate.py to out_dir (.tmp) - print("\n4. Copying validate.py to out_dir...") - if not copy_test_runner_dep_tree(out_dir): - raise Exception("Failed to copy test runner dependency tree") - - print("\n✓ Successfully built all validation components") - return True - except Exception as e: - print(f"\n✗ Failed to build validation: {e}") - return False - - -class CvValidator: - """Class for validating CV inference results""" - - @staticmethod - def is_float_equal(a: float, b: float, tolerance: float = 1e-5) -> bool: - """Compare floats with relative and absolute tolerance, similar to numpy.isclose - - Uses both relative and absolute tolerance for robust comparison: - - Relative tolerance scales with the magnitude of the values - - Absolute tolerance handles cases where values are close to zero - - With large tolerance values, most comparisons will pass - - Formula: abs(a - b) <= (atol + rtol * max(abs(a), abs(b))) - - Args: - a: First float value - b: Second float value - tolerance: Relative tolerance parameter (default: 1e-5). - Also used as absolute tolerance scaled down by 1000x - - Returns: - bool: True if values are close within tolerance - - Examples: - # Basic usage - is_float_equal(1.0, 1.00001, 1e-4) # True - - # Scales with magnitude (relative tolerance) - is_float_equal(1000.0, 1000.01, 1e-4) # True - is_float_equal(0.001, 0.00100001, 1e-4) # True - - # Handles near-zero values (absolute tolerance) - is_float_equal(1e-10, 2e-10, 1e-5) # True - - # Large tolerance passes most cases - is_float_equal(1.0, 2.0, 1.0) # True - """ - # Use tolerance as relative tolerance (rtol) - rtol = tolerance - # Use much smaller absolute tolerance to handle near-zero values - atol = tolerance * 1e-3 - - return abs(a - b) <= (atol + rtol * max(abs(a), abs(b))) - - @staticmethod - def compute_iou(box1: List, box2: List) -> float: - """Compute Intersection over Union between two bounding boxes. - - Args: - box1: First bounding box as [x1, y1, x2, y2] - box2: Second bounding box as [x1, y1, x2, y2] - - Returns: - float: IoU value in [0.0, 1.0] - """ - x1 = max(box1[0], box2[0]) - y1 = max(box1[1], box2[1]) - x2 = min(box1[2], box2[2]) - y2 = min(box1[3], box2[3]) - - intersection = max(0, x2 - x1) * max(0, y2 - y1) - - area1 = (box1[2] - box1[0]) * (box1[3] - box1[1]) - area2 = (box2[2] - box2[0]) * (box2[3] - box2[1]) - union = area1 + area2 - intersection - - if union == 0: - return 0.0 - return intersection / union - - @staticmethod - def tolerance_to_iou_params(tolerance: float) -> tuple: - """Project numeric tolerance to IoU matching parameters. - - Uses two mathematical projections: - - 1. iou_threshold = 1 / (1 + sqrt(tolerance)) - Inverse-sqrt decay — maps tolerance to the (0, 1] range. - The square root softens the curve so that moderate tolerances - still produce workable IoU thresholds for small objects, where - even 1-pixel shifts cause large IoU drops. - At tolerance=1.0 this yields 0.5, the standard PASCAL VOC threshold. - - 2. max_unmatched = floor(log2(1 + tolerance)) - Logarithmic growth — allows more unmatched detections as tolerance - increases, but grows slowly to prevent over-permissiveness. - - Reference table: - tolerance iou_threshold max_unmatched - 0 1.000 0 - 1e-5 ≈1.000 0 - 0.01 ≈0.909 0 - 0.1 ≈0.760 0 - 0.5 ≈0.586 0 - 1.0 0.500 1 - 3.0 ≈0.366 2 - 7.0 ≈0.274 3 - - Args: - tolerance: The numeric float-comparison tolerance - - Returns: - Tuple of (iou_threshold, max_unmatched) - """ - iou_threshold = 1.0 / (1.0 + math.sqrt(tolerance)) - max_unmatched = int(math.floor(math.log2(1.0 + tolerance))) if tolerance > 0 else 0 - return iou_threshold, max_unmatched - - @classmethod - def compare_lists(cls, list1: List[Any], list2: List[Any], tolerance: float) -> bool: - """Compare two lists with tolerance for float values. - For nested lists (list of lists): - - Outer list order doesn't matter (will be sorted) - - Inner list order DOES matter (will not be sorted) - For regular lists (non-nested): - - Order doesn't matter (will be sorted) - - Examples: - # These are equal (outer order doesn't matter): - list1 = [[1, 2], [3, 4]] - list2 = [[3, 4], [1, 2]] # True - - # These are NOT equal (inner order matters): - list1 = [[1, 2], [3, 4]] - list2 = [[2, 1], [3, 4]] # False - - # These are equal (floats within tolerance): - list1 = [[1.001, 2.002], [3.003, 4.004]] - list2 = [[3.004, 4.003], [1.002, 2.001]] # True - - # Regular lists (order doesn't matter): - list1 = [1.0, 2.0, 3.0] - list2 = [3.0, 1.0, 2.0] # True - - # Regular lists with tolerance: - list1 = [1.001, 2.002] - list2 = [2.001, 1.000] # True (within tolerance) - - Args: - list1: First list to compare - list2: Second list to compare - tolerance: Float tolerance for comparing float values - - Returns: - bool: True if lists are equal within tolerance, False otherwise - """ - if len(list1) != len(list2): - return False - - # If lists contain more lists/tuples, keep inner items as tuples but don't sort them - if all(isinstance(x, (list, tuple)) for x in list1 + list2): - tuples1 = [tuple(x) for x in list1] - tuples2 = [tuple(x) for x in list2] - # Sort the outer list only - sorted1 = sorted(tuples1) - sorted2 = sorted(tuples2) - - # Use tolerance-aware matching instead of simple pairing - # This handles cases where sorting order might differ slightly - # due to float precision - used = [False] * len(sorted2) - for item1 in sorted1: - matched = False - for i, item2 in enumerate(sorted2): - if used[i]: - continue - # Check if all elements match within tolerance - if len(item1) == len(item2): - all_match = all( - cls.is_float_equal(a, b, tolerance) - if isinstance(a, (float, int)) and - isinstance(b, (float, int)) - else a == b - for a, b in zip(item1, item2) - ) - if all_match: - used[i] = True - matched = True - break - if not matched: - return False - return True - - # For lists of floats, compare with tolerance - if all(isinstance(x, (float, int)) for x in list1 + list2): - sorted1 = sorted(list1) - sorted2 = sorted(list2) - return all( - cls.is_float_equal(float(a), float(b), tolerance) - for a, b in zip(sorted1, sorted2) - ) - - # For other types, use regular comparison - return sorted(list1) == sorted(list2) - - @classmethod - def match_detections(cls, actual_bboxes: List[List], expected_bboxes: List[List], - iou_threshold: float = 0.5, - actual_labels: List = None, - expected_labels: List = None) -> tuple: - """Match actual detections to expected detections using greedy IoU matching. - - Pairs detections by highest IoU first. A pair is only accepted if - IoU >= iou_threshold. When labels are provided, pairs must also - have matching labels — this prevents cross-matching at locations - where multiple classes are detected on the same bbox. - - Args: - actual_bboxes: List of actual bounding boxes [x1, y1, x2, y2] - expected_bboxes: List of expected bounding boxes [x1, y1, x2, y2] - iou_threshold: Minimum IoU to consider a valid match - actual_labels: Optional per-detection labels for actual results - expected_labels: Optional per-detection labels for expected results - - Returns: - Tuple of (matches, unmatched_actual, unmatched_expected) where - matches is a list of (actual_idx, expected_idx, iou) tuples - """ - use_labels = actual_labels is not None and expected_labels is not None - - candidates = [] - for i, a_box in enumerate(actual_bboxes): - for j, e_box in enumerate(expected_bboxes): - if use_labels and actual_labels[i] != expected_labels[j]: - continue - iou = cls.compute_iou(a_box, e_box) - if iou >= iou_threshold: - candidates.append((iou, i, j)) - - candidates.sort(reverse=True) - - matched_actual = set() - matched_expected = set() - matches = [] - - for iou, i, j in candidates: - if i not in matched_actual and j not in matched_expected: - matches.append((i, j, iou)) - matched_actual.add(i) - matched_expected.add(j) - - unmatched_actual = set(range(len(actual_bboxes))) - matched_actual - unmatched_expected = set(range(len(expected_bboxes))) - matched_expected - - return matches, unmatched_actual, unmatched_expected - - @classmethod - def _compare_detections_iou(cls, actual: Dict, expected: Dict, tolerance: float, - iou_threshold: float, max_unmatched: int) -> bool: - """Compare detection results using IoU-based matching. - - Matches detections holistically — each detection's bbox, label, and - probability are validated together as a unit. - - Args: - actual: Actual inference result dict (must contain 'bboxes') - expected: Expected inference result dict (must contain 'bboxes') - tolerance: Float tolerance for probability comparison - iou_threshold: Minimum IoU for a bbox match - max_unmatched: Max total unmatched detections allowed (actual + expected) - - Returns: - bool: True if detection results match within constraints - """ - per_detection_keys = {'bboxes', 'probs', 'labels', 'masks', 'objects'} - - for key in actual: - if key in per_detection_keys: - continue - actual_value = actual[key] - expected_value = expected[key] - - if key == 'shape': - if actual_value != expected_value: - print(f" ✗ {key} mismatch: {actual_value} vs {expected_value}") - return False - print(f" ✓ {key} passed") - continue - - if isinstance(actual_value, list): - if not cls.compare_lists(actual_value, expected_value, tolerance): - print(f" ✗ {key} mismatch") - return False - print(f" ✓ {key} passed") - continue - - if actual_value != expected_value: - print(f" ✗ {key} mismatch: {actual_value} vs {expected_value}") - return False - - actual_labels = actual.get('labels') - expected_labels = expected.get('labels') - matches, unmatched_actual, unmatched_expected = cls.match_detections( - actual['bboxes'], expected['bboxes'], iou_threshold, - actual_labels, expected_labels - ) - - total_unmatched = len(unmatched_actual) + len(unmatched_expected) - if total_unmatched > max_unmatched: - print(f" ✗ Too many unmatched detections: " - f"{len(unmatched_actual)} extra in actual + " - f"{len(unmatched_expected)} missing from expected " - f"= {total_unmatched} (max allowed: {max_unmatched})") - for idx in sorted(unmatched_actual): - prob = actual['probs'][idx] if 'probs' in actual else 'N/A' - label = actual['labels'][idx] if 'labels' in actual else 'N/A' - print(f" Extra actual[{idx}]: bbox={actual['bboxes'][idx]}, " - f"prob={prob}, label={label}") - for idx in sorted(unmatched_expected): - prob = expected['probs'][idx] if 'probs' in expected else 'N/A' - label = expected['labels'][idx] if 'labels' in expected else 'N/A' - print(f" Missing expected[{idx}]: bbox={expected['bboxes'][idx]}, " - f"prob={prob}, label={label}") - return False - - if unmatched_actual or unmatched_expected: - print(f" ⚠ Unmatched detections within tolerance: " - f"{len(unmatched_actual)} extra in actual, " - f"{len(unmatched_expected)} missing from expected") - - for actual_idx, expected_idx, iou in matches: - if 'probs' in actual and 'probs' in expected: - a_prob = actual['probs'][actual_idx] - e_prob = expected['probs'][expected_idx] - if isinstance(a_prob, (int, float)) and isinstance(e_prob, (int, float)): - if not cls.is_float_equal(float(a_prob), float(e_prob), tolerance): - print(f" ✗ prob mismatch at IoU={iou:.3f}: " - f"actual[{actual_idx}]={a_prob} vs expected[{expected_idx}]={e_prob}") - return False - - if 'labels' in actual and 'labels' in expected: - a_label = actual['labels'][actual_idx] - e_label = expected['labels'][expected_idx] - if a_label != e_label: - print(f" ✗ label mismatch at IoU={iou:.3f}: " - f"actual[{actual_idx}]={a_label} vs expected[{expected_idx}]={e_label}") - return False - - if 'masks' in actual and 'masks' in expected: - a_masks = actual['masks'] - e_masks = expected['masks'] - if a_masks and e_masks and \ - len(a_masks) > actual_idx and len(e_masks) > expected_idx: - a_mask = a_masks[actual_idx] - e_mask = e_masks[expected_idx] - if isinstance(a_mask, list) and isinstance(e_mask, list): - if not cls.compare_lists(a_mask, e_mask, tolerance): - print(f" ✗ mask mismatch at IoU={iou:.3f}: " - f"actual[{actual_idx}] vs expected[{expected_idx}]") - return False - - print(f" ✓ detections passed: {len(matches)} matched by IoU " - f"(iou_threshold={iou_threshold}, prob_tolerance={tolerance}, " - f"unmatched={total_unmatched}/{max_unmatched})") - return True - - @classmethod - def compare_inference_result(cls, actual: Dict, expected: Dict, tolerance: float, - iou_threshold: float = None, - max_unmatched: int = None) -> bool: - """Compare inference result dictionaries with special handling for different field types. - - When bboxes are present, uses IoU-based matching to pair detections - holistically (bbox + label + prob as a unit). Otherwise falls back - to independent list comparison. - - If iou_threshold or max_unmatched are not provided, they are - automatically derived from tolerance via tolerance_to_iou_params(). - - Args: - actual: Dictionary containing actual inference results - expected: Dictionary containing expected inference results - tolerance: Float tolerance for comparing numeric values - iou_threshold: Minimum IoU to consider a bbox match (default: derived from tolerance) - max_unmatched: Max allowed unmatched detections (default: derived from tolerance) - - Returns: - bool: True if dictionaries match within tolerance - """ - actual.pop('timestamp', None) - expected.pop('timestamp', None) - - if actual.keys() != expected.keys(): - return False - - if 'bboxes' in actual and actual['bboxes'] and expected.get('bboxes'): - if iou_threshold is None or max_unmatched is None: - derived_iou, derived_max = cls.tolerance_to_iou_params(tolerance) - if iou_threshold is None: - iou_threshold = derived_iou - if max_unmatched is None: - max_unmatched = derived_max - print(f" IoU params (from tolerance={tolerance}): " - f"iou_threshold={iou_threshold:.4f}, max_unmatched={max_unmatched}") - return cls._compare_detections_iou( - actual, expected, tolerance, iou_threshold, max_unmatched - ) - - for key, actual_value in actual.items(): - expected_value = expected[key] - - if key == 'shape': - if actual_value != expected_value: - return False - continue - - if isinstance(actual_value, list): - if not cls.compare_lists(actual_value, expected_value, tolerance): - return False - else: - print(f" ✓ {key} passed") - continue - - if actual_value != expected_value: - return False - - return True - - @classmethod - def compare_responses(cls, actual: Dict, expected: Dict, tolerance: float = 1e-5, - iou_threshold: float = None, max_unmatched: int = None) -> bool: - """Compare actual and expected inference API responses. - - If iou_threshold or max_unmatched are not provided, they are - automatically derived from tolerance via tolerance_to_iou_params(). - - Args: - actual: Dictionary containing actual API response - expected: Dictionary containing expected API response - tolerance: Float tolerance for comparing numeric values - iou_threshold: Minimum IoU for bbox matching (default: derived from tolerance) - max_unmatched: Max allowed unmatched detections (default: derived from tolerance) - - Returns: - bool: True if responses match within tolerance - """ - if actual.keys() != expected.keys(): - return False - - for key, value in actual.items(): - if key == 'data': - if len(actual['data']) != len(expected['data']): - return False - for actual_item, expected_item in zip(actual['data'], expected['data']): - if not cls.compare_inference_result(actual_item, expected_item, tolerance, - iou_threshold, max_unmatched): - return False - else: - if actual[key] != expected[key]: - return False - return True - - - -def main(): - parser = argparse.ArgumentParser(description='Build validator components') - parser.add_argument('--api-spec', - type=str, - required=True, - help='Path to OpenAPI specification file') - parser.add_argument('--base-dir', - type=str, - default='.', - help='Base directory for validation') - - args = parser.parse_args() - - with open(args.api_spec) as f: - api_schema = f.read() - - if build_validation(api_schema, Path(args.base_dir)): - print("✓ Successfully built validation") - else: - print("✗ Failed to build validation") - sys.exit(1) - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/tools/inference_builder/cursor-mcp-config.json b/tools/inference_builder/cursor-mcp-config.json deleted file mode 100644 index 8dc6aaca..00000000 --- a/tools/inference_builder/cursor-mcp-config.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "mcpServers": { - "deepstream-inference-builder": { - "command": ".venv/bin/python", - "args": ["mcp/mcp_server.py"], - "cwd": ".", - "env": { - "PYTHONPATH": "." - } - } - } -} - diff --git a/tools/inference_builder/doc/architecture.md b/tools/inference_builder/doc/architecture.md deleted file mode 100644 index 8cc994c0..00000000 --- a/tools/inference_builder/doc/architecture.md +++ /dev/null @@ -1,38 +0,0 @@ -# Inference Builder Architecture - -## Overview - -Inference Builder generates the Python package that includes both the inference logic and server integration. The package is constructed from a standardized inference flow combined with a variety of well tested source code templates—including server templates and inference templates. - -The generated code follows the architecture illustrated in the diagram below. - -![Architecture](assets/architecture.png) - -## Inference Flow - -The inference flow is standardized by the base class `InferenceBase`, which supports inference through multiple models. Each model is managed by a `ModelOperator` instance, and is connected with one another through data flows. Each data flow is managed by various implementations of `DataFlow`, handling various types of data, such as images, videos, speech, etc. -The inference flow is standardized by the base class `InferenceBase`, which supports execution of inference across multiple models. Each model is managed by a dedicated `ModelOperator` instance, and models are connected through well-defined data flows. The output data from one model is passed to other models through data flows, using Python dictionaries that map names to tensors. This allows each model to receive its required inputs in a structured and consistent format. - -## Inference Backend - -The inference backend can vary depending on the model type, user requirements, and hardware platform. To support a wide range of SDKs and frameworks, Inference Builder defines a standard interface: 'ModelBackend'. This interface allows the ModelOperator to interact with models regardless of their underlying implementation. - -## API Server - -The API server handles incoming requests from end users and returns responses. It supports communication protocols such as HTTP and gRPC, and conforms to standards like OpenAPI specifications or Protocol Buffers (protobuf) definitions. - -Multiple server implementations are supported, provided they comply with the API specification and interact with the inference pipeline through a defined abstract interface. - -Additionally, Inference Builder supports a server type called `serverless`, which enables users to run inference as a standalone command-line interface without deploying a server. - -The interactions among the components described above are illustrated in the diagram below: - -![Block Diagram](assets/block_digram.png) - -### Integration with Customized Code - -Custom logic can be integrated into the inference flow through the Processor Interface. In the inference configuration file, multiple named pre-processors and post-processors can be assigned to each `ModelOperator` instance. Corresponding Python code snippets implementing these processors must be provided prior to code generation. - -When custom processors are configured, the `ModelOperator` sequentially passes named tensors from the input data flow through the assigned pre-processors before invoking the model backend. Similarly, after receiving output tensors from the backend, the `ModelOperator` passes them through the configured post-processors in sequence. - -Users can define a processor in the configuration file in a way similar to declaring a function interface—by specifying input names and return value names in order. 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GB>6vu=#b0+ diff --git a/tools/inference_builder/doc/platform-guide.md b/tools/inference_builder/doc/platform-guide.md deleted file mode 100644 index 0c1c0ec4..00000000 --- a/tools/inference_builder/doc/platform-guide.md +++ /dev/null @@ -1,99 +0,0 @@ -# Platform Guide: Choosing the Right Dockerfile and Base Image - -Use this guide to select the correct Dockerfile template and DeepStream base image -for your target hardware **before** writing any Dockerfile or pipeline config. - ---- - -## Quick Selection Table - -| Platform | Architecture | Example Hardware | Dockerfile Template | DeepStream Base Image | -|---|---|---|---|---| -| **x86_64 datacenter** | x86_64 | A100, H100, B200, RTX | `samples://dockerfile/ds_app/Dockerfile` | `nvcr.io/nvidia/deepstream:9.0-triton-multiarch` | -| **Jetson / Tegra** | aarch64 | Orin, AGX Orin, Thor | `samples://dockerfile/ds_app/Dockerfile.tegra` | `nvcr.io/nvidia/deepstream:9.0-triton-multiarch` | -| **arm-sbsa server** | aarch64 | GB10, GB300, DGX Spark | `samples://dockerfile/ds_app/Dockerfile.dgxspark` | `nvcr.io/nvidia/deepstream:9.0-triton-sbsa-dgx-spark` | - ---- - -## How to Identify Your Platform - -### Step 1 — Check architecture -```bash -uname -m -``` -- `x86_64` → use the **x86_64 datacenter** Dockerfile -- `aarch64` → continue to Step 2 - -### Step 2 — Distinguish Tegra (Jetson) from arm-sbsa (server) -```bash -# Tegra devices expose a device-tree model string: -cat /proc/device-tree/model 2>/dev/null -# e.g. "NVIDIA Jetson AGX Orin" → Tegra - -# Server ARM64 (GB10, GB300, DGX Spark) report GPU via nvidia-smi: -nvidia-smi -L -# e.g. "GPU 0: NVIDIA GB10" → arm-sbsa server -``` - -If `/proc/device-tree/model` contains "Jetson" → use **Dockerfile.tegra**. -If `nvidia-smi` reports GB10 / GB300 / GH200 / Grace and no Jetson device-tree → use **Dockerfile.dgxspark**. - ---- - -## Key Differences by Platform - -### x86_64 (`Dockerfile`) -- PyTorch installed via pip from `https://download.pytorch.org/whl/cu130` -- TRT OSS GPU archs: `80;86;90;100` (Ampere, Ada, Hopper, Blackwell-x86) -- CUDA headers at `/usr/local/cuda-/include` - -### Jetson / Tegra (`Dockerfile.tegra`) -- PyTorch **copied** from `nvcr.io/nvidia/pytorch:25.08-py3` (no pip wheel for Tegra) -- TRT OSS GPU archs: `110;120;121` (Ampere/Orin, Blackwell/Thor) -- Tegra BSP libraries (`libnvbufsurface.so`, etc.) **mounted at runtime** by the - NVIDIA Container Toolkit — container will fail without `--runtime nvidia` on - actual Jetson hardware -- Base image: Tegra multiarch variant (`-ma` suffix) - -### arm-sbsa server (`Dockerfile.dgxspark`) -- PyTorch **copied** from `nvcr.io/nvidia/pytorch:25.08-py3` (same as Tegra) -- TRT OSS GPU archs: `110;120;121` (same as Tegra) -- CUDA headers present in the arm-sbsa base image (no Tegra BSP dependency) -- Base image: `arm-sbsa-spark` variant — **not** the `-ma` Tegra image -- Requires `apt-get install cmake libyaml-cpp-dev pkg-config` before TRT OSS build - ---- - -## Common Mistakes to Avoid - -| Mistake | Symptom | Fix | -|---|---|---| -| Using Tegra base image on GB10/GB300 | `libnvbufsurface.so.1.0.0: cannot open shared object file` | Switch to `Dockerfile.dgxspark` and the `arm-sbsa-spark` base image | -| Installing PyTorch via pip on Tegra/arm-sbsa | No compatible wheel found, or wrong CUDA variant | Copy torch from `nvcr.io/nvidia/pytorch:25.08-py3` | -| Missing `cmake` before TRT OSS build | `cmake: command not found` | Add `apt-get install cmake` (or skip TRT OSS build if not needed) | -| Using wrong GPU arch flags | TRT plugins fail to load or slow JIT compilation | Match `-DGPU_ARCHS` to the GPU table above | -| Tegra container without `--runtime nvidia` | Missing Tegra BSP `.so` files at runtime | Run with `--runtime nvidia` via NVIDIA Container Toolkit on Jetson | - ---- - -## TAO Post-Processor Library (`libnvds_infercustomparser_tao.so`) - -Required for TAO-trained detection models (e.g. PeopleNet Transformer) that use -`NvDsInferParseCustomDDETRTAO`. It is **not** bundled in any base image and must -be built from source: - -```bash -# Inside the container (or in the Dockerfile RUN step): -git clone https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git -cd deepstream_tao_apps/post_processor -CUDA_VER=. make DS_SRC_PATH=/opt/nvidia/deepstream/deepstream -cp libnvds_infercustomparser_tao.so /opt/nvidia/deepstream/deepstream/lib/ -``` - -CUDA version by platform: -- x86_64 datacenter: `CUDA_VER=13.1` -- Jetson / arm-sbsa server: `CUDA_VER=13.0` - -On arm-sbsa (GB10/GB300), if CUDA headers are not in the container, mount them -from the host at `/usr/local/cuda-13.0` or pre-build the `.so` on the host and -`COPY` it into the Dockerfile. diff --git a/tools/inference_builder/doc/usage.md b/tools/inference_builder/doc/usage.md deleted file mode 100644 index 48253ab8..00000000 --- a/tools/inference_builder/doc/usage.md +++ /dev/null @@ -1,204 +0,0 @@ -# Inference Builder Usage - -## Command Line Arguments - -For generating the inference code with the corresponding server implementation, you can run the following command: - -```bash -python builder/main.py -h -usage: Inference Builder [-h] [--server-type [{triton,fastapi,serverless}]] [-o [OUTPUT_DIR]] [-a [API_SPEC]] [-c [CUSTOM_MODULE ...]] [-x] [-t] [-v VALIDATION_DIR] [--no-docker] [--test-cases-abs-path] config - -positional arguments: - config Path the the configuration - -options: - -h, --help show this help message and exit - --server-type [{triton,fastapi,serverless}] - Choose the server type - -o [OUTPUT_DIR], --output-dir [OUTPUT_DIR] - Output directory - -a [API_SPEC], --api-spec [API_SPEC] - File for OpenAPI specification - -c [CUSTOM_MODULE ...], --custom-module [CUSTOM_MODULE ...] - Custom python modules - -x, --exclude-lib Do not include common lib to the generated code. - -t, --tar-output Zip the output to a single file - -v, --version show program's version number and exit - --validation-dir VALIDATION_DIR - valid validation directory path to build validator - --no-docker Use local OpenAPI Generator instead of Docker for OpenAPI client generation - --test-cases-abs-path - Use absolute paths in generated test_cases.yaml -``` -## Configuration File - -Before using the tool, you must prepare a YAML configuration file to define the inference flow. If server integration is required, you also need to provide an OpenAPI specification that defines the server and update the configuration with server templates based on the OpenAPI specification. - -For comprehensive documentation on configuration schemas, including validation, IDE support, and detailed backend specifications, see [schemas/README.md](../schemas/README.md). - -The configuration file is a YAML file that defines the inference flow and server implementation. It contains the following sections: - -- **name**: The name of the inference pipeline, which will be also used as the name of the folder for saving the generated inference code or the name of the tarball if '-t' is specified. -- **model_repo**: Specifies the path to the model repository from which the inference pipeline searches and loads the required model files. Each model has a separate folder for their files. -- **models**: List of model definitions. A inference flow can incorporate multiple models, each might be implemented with different backends to achieve optimal performance. -- **input** (optional): Defines the top-level inputs of the inference flow. This field is required only when the pipeline includes multiple models or when at least one input is a custom type—such as in cases that require standard preprocessing like video decoding before the input data is passed to the model. -- **output** (optional): Defines the top-level outputs of the inference flow. This field is required only when the pipeline includes multiple models or when at least one output is custom type—such as in cases that require standard preprocessing like video encoding before the output data is passed out. -- **server** (optional): Defines the endpoint templates for the server implementation. This field is not required if the server type is set to "serverless". -- **routes** (optional): Defines the routing rules for the inference flow when multiple models are involved. -- **postprocessors** (optional): Defines the top-level post-processors for the inference flow. This field is required only when the pipeline includes multiple models and the output of these models need to be consolidated. - -A configuration file can be as simple as the following example: - -```yaml - -name: "detector" -model_repo: "/workspace/models" - -models: -- name: rtdetr - backend: deepstream/nvinfer - max_batch_size: 4 - input: - - name: media_url - data_type: TYPE_CUSTOM_DS_MEDIA_URL - dims: [ -1 ] - - name: mime - data_type: TYPE_CUSTOM_DS_MIME - dims: [ -1 ] - output: - - name: output - data_type: TYPE_CUSTOM_DS_METADATA - dims: [ -1 ] - parameters: - infer_config_path: - - nvdsinfer_config.yaml -``` - -With the above configuration file, you can generate inference code for the RT-DETR object detection model using the DeepStream backend, which takes image or video url as input and produces bounding boxes as output. (Step by step guide can be found in the [detection README](./builder/samples/ds_app/detection/README.md)) - -Breakdown of the configuration: - -- The inference package is named `detector`, and the model search path is set to `/workspace/models`. The pipeline uses one model and will look for the model file (in this case, an ONNX file and a yaml config for Deepstream) under the `rtdetr` directory under `/workspace/models`, as specified by the model name and model_repo. -- Inference is performed using nvinfer from the NVIDIA DeepStream SDK. A corresponding configuration file named `nvdsinfer_config.yaml` must be present under `/workspace/models/rtdetr`. -- The pipeline supports two inputs: - - media_url: the path or URL to the input media. - - mime: the media type (e.g., "video/mp4" or "image/jpeg"). -- The pipeline supports batch processing with up to 4 media items at a time as indicated by max_batch_size, and the number can be adjusted to suit the capabilities of the hardware platform and the requirements of the model. -- The output is a custom DeepStream metadata dictionary (`TYPE_CUSTOM_DS_METADATA`), which carries per-frame information such as detected bounding boxes, confidence scores, labels, segmentation maps, tracking IDs, and timestamps. The structure is flexible and returned as a dictionary with fields like `shape`, `bboxes`, `probs`, `labels`, `seg_maps`, `objects`, and `timestamp`. For complete structure details, see the [DeepStream-Specific Types section in schemas/README.md](../schemas/README.md#type_custom_ds_metadata). - -### Input and Output Definition - -Input and output definitions are required at the model level, and in some cases also at the top level of the pipeline. Each definition specifies the name, data type, dimensions, and optional flags for tensors flowing through the pipeline. - -For complete documentation on input/output specifications, supported data types (including custom types like TYPE_CUSTOM_IMAGE_BASE64, TYPE_CUSTOM_LONG_VIDEO_ASSETS, etc.), and field descriptions, see the [Data Types section in schemas/README.md](../schemas/README.md#data-types). - -### Model Definition - -The model definition specifies the inference backend, input/output tensors, batch size, and optional preprocessors/postprocessors. The definition is derived from the Triton model configuration and extended to support various backends including DeepStream, TensorRT, TensorRT-LLM, vLLM, and others. - -For complete documentation on model specifications, including: -- Supported backend types and their hierarchical structure (e.g., `deepstream/nvinfer`, `triton/tensorrt`, `tensorrtllm/pytorch`, `vllm`) -- Backend-specific parameters and configuration options -- Input/output tensor definitions -- Preprocessor and postprocessor specifications - -See the [Backend-Specific Schemas section in schemas/README.md](../schemas/README.md#backend-specific-schemas). - - -### Custom Preprocessors and Postprocessors - -Custom preprocessors and postprocessors integrate user-defined Python code into the inference flow, offering a new paradigm for programming with neural network models. - -Both custom preprocessors and postprocessors are defined using the following specification: -- **kind**: The kind of the processor: "custom" or "auto". Only "custom" processors are supported in the current release. -- **name**: The name of the processor. It must match the "name" defined in the user-implemented processor class. -- **input**: Specifies the names of the processor's inputs in order. This defines how tensors from the inference flow are passed to the processor. -- **output**: Specifies the names of the processor's outputs in order. This defines how the inference flow extracts the tensors from the processor. -- **config**: Defines the processor's configuration as a dictionary. The contents are implementation-specific. - -For a list of common preprocessors (image normalizers, tokenizers, VLM loaders) and postprocessors (masking, embedding processors) with their configurations, see the [Preprocessors and Postprocessors section in schemas/README.md](../schemas/README.md#preprocessors-and-postprocessors). - -#### Custom Preprocessor/Postprocessor Implementation Requirements - -When implementing a custom preprocessor or postprocessor, the class must adhere to the following specification: -- A class variable named `"name"` must be defined to uniquely identify the processor. -- An `__init__` method must be implemented to accept a single argument: a dictionary containing the processor's configuration (`config`). -- A `__call__` method must be defined to accept multiple positional arguments. The number and order of these arguments must match the input definition of the processor in the configuration file. -- The `__call__` method must return a tuple of tensors, in the order specified by the output definition of the processor in the configuration file. -- Input and output data are expected to be either NumPy arrays or PyTorch tensors, unless a custom data type is explicitly specified. - -### Server Definition - -The server definition is required at the top level unless the server type is set to "serverless". It specifies the server implementation along with the corresponding request and response templates. Both templates must be written in Jinja2, and are used to: -- Extract the necessary inputs from the server request. -- Format the inference outputs into the desired server response. - -The "responders" section allows users to map server implementations to operations defined in the OpenAPI specification. Each responder corresponds to a specific endpoint or operation. -For detailed information about responders, operations, and examples, see the [Server Configuration section in schemas/README.md](../schemas/README.md#server-configuration). - -### Routing - -The routes section is optional and typically used for multi-model inference flows. It defines custom routing rules that control how data flows between different models in the pipeline. - -The routes definition is a map where each entry specifies a connection between a source and destination: -- Format: `: : :` -- If the model name is omitted (e.g., `:`), the tensors are at the top level of the inference flow. -- Tensor lists are optional and specified as JSON arrays (e.g., `["tensor1", "tensor2"]`). - -For detailed examples and explanation of routing logic, see the [Routes section in schemas/README.md](../schemas/README.md#routes). - -## Runtime Environment Variables - -The following environment variables can be used to control runtime behavior of the generated inference pipelines. - -### Logging & Debugging - -| Variable | Default | Description | -|----------|---------|-------------| -| `DEBUG` | `0` | Enable debug mode (sets log level to DEBUG when non-zero) | -| `LOG_LEVEL` | `INFO` | Log verbosity level: `DEBUG`, `INFO`, `WARNING`, `ERROR`, `CRITICAL` | - -### Inference Performance - -| Variable | Default | Description | -|----------|---------|-------------| -| `N_CODEC_INSTANCES` | `1` | Number of video codec instances for parallel decoding. Increase for higher throughput when processing multiple video streams. | -| `MAX_BATCH_SIZE` | `16` | Maximum batch size for live stream frame collection. Controls memory usage vs throughput trade-off. | - -### Server Configuration - -| Variable | Default | Description | -|----------|---------|-------------| -| `HTTP_PORT` | `8000` | HTTP port for FastAPI server when using `fastapi` server type. | - -### Testing & Validation - -| Variable | Default | Description | -|----------|---------|-------------| -| `TEST_HOST` | `http://127.0.0.1:8800` | Target host URL for the test runner to connect to. | -| `TEST_TOLERANCE` | `1e-5` | Numerical tolerance for floating-point comparisons in test assertions. | -| `ERROR_EXPORT_PATH` | `/tmp/inference_errors.json` | File path to export collected errors for test validation and debugging. | - -### Dynamic Configuration Override - -The generated configuration module (`config.py`) supports dynamic environment variable substitution using the pattern `$ENV_VAR|default_value`. This allows any configuration value to be overridden at runtime. - -**Format:** `$VARIABLE_NAME|default_value` - -**Example configuration:** -```yaml -models: -- name: my_model - parameters: - batch_size: $MODEL_BATCH_SIZE|8 - precision: $MODEL_PRECISION|fp16 -``` - -In this example: -- `batch_size` will use the value of `MODEL_BATCH_SIZE` environment variable if set, otherwise defaults to `8` -- `precision` will use `MODEL_PRECISION` if set, otherwise defaults to `fp16` - -Type conversion is automatic based on the default value: -- `"true"`/`"false"` → boolean -- Numeric strings → int or float -- Everything else → string \ No newline at end of file diff --git a/tools/inference_builder/lib/__init__.py b/tools/inference_builder/lib/__init__.py deleted file mode 100644 index 3159bfe6..00000000 --- a/tools/inference_builder/lib/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tools/inference_builder/lib/asset_manager.py b/tools/inference_builder/lib/asset_manager.py deleted file mode 100644 index 38ee5957..00000000 --- a/tools/inference_builder/lib/asset_manager.py +++ /dev/null @@ -1,363 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from .utils import get_logger -import os -from dataclasses import dataclass, field -from typing import Dict, TYPE_CHECKING -import json -import uuid -import shutil - -if TYPE_CHECKING: - from fastapi import UploadFile -from pyservicemaker.utils import MediaInfo -import tempfile -from urllib.parse import urlparse -import threading - -logger = get_logger(__name__) - -DEFAULT_ASSET_DIR = "/tmp/assets" -@dataclass -class Asset: - id: str - file_name: str - mime_type: str - size: int - duration: int - path: str - use_count: int - asset_dir: str - description: str - username: str - password: str - live: bool - managed: bool - _lock: threading.Lock = field(init=False, repr=False, compare=False, default=None) - _condition: threading.Condition = field(init=False, repr=False, compare=False, default=None) - _marked_for_deletion: bool = field(init=False, repr=False, compare=False, default=False) - - def __post_init__(self): - self._lock = threading.Lock() - self._condition = threading.Condition(self._lock) - self._marked_for_deletion = False - - def to_dict(self): - """Convert Asset to dictionary, excluding non-serializable fields.""" - return { - "id": self.id, - "file_name": self.file_name, - "mime_type": self.mime_type, - "size": self.size, - "duration": self.duration, - "path": self.path, - "use_count": self.use_count, - "asset_dir": self.asset_dir, - "description": self.description, - "username": self.username, - "password": self.password, - "live": self.live, - "managed": self.managed - } - - def lock(self): - with self._condition: - self.use_count += 1 - - def unlock(self): - with self._condition: - if self.use_count <= 0: - logger.warning("Attempted to unlock asset %s with use_count %d", - self.id, self.use_count) - return - self.use_count -= 1 - if self.use_count == 0: - # Notify waiting threads (e.g., delete_asset) - self._condition.notify_all() - - # Safety net: cleanup unmanaged assets when object is destroyed - if not self.managed and os.path.exists(self.path): - try: - os.remove(self.path) - logger.info("Removed unmanaged asset %s from %s", - self.id, self.path) - except OSError as e: - logger.exception( - "Failed to cleanup asset %s: %s", self.id, e) - - def is_marked_for_deletion(self): - """Check if this asset is marked for deletion.""" - with self._condition: - return self._marked_for_deletion - - def mark_for_deletion(self): - """Mark this asset for deletion, signaling processing loops to stop.""" - with self._condition: - self._marked_for_deletion = True - logger.info("Marked asset %s for deletion", self.id) - - @classmethod - def fromdir(cls, asset_dir): - with open(os.path.join(asset_dir, "info.json")) as f: - info = json.load(f) - - return Asset(id=info["assetId"], - path=info["path"], - file_name=info["fileName"], - mime_type=info["mimeType"], - duration=info["duration"], - username=info["username"], - password=info["password"], - description=info["description"], - asset_dir=asset_dir, - use_count=0, - size=info["size"], - live=info["live"], - managed=True) - - -class AssetManager: - _instance = None - - def __new__(cls): - if cls._instance is None: - cls._instance = super(AssetManager, cls).__new__(cls) - cls._instance._initialized = False - return cls._instance - - def __init__(self): - if self._initialized: - return - - self._asset_dir = DEFAULT_ASSET_DIR - self._asset_map:Dict[str, Asset] = dict() - os.makedirs(self._asset_dir, exist_ok=True) - asset_ids = self._get_existing_asset_ids() - self._asset_map: dict[str, Asset] = { - asset_id: Asset.fromdir(os.path.join(self._asset_dir, asset_id)) - for asset_id in asset_ids - } - self._initialized = True - - def save_file(self, file: "UploadFile", file_name: str, mime_type: str) -> Asset | None: - asset_id = str(uuid.uuid4()) - while asset_id in self._asset_map: - asset_id = str(uuid.uuid4()) - asset_dir = os.path.join(self._asset_dir, asset_id) - try: - os.makedirs(asset_dir) - except: - logger.error(f"Failed to create asset directory: {asset_dir}") - return None - - with open(os.path.join(asset_dir, file_name), "wb") as f: - shutil.copyfileobj(file, f) - try: - size = os.path.getsize(os.path.join(asset_dir, file_name)) - except: - size = 0 - mediainfo = MediaInfo.discover(os.path.join(asset_dir, file_name)) - - with open(os.path.join(asset_dir, "info.json"), "w") as f: - json.dump( - { - "assetId": asset_id, - "path": os.path.join(asset_dir, file_name), - "fileName": file_name, - "mimeType": mime_type, - "duration": mediainfo.duration, - "username": "", - "password": "", - "description": "", - "size": size, - "live": False, - }, f) - - self._asset_map[asset_id] = Asset.fromdir(asset_dir) - - logger.info(f"Saved file - asset-id: {asset_id} name: {file_name}") - - return self._asset_map[asset_id] - - def add_live_stream(self, url: str, description="", username="", password="") -> Asset | None: - asset_id = str(uuid.uuid4()) - while asset_id in self._asset_map: - asset_id = str(uuid.uuid4()) - asset_dir = os.path.join(self._asset_dir, asset_id) - try: - os.makedirs(asset_dir) - except Exception as e: - logger.error(f"Failed to create asset directory: {asset_dir}") - return None - - with open(os.path.join(asset_dir, "info.json"), "w") as f: - json.dump( - { - "assetId": asset_id, - "path": url, - "fileName": "", - "mimeType": "", - "duration": 0, - "username": username, - "password": password, - "description": description, - "size": 0, - "live": True - }, f) - - self._asset_map[asset_id] = Asset.fromdir(asset_dir) - - logger.info(f"Added live stream - asset-id: {asset_id} url: {url}") - - return self._asset_map[asset_id] - - - def list_assets(self): - return list(self._asset_map.values()) - - def get_asset(self, asset_id): - if asset_id in self._asset_map: - return self._asset_map[asset_id] - else: - # check if the asset is an id or a path - # Handle file:// URIs by converting them to paths - file_path = asset_id - if asset_id.startswith("file://"): - parsed = urlparse(asset_id) - file_path = parsed.path - - if file_path.startswith("/") and os.path.exists(file_path): - # Check if this path already exists in the asset map - for existing_asset in self._asset_map.values(): - if existing_asset.path == file_path: - return existing_asset - - mediainfo = MediaInfo.discover(file_path) - asset = Asset(id=str(uuid.uuid4()), - path=file_path, - file_name=os.path.basename(file_path), - mime_type="", - duration=mediainfo.duration, - username="", - password="", - description="", - asset_dir=file_path, - use_count=0, - size=os.path.getsize(file_path), - live=False, - managed=True) - self._asset_map[asset.id] = asset - return asset - elif (asset_id.startswith("http://") or - asset_id.startswith("https://")): - # Download the video from the web - try: - # Extract filename from URL - parsed_url = urlparse(asset_id) - file_name = os.path.basename(parsed_url.path) - if not file_name: - file_name = "downloaded_video" - - # Create a temporary directory - temp_dir = tempfile.mkdtemp(prefix="web_asset_") - temp_file_path = os.path.join(temp_dir, file_name) - - # Download the file - logger.info(f"Downloading video from {asset_id}") - import requests # lazy import to avoid GIL error - response = requests.get( - asset_id, stream=True, timeout=30) - response.raise_for_status() - - with open(temp_file_path, "wb") as f: - for chunk in response.iter_content(chunk_size=8192): - f.write(chunk) - - # Get media info - mediainfo = MediaInfo.discover(temp_file_path) - file_size = os.path.getsize(temp_file_path) - - logger.info(f"Downloaded video to {temp_file_path}") - - return Asset(id=str(uuid.uuid4()), - path=temp_file_path, - file_name=file_name, - mime_type="", - duration=mediainfo.duration, - username="", - password="", - description=f"Downloaded from {asset_id}", - asset_dir=temp_dir, - use_count=0, - size=file_size, - live=False, - managed=False) - except Exception as e: - logger.exception( - "Failed to download video from %s: %s", asset_id, e) - return None - elif asset_id.startswith("rtsp://"): - return self.add_live_stream(asset_id) - else: - logger.error(f"Invalid asset ID: {asset_id}") - return None - - def delete_asset(self, asset_id, timeout=None): - """ - Delete an asset, waiting for it to be released if currently in use. - - Args: - asset_id: The ID of the asset to delete - timeout: Maximum time to wait in seconds (None for indefinite wait) - - Returns: - bool: True if asset was deleted, False if asset not found or timeout occurred - """ - if asset_id not in self._asset_map: - return False - - asset = self._asset_map[asset_id] - - # Mark asset for deletion first (signals processing loops to stop) - logger.info(f"Marking asset {asset_id} for deletion") - asset.mark_for_deletion() - logger.info(f"Marked asset {asset_id} for deletion") - - # Wait for asset to be released - with asset._condition: - if asset.use_count > 0: - logger.info(f"Asset {asset_id} is in use (use_count={asset.use_count}), waiting for release...") - # Wait until use_count becomes 0 - if not asset._condition.wait_for(lambda: asset.use_count == 0, timeout=timeout): - logger.error(f"Timeout waiting for asset {asset_id} to be released") - return False - logger.info(f"Asset {asset_id} released, proceeding with deletion") - - # Now safe to delete - asset_dir = os.path.join(self._asset_dir, asset_id) - if os.path.exists(asset_dir): - shutil.rmtree(asset_dir) - self._asset_map.pop(asset_id) - logger.info(f"Removed asset {asset_id} and cleaned up associated resources") - return True - - def _get_existing_asset_ids(self): - entries = os.listdir(self._asset_dir) - return [ - entry for entry in entries if os.path.isdir(os.path.join(self._asset_dir, entry)) - and os.path.isfile(os.path.join(self._asset_dir, entry, "info.json")) - ] diff --git a/tools/inference_builder/lib/codec.py b/tools/inference_builder/lib/codec.py deleted file mode 100644 index e46e4659..00000000 --- a/tools/inference_builder/lib/codec.py +++ /dev/null @@ -1,100 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from pyservicemaker import BufferProvider, Buffer, Pipeline, Flow, BufferRetriever, as_tensor, ColorFormat -from queue import Queue, Empty, Full -import numpy as np -from .utils import get_logger -from typing import List -import base64 -import torch - -logger = get_logger(__name__) - -png_data = base64.b64decode("iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAIAAACQd1PeAAAAEElEQVR4nGK6HcwNCAAA//8DTgE8HuxwEQAAAABJRU5ErkJggg==") -jpg_data = base64.b64decode("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") - - -class ImageInput(BufferProvider): - DEFAULT_HEIGHT = 1080 - DEFAULT_WIDTH = 1920 - - def __init__(self, format): - super().__init__() - self.format = format - self.height = ImageInput.DEFAULT_HEIGHT - self.width = ImageInput.DEFAULT_WIDTH - self.framerate = 1 - self.device = 'cpu' - self.queue = Queue(maxsize=1) - - def generate(self, size): - tensor = self.queue.get() - return tensor.wrap(ColorFormat.I420) - - def send(self, data): - self.queue.put(data) - -class ImageOutput(BufferRetriever): - def __init__(self, max_queue_size: int=1): - super().__init__() - self._timeout = 10 - self._output = Queue(maxsize=max_queue_size) - - def consume(self, buffer): - try: - tensor = buffer.extract(0).clone() - torch_tensor = torch.utils.dlpack.from_dlpack(tensor) - self._output.put(torch_tensor) - except Full: - logger.error(f"ImageOutput queue is full, buffer dropped") - return 1 - - def get(self): - try: - data = self._output.get(timeout=self._timeout) - except Empty: - logger.error("ImageOutput timeout, failed to decode the image, input data may be corrupted") - return None - return data - - -class ImageDecoder: - def __init__(self, formats: List[str], device_id: int=0): - self._piplines = [Pipeline(f"image_decoder_{format}") for format in formats] - self._image_inputs = {format: ImageInput(format) for format in formats} - self._image_output = ImageOutput() - self._flows = [] - for pipline, format in zip(self._piplines, formats): - image_input = self._image_inputs[format] - flow = Flow(pipline).inject([image_input]).decode().retrieve(self._image_output, gpu_id=device_id) - self._flows.append(flow) - pipline.start() - - if "PNG" in formats: - logger.info("PNG decoder warmup:") - warmup_data = np.frombuffer(png_data, dtype=np.uint8) - self.decode(as_tensor(warmup_data.copy(), "PNG"), "PNG") - if "JPEG" in formats: - logger.info("JPEG decoder warmup:") - warmup_data = np.frombuffer(jpg_data, dtype=np.uint8) - self.decode(as_tensor(warmup_data.copy(), "JPEG"), "JPEG") - logger.info("Image decoder initialized") - - - def decode(self, tensor, format: str): - self._image_inputs[format].send(tensor) - return self._image_output.get() diff --git a/tools/inference_builder/lib/errors.py b/tools/inference_builder/lib/errors.py deleted file mode 100644 index cc4c37a8..00000000 --- a/tools/inference_builder/lib/errors.py +++ /dev/null @@ -1,1163 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Enhanced Error Handling Module for Inference Builder - -This module provides rich error context, categorization, and troubleshooting guidance -to make debugging easier for both humans and AI tools. -""" - -import traceback -import time -import uuid -import json -import threading -import inspect -import os -from dataclasses import dataclass, field, asdict -from typing import ClassVar, Dict, List, Optional, Any, Union -from enum import Enum -import numpy as np - -# Try to import torch, but make it optional -try: - import torch - TORCH_AVAILABLE = True -except ImportError: - TORCH_AVAILABLE = False - - -class ErrorCategory(Enum): - """Categorize errors for easier filtering and analysis""" - CONFIGURATION = "configuration" - DATA_VALIDATION = "data_validation" - ASSET_NOT_FOUND = "asset_not_found" - PROCESSING = "processing" - BACKEND = "backend" - TIMEOUT = "timeout" - RESOURCE = "resource" - NETWORK = "network" - INTERNAL = "internal" - CODEC = "codec" - QUEUE = "queue" - - -class ErrorSeverity(Enum): - """Error severity levels""" - CRITICAL = "critical" # Service-breaking - ERROR = "error" # Request-breaking - WARNING = "warning" # Degraded but continuing - INFO = "info" # Informational only - - -@dataclass -class Error: - """ - Base Error class for simple error messages. - - This is the base class that EnhancedError inherits from. - Use this for simple errors that don't need rich context. - Use EnhancedError (via ErrorFactory) for structured errors with full context. - """ - message: str - - def __bool__(self): - """Return False so error objects evaluate to False in boolean context""" - return False - - def __str__(self): - """Human-readable string representation""" - return self.message - - -@dataclass -class EnhancedError(Error): - """ - Enhanced error class with rich context for AI-powered troubleshooting - - This class provides comprehensive error information including: - - Categorization and severity - - Contextual information (component, operation, model) - - Technical details (stack traces, error codes) - - Data context (inputs, expected vs actual) - - Troubleshooting guidance and remediation steps - - Error chaining for root cause analysis - - Inherits from Error base class, which provides: - - message field - - __bool__() override (returns False for boolean checks) - """ - - # Core error information (message inherited from Error) - category: ErrorCategory - severity: ErrorSeverity = ErrorSeverity.ERROR - - # Contextual information - component: str = "" # e.g., "DataFlow", "ModelOperator", "ImageInputDataFlow" - operation: str = "" # e.g., "bind_input", "_process_custom_data" - request_id: str = field(default_factory=lambda: str(uuid.uuid4())) - timestamp: float = field(default_factory=time.time) - - # Technical details - error_code: str = "" # e.g., "ERR_DF_001", "ERR_MO_005" - stack_trace: Optional[str] = None - exception_type: Optional[str] = None - - # Data context - input_data: Dict[str, Any] = field(default_factory=dict) - expected_data: Dict[str, Any] = field(default_factory=dict) - actual_data: Dict[str, Any] = field(default_factory=dict) - - # Pipeline context - model_name: Optional[str] = None - dataflow_names: List[str] = field(default_factory=list) - tensor_names: List[str] = field(default_factory=list) - - # Troubleshooting guidance - troubleshooting_hints: List[str] = field(default_factory=list) - related_config: Dict[str, Any] = field(default_factory=dict) - remediation_steps: List[str] = field(default_factory=list) - - # Error chain for root cause analysis - caused_by: Optional['EnhancedError'] = None - - # Additional metadata - metadata: Dict[str, Any] = field(default_factory=dict) - - def __str__(self): - """Human-readable string representation""" - parts = [f"[{self.error_code or 'ERROR'}]"] - if self.component: - parts.append(f"{self.component}") - if self.operation: - parts.append(f".{self.operation}()") - parts.append(f": {self.message}") - return " ".join(parts) - - def to_dict(self, include_stack_trace: bool = True, sanitize_data: bool = True) -> Dict: - """ - Convert to dictionary for structured logging - - Args: - include_stack_trace: Whether to include full stack trace (can be verbose) - sanitize_data: Whether to sanitize large data structures - - Returns: - Dictionary representation suitable for JSON serialization - """ - result = { - "error_id": self.request_id, - "timestamp": self.timestamp, - "iso_timestamp": time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(self.timestamp)), - "category": self.category.value, - "severity": self.severity.value, - "component": self.component, - "operation": self.operation, - "message": self.message, - "error_code": self.error_code, - "exception_type": self.exception_type, - } - - if include_stack_trace and self.stack_trace: - result["stack_trace"] = self.stack_trace - - # Add data context - if sanitize_data: - result["input_data"] = self._sanitize_data(self.input_data) - result["actual_data"] = self._sanitize_data(self.actual_data) - else: - result["input_data"] = self.input_data - result["actual_data"] = self.actual_data - - result["expected_data"] = self.expected_data - - # Add pipeline context - if self.model_name: - result["model_name"] = self.model_name - if self.dataflow_names: - result["dataflow_names"] = self.dataflow_names - if self.tensor_names: - result["tensor_names"] = self.tensor_names - - # Add troubleshooting information - if self.troubleshooting_hints: - result["troubleshooting_hints"] = self.troubleshooting_hints - if self.remediation_steps: - result["remediation_steps"] = self.remediation_steps - if self.related_config: - result["related_config"] = self._sanitize_config(self.related_config) - - # Add metadata - if self.metadata: - result["metadata"] = self.metadata - - # Add error chain - if self.caused_by: - result["caused_by"] = self.caused_by.to_dict( - include_stack_trace=False, # Don't include nested stack traces - sanitize_data=sanitize_data - ) - - return result - - def _sanitize_data(self, data: Dict) -> Dict: - """Sanitize large tensors/arrays for logging""" - if not data: - return {} - - sanitized = {} - for k, v in data.items(): - try: - if isinstance(v, np.ndarray): - sanitized[k] = { - "type": "numpy.ndarray", - "shape": str(v.shape), - "dtype": str(v.dtype), - "sample": self._safe_sample(v) - } - elif TORCH_AVAILABLE and isinstance(v, torch.Tensor): - sanitized[k] = { - "type": "torch.Tensor", - "shape": str(list(v.shape)), - "dtype": str(v.dtype), - "device": str(v.device), - "sample": self._safe_sample(v.cpu().numpy() if v.is_cuda else v.numpy()) - } - elif isinstance(v, (list, tuple)): - if len(v) > 10: - sanitized[k] = { - "type": type(v).__name__, - "length": len(v), - "sample": str(v[:3]) + " ... " + str(v[-1:]) - } - else: - sanitized[k] = v - elif isinstance(v, dict): - sanitized[k] = self._sanitize_data(v) - elif isinstance(v, str) and len(v) > 500: - sanitized[k] = v[:500] + "... [truncated]" - else: - sanitized[k] = v - except Exception as e: - sanitized[k] = f"" - - return sanitized - - def _safe_sample(self, arr: np.ndarray, max_elements: int = 5) -> str: - """Get a safe sample from array""" - try: - flat = arr.flatten() - if len(flat) <= max_elements: - return str(flat.tolist()) - else: - sample = flat[:max_elements].tolist() - return f"{sample} ... (total: {len(flat)} elements)" - except Exception: - return "" - - def _sanitize_config(self, config: Dict) -> Dict: - """Sanitize configuration data""" - if not config: - return {} - - sanitized = {} - for k, v in config.items(): - if isinstance(v, dict): - sanitized[k] = self._sanitize_config(v) - elif isinstance(v, (list, tuple)) and len(v) > 20: - sanitized[k] = f"{type(v).__name__}(length={len(v)})" - else: - sanitized[k] = v - return sanitized - - def to_json(self, **kwargs) -> str: - """Convert to JSON string""" - return json.dumps(self.to_dict(**kwargs), indent=2, default=str) - - def log(self, logger, as_json: bool = True): - """ - Log error with appropriate level - - Args: - logger: Logger instance - as_json: If True, log as structured JSON; otherwise log as formatted text - """ - if as_json: - error_dict = self.to_dict() - log_msg = json.dumps(error_dict, indent=2, default=str) - else: - log_msg = self._format_text() - - if self.severity == ErrorSeverity.CRITICAL: - logger.critical(log_msg) - elif self.severity == ErrorSeverity.ERROR: - logger.error(log_msg) - elif self.severity == ErrorSeverity.WARNING: - logger.warning(log_msg) - else: - logger.info(log_msg) - - def _format_text(self) -> str: - """Format error as human-readable text""" - lines = [ - "=" * 80, - f"ERROR: {self.error_code or 'UNKNOWN'}", - f"Severity: {self.severity.value.upper()}", - f"Category: {self.category.value}", - "=" * 80, - f"Message: {self.message}", - ] - - if self.component or self.operation: - lines.append(f"Location: {self.component}.{self.operation}()") - - if self.model_name: - lines.append(f"Model: {self.model_name}") - - if self.dataflow_names: - lines.append(f"Dataflow: {', '.join(self.dataflow_names)}") - - if self.tensor_names: - lines.append(f"Tensors: {', '.join(self.tensor_names)}") - - if self.exception_type: - lines.append(f"Exception Type: {self.exception_type}") - - if self.input_data: - sanitized_input = self._sanitize_data(self.input_data) - lines.append(f"\nInput Data: {sanitized_input}") - - if self.expected_data: - lines.append(f"\nExpected Data: {self.expected_data}") - - if self.actual_data: - sanitized_actual = self._sanitize_data(self.actual_data) - lines.append(f"\nActual Data: {sanitized_actual}") - - if self.related_config: - lines.append(f"\nRelated Config: {self.related_config}") - - if self.troubleshooting_hints: - lines.append("\nTroubleshooting Hints:") - for i, hint in enumerate(self.troubleshooting_hints, 1): - lines.append(f" {i}. {hint}") - - if self.remediation_steps: - lines.append("\nRemediation Steps:") - for i, step in enumerate(self.remediation_steps, 1): - lines.append(f" {i}. {step}") - - if self.stack_trace: - lines.append("\nStack Trace:") - lines.append(self.stack_trace) - - if self.caused_by: - lines.append("\nCaused by:") - lines.append(f" {self.caused_by}") - - lines.append("=" * 80) - - return "\n".join(lines) - - -class ErrorFactory: - """ - Factory for creating standardized errors with consistent codes and hints - - This factory maintains an error catalog with predefined error codes, categories, - and troubleshooting guidance. It ensures consistent error handling across the codebase. - - The factory can automatically extract component and operation names from context: - - Pass `caller=self` to auto-extract class name as component - - Pass `auto_operation=True` to auto-detect calling method name - """ - - # Error code definitions with troubleshooting hints - ERROR_CATALOG: ClassVar[Dict[str, Dict[str, Any]]] = { - # DataFlow errors (DF) - "ERR_DF_001": { - "message": "Required tensor not found in dataflow input", - "category": ErrorCategory.DATA_VALIDATION, - "severity": ErrorSeverity.ERROR, - "hints": [ - "Check that all required input tensors are provided", - "Verify the tensor names match the configuration", - "Consider marking the tensor as optional if it's not always needed", - "Review the model input configuration" - ], - "remediation": [ - "Add the missing tensor to the input data", - "Update configuration to mark tensor as optional", - "Check upstream processor output", - "Verify route configuration" - ] - }, - "ERR_DF_002": { - "message": "Invalid data type in custom data processing", - "category": ErrorCategory.DATA_VALIDATION, - "severity": ErrorSeverity.ERROR, - "hints": [ - "Ensure base64 binary data is provided as bytes or string", - "Check data encoding format", - "Verify MIME type matches data format", - "Review data type conversions" - ], - "remediation": [ - "Convert data to expected format (bytes or string)", - "Verify base64 encoding is correct", - "Check data type in request payload" - ] - }, - "ERR_DF_003": { - "message": "Asset not found", - "category": ErrorCategory.ASSET_NOT_FOUND, - "severity": ErrorSeverity.ERROR, - "hints": [ - "Verify asset ID exists in AssetManager", - "Check if asset was properly uploaded/registered", - "Ensure asset hasn't been deleted or expired", - "Verify asset path and permissions" - ], - "remediation": [ - "Upload the asset before referencing it", - "Check asset ID spelling/format", - "Verify asset lifecycle management", - "Check AssetManager logs for registration issues" - ] - }, - "ERR_DF_004": { - "message": "Multiple result queues not supported in single dataflow", - "category": ErrorCategory.CONFIGURATION, - "severity": ErrorSeverity.ERROR, - "hints": [ - "Restructure dataflow to have single queue output", - "Split into multiple dataflows if needed" - ], - "remediation": [ - "Refactor to use single queue per dataflow", - "Create separate dataflows for multiple queues" - ] - }, - "ERR_DF_005": { - "message": "Queue full, dropping frames", - "category": ErrorCategory.QUEUE, - "severity": ErrorSeverity.WARNING, - "hints": [ - "Consumer is too slow, increase processing speed", - "Increase queue size if appropriate", - "Check for bottlenecks in pipeline" - ], - "remediation": [ - "Optimize downstream processing", - "Increase queue maxsize", - "Add backpressure handling" - ] - }, - "ERR_DF_006": { - "message": "Failed to inject tensors into input flows", - "category": ErrorCategory.DATA_VALIDATION, - "severity": ErrorSeverity.ERROR, - "hints": [ - "No matching input tensors found in the request" - ], - "remediation": [ - "Provide at least one valid tensor in the request", - "Check input tensor names against model configuration", - "Verify request payload format", - "Review input flow configuration" - ] - }, - - # Model Operator errors (MO) - "ERR_MO_001": { - "message": "Empty result from preprocessing", - "category": ErrorCategory.PROCESSING, - "severity": ErrorSeverity.ERROR, - "hints": [ - "Check preprocessor logic and return values", - "Verify input data is valid", - "Review preprocessor configuration", - "Ensure preprocessor returns non-empty data" - ], - "remediation": [ - "Debug preprocessor implementation", - "Verify input data format", - "Check preprocessor output configuration", - "Review preprocessing chain" - ] - }, - "ERR_MO_002": { - "message": "Model output incomplete", - "category": ErrorCategory.PROCESSING, - "severity": ErrorSeverity.ERROR, - "hints": [ - "Verify model generates all required outputs", - "Check if postprocessor is needed", - "Review model configuration", - "Ensure all output tensors are generated" - ], - "remediation": [ - "Add missing output tensors", - "Implement required postprocessor", - "Update model output configuration", - "Check model implementation" - ] - }, - "ERR_MO_003": { - "message": "No output dataflow bound to model", - "category": ErrorCategory.CONFIGURATION, - "severity": ErrorSeverity.CRITICAL, - "hints": [ - "Check route configuration", - "Ensure model output is connected to downstream", - "Review pipeline setup" - ], - "remediation": [ - "Add output binding in route configuration", - "Connect model to output dataflow", - "Review inference pipeline setup" - ] - }, - "ERR_MO_004": { - "message": "Invalid result from preprocessing", - "category": ErrorCategory.PROCESSING, - "severity": ErrorSeverity.ERROR, - "hints": [ - "Preprocessor must return valid data structure", - "Check for errors in preprocessing chain", - "Verify data format compatibility" - ] - }, - - # Processor errors (PROC) - "ERR_PROC_001": { - "message": "Processor output count mismatch", - "category": ErrorCategory.CONFIGURATION, - "severity": ErrorSeverity.WARNING, - "hints": [ - "Verify processor returns correct number of outputs", - "Check processor configuration matches implementation", - "Review processor output specification in the configuration", - "Ensure output list length matches config" - ], - "remediation": [ - "Update processor to return correct number of outputs", - "Fix processor configuration", - "Review processor implementation" - ] - }, - "ERR_PROC_002": { - "message": "Failed to load AutoProcessor", - "category": ErrorCategory.CONFIGURATION, - "severity": ErrorSeverity.CRITICAL, - "hints": [ - "Verify model home path is correct", - "Check if model supports AutoProcessor", - "Ensure transformers library is installed", - "Verify model files are present" - ], - "remediation": [ - "Check model_home path", - "Download model files", - "Use custom processor instead", - "Install required dependencies" - ] - }, - "ERR_PROC_003": { - "message": "Custom processor module invalid", - "category": ErrorCategory.CONFIGURATION, - "severity": ErrorSeverity.CRITICAL, - "hints": [ - "Custom module must have create_instance() method", - "Check custom processor implementation", - "Verify module imports correctly" - ], - "remediation": [ - "Implement create_instance() in custom module", - "Fix custom processor module", - "Check module path and imports" - ] - }, - "ERR_PROC_004": { - "message": "Failed to create custom processor", - "category": ErrorCategory.CONFIGURATION, - "severity": ErrorSeverity.CRITICAL, - "hints": [ - "Check processor configuration", - "Verify processor class exists", - "Verify the processor name in the configuration matches the name in the processor class" - ] - }, - - # Image processing errors (IMG) - "ERR_IMG_001": { - "message": "Unsupported image format", - "category": ErrorCategory.DATA_VALIDATION, - "severity": ErrorSeverity.ERROR, - "hints": [ - "Supported formats: JPEG, PNG", - "Check MIME type of uploaded image", - "Convert image to supported format", - "Verify image encoding" - ], - "remediation": [ - "Convert image to JPEG or PNG", - "Check image MIME type", - "Re-encode image in supported format" - ] - }, - "ERR_IMG_002": { - "message": "Invalid base64 image data", - "category": ErrorCategory.DATA_VALIDATION, - "severity": ErrorSeverity.ERROR, - "hints": [ - "Base64 image must be bytes or string", - "Check data encoding", - "Verify base64 format: data:image/[type];base64,[data]" - ], - "remediation": [ - "Encode image as valid base64 string", - "Check data URL format", - "Verify data type (bytes/string)" - ] - }, - "ERR_IMG_003": { - "message": "Image decoder not initialized", - "category": ErrorCategory.INTERNAL, - "severity": ErrorSeverity.CRITICAL, - "hints": [ - "Image decoder failed to initialize", - "Check codec dependencies", - "Verify system libraries" - ] - }, - - # Video processing errors (VIDEO) - "ERR_VIDEO_001": { - "message": "Invalid video frame sampling parameters", - "category": ErrorCategory.CONFIGURATION, - "severity": ErrorSeverity.WARNING, - "hints": [ - "Frames or chunks parameters differ across assets", - "Ensure consistent parameters for batch processing", - "Review asset query parameters" - ] - }, - - # Route configuration errors (ROUTE) - "ERR_ROUTE_001": { - "message": "Invalid route configuration", - "category": ErrorCategory.CONFIGURATION, - "severity": ErrorSeverity.CRITICAL, - "hints": [ - "Check route format: 'model:[tensor, ...]' -> 'model:[tensor, ...]'", - "Verify JSON syntax in data specifications", - "Review route configuration documentation" - ], - "remediation": [ - "Fix route configuration format", - "Validate JSON in route data", - "Review routing examples" - ] - }, - "ERR_ROUTE_002": { - "message": "Model not found in route configuration", - "category": ErrorCategory.CONFIGURATION, - "severity": ErrorSeverity.CRITICAL, - "hints": [ - "Model referenced in route doesn't exist", - "Check model name spelling", - "Verify model is defined in configuration" - ], - "remediation": [ - "Add model to configuration", - "Fix model name in route", - "Review model definitions" - ] - }, - - # AsyncDispatcher errors (ASYNC) - "ERR_ASYNC_001": { - "message": "AsyncDispatcher failed to start - collector is None", - "category": ErrorCategory.INTERNAL, - "severity": ErrorSeverity.CRITICAL, - "hints": [ - "Collector not properly initialized", - "Check pipeline setup", - "Review initialization order" - ] - }, - "ERR_ASYNC_002": { - "message": "AsyncDispatcher failed to start - event loop is None", - "category": ErrorCategory.INTERNAL, - "severity": ErrorSeverity.CRITICAL, - "hints": [ - "Event loop not properly initialized", - "Check async setup", - "Review initialization order" - ] - }, - "ERR_ASYNC_003": { - "message": "Consumer queue reached maximum size", - "category": ErrorCategory.QUEUE, - "severity": ErrorSeverity.CRITICAL, - "hints": [ - "Too many concurrent requests", - "Increase queue size", - "Add rate limiting", - "Check consumer processing speed" - ], - "remediation": [ - "Increase n_max_async_queues parameter", - "Implement request throttling", - "Optimize consumer processing" - ] - }, - - # System errors (SYS) - "ERR_SYS_001": { - "message": "Model repository does not exist", - "category": ErrorCategory.CONFIGURATION, - "severity": ErrorSeverity.CRITICAL, - "hints": [ - "Check model_repo path in configuration", - "Verify directory exists and is accessible", - "Check file permissions" - ], - "remediation": [ - "Create the model repository directory and correctly mount it into the container.", - "Fix model_repo path in configuration", - "Check directory permissions" - ] - }, - "ERR_SYS_002": { - "message": "Inference pipeline incomplete", - "category": ErrorCategory.CONFIGURATION, - "severity": ErrorSeverity.CRITICAL, - "hints": [ - "Either input or output is empty", - "Check route configuration", - "Review pipeline setup" - ], - "remediation": [ - "Add input/output bindings", - "Fix route configuration", - "Review pipeline configuration" - ] - }, - } - - @classmethod - def _get_caller_info(cls, caller=None, auto_operation: bool = False): - """ - Extract component and operation names from caller context - - Args: - caller: The calling object (usually `self`). If provided, extracts class name. - auto_operation: If True, extracts the calling method name from stack - - Returns: - tuple: (component_name, operation_name) - """ - component = None - operation = None - - # Extract component from caller object - if caller is not None: - component = caller.__class__.__name__ - - # Extract operation from call stack - if auto_operation: - # Get the calling frame (skip this method and the create/from_exception method) - frame = inspect.currentframe() - try: - # Go up the stack: _get_caller_info -> create/from_exception -> actual caller - caller_frame = frame.f_back.f_back - if caller_frame: - operation = caller_frame.f_code.co_name - finally: - del frame # Avoid reference cycles - - return component, operation - - @classmethod - def create( - cls, - error_code: str, - message: Optional[str] = None, - caller: Optional[Any] = None, - auto_operation: bool = True, - **kwargs - ) -> EnhancedError: - """ - Create error from catalog with additional context - - Args: - error_code: Error code from ERROR_CATALOG - message: Custom message (overrides catalog message) - caller: The calling object (usually `self`). Auto-extracts class name as component. - auto_operation: If True, auto-detects calling method name as operation. - **kwargs: Additional context fields for EnhancedError - - Returns: - EnhancedError instance with all context - - Example: - # Automatic component/operation detection: - error = ErrorFactory.create( - "ERR_DF_001", - caller=self, # Extracts "DataFlow" from self.__class__.__name__ - auto_operation=True, # Extracts "put" from calling method - tensor_names=["image"] - ) - - # Manual specification (still supported): - error = ErrorFactory.create( - "ERR_DF_001", - component="DataFlow", - operation="put", - tensor_names=["image"] - ) - """ - # Auto-detect component and operation if requested - auto_component, auto_operation_name = cls._get_caller_info(caller, auto_operation) - - # Use auto-detected values if not explicitly provided - if auto_component and 'component' not in kwargs: - kwargs['component'] = auto_component - if auto_operation_name and 'operation' not in kwargs: - kwargs['operation'] = auto_operation_name - - if error_code not in cls.ERROR_CATALOG: - # Remove conflicting keys from kwargs - final_category = kwargs.pop('category', ErrorCategory.INTERNAL) - final_severity = kwargs.pop('severity', ErrorSeverity.ERROR) - error = EnhancedError( - message=message or f"Unknown error code: {error_code}", - category=final_category, - severity=final_severity, - error_code=error_code, - troubleshooting_hints=[ - "This error code is not in the catalog", - "Check error code spelling", - "Review error handling implementation" - ], - **kwargs - ) - # Add to global collector if enabled - cls._add_to_collector(error) - return error - - template = cls.ERROR_CATALOG[error_code] - - # Use custom message or template message - final_message = message or template["message"] - - # If custom message provided, append template message as hint - hints = list(template.get("hints", [])) - if message and message != template["message"]: - hints.insert(0, f"Base error: {template['message']}") - - # Merge catalog values with provided kwargs - # User-provided kwargs override catalog defaults - final_category = kwargs.pop('category', template.get("category", ErrorCategory.INTERNAL)) - final_severity = kwargs.pop('severity', template.get("severity", ErrorSeverity.ERROR)) - - error = EnhancedError( - message=final_message, - category=final_category, - severity=final_severity, - error_code=error_code, - troubleshooting_hints=hints, - remediation_steps=list(template.get("remediation", [])), - **kwargs - ) - # Add to global collector if enabled - cls._add_to_collector(error) - return error - - @classmethod - def _add_to_collector(cls, error: EnhancedError): - """Add error to global collector if enabled""" - global _global_error_collector - if _global_error_collector is not None: - _global_error_collector.add(error) - - @classmethod - def from_exception( - cls, - exc: Exception, - component: Optional[str] = None, - operation: Optional[str] = None, - caller: Optional[Any] = None, - auto_operation: bool = True, - error_code: Optional[str] = None, - category: ErrorCategory = ErrorCategory.INTERNAL, - severity: ErrorSeverity = ErrorSeverity.ERROR, - **kwargs - ) -> EnhancedError: - """ - Create EnhancedError from Python exception - - Args: - exc: The exception object - component: Component where error occurred (or use caller for auto-detection) - operation: Operation that failed (or use auto_operation=True) - caller: The calling object (usually `self`). Auto-extracts class name as component. - auto_operation: If True, auto-detects calling method name as operation. - error_code: Optional error code - category: Error category - severity: Error severity - **kwargs: Additional context - - Returns: - EnhancedError with exception details - - Example: - # Automatic component/operation detection: - except Exception as e: - error = ErrorFactory.from_exception( - e, - caller=self, # Extracts class name - auto_operation=True # Extracts method name - ) - - # Manual specification (still supported): - except Exception as e: - error = ErrorFactory.from_exception( - e, - component="ModelOperator", - operation="run" - ) - """ - # Auto-detect component and operation if requested - auto_component, auto_operation_name = cls._get_caller_info(caller, auto_operation) - - # Use auto-detected values if not explicitly provided - if component is None and auto_component: - component = auto_component - if operation is None and auto_operation_name: - operation = auto_operation_name - - # Remove conflicting keys from kwargs to avoid duplicate parameter errors - # (kwargs might contain category/severity if passed by caller) - kwargs.pop('category', None) - kwargs.pop('severity', None) - kwargs.pop('component', None) - kwargs.pop('operation', None) - kwargs.pop('error_code', None) - - error = EnhancedError( - message=str(exc), - category=category, - severity=severity, - component=component, - operation=operation, - error_code=error_code or "ERR_EXCEPTION", - exception_type=type(exc).__name__, - stack_trace=traceback.format_exc(), - troubleshooting_hints=[ - f"Exception type: {type(exc).__name__}", - f"Exception message: {str(exc)}", - "Review stack trace for details" - ], - **kwargs - ) - # Add to global collector if enabled - cls._add_to_collector(error) - return error - - @classmethod - def get_catalog_summary(cls) -> Dict[str, Any]: - """Get summary of error catalog for documentation""" - summary = { - "total_errors": len(cls.ERROR_CATALOG), - "by_category": {}, - "by_severity": {}, - "errors": {} - } - - for code, info in cls.ERROR_CATALOG.items(): - category = info.get("category", ErrorCategory.INTERNAL).value - severity = info.get("severity", ErrorSeverity.ERROR).value - - summary["by_category"][category] = summary["by_category"].get(category, 0) + 1 - summary["by_severity"][severity] = summary["by_severity"].get(severity, 0) + 1 - - summary["errors"][code] = { - "message": info["message"], - "category": category, - "severity": severity, - "hints_count": len(info.get("hints", [])), - "remediation_count": len(info.get("remediation", [])) - } - - return summary - - -class ErrorCollector: - """ - Collect and analyze errors for patterns and statistics - - This class maintains a thread-safe collection of errors and provides - statistics and analysis capabilities for monitoring and debugging. - """ - - def __init__(self, max_errors: int = 1000): - """ - Initialize error collector - - Args: - max_errors: Maximum number of errors to keep in memory - """ - self._errors: List[EnhancedError] = [] - self._error_counts: Dict[str, int] = {} - self._max_errors = max_errors - self._lock = threading.RLock() # RLock allows same thread to acquire multiple times - self._start_time = time.time() - - def add(self, error: EnhancedError): - """Add error to collection (thread-safe)""" - with self._lock: - self._errors.append(error) - self._error_counts[error.error_code] = \ - self._error_counts.get(error.error_code, 0) + 1 - - # Trim if exceeds max size (keep most recent) - if len(self._errors) > self._max_errors: - self._errors = self._errors[-self._max_errors:] - - def get_stats(self, include_recent: int = 10) -> Dict: - """ - Get error statistics for monitoring - - Args: - include_recent: Number of recent errors to include - - Returns: - Dictionary with error statistics - """ - with self._lock: - stats = { - "collection_period_seconds": time.time() - self._start_time, - "total_errors": len(self._errors), - "unique_error_codes": len(self._error_counts), - "by_category": self._count_by("category"), - "by_severity": self._count_by("severity"), - "by_component": self._count_by("component"), - "by_model": self._count_by("model_name"), - "by_code": dict(sorted( - self._error_counts.items(), - key=lambda x: x[1], - reverse=True - )[:20]), # Top 20 error codes - } - - if include_recent > 0: - stats["recent_errors"] = [ - e.to_dict(include_stack_trace=False) - for e in self._errors[-include_recent:] - ] - - return stats - - def get_errors_by_category(self, category: ErrorCategory) -> List[EnhancedError]: - """Get all errors of a specific category""" - with self._lock: - return [e for e in self._errors if e.category == category] - - def get_errors_by_code(self, error_code: str) -> List[EnhancedError]: - """Get all errors with specific error code""" - with self._lock: - return [e for e in self._errors if e.error_code == error_code] - - def get_critical_errors(self) -> List[EnhancedError]: - """Get all critical errors""" - with self._lock: - return [e for e in self._errors if e.severity == ErrorSeverity.CRITICAL] - - def clear(self): - """Clear all collected errors""" - with self._lock: - self._errors.clear() - self._error_counts.clear() - self._start_time = time.time() - - def _count_by(self, field: str) -> Dict[str, int]: - """Count errors by field value""" - counts = {} - for error in self._errors: - value = getattr(error, field, None) - if value is None or value == "": - continue - - # Handle enum values - if hasattr(value, 'value'): - key = value.value - # Handle lists - elif isinstance(value, list): - if len(value) == 0: - continue - key = ", ".join(str(v) for v in value[:3]) # First 3 items - if len(value) > 3: - key += "..." - else: - key = str(value) - - counts[key] = counts.get(key, 0) + 1 - - return dict(sorted(counts.items(), key=lambda x: x[1], reverse=True)[:20]) - - def export_to_json(self, filepath: str, include_stack_traces: bool = False): - """Export all errors to JSON file""" - with self._lock: - data = { - "exported_at": time.time(), - "stats": self.get_stats(include_recent=0), - "errors": [ - e.to_dict(include_stack_trace=include_stack_traces) - for e in self._errors - ] - } - - with open(filepath, 'w') as f: - json.dump(data, f, indent=2, default=str) - f.flush() - os.fsync(f.fileno()) - - -# Global error collector instance (optional) -_global_error_collector = None - -def get_global_error_collector() -> ErrorCollector: - """Get or create global error collector""" - global _global_error_collector - if _global_error_collector is None: - _global_error_collector = ErrorCollector() - return _global_error_collector - - -def enable_global_error_collection(max_errors: int = 1000): - """Enable global error collection""" - global _global_error_collector - _global_error_collector = ErrorCollector(max_errors=max_errors) - - -def disable_global_error_collection(): - """Disable global error collection""" - global _global_error_collector - _global_error_collector = None - diff --git a/tools/inference_builder/lib/inference.py b/tools/inference_builder/lib/inference.py deleted file mode 100644 index 9b25f29e..00000000 --- a/tools/inference_builder/lib/inference.py +++ /dev/null @@ -1,2051 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import atexit -import base64 -from concurrent.futures import ThreadPoolExecutor, Future -import os -import threading -from queue import Queue, Empty, Full -from abc import ABC, abstractmethod -from config import global_config -from .utils import get_logger, split_tensor_in_dict, FutureConsumer, QueueConsumer -from .codec import ImageDecoder -import custom -from omegaconf import OmegaConf -from dataclasses import dataclass -from typing import Dict, List, Tuple, Union, Generator -import numpy as np -from collections import namedtuple -import json -from pyservicemaker import Pipeline, as_tensor -from pyservicemaker.utils import MediaExtractor, MediaChunk -import torch -from .asset_manager import AssetManager -import time -import asyncio -import inspect -from .errors import ( - Error, - EnhancedError, - ErrorFactory, - ErrorCategory, - ErrorSeverity, - enable_global_error_collection, - get_global_error_collector -) - - -logger = get_logger(__name__) - -py_datatype_mapping = { - "TYPE_UINT8": int, - "TYPE_UINT16": int, - "TYPE_UINT32": int, - "TYPE_UINT64": int, - "TYPE_INT8": int, - "TYPE_INT16": int, - "TYPE_INT32": int, - "TYPE_INT64": int, - "TYPE_FP16": float, - "TYPE_FP32": float, - "TYPE_FP64": float, - "TYPE_STRING": str, - "TYPE_CUSTOM_DS_IMAGE": str, - "TYPE_CUSTOM_DS_MIME": str, - "TYPE_CUSTOM_LIST": str, - "TYPE_CUSTOM_DS_MEDIA_URL": str, - "TYPE_CUSTOM_DS_SOURCE_CONFIG": str, - "TYPE_CUSTOM_BINARY_BASE64": str, - "TYPE_CUSTOM_VIDEO_CHUNK_ASSETS": str, - "TYPE_CUSTOM_LONG_VIDEO_ASSETS": str, - "TYPE_CUSTOM_IMAGE_ASSETS": str, - "TYPE_CUSTOM_IMAGE_BASE64": str -} - -np_datatype_mapping = { - "TYPE_INVALID": None, - "TYPE_BOOL": np.bool_, - "TYPE_UINT8": np.ubyte, - "TYPE_UINT16": np.uint16, - "TYPE_UINT32": np.uint32, - "TYPE_UINT64": np.uint64, - "TYPE_INT8": np.byte, - "TYPE_INT16": np.int16, - "TYPE_INT32": np.int32, - "TYPE_INT64": np.int64, - "TYPE_FP16": np.float16, - "TYPE_FP32": np.float32, - "TYPE_FP64": np.float64, - "TYPE_STRING": np.string_, - "TYPE_CUSTOM_DS_IMAGE": np.ubyte, - "TYPE_CUSTOM_DS_MIME": np.string_, - "TYPE_CUSTOM_DS_MEDIA_URL": np.string_, - "TYPE_CUSTOM_DS_SOURCE_CONFIG": str, - "TYPE_BF16": None, - "TYPE_CUSTOM_OBJECT": None -} - -torch_datatype_mapping = { - "TYPE_INVALID": None, - "TYPE_BOOL": torch.bool, - "TYPE_UINT8": torch.uint8, - "TYPE_UINT16": torch.int16, - "TYPE_UINT32": torch.int32, - "TYPE_UINT64": torch.int64, - "TYPE_INT8": torch.int8, - "TYPE_INT16": torch.int16, - "TYPE_INT32": torch.int32, - "TYPE_INT64": torch.int64, - "TYPE_FP16": torch.float16, - "TYPE_FP32": torch.float32, - "TYPE_FP64": torch.float64, - "TYPE_STRING": None, - "TYPE_CUSTOM_DS_IMAGE": torch.int8, - "TYPE_CUSTOM_DS_MIME": None, - "TYPE_CUSTOM_DS_MEDIA_URL": None, - "TYPE_CUSTOM_DS_SOURCE_CONFIG": str, - "TYPE_BF16": None, - "TYPE_CUSTOM_OBJECT": None -} - -@dataclass -class Stop: - reason: str - - def __bool__(self): - return False - -Path = namedtuple('Path', ['source', 'target']) -Route = namedtuple('Route', ['model', 'data']) - -class DataFlow: - """A single data flow from or to a model""" - def __init__( - self, - configs: List[Dict], - tensor_names: List[Tuple[str, str]], - inbound: bool = False, - outbound: bool = False, - timeout=1.0 - ): - self._configs = configs - self._tensor_names = tensor_names - self._inbound = inbound - self._outbound = outbound - self._timeout = timeout - self._queue = Queue() - self._optional = False - self._stop_event = threading.Event() - if self._inbound or self._outbound: - self._optional = all([ - config["optional"] if "optional" in config else False - for config in self._configs - ]) - self._queue_consumer = None - - def _process_custom_data(self, tensor: np.ndarray, data_type: str) -> Union[List, np.ndarray, Queue, EnhancedError]: - """ - Process custom data types that are not standard numpy types. - - Args: - tensor: Input tensor data - data_type: The custom data type string (e.g., "TYPE_CUSTOM_BINARY_BASE64") - - Returns: - Union[List, np.ndarray, Queue, EnhancedError]: - - List: For processed data like base64 decoded arrays - - np.ndarray: For tensor data - - Queue: For streaming data (in subclasses) - - EnhancedError: If processing fails (e.g., decoder not initialized) - - Note: - Subclasses override this method to handle specific data types. - The return value is assigned to collected[o_name] in the put() method. - """ - processed = tensor - if self._inbound: - if data_type == "TYPE_CUSTOM_LIST": - logger.debug("DataFlow _process_custom_data: %s", data_type) - processed = [input for input in tensor] - elif data_type == "TYPE_CUSTOM_BINARY_BASE64": - logger.debug("DataFlow _process_custom_data: %s", data_type) - processed = [] - for input in tensor: - if not isinstance(input, np.str_): - error = ErrorFactory.create( - "ERR_DF_002", - message=f"base64 binary must be bytes or string, got {type(input).__name__}", - caller=self, - input_data={"input_type": type(input).__name__}, - expected_data={"valid_types": ["bytes", "np.bytes_", "str", "np.str_"]} - ) - error.log(logger, as_json=False) - return error - try: - decoded = base64.b64decode(input) - except Exception as e: - error = ErrorFactory.create( - "ERR_DF_002", - message=f"Failed to decode base64 string: {e}", - caller=self, - input_data={"input": input}, - expected_data={"valid_encoding": "base64"} - ) - error.log(logger, as_json=False) - return error - processed.append(np.frombuffer(decoded, dtype=np.uint8)) - return processed - - def _is_collected_valid(self, collected: Dict): - if not collected: - logger.info("No data collected from the dataflow: %s", self.in_names) - return False - # check output data integrity - if self._outbound: - for config in self._configs: - name = config["name"] - optional = "optional" in config and config["optional"] - if name not in collected and not optional: - return False - return True - - @property - def in_names(self): - return [i[0] for i in self._tensor_names] - - @property - def o_names(self): - return [i[1] for i in self._tensor_names] - - @property - def optional(self): - return self._optional - - def get_config(self, name: str): - if not self._configs: - return None - return next((config for config in self._configs if config["name"] == name), None) - - def put(self, item: Union[Dict, Error, Stop]): - logger.debug("[DataFlow.put] in_names=%s, o_names=%s, item_type=%s", self.in_names, self.o_names, type(item).__name__) - if not item: - if self._queue_consumer is None: - # pass Error or Stop to the downstream - self._queue.put(item, timeout=self._timeout) - return - # check input data integrity - if self._inbound: - for config in self._configs: - name = config["name"] - optional = "optional" in config and config["optional"] - if name not in item and not optional: - error = ErrorFactory.create( - "ERR_DF_001", - message=f"{name} is not optional and not found from the dataflow input configs", - caller=self, - dataflow_names=self.in_names, - tensor_names=[name], - expected_data={"required_tensors": [c["name"] for c in self._configs if not c.get("optional", False)]}, - actual_data={"provided_tensors": list(item.keys())}, - related_config={"config": config, "all_configs": self._configs} - ) - error.log(logger, as_json=False) - return - # collect data and deposit it to the queue - collected = {} - for i_name, o_name in self._tensor_names: - if i_name not in item: - continue - tensor = item[i_name] - # handling custom data type - if self._inbound or self._outbound: - config = self.get_config(i_name) - if config and not config["data_type"] in np_datatype_mapping: - result = self._process_custom_data(tensor, config["data_type"]) - # Check if processing returned an error - if isinstance(result, EnhancedError): - self._queue.put(result) - return - collected[o_name] = result - else: - collected[o_name] = tensor - else: - collected[o_name] = tensor - # check output data integrity - if not self._is_collected_valid(collected): - error = ErrorFactory.create( - "ERR_DF_007", - message=f"Invalid data collected from the dataflow: {self.in_names}", - caller=self, - severity=ErrorSeverity.WARNING, - dataflow_names=self.in_names, - actual_data={"collected_keys": list(collected.keys()) if collected else []} - ) - error.log(logger, as_json=False) - self._queue.put(error) - return - - # deposit the collected data to the queue - values = {} - results_queues = {} - for k, v in collected.items(): - if isinstance(v, Queue): - results_queues[k] = v - else: - values[k] = v - - if results_queues: - if len(results_queues) > 1: - error = ErrorFactory.create( - "ERR_DF_004", - message=f"Multiple result queues are not supported in one dataflow yet, got {len(results_queues)} queues", - caller=self, - dataflow_names=self.in_names, - actual_data={"n_queues": len(results_queues), "queue_keys": list(results_queues.keys())} - ) - error.log(logger, as_json=False) - self._queue.put(error) - return - q_key, q = next(iter(results_queues.items())) - def on_result_queue_result(item, user_data): - if not item: - # end of the inference - self._queue.put(item, timeout=self._timeout) - return - result_values = user_data[1] - result_values[q_key] = item - self._queue.put(result_values, timeout=self._timeout) - if self._queue_consumer is None: - self._queue_consumer = QueueConsumer("dataflow_queue_consumer", self._stop_event) - collected.pop(q_key) - self._queue_consumer.append_queue(q, on_result_queue_result, (q_key, collected)) - else: - self._queue.put(values, timeout=self._timeout) - return - - def get(self): - logger.debug("[DataFlow.get] Attempting get from queue: in_names=%s, o_names=%s, qsize=%s", self.in_names, self.o_names, self._queue.qsize()) - result = self._queue.get(timeout=self._timeout) - logger.debug("[DataFlow.get] Got result: in_names=%s, item_type=%s", self.in_names, type(result).__name__) - return result - - def stop(self): - self._stop_event.set() - self._queue.put(Stop("Shutdown")) - - def is_stopped(self): - return self._stop_event.is_set() - - def parse_asset_string(self, asset: str): - pieces = asset.split("?") - asset = pieces[0] - params = {} - if len(pieces) > 1: - query_string = pieces[1] - query_params = query_string.split("&") - for param in query_params: - key, value = param.split("=") - params[key] = value - return asset, params - -class VideoInputDataFlow(DataFlow): - """A data flow for live stream data""" - def __init__(self, configs: List[Dict], tensor_names: List[Tuple[str, str]], key_tensor_type: str, timeout=1.0): - super().__init__(configs, tensor_names, True, False, timeout) - self._video_tensor_type = key_tensor_type - self._video_tensor_names = [] - for tensor_name in tensor_names: - config = next((c for c in configs if c["name"] == tensor_name[0]), None) - if config and config["data_type"] == key_tensor_type: - self._video_tensor_names.append(tensor_name[1]) - # Thread pool for collecting frames in background - n_codec_instances = int(os.getenv("N_CODEC_INSTANCES", 1)) - self._frame_collector = ThreadPoolExecutor(max_workers=n_codec_instances) - logger.info(f"VideoInputDataFlow initialized with {n_codec_instances} codec instances") - - def _process_custom_data(self, tensor: np.ndarray, data_type: str): - """ - Process custom video data types. - - Returns: - Union[Queue, np.ndarray, EnhancedError]: - - Queue: Results queue for live video streams (non-blocking) - - np.ndarray: Fallback to parent processing - - EnhancedError: If asset not found or processing fails - """ - if data_type == self._video_tensor_type: - return self._process_video_assets(tensor) - else: - return super()._process_custom_data(tensor, data_type) - - def _collect_live_stream_frames(self, asset_list, chunk_params, media_chunks, results_queue, scaling=None): - """ - Collect frames from live streams in a background thread. - - Args: - asset_list: List of locked assets - chunk_params: List of dicts with n_frames and max_chunks for each asset - media_chunks: List of MediaChunk configurations - results_queue: Queue to put results into - scaling: Optional target resolution as (width, height) tuple - """ - unlocked = False - try: - max_batch_size = int(os.getenv("MAX_BATCH_SIZE", 16)) - batch_size = min(max_batch_size, len(media_chunks)) - extractor_kwargs = {"n_thread": 1, "batch_size": batch_size} - if scaling is not None: - extractor_kwargs["scaling"] = scaling - with MediaExtractor(media_chunks, **extractor_kwargs) as media_extractor: - qs = media_extractor() - eos_flags = [False] * len(qs) - chunks_yielded = [0] * len(qs) # Track chunks per stream - - while not all(eos_flags): - results = [] - for idx, q in enumerate(qs): - # Check if this specific asset is marked for deletion - if asset_list[idx].is_marked_for_deletion(): - logger.info(f"Stream {idx} asset marked for deletion, stopping processing for this stream") - eos_flags[idx] = True - continue - - # Check if this stream has reached its maximum chunks limit - max_chunks = chunk_params[idx].get("max_chunks") - if max_chunks is not None and chunks_yielded[idx] >= max_chunks: - logger.info("Stream %s reached maximum chunks limit: %s", idx, max_chunks) - eos_flags[idx] = True - continue - - n_frames = chunk_params[idx]["n_frames"] - frames = [] - frame_count = 0 - - while not eos_flags[idx]: - try: - frame = q.get(timeout=5.0) - if frame is None: - logger.info("Frame extraction completed for stream %s", idx) - eos_flags[idx] = True - break - frames.append(frame) - frame_count += 1 - - # Put to results queue when we reach n_frames - if n_frames and frame_count >= n_frames: - results.append(frames) - chunks_yielded[idx] += 1 - frames = [] - frame_count = 0 - break # Exit inner loop - - except Empty: - logger.info("EOS on live stream %s", idx) - eos_flags[idx] = True - break - - # TODO WAR on pyservicemaker error - time.sleep(0) - - if results: # Only put if we have collected frames - try: - results_queue.put(results, timeout=10.0) - except Full: - logger.warning(f"Results queue is full, dropping frame batch") - break - - # Unlock assets BEFORE MediaExtractor cleanup to avoid blocking on __exit__ - results_queue.put(Stop("end")) - for asset in asset_list: - asset.unlock() - unlocked = True - - logger.info("Media processing completed") - except Exception as e: - logger.exception(f"Error in live stream frame collection: {e}") - error = ErrorFactory.from_exception(e, caller=self) - error.log(logger, as_json=False) - results_queue.put(error) - finally: - # Fallback: ensure assets are unlocked if exception occurred before unlocking - if not unlocked: - results_queue.put(Stop("end")) - for asset in asset_list: - asset.unlock() - - def _process_video_assets(self, assets: np.ndarray): - """ - Process video assets using Queue-based approach (non-blocking). - - Args: - assets: Array of asset strings with query parameters - - Returns: - Union[Queue, EnhancedError]: Results queue or error - """ - media_chunks = [] - chunk_params = [] # Store n_frames and max_chunks for each asset - asset_list = [] - expected_n_frames = None - expected_scaling = None - - for a in assets: - asset_manager = AssetManager() - asset_id, params = self.parse_asset_string(a) - asset = asset_manager.get_asset(asset_id) - if asset: - asset_list.append(asset) - n = int(params.get("frames", None)) - max_chunks = params.get("chunks", None) - if max_chunks is not None: - max_chunks = int(max_chunks) - - # Validate that all assets have the same n_frames - if expected_n_frames is None: - expected_n_frames = n - elif expected_n_frames != n: - error = ErrorFactory.create( - "ERR_VIDEO_001", - message=f"frames mismatch on multiple assets: {expected_n_frames} != {n}", - caller=self, - severity=ErrorSeverity.WARNING, - actual_data={"n_frames_first": expected_n_frames, "n_frames_current": n} - ) - error.log(logger, as_json=False) - return error - - interval = int(params.get("interval", 0)) - - # Parse scaling parameter (format: widthxheight, e.g., 1920x1080) - scaling = None - scale_param = params.get("scale", None) - if scale_param: - try: - width, height = scale_param.split("x") - scaling = (int(width), int(height)) - except (ValueError, AttributeError) as e: - error = ErrorFactory.create( - "ERR_VIDEO_001", - message=f"Invalid scale parameter format: {scale_param}. Expected format: widthxheight (e.g., 1920x1080)", - caller=self, - severity=ErrorSeverity.WARNING, - input_data={"scale_param": scale_param} - ) - error.log(logger, as_json=False) - return error - - # Validate that all assets have the same scaling - if expected_scaling is None: - expected_scaling = scaling - elif expected_scaling != scaling: - error = ErrorFactory.create( - "ERR_VIDEO_001", - message=f"scale mismatch on multiple assets: {expected_scaling} != {scaling}", - caller=self, - severity=ErrorSeverity.WARNING, - actual_data={"scale_first": expected_scaling, "scale_current": scaling} - ) - error.log(logger, as_json=False) - return error - - media_chunks.append(MediaChunk( - asset.path, - start_pts=0, - interval=interval - )) - chunk_params.append({"n_frames": n, "max_chunks": max_chunks}) - else: - error = ErrorFactory.create( - "ERR_DF_003", - message=f"Asset not found: {asset_id}", - caller=self, - input_data={ - "asset_id": asset_id, - "asset_string": a, - "params": params - }, - metadata={ - "n_frames": params.get("frames"), - "interval": params.get("interval") - } - ) - error.log(logger, as_json=False) - return error - - # Create results queue and submit background thread - results_queue = Queue(maxsize=32) - for asset in asset_list: - asset.lock() - - self._frame_collector.submit( - self._collect_live_stream_frames, asset_list, chunk_params, media_chunks, results_queue, expected_scaling - ) - return results_queue - - def _is_collected_valid(self, collected: Dict): - result = super()._is_collected_valid(collected) - if not result: - return False - return all([n in collected for n in self._video_tensor_names]) - - def stop(self): - super().stop() - if hasattr(self, '_frame_collector') and self._frame_collector: - logger.info("VideoInputDataFlow: shutting down frame collector") - self._frame_collector.shutdown(wait=True) - logger.info("VideoInputDataFlow: frame collector shut down") - self._frame_collector = None - - -class VideoFrameSamplingDataFlow(DataFlow): - """A data flow for video frame sampling - for short videos without chunking""" - def __init__(self, configs: List[Dict], tensor_names: List[Tuple[str, str]], key_tensor_type: str, timeout=1.0): - super().__init__(configs, tensor_names, True, False, timeout) - n_codec_instances = int(os.getenv("N_CODEC_INSTANCES", 1)) - self._media_extractor = MediaExtractor(chunks=[], n_thread=n_codec_instances) - self._video_tensor_type = key_tensor_type - self._video_tensor_names = [] - for tensor_name in tensor_names: - config = next((c for c in configs if c["name"] == tensor_name[0]), None) - if config and config["data_type"] == key_tensor_type: - self._video_tensor_names.append(tensor_name[1]) - self._media_extractor() - self._frame_collector = ThreadPoolExecutor(max_workers=n_codec_instances) - logger.info(f"VideoFrameSamplingDataFlow initialized for short video handling") - - def _process_custom_data(self, tensor: np.ndarray, data_type: str) -> Union[Queue, List, np.ndarray, EnhancedError]: - """ - Process custom video frame sampling data types. - - Returns: - Union[Queue, List, np.ndarray, EnhancedError]: - - Queue: Results queue for frame sampling (streaming) - - List: Fallback to parent processing - - np.ndarray: Fallback to parent processing - - EnhancedError: If asset not found or processing fails - """ - logger.debug("VideoFrameSamplingDataFlow._process_custom_data: %s", data_type) - if data_type == self._video_tensor_type: - return self._do_video_frame_sampling(tensor) - else: - return super()._process_custom_data(tensor, data_type) - - def _collect_frames_from_queue(self, assets, qs, n_frames, results_queue): - """Collect frames from queues in a separate thread - single batch, no chunking""" - total_frames = [[] for _ in qs] - - # Collect n_frames from each queue - for frame_idx in range(n_frames): - for i, q in enumerate(qs): - try: - frame = q.get(timeout=10.0) - if frame is None: - logger.info("Frame extraction completed for stream %s", i) - break - total_frames[i].append(frame) - except Empty: - logger.warning("Frame queue empty for stream %s", i) - break - - # Put the collected frames to results queue - if any(total_frames): - try: - results_queue.put(total_frames, timeout=10.0) - except Full: - error = ErrorFactory.create( - "ERR_DF_005", - caller=self, - severity=ErrorSeverity.WARNING, - metadata={ - "n_frames": n_frames, - "queue_info": "Results queue is full" - } - ) - error.log(logger, as_json=False) - - # Signal completion and unlock assets - results_queue.put(Stop("end")) - for asset in assets: - asset.unlock() - return - - - def _do_video_frame_sampling(self, assets: np.ndarray): - """Extract frames from videos - single batch, no chunking. Parameters: frames, start, duration""" - qs = [] - asset_list = [] - start_times = [] # Store per-asset start times - n_frames = None - duration = None - - for asset_str in assets: - asset_manager = AssetManager() - asset_id, params = self.parse_asset_string(asset_str) - asset = asset_manager.get_asset(asset_id) - if not asset: - error = ErrorFactory.create( - "ERR_DF_003", - message=f"Asset not found: {asset_id}", - caller=self, - input_data={"asset_id": asset_id, "asset_string": asset_str, "params": params} - ) - error.log(logger, as_json=False) - return error - - asset_list.append(asset) - - # Validate frames parameter (required) - if "frames" not in params: - error = ErrorFactory.create( - "ERR_VIDEO_001", - message="Missing required parameter 'frames' for video frame sampling", - caller=self, - severity=ErrorSeverity.CRITICAL, - input_data={"asset_id": asset_id, "params": params}, - expected_data={"required_params": ["frames"]} - ) - error.log(logger, as_json=False) - return error - - try: - n = int(params["frames"]) - if n <= 0: - raise ValueError("frames must be positive") - except (TypeError, ValueError) as e: - error = ErrorFactory.create( - "ERR_VIDEO_001", - message=f"Invalid 'frames' value: {params['frames']} - {e}", - caller=self, - severity=ErrorSeverity.CRITICAL, - input_data={"asset_id": asset_id, "frames": params["frames"]} - ) - error.log(logger, as_json=False) - return error - - if n_frames is None: - n_frames = n - elif n_frames != n: - error = ErrorFactory.create( - "ERR_VIDEO_001", - message=f"frames mismatch on multiple assets: {n_frames} != {n}", - caller=self, - severity=ErrorSeverity.WARNING - ) - error.log(logger, as_json=False) - return error - - # Validate duration parameter (optional, default asset duration) - n = int(params.get("duration", asset.duration)) - if duration is None: - duration = n - elif duration != n: - error = ErrorFactory.create( - "ERR_VIDEO_001", - message=f"duration mismatch on multiple assets: {duration} != {n}", - caller=self, - severity=ErrorSeverity.WARNING - ) - error.log(logger, as_json=False) - return error - - # Extract start parameter (optional, default 0) - can vary per asset - start = int(params.get("start", 0)) - start_times.append(start) - - # Calculate frame sampling interval - interval = duration / n_frames if n_frames > 0 else 0 - - # Setup MediaExtractor queues for each asset - results_queue = Queue() - for asset, start_time in zip(asset_list, start_times): - asset.lock() - chunk = MediaChunk( - asset.path, - start_pts=start_time, - duration=duration if duration else -1, - interval=interval if interval else 0 - ) - qs.append(self._media_extractor.append(chunk)) - - # Submit frame collection task to thread pool - self._frame_collector.submit( - self._collect_frames_from_queue, asset_list, qs, n_frames, results_queue - ) - return results_queue - - def _is_collected_valid(self, collected: Dict): - result = super()._is_collected_valid(collected) - if not result: - return False - return all([n in collected for n in self._video_tensor_names]) - - def stop(self): - super().stop() - if self._media_extractor: - # explicitly call __exit__ due to MediaExtractor internal threads holding the reference - self._media_extractor.__exit__(None, None, None) - self._media_extractor = None - if self._frame_collector is not None: - logger.info(f"VideoFrameSamplingDataFlow: shutting down frame collector") - self._frame_collector.shutdown(wait=True) - logger.info(f"VideoFrameSamplingDataFlow: frame collector shut down") - self._frame_collector = None - -class ImageInputDataFlow(DataFlow): - """A data flow for image data""" - def __init__(self, configs: List[Dict], tensor_names: List[Tuple[str, str]], key_tensor_type: str, timeout=1.0): - super().__init__(configs, tensor_names, True, False, timeout) - self._image_decoder = ImageDecoder(["JPEG", "PNG"]) - self._image_tensor_names = [] - for tensor_name in tensor_names: - config = next((c for c in configs if c["name"] == tensor_name[0]), None) - if config and config["data_type"] == key_tensor_type: - self._image_tensor_names.append(tensor_name[1]) - if not self._image_tensor_names: - raise Exception("Image tensor name not found in the ImageInputDataFlow") - - def _process_custom_data(self, images: np.ndarray, data_type: str) -> Union[List, np.ndarray, EnhancedError]: - """ - Process custom image data types. - - Args: - images: Input image data (base64 or asset references) - data_type: The custom data type string - - Returns: - Union[List, np.ndarray, EnhancedError]: - - List: Decoded images (list of tensors) - - np.ndarray: Fallback to parent processing - - EnhancedError: If decoder not initialized or processing fails - - Note: - Returns EnhancedError (not raises) when decoder is not initialized, - allowing the error to be propagated through the pipeline. - """ - logger.debug("ImageInputDataFlow._process_custom_data: %s", data_type) - if self._image_decoder is None: - return ErrorFactory.create( - "ERR_IMG_003", - caller=self, - severity=ErrorSeverity.CRITICAL - ) - if data_type == "TYPE_CUSTOM_IMAGE_BASE64": - return self._process_base64_image(images) - elif data_type == "TYPE_CUSTOM_IMAGE_ASSETS": - return self._process_image_assets(images) - else: - return super()._process_custom_data(images, data_type) - - def _is_collected_valid(self, collected: Dict): - result = super()._is_collected_valid(collected) - if not result: - return False - return all([n in collected for n in self._image_tensor_names]) - - def _process_base64_image(self, images: np.ndarray): - result = [] - for image in images: - if not isinstance(image, np.str_): - error = ErrorFactory.create( - "ERR_IMG_002", - message=f"base64 image must be bytes or string, got {type(image).__name__}", - caller=self, - input_data={"image_type": type(image).__name__}, - expected_data={"valid_types": ["bytes", "np.bytes_", "str", "np.str_"]} - ) - error.log(logger, as_json=False) - return error - try: - data_prefix, data_payload = image.split(",") - mime_type = data_prefix.split(";")[0].split(":")[1] - format = None - if mime_type == "image/jpeg" or mime_type == "image/jpg": - format = "JPEG" - elif mime_type == "image/png": - format = "PNG" - else: - error = ErrorFactory.create( - "ERR_IMG_001", - message=f"Unsupported image format: {mime_type}", - caller=self, - input_data={"mime_type": mime_type, "data_prefix": data_prefix}, - expected_data={"supported_formats": ["image/jpeg", "image/jpg", "image/png"]} - ) - error.log(logger, as_json=False) - return error - data_payload = base64.b64decode(data_payload) - except Exception: - error = ErrorFactory.create( - "ERR_DF_002", - message=f"Failed to decode base64 string for image data: {image}", - caller=self, - input_data={"input": image}, - expected_data={"valid_encoding": "base64"} - ) - error.log(logger, as_json=False) - return error - tensor = as_tensor(np.frombuffer(data_payload, dtype=np.uint8).copy(), format) - result.append(self._image_decoder.decode(tensor, format)) - logger.debug("ImageInputDataFlow._process_base64_image generates %s tensors", len(result)) - return result - - def _process_image_assets(self, assets: np.ndarray): - result = [] - for asset in assets: - asset_manager = AssetManager() - asset = asset_manager.get_asset(asset) - if not asset: - error = ErrorFactory.create( - "ERR_DF_003", - message=f"Asset not found: {asset}", - caller=self, - input_data={"asset_id": asset} - ) - error.log(logger, as_json=False) - return error - asset.lock() - format = None - if asset.mime_type == "image/jpeg" or asset.mime_type == "image/jpg": - format = "JPEG" - elif asset.mime_type == "image/png": - format = "PNG" - else: - error = ErrorFactory.create( - "ERR_IMG_001", - message=f"Unsupported image format: {asset.mime_type}", - caller=self, - input_data={"mime_type": asset.mime_type, "asset_path": asset.path}, - expected_data={"supported_formats": ["image/jpeg", "image/jpg", "image/png"]} - ) - error.log(logger, as_json=False) - asset.unlock() - return error - with open(asset.path, "rb") as f: - data = f.read() - tensor = as_tensor(np.frombuffer(data, dtype=np.uint8).copy(), format) - result.append(self._image_decoder.decode(tensor, format)) - asset.unlock() - return result - -inbound_dataflow_mapping = { - "TYPE_CUSTOM_IMAGE_BASE64": ImageInputDataFlow, - "TYPE_CUSTOM_IMAGE_ASSETS": ImageInputDataFlow, - "TYPE_CUSTOM_LONG_VIDEO_ASSETS": VideoInputDataFlow, - "TYPE_CUSTOM_VIDEO_CHUNK_ASSETS": VideoFrameSamplingDataFlow -} -class ModelBackend(ABC): - """Interface for standardizing the model backend """ - def __init__(self, model_config: Dict, model_home: str, device_id=0): - self._model_config = model_config - self._model_home = model_home - self._device_id = device_id - - @property - def device_id(self): - return self._device_id - - @property - def model_home(self): - return self._model_home - - @abstractmethod - def __call__(self, *args, **kwargs): - raise Exception("Not Implemented") - - def stop(self): - logger.info(f'Backend for {self._model_config["name"]} stopped') - -class Processor(ABC): - def __init__(self, config: Dict, model_home: str): - self._name = config['name'] - self._kind = config['kind'] if 'kind' in config else 'auto' - self._input = config['input'] if 'input' in config else [] - self._output = config['output'] if 'output' in config else [] - self._config = { 'device_id': 0, 'model_home': model_home } - if 'config' in config: - self._config.update(config['config']) - self._name = config['name'] - self._processor = None - - @property - def name(self): - return self._name - - @property - def input(self): - return self._input - - @property - def output(self): - return self._output - - @property - def kind(self): - return self._kind - - @property - def config(self): - return self._config - - @abstractmethod - def __call__(self, *args, **kwargs): - pass - -class AutoProcessor(Processor): - """AutoPrrocessor loads the preprocessor from pretrained""" - def __init__(self, config: Dict, model_home: str): - super().__init__(config, model_home) - import transformers - self._processor = transformers.AutoProcessor.from_pretrained(model_home) - if self._processor is None: - error = ErrorFactory.create( - "ERR_PROC_002", - message=f"Failed to load AutoProcessor from {model_home}", - caller=self, - severity=ErrorSeverity.CRITICAL, - related_config={"model_home": model_home, "config": config} - ) - error.log(logger, as_json=False) - raise RuntimeError(str(error)) - - def __call__(self, *args): - # TODO value parser should be configurable - return self._processor(*args) - -class CustomProcessor(Processor): - """CustomProcessor loads the processor from custom module""" - def __init__(self, config: Dict, model_home: str): - super().__init__(config, model_home) - if not hasattr(custom, "create_instance"): - error = ErrorFactory.create( - "ERR_PROC_003", - caller=self, - severity=ErrorSeverity.CRITICAL, - related_config={"config": config, "model_home": model_home} - ) - error.log(logger, as_json=False) - raise RuntimeError(str(error)) - self._processor = custom.create_instance(self.name, self.config) - if self._processor is not None: - logger.info(f"Custom processor {self._processor.name} created") - else: - error = ErrorFactory.create( - "ERR_PROC_004", - message=f"Failed to create processor {self.name}", - caller=self, - severity=ErrorSeverity.CRITICAL, - related_config={"processor_name": self.name, "config": config} - ) - error.log(logger, as_json=False) - raise RuntimeError(str(error)) - - def __call__(self, *args): - ret = self._processor(*args) - if not isinstance(ret, tuple): - ret = ret, - return ret - -class Collector(ABC): - """Collector is an interface for collecting data from the data flow""" - def collect(self): - raise NotImplementedError("Not implemented") - - def stop(self): - raise NotImplementedError("Not implemented") - -class SingleFlowCollector(Collector): - """SingleFlowCollector is a collector for a single data flow""" - def __init__(self, data_flow: DataFlow): - self._data_flow = data_flow - - def collect(self): - return self._data_flow.get() - - def stop(self): - return self._data_flow.stop() - -class AggregationFlowCollector(Collector): - """AggregationFlowCollector is a collector aggregating multiple data flows""" - def __init__(self, data_flows: List[DataFlow], timeout=None): - self._data_flows = data_flows - self._executor = ThreadPoolExecutor(max_workers=1) - self._queue = Queue() - self._stop_event = threading.Event() - self._timeout = timeout - self._futures = self._executor.submit(self._collect) - - def collect(self): - return self._queue.get(timeout=self._timeout) - - def _collect(self): - logger.info(f"AggregationFlowCollector starts collecting data") - while not self._stop_event.is_set(): - result = {} - completed = [] - for data_flow in self._data_flows: - # continue reading from each data flow until we get some data - while not self._stop_event.is_set(): - try: - data = data_flow.get() - if isinstance(data, Error) or isinstance(data, Stop): - completed.append(data) - else: - result.update(data) - break - except Empty: - continue - if result: - self._queue.put(result) - if completed and all([isinstance(c, Stop) for c in completed]): - self._queue.put(Stop("All data flows completed")) - elif any([isinstance(c, Error) for c in completed]): - self._queue.put(Error("One or more data flows ended in error")) - - def stop(self): - logger.info(f"AggregationFlowCollector destructing...") - self._stop_event.set() - for data_flow in self._data_flows: - data_flow.stop() - self._executor.shutdown(wait=True) - self._queue.put(Stop("Shutdown")) - logger.info(f"AggregationFlowCollector destructed") - -class MultiFlowCollector(Collector): - """MultiFlowCollector is a collector for multiple data flows""" - def __init__(self, data_flows: List[DataFlow], timeout=None): - self._data_flows = data_flows - self._executor = ThreadPoolExecutor(max_workers=len(data_flows)) - self._queue = Queue() - self._stop_event = threading.Event() - self._condition = threading.Condition() - self._active_flow = -1 - self._timeout = timeout - self._futures = [self._executor.submit(self._poll, i) for i in range(len(self._data_flows))] - - def collect(self): - data = self._queue.get(timeout=self._timeout) - return data - - def _poll(self, index: int): - logger.info("Start polling data flow %s", self._data_flows[index].in_names) - data_flow = self._data_flows[index] - n_data = 0 - while not self._stop_event.is_set(): - try: - data = data_flow.get() - is_stop = isinstance(data, Stop) - if is_stop and n_data == 0: - # empty data flow, skip it - logger.info(f"Empty data flow {data_flow.in_names}, skip it") - continue - elif not is_stop: - n_data += 1 - # try grabbing the queue - with self._condition: - while self._active_flow != index and self._active_flow != -1: - self._condition.wait() - if self._active_flow == -1: - self._active_flow = index - self._queue.put(data) - with self._condition: - if isinstance(data, Stop): - logger.info("Data flow %s ended in: %s", data_flow.in_names, data) - self._active_flow = -1 - n_data = 0 - self._condition.notify_all() - except Empty: - continue - - - def stop(self): - logger.info(f"MultiFlowCollector destructing...") - self._stop_event.set() - for data_flow in self._data_flows: - data_flow.stop() - logger.info(f"MultiFlowCollector data flows stopped") - self._executor.shutdown(wait=True) - self._queue.put(Stop("Shutdown")) - logger.info(f"MultiFlowCollector destructed") - - -class AsyncDispatcher: - """AsyncDispatcher is a dispatcher of synchronous data flows to asynchronous queues""" - def __init__(self, collector: Collector, loop: asyncio.AbstractEventLoop, n_max_async_queues: int = 100): - self._collector = collector - self._loop = loop - self._consumer_queues = Queue(maxsize=n_max_async_queues) - self._stop_event = threading.Event() - self._dispatcher_thread = threading.Thread(target=self.run, daemon=True) - self._dispatcher_thread.start() - - def append_async_queue(self, async_queue: asyncio.Queue): - try: - self._consumer_queues.put_nowait(async_queue) - except Full: - error = ErrorFactory.create( - "ERR_ASYNC_003", - caller=self, - severity=ErrorSeverity.CRITICAL, - metadata={"queue_maxsize": self._consumer_queues.maxsize} - ) - error.log(logger, as_json=False) - raise RuntimeError(str(error)) - - def run(self): - if self._collector is None: - error = ErrorFactory.create( - "ERR_ASYNC_001", - caller=self, - severity=ErrorSeverity.CRITICAL - ) - error.log(logger, as_json=False) - raise RuntimeError(str(error)) - if self._loop is None: - error = ErrorFactory.create( - "ERR_ASYNC_002", - caller=self, - severity=ErrorSeverity.CRITICAL - ) - error.log(logger, as_json=False) - raise RuntimeError(str(error)) - logger.info("AsyncDispatcher started") - while not self._stop_event.is_set(): - try: - async_queue = self._consumer_queues.get(timeout=1.0) - except Empty: - continue - except Exception as e: - logger.exception(e) - continue - - # For this consumer queue, keep collecting until Stop/Error is received - logger.debug("[AsyncDispatcher] Starting collection loop for new async_queue") - collect_attempts = 0 - while not self._stop_event.is_set(): - try: - logger.debug("[AsyncDispatcher] Attempting to collect (attempt #%s)", collect_attempts) - data = self._collector.collect() - logger.debug("[AsyncDispatcher] Collected data: %s", type(data).__name__) - except Empty: - collect_attempts += 1 - if collect_attempts % 100 == 0: - logger.debug("[AsyncDispatcher] Still waiting for data after %s attempts", collect_attempts) - continue - except Exception as e: - logger.exception(e) - data = Error(str(e)) - try: - logger.debug("[AsyncDispatcher] Putting data into async_queue") - async def _async_put(q, item): - await q.put(item) - asyncio.run_coroutine_threadsafe(_async_put(async_queue, data), self._loop) - logger.debug("[AsyncDispatcher] Successfully put data into async_queue") - except Exception as e: - logger.exception(e) - continue - - if isinstance(data, Stop): - logger.info("AsyncDispatcher delivered Stop message: %s", data) - break - - logger.info("AsyncDispatcher stopped") - - def stop(self): - self._stop_event.set() - logger.info("AsyncDispatcher stopping") - -class ModelOperator: - """An model operator runs a single model""" - def __init__(self, model_config:Dict, model_repo: str): - self._model_name = model_config["name"] - self._model_home = os.path.join(model_repo, self._model_name) - self._in: List[DataFlow] = [] - self._out: List[DataFlow] = [] - self._running = False - self._preprocessors = [] - self._postprocessors = [] - self._model_config = model_config - self._backend = None - self._stop_event = threading.Event() - self._collector = None - self._future_consumer = None - - - @property - def model_name(self): - return self._model_name - - @property - def model_config(self): - return self._model_config.copy() - - @property - def inputs(self): - return self._in.copy() - - @property - def outputs(self): - return self._out.copy() - - def bind_input(self, configs: List[Dict], targets: List[str]=[]): - if not targets: - targets = [i['name'] for i in configs] - flow = None - tensor_names = [(i['name'], o) for i, o in zip(configs, targets)] - tensor_types = [i['data_type'] for i in configs] - image_tensor_type = None - for tensor_type in tensor_types: - if tensor_type in inbound_dataflow_mapping: - image_tensor_type = tensor_type - break - if image_tensor_type is None: - flow = DataFlow(configs, tensor_names, inbound=True) - else: - # customized inbound data flow - flow = inbound_dataflow_mapping[image_tensor_type](configs, tensor_names, image_tensor_type) - self._in.append(flow) - logger.info("Data flow < %s -> %s > connected to model %s", flow.in_names, flow.o_names, self._model_name) - return flow - - def bind_output(self, configs: List[Dict], sources: List[str]=[]): - if not sources: - sources = [i['name'] for i in configs] - tensor_names = [(i, o['name']) for i, o in zip(sources, configs)] - flow = DataFlow(configs, tensor_names, outbound=True) - self._out.append(flow) - logger.info(f"model {self._model_name} connected to Data flow < {flow.in_names} -> {flow.o_names} >") - return flow - - def import_input(self, input: DataFlow): - self._in.append(input) - - def import_output(self, output: DataFlow): - self._out.append(output) - - def set_backend(self, backend: ModelBackend): - self._backend = backend - - def _pass_error_or_stop_downstream(self, data: Union[Error, Stop]): - """ - Pass Error or Stop message to downstream dataflows. - - Args: - data: Error or Stop object to pass downstream - - Note: - If _future_consumer exists, the error/stop is wrapped in a Future - and sent through the consumer. Otherwise, it's sent directly to - output dataflows. - """ - if self._future_consumer is None: - # pass the error or stop message downstream - for out in self._out: - logger.debug("Passing error or stop message to %s", out.in_names) - out.put(data) - else: - f = Future() - f.set_result(data) - self._future_consumer.append_future(f) - - def run(self): - logger.debug("Model operator for %s started", self._model_name) - - # create preprocessors - for kind, processors in [("preprocessors", self._preprocessors), ("postprocessors", self._postprocessors)]: - if kind in self._model_config: - for config in self._model_config[kind]: - ProcessorClass = None - if config["kind"] == "auto": - ProcessorClass = AutoProcessor - elif config["kind"] == "custom": - ProcessorClass = CustomProcessor - if ProcessorClass is not None: - processors.append( - ProcessorClass( - config=config, - model_home=self._model_home - ) - ) - else: - raise Exception("Invalid Processor") - # backend loop - self._collector = self._create_collector() - if not self._out: - error = ErrorFactory.create( - "ERR_MO_003", - caller=self, - model_name=self._model_name, - severity=ErrorSeverity.CRITICAL, - related_config={"model_config": self._model_config} - ) - error.log(logger, as_json=False) - raise RuntimeError(str(error)) - while not self._stop_event.is_set(): - try: - # collect input data until Stop is received - data = self._collector.collect() - if isinstance(data, Stop) or isinstance(data, Error): - self._pass_error_or_stop_downstream(data) - continue - logger.info("Input collected from %s: %s", self._collector.__class__.__name__, data) - - # convert data to args and kwargs based on if explicit batching is required - args = [] - kwargs = data - values = [v for v in data.values()] - lengths = [ - len(v) if isinstance(v, list) or v.ndim > 0 else 0 - for v in values - ] - if any(isinstance(v, list) for v in values) and \ - all(length == lengths[0] for length in lengths): - logger.info( - "Explicit batching detected, " - "splitting the data into multiple inference requests" - ) - # construct multiple inference requests - args = split_tensor_in_dict(kwargs) - kwargs = {} - # call preprocess() before passing args to the backend - processed, passthrough_tensors = self._preprocess(args if args else [kwargs]) - in_names = [i["name"] for i in self._model_config["input"]] - if not processed: - error = ErrorFactory.create( - "ERR_MO_001", - caller=self, - model_name=self._model_name, - input_data={ - "args": str(args)[:500] if args else "", - "kwargs_keys": list(kwargs.keys()) if kwargs else [] - }, - related_config={ - "preprocessors": [p.name for p in self._preprocessors], - "model_input": in_names - } - ) - error.log(logger, as_json=False) - self._pass_error_or_stop_downstream(error) - continue - if args: - # explicitly batched data - args = processed - elif kwargs: - # implicitly batched data - kwargs = processed[0] - else: - error = ErrorFactory.create( - "ERR_MO_004", - message="Invalid result from preprocess: both args and kwargs are empty", - caller=self, - model_name=self._model_name, - input_data={ - "processed": str(processed)[:500] if processed else "", - "passthrough_tensors_keys": list(passthrough_tensors[0].keys()) if passthrough_tensors else [] - } - ) - error.log(logger, as_json=False) - self._pass_error_or_stop_downstream(error) - continue - # execute inference backend and collect result - logger.debug("Model %s invokes backend %s with %s", self._model_name, self._backend.__class__.__name__, args if args else kwargs) - rs = self._backend(*args, **kwargs) - if isinstance(rs, Future): - def on_future_result(result: Dict|Error|Stop, user_data: List): - if isinstance(result, Error) or isinstance(result, Stop): - # Pass error/stop directly - already in FutureConsumer callback context - for out in self._out: - out.put(result) - return - self._on_inference_result(result, user_data[0]) - if self._future_consumer is None: - self._future_consumer = FutureConsumer( - name=self._model_name, - stop_event=self._stop_event, - result_callback=on_future_result - ) - self._future_consumer.append_future(rs, passthrough_tensors) - elif inspect.isgenerator(rs): - self._on_inference_result(rs, passthrough_tensors) - elif isinstance(rs, Error) or isinstance(rs, Stop): - self._pass_error_or_stop_downstream(rs) - continue - else: - error_msg = ( - f"Invalid result from backend {self._backend.__class__.__name__}: {rs}" - " Expected a Future or a generator" - ) - raise ValueError(error_msg) - except Empty: - continue - except Exception as e: - error = ErrorFactory.from_exception( - e, - caller=self, - category=ErrorCategory.BACKEND if "backend" in str(e).lower() else ErrorCategory.INTERNAL, - model_name=self._model_name, - dataflow_names=[name for o in self._out for name in o.in_names], - input_data={ - "data_keys": list(data.keys()) if 'data' in locals() and isinstance(data, dict) else [] - }, - related_config={ - "model_config": { - "name": self._model_config.get("name"), - "input": [i["name"] for i in self._model_config.get("input", [])], - "output": [o["name"] for o in self._model_config.get("output", [])] - }, - "backend": self._backend.__class__.__name__ if self._backend else None, - "n_preprocessors": len(self._preprocessors), - "n_postprocessors": len(self._postprocessors) - } - ) - error.log(logger, as_json=False) - - # Pass error downstream - self._pass_error_or_stop_downstream(error) - - logger.info(f"Model operator {self._model_name} stopped") - - def stop(self): - logger.info(f"Model operator {self._model_name} is stopping") - self._stop_event.set() - if self._collector is not None: - self._collector.stop() - if self._backend is not None: - self._backend.stop() - self._in.clear() - self._out.clear() - self._preprocessors.clear() - self._postprocessors.clear() - - def _on_inference_result(self, results: Generator, passthrough_tensors: Dict): - for r in results: - logger.debug( - "Model %s generated result from backend %s: %s", - self._model_name, self._backend.__class__.__name__, r - ) - # iterate the result list and postprocess each of them - for out in self._out: - output_data = {n : [] for n in out.in_names} - if isinstance(r, list): - # we get a batch - if len(passthrough_tensors) == 1: - passthrough_tensors = passthrough_tensors*len(r) - for i, result in enumerate(r): - if passthrough_tensors: - result.update(passthrough_tensors[i]) - result = self._postprocess(result) - if not all([n in result for n in out.in_names]): - error = ErrorFactory.create( - "ERR_MO_002", - message=f"Model output incomplete (batch item {i})", - caller=self, - model_name=self._model_name, - dataflow_names=out.in_names, - expected_data={"required_outputs": out.in_names}, - actual_data={"provided_outputs": list(result.keys())}, - related_config={"postprocessors": [p.name for p in self._postprocessors]} - ) - error.log(logger, as_json=False) - continue - # collect the result - for n, v in output_data.items(): - if n in result: - v.append(result[n]) - else: - v.append(None) - else: - # implicit batching - if passthrough_tensors: - r.update(passthrough_tensors[0]) - output_data = self._postprocess(r) - if not all([n in output_data for n in out.in_names]): - error = ErrorFactory.create( - "ERR_MO_002", - message="Model output incomplete", - caller=self, - model_name=self._model_name, - dataflow_names=out.in_names, - expected_data={"required_outputs": out.in_names}, - actual_data={"provided_outputs": list(output_data.keys())}, - related_config={"postprocessors": [p.name for p in self._postprocessors]} - ) - error.log(logger, as_json=False) - continue - logger.debug("ModelOperator of %s deposits result: %s", self._model_name, output_data) - out.put(output_data) - - def _preprocess(self, args: List): - # go through the preprocess chain - outcome = args - for preprocessor in self._preprocessors: - result = [] - for data in outcome: - # initialize the processed as the original values - processed = {k: v for k, v in data.items()} - # trigger the preprocessor if all the input tensors are present - if all([i in data for i in preprocessor.input]): - input = [processed.pop(i) for i in preprocessor.input] - logger.debug("%s invokes preprocessor %s with given input %s", self._model_name, preprocessor.name, input) - output = preprocessor(*input) - logger.debug("%s preprocessor %s generated output %s", self._model_name, preprocessor.name, output) - if len(output) != len(preprocessor.output): - error = ErrorFactory.create( - "ERR_PROC_001", - message=f"Number of preprocessing output doesn't match the configuration, expecting {len(preprocessor.output)}, while getting {len(output)}", - caller=self, - severity=ErrorSeverity.WARNING, - model_name=self._model_name, - expected_data={"output_count": len(preprocessor.output), "output_names": preprocessor.output}, - actual_data={"output_count": len(output)}, - related_config={"preprocessor": preprocessor.name} - ) - error.log(logger, as_json=False) - continue - # update as processed - for key, value in zip(preprocessor.output, output): - if value is not None: - processed[key] = value - else: - logger.info( - "Pre-processor %s skipped on mismatching input", - preprocessor.name - ) - result.append(processed) - # update outcome - outcome = result - # correct the data type and extract the passthrough tensors - passthrough_tensors = [] - for data in outcome: - passthrough_tensor = {} - for key in data: - value = data[key] - i_config = next((i for i in self._model_config['input'] if i["name"] == key), None) - if i_config is None: - logger.info("%s from preprocessed is not found in the model input config, adding it as a passthrough tensor", key) - passthrough_tensor[key] = value - else: - data_type = i_config["data_type"] - if isinstance(value, np.ndarray): - data_type = np_datatype_mapping[data_type] - if value.dtype != data_type: - data[key] = value.astype(data_type) - elif isinstance(value, torch.Tensor): - data_type = torch_datatype_mapping[data_type] - if value.dtype != data_type: - data[key] = value.to(data_type) - for key in passthrough_tensor: - data.pop(key) - passthrough_tensors.append(passthrough_tensor) - return outcome, passthrough_tensors - - def _postprocess(self, data: Dict): - processed = {k: v for k, v in data.items()} - for processor in self._postprocessors: - if not all([i in processed for i in processor.input]): - logger.info( - "Post-processor %s skipped on mismatching input", - processor.name - ) - continue - input = [processed.pop(i) for i in processor.input] - logger.debug("Post-processor %s invoked with given input %s", processor.name, input) - output = processor(*input) - logger.debug("Post-processor generated output %s", output) - if len(output) != len(processor.output): - error = ErrorFactory.create( - "ERR_PROC_001", - message=f"Number of postprocessing output doesn't match the configuration, expecting {len(processor.output)}, while getting {len(output)}", - caller=self, - severity=ErrorSeverity.WARNING, - model_name=self._model_name, - expected_data={"output_count": len(processor.output), "output_names": processor.output}, - actual_data={"output_count": len(output)}, - related_config={"postprocessor": processor.name} - ) - error.log(logger, as_json=False) - continue - # update as processed - for key, value in zip(processor.output, output): - processed[key] = value - return processed - - def _create_collector(self): - if len(self._in) == 1: - logger.info("Single data flow input detected, using single flow collector on model %s", self._model_name) - return SingleFlowCollector(self._in[0]) - else: - outputs = [set(d.o_names) for d in self._in] - intersection = set.intersection(*outputs) - if len(intersection) == 0 and not any([d.optional for d in self._in]): - logger.info("Aggregation data flow input detected, using aggregation flow collector on model %s", self._model_name) - return AggregationFlowCollector(self._in, timeout=1.0) - else: - logger.info("Multi data flow input detected, using multi flow collector on model %s", self._model_name) - return MultiFlowCollector(self._in, timeout=1.0) - -class InferenceBase: - """The base model that drives the inference flow""" - def initialize(self): - # Enable global error collection FIRST - before any error can occur - enable_global_error_collection(max_errors=5000) - atexit.register(InferenceBase._export_errors) - logger.info("Enhanced error collection enabled (with atexit export)") - - def parse_route(i1, i2) -> Route: - m1 = '' - m2 = '' - d1 = [] - d2 = [] - s = i1.split(':') - t = i2.split(':') - if len(s) > 2 or len(t) > 2: - error = ErrorFactory.create( - "ERR_ROUTE_001", - message=f"Invalid route format: {i1} -> {i2}", - component="InferenceBase", - operation="initialize", - severity=ErrorSeverity.CRITICAL, - input_data={"source": i1, "target": i2} - ) - error.log(logger, as_json=False) - raise RuntimeError(str(error)) - else: - m1 = s[0] - m2 = t[0] - if len(s) == 2 and s[1]: - data = json.loads(s[1]) - if isinstance(data, list): - d1 = data - else: - error = ErrorFactory.create( - "ERR_ROUTE_001", - message="Invalid route: source data must be a list", - component="InferenceBase", - operation="initialize.parse_route", - severity=ErrorSeverity.CRITICAL, - input_data={"source_data": data} - ) - error.log(logger, as_json=False) - raise RuntimeError(str(error)) - if len(t) == 2 and t[1]: - data = json.loads(t[1]) - if isinstance(data, list): - d2 = data - else: - error = ErrorFactory.create( - "ERR_ROUTE_001", - message="Invalid route: target data must be a list", - component="InferenceBase", - operation="initialize.parse_route", - severity=ErrorSeverity.CRITICAL, - input_data={"target_data": data} - ) - error.log(logger, as_json=False) - raise RuntimeError(str(error)) - - return Route(Path(m1, m2), Path(d1, d2)) - - - self._operators: ModelOperator = [] - self._inputs: List[DataFlow] = [] - self._outputs: List[DataFlow] = [] - self._input_config = OmegaConf.to_container(global_config.input) - self._output_config = OmegaConf.to_container(global_config.output) - self._model_repo = global_config.model_repo - self._ready = False - - if not os.path.exists(self._model_repo): - error = ErrorFactory.create( - "ERR_SYS_001", - message=f"Model repository {self._model_repo} does not exist", - caller=self, - severity=ErrorSeverity.CRITICAL, - related_config={"model_repo": self._model_repo} - ) - error.log(logger, as_json=False) - raise RuntimeError(str(error)) - - # set up the inference flow - for model_config in global_config.models: - self._operators.append(ModelOperator(OmegaConf.to_container(model_config), self._model_repo)) - - # initialize the operators - for operator in self._operators: - model_config = next((m for m in global_config.models if m.name == operator.model_name), None) - model_home = os.path.join(self._model_repo, operator.model_name) - backend_spec = model_config.backend.split('/') - backend_instance = self._create_backend(backend_spec, model_config, model_home) - if backend_instance is None: - raise RuntimeError(f"Unable to create backend {model_config.backend}") - operator.set_backend(backend_instance) - - if hasattr(global_config, "routes"): - # go through the routing table - for k, v in global_config.routes.items(): - route = parse_route(k, v) - logger.debug("Adding route %s", route) - # neither source nor target model is specified. - if not route.model.source and not route.model.target: - # this is a direct passthrough in the top level, we can use a standalone dataflow - if not route.data.source and not route.data.target: - logger.error(f"Invalid route: {route}, source or target is required for a direct pass") - continue - dataflow = None - if route.data.source: - s_configs = [OmegaConf.to_container(c) for c in global_config.input if c.name in route.data.source] - if len(s_configs) != len(route.data.source): - logger.error(f"Not all the sources are found in the input configs, unable to create passthrough dataflow") - continue - o_tensor_names = route.data.target if route.data.target else route.data.source - if any(n not in [c.name for c in global_config.output] for n in o_tensor_names): - logger.warning(f"Not all the output tensors from {o_tensor_names} are found in the output configs, consider adding a postprocessor to generate the missing tensors") - tensor_names = [(i['name'], o) for i, o in zip(s_configs, o_tensor_names)] - dataflow = DataFlow(configs=s_configs, tensor_names=tensor_names, inbound=True) - elif route.data.target: - t_configs = [OmegaConf.to_container(c) for c in global_config.output if c.name in route.data.target] - if len(t_configs) != len(route.data.target): - logger.error(f"Not all the targets are found in the output configs, unable to create passthrough dataflow") - continue - if any(n not in [c.name for c in global_config.input] for n in route.data.target): - logger.error(f"Not all the targets are found in the input configs, unable to create passthrough dataflow") - continue - tensor_names = [(i, i) for i in route.data.target] - dataflow = DataFlow(configs=t_configs, tensor_names=tensor_names, outbound=True) - else: - logger.error(f"Invalid route: {route}, source or target is required for a direct pass") - continue - if dataflow is not None: - self._inputs.append(dataflow) - self._outputs.append(dataflow) - elif route.model.target: - operator1 = next((o for o in self._operators if o.model_name == route.model.target), None) - if operator1 is None: - error = ErrorFactory.create( - "ERR_ROUTE_002", - message=f"Model {route.model.target} in the routes not found", - caller=self, - severity=ErrorSeverity.CRITICAL, - input_data={"model_name": route.model.target}, - related_config={"available_models": [o.model_name for o in self._operators]} - ) - error.log(logger, as_json=False) - raise RuntimeError(str(error)) - # both source and target model are specified. - if route.model.source: - # this is the model provides data - operator2 = next((o for o in self._operators if o.model_name == route.model.source), None) - if operator2 is None: - error = ErrorFactory.create( - "ERR_ROUTE_002", - message=f"Model {route.model.source} in the routes not found", - caller=self, - severity=ErrorSeverity.CRITICAL, - input_data={"model_name": route.model.source}, - related_config={"available_models": [o.model_name for o in self._operators]} - ) - error.log(logger, as_json=False) - raise RuntimeError(str(error)) - flow = None - if route.data.source and route.data.target: - tensor_names = [(i, o) for i, o in zip(route.data.source, route.data.target)] - flow = DataFlow(configs=None, tensor_names=tensor_names) - elif route.data.source: - tensor_names = [(i, i) for i in route.data.source] - flow = DataFlow(configs=None, tensor_names=tensor_names) - elif route.data.target: - tensor_names = [(i, i) for i in route.data.target] - flow = DataFlow(configs=None, tensor_names=tensor_names) - else: - logger.error(f"Invalid route: {route}, source or target is required for connecting two models, {operator2.model_name} and {operator1.model_name}") - continue - operator2.import_output(flow) - operator1.import_input(flow) - else: - # this is the top level input - if route.data.source: - s_configs = [] - for s in route.data.source: - c = next((c for c in global_config.input if c.name == s), None) - if c is None: - logger.error(f"Input {s} not found in the input configs, unable to create dataflow") - continue - s_configs.append(OmegaConf.to_container(c)) - else: - s_configs = [OmegaConf.to_container(c) for c in global_config.input] - self._inputs.append(operator1.bind_input(s_configs, route.data.target)) - # only source model is specified. - elif route.model.source: - # this is the top level output - operator = next((o for o in self._operators if o.model_name == route.model.source), None) - if operator is None: - error = ErrorFactory.create( - "ERR_ROUTE_002", - message=f"Model {route.model.source} in the routes not found", - caller=self, - severity=ErrorSeverity.CRITICAL, - input_data={"model_name": route.model.source}, - related_config={"available_models": [o.model_name for o in self._operators]} - ) - error.log(logger, as_json=False) - raise RuntimeError(str(error)) - if route.data.target: - configs = [OmegaConf.to_container(c) for c in global_config.output if c.name in route.data.target] - self._outputs.append(operator.bind_output(configs, route.data.source)) - else: - configs = [OmegaConf.to_container(c) for c in global_config.output if c.name in route.data.source] - if route.data.source == [c['name'] for c in configs]: - self._outputs.append(operator.bind_output(configs)) - else: - logger.warning(f"Output of {operator.model_name} is not compatible with top level output, be sure to add a top level postprocessor") - dataflow = DataFlow(configs=None, tensor_names=[(i, i) for i in route.data.source]) - operator.import_output(dataflow) - self._outputs.append(dataflow) - else: - logger.warning("Empty route entry") - elif len(self._operators) == 1: - # default flow for single model use case without an explicit route - operator = self._operators[0] - # direct connection from top level input to the model - self._inputs.append(operator.bind_input(OmegaConf.to_container(global_config.input))) - # direct connection from the model to top level output - self._outputs.append(operator.bind_output(OmegaConf.to_container(global_config.output))) - else: - logger.error("Unable to set up inference routes") - if len(self._inputs) == 0 or len(self._outputs) == 0: - error = ErrorFactory.create( - "ERR_SYS_002", - message="Either input or output is empty, inference pipeline is not complete", - caller=self, - severity=ErrorSeverity.CRITICAL, - actual_data={ - "n_inputs": len(self._inputs), - "n_outputs": len(self._outputs) - }, - related_config={ - "models": [o.model_name for o in self._operators] - } - ) - error.log(logger, as_json=False) - raise RuntimeError(str(error)) - self._executor = ThreadPoolExecutor(max_workers=len(self._operators)) - self._future = None - - # sanity check on vision pipeline - try: - Pipeline("vision") - except Exception as e: - logger.exception(e) - - # post processing: - self._processors = [] - if hasattr(global_config, "postprocessors"): - configs = OmegaConf.to_container(global_config.postprocessors) - for config in configs: - if config["kind"] == "custom": - self._processors.append( - CustomProcessor(config, self._model_repo) - ) - - # data collector - self._collector = ( - AggregationFlowCollector(self._outputs) - if len(self._outputs) > 1 - else SingleFlowCollector(self._outputs[0]) - ) - # async dispatcher - self._async_dispatcher = None - # stop event for request lifecycle - self._stop_event = threading.Event() - - - # start the inference flow - for operator in self._operators: - self._submit(operator) - - self._ready = True - logger.info("Inference Engine initialized for %s", global_config.name) - logger.info("Inputs: %s", [f.o_names for f in self._inputs]) - logger.info("Outputs: %s", [f.o_names for f in self._outputs]) - - def is_healthy(self): - return self._ready - - def finalize(self): - if self._stop_event is not None: - self._stop_event.set() - if self._async_dispatcher is not None: - self._async_dispatcher.stop() - self._async_dispatcher = None - if self._collector is not None: - self._collector.stop() - self._collector = None - for operator in self._operators: - operator.stop() - self._executor.shutdown() - - logger.info("Inference pipeline is finalized") - - @staticmethod - def _export_errors(): - """Export collected errors to JSON file for test validation. - - Uses ERROR_EXPORT_PATH env var to determine the full file path - (defaults to /tmp/inference_errors.json). - """ - error_export_path = os.environ.get("ERROR_EXPORT_PATH", "/tmp/inference_errors.json") - try: - collector = get_global_error_collector() - if collector: - stats = collector.get_stats(include_recent=100) - collector.export_to_json(error_export_path, include_stack_traces=False) - logger.info("Exported %s errors to %s", stats['total_errors'], error_export_path) - except Exception as e: - logger.warning("Failed to export errors: %s", e) - - def _create_backend(self, backend_spec: List[str], model_config: Dict, model_home: str) -> ModelBackend | None: - raise NotImplementedError("Subclass must implement this method") - - def _on_operator_done(self, future: Future, operator: ModelOperator): - """Callback when an operator thread completes. Handles exceptions by logging to error JSON.""" - try: - future.result() # This raises if the thread had an exception - except Exception as e: - error = ErrorFactory.from_exception( - e, - caller=self, - category=ErrorCategory.INTERNAL, - model_name=operator.model_name, - related_config={ - "model_config": { - "name": operator.model_config.get("name"), - "preprocessors": operator.model_config.get("preprocessors", []), - "postprocessors": operator.model_config.get("postprocessors", []) - } - } - ) - error.log(logger, as_json=False) - - logger.critical(f"Operator {operator.model_name} thread failed with exception: {e}") - logger.critical("Forcing application exit due to model thread failure") - - # Export errors and force exit - no graceful shutdown for unrecoverable critical exception - InferenceBase._export_errors() - os._exit(1) - - def _submit(self, op: ModelOperator): - future = self._executor.submit(lambda: op.run()) - future.add_done_callback(lambda f: self._on_operator_done(f, op)) \ No newline at end of file diff --git a/tools/inference_builder/lib/responder.py b/tools/inference_builder/lib/responder.py deleted file mode 100644 index 2e5c24ca..00000000 --- a/tools/inference_builder/lib/responder.py +++ /dev/null @@ -1,137 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from lib.asset_manager import AssetManager -from lib.utils import create_jinja2_env, stack_tensors_in_dict, convert_list, get_logger -from omegaconf.errors import ConfigKeyError -from omegaconf import OmegaConf -import base64 -from config import global_config -import json -import re -from pydantic import BaseModel -from typing import Dict, Any - -jinja2_env = create_jinja2_env() - -class ResponderBase: - def __init__(self): - self._action_map = {} - self._inference = None - self._request_templates = {} - self._response_templates = {} - self._asset_manager = AssetManager() - self.logger = get_logger(__name__) - - # load the request and response templates - try: - input_config = OmegaConf.to_container(global_config.server.responders) - except ConfigKeyError: - raise ValueError("No responders found in the config") - for rp, value in input_config.items(): - req_tpls = value.get("requests", {}) - for k, tpl in req_tpls.items(): - if rp not in self._request_templates: - self._request_templates[rp] = {} - if isinstance(tpl, list): - self._request_templates[rp][k] = tpl - else: - self._request_templates[rp][k] = base64.b64decode(tpl).decode() - res_tpls = value.get("responses", {}) - for k, tpl in res_tpls.items(): - if rp not in self._response_templates: - self._response_templates[rp] = {} - if isinstance(tpl, list): - self._response_templates[rp][k] = tpl - else: - self._response_templates[rp][k] = base64.b64decode(tpl).decode() - - async def take_action(self, action_name:str, *args): - action = self._action_map.get(action_name, None) - if not action: - raise ValueError(f"Unknown action: {action_name}") - return await action(*args) - - def healthy_ready(self): - return self._inference and self._inference.is_healthy() - - def process_request(self, responder: str, request: BaseModel) -> Dict[str, Any]: - self.logger.debug(f"Processing request {request}") - - result = json.loads(request.model_dump_json()) - - # find the request template for the endpoint - templates = self._request_templates.get(responder, None) - if not templates: - # no template to be applied on the request - return result - - request_class = next(iter(templates.keys())) - template = templates[request_class] - json_string = jinja2_env.from_string(template).render(request=result) - try: - result = json.loads(json_string) - except Exception as e: - self.logger.error(f"Error parsing request template: {e}") - self.logger.error(f"Json string: {json_string}") - raise e - - # template filters on each field - for key, value in templates.items(): - if not key in result: - continue - if isinstance(value, list): - # this is regex filter for fields - text = result.pop(key) - if not isinstance(text, str): - continue - regx = value[0] - keys = value[1:] - matches = re.findall(regx, text) - for match in matches: - if len(keys) != len(match): - continue - for x, y in zip(keys, match): - if y: - result.setdefault(x, []).append(y) - return result - - def process_response(self, responder: str, request, response: Dict[str, Any]) -> str: - self.logger.debug(f"Processing response {response}") - # Load the response template for the endpoint - templates = self._response_templates.get(responder, None) - if not templates: - # no template to be applied on the response - return response - - response_class = next(iter(templates.keys())) - template = templates[response_class] - json_string = jinja2_env.from_string(template).render(request=request, response=response) - return json_string - - def process_streamed_response(self, responder: str, request, response: Dict[str, Any]) -> str: - self.logger.debug(f"Processing streamed response {response}") - # Load the response template for the endpoint - templates = self._response_templates.get(responder, None) - if not templates: - # no template to be applied on the response - return response - keys = list(templates.keys()) - if len(keys) < 2: - return response - template = templates[keys[1]] - json_string = jinja2_env.from_string(template).render(request=request, response=response) - return json_string diff --git a/tools/inference_builder/lib/utils.py b/tools/inference_builder/lib/utils.py deleted file mode 100644 index 56bd152c..00000000 --- a/tools/inference_builder/lib/utils.py +++ /dev/null @@ -1,387 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import os -import re -import sys -import logging -import time -from typing import Callable, Optional, List, Dict, Any -import importlib -import numpy as np -import torch -import jinja2 -import json -from queue import Queue, Full, Empty -from concurrent.futures import Future -import threading - -kDebug = int(os.getenv("DEBUG", "0")) -PACKAGE_NAME = "ib" - -class NumpyFlatEncoder(json.JSONEncoder): - def default(self, obj): - if isinstance(obj, np.ndarray): - return obj.flatten().tolist() - return super().default(obj) - - -def concat_tensors_in_dict(list_of_tensor_dicts: List) -> Dict: - result = {} - - # Iterate over each dictionary in the list - for d in list_of_tensor_dicts: - for key, value in d.items(): - if isinstance(value, np.ndarray): - result[key] = np.concatenate(result[key], value) if key in result else value - elif isinstance(value, torch.Tensor): - result[key] = torch.cat((result[key], value)) if key in result else value - else: - result[key] = result[key] + value if key in result else value - return result - -def stack_tensors_in_dict(list_of_tensor_dicts: List) -> Dict: - """ - [{'k1': 'v1', 'k2': 'v2'}, {'k1': 'v3', 'k2': 'v4'}] -> - {'k1': ['v1', 'v3'], 'k2': ['v2', 'v4']} - """ - result = {} - - # Iterate over each dictionary in the list - for d in list_of_tensor_dicts: - for key, value in d.items(): - if key not in result: - result[key] = [] # Create a new list for each new key - result[key].append(value) # Append the value to the list - # Iterate over the combined dictionary - for key in result: - tensor_list = result[key] - if isinstance(tensor_list[0], np.ndarray): - result[key] = np.stack(tensor_list, axis=0) - elif isinstance(tensor_list[0], torch.Tensor): - result[key] = torch.stack(tensor_list, dim=0) - - return result - -def split_tensor_in_dict(dict_of_tensor_list: Dict) -> List: - """ - {'k1': ['v1', 'v2'], 'k2': ['v3', 'v4']} -> - [{'k1': 'v1', 'k2': 'v3'}, {'k1': 'v2', 'k2': 'v4'}] - """ - values = [dict_of_tensor_list[k] for k in dict_of_tensor_list] - result = [] - - def _len(v): - if isinstance(v, list): - return len(v) - if isinstance(v, np.ndarray) and v.ndim > 0: - return len(v) - return 1 - - length = min(_len(v) for v in values) - for i in range(length): - result.append({ - k: v.item() if isinstance(v, np.ndarray) and v.ndim == 0 else v[i] - for k, v in dict_of_tensor_list.items() - }) - - return result - - -def convert_list(i: List, f: Callable): - # If the item is a list, apply the function recursively - if isinstance(i, list): - return [convert_list(item, f) for item in i] - else: - return f(i) - -def import_class(module_name, class_name): - # Import the module using importlib - module = importlib.import_module(module_name) - - # Get the class from the module using getattr - class_ = getattr(module, class_name) - - return class_ - -def create_jinja2_env(): - def start_with(field, s): - return field.startswith(s) - - def replace(value, pattern, text): - return re.sub(pattern, text, value) - - def extract(value, pattern): - match = re.search(pattern, value) - return match.group(1) if match else '' - - def raise_helper(message): - raise Exception(message) - - def tolist(value): - if hasattr(value, 'tolist'): - # Convert NumPy array to list and flatten to 1D - return value.flatten().tolist() - elif isinstance(value, list): - # Already a list, return as is - return value - else: - # Fallback: try to convert to list - return list(value) - - jinja2_env = jinja2.Environment() - jinja2_env.tests["startswith"] = start_with - jinja2_env.filters["replace"] = replace - jinja2_env.filters["extract"] = extract - jinja2_env.filters["tolist"] = tolist - jinja2_env.filters["zip"] = zip - jinja2_env.globals["raise"] = raise_helper - - return jinja2_env - -def get_logger(name: Optional[str] = None) -> logging.Logger: - """ Get component logger - - Parameters: - name: a module name - - Returns: A Logger Instance - """ - - log_level = os.getenv("LOG_LEVEL", "INFO").upper() - if kDebug: - log_level = logging.DEBUG - name = f"{PACKAGE_NAME}.{name}" - log_format = "%(asctime)s [%(levelname)s] [%(name)s]: %(message)s" - # sys.stdout.reconfigure(encoding="utf-8") - sys.stderr.reconfigure(encoding="utf-8") - print(f"logger {str(name)}, log_level: {str(log_level)}") - logging.basicConfig( - level=log_level, - format=log_format, - handlers=[logging.StreamHandler(sys.stderr)], - ) - logger = logging.getLogger(name) - logger.propagate = True - # formatter = logging.Formatter(log_format) - # stream_handler = logging.StreamHandler(sys.stdout) - # stream_handler.setFormatter(formatter) - # stream_handler.setLevel(log_level) - # logger.handlers.clear() - # logger.addHandler(stream_handler) - return logger - -def flush(logger): - for h in logger.handlers: - h.flush() - - -class SimpleLogger: - def __init__(self, name: str = ""): - self.name = name - def log_print(self, level, *args, **kwargs): - asctime = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) - prefix = f"{asctime} [{str(level)}] [{self.name}]: " - print(prefix, *args, **kwargs) - - def debug(self, *args, **kwargs): - if kDebug: - self.log_print("DEBUG", *args, **kwargs) - - def info(self, *args, **kwargs): - self.log_print("INFO", *args, **kwargs) - - def warning(self, *args, **kwargs): - self.log_print("WARNING", *args, **kwargs) - - def error(self, *args, **kwargs): - self.log_print("ERROR", *args, **kwargs) - -def tensor_info(tensor): - return f" shape: {tensor.shape}, dtype: {tensor.dtype}, dtype: {tensor.device}" - - -logger = get_logger(__name__) - -class FutureWrapper: - """A future wrapper for collecting results from an asynchronous operation""" - def __init__(self, future: Future, depot: Queue, *user_data): - self._future = future - self._depot = depot - self._user_data = list(user_data) - self._future.add_done_callback(self._on_done) - - - def __repr__(self): - return f"FutureWrapper(done={self._future.done()})" - - def _on_done(self, future: Future): - try: - self._depot.put_nowait(self) - except Full: - # fallback policy: best-effort enqueue with small timeout or log-and-drop - try: - self._depot.put(self, timeout=1) - except Full: - logger.warning( - f"FutureWrapper failed to enqueue to depot, dropped" - "consider increasing the future depot size" - ) - - @property - def user_data(self): - return self._user_data - - def result(self): - return self._future.result() - - def done(self): - return self._future.done() - - def cancel(self): - self._future.cancel() - -class FutureConsumer: - def __init__(self, - name: str, - stop_event: threading.Event, - result_callback: Callable[[Any, Any], None]): - self._name = name - self._stop_event = stop_event - self._future_depot = Queue() - self._future_lock = threading.Lock() - self._future_list = [] - self._result_callback = result_callback - self._future_consumer_thread = threading.Thread( - target=self.run, - daemon=True - ) - self._future_consumer_thread.start() - - def append_future(self, future: Future, *user_data): - future_wrapper = FutureWrapper(future, self._future_depot, *user_data) - with self._future_lock: - self._future_list.append(future_wrapper) - - def run(self): - if self._stop_event is None: - logger.error(f"Future consumer {self._name} failed to start, stopped event is None") - return - if not callable(self._result_callback): - logger.error(f"Future consumer {self._name} failed to start, result callback is not callable") - return - - logger.info(f"Future consumer for {self._name} started") - # Keep consuming completion signals and emit results in FIFO order of submission - while not self._stop_event.is_set(): - try: - # Wait for any future to complete; we only use this as a signal - future_wrapper = self._future_depot.get(timeout=1) - except Empty: - continue - except Exception as e: - logger.exception(e) - continue - - # Flush all completed futures from the head of the list (preserving order) - while True: - with self._future_lock: - if not self._future_list: - logger.info(f"Future consumer {self._name} future list is empty.") - break - if not self._future_list[0].done(): - logger.info(f"Future consumer {self._name} the first future in the list is not actually done. {self._future_list}") - break - future_wrapper = self._future_list.pop(0) - self._result_callback(future_wrapper.result(), future_wrapper.user_data) - - logger.info(f"Future consumer for {self._name} stopped") - -class ResultQueue: - def __init__(self, - queue: Queue, - result_callback: Callable[[Any, Any], None], - user_data: Any, - stop_event: Optional[threading.Event] = None): - self._queue = queue - self._result_callback = result_callback - self._user_data = user_data - self._stop_event = stop_event - - def __repr__(self): - return f"ResultQueue(queue={self._queue}, result_callback={self._result_callback}, user_data={self._user_data})" - - def get_all(self): - while self._stop_event is None or not self._stop_event.is_set(): - try: - item = self._queue.get(timeout=1.0) - except Empty: - # nothing to drain right now - continue - except Exception as e: - logger.exception(e) - break - self._result_callback(item, self._user_data) - if not item: - break - logger.debug(f"ResultQueue drained") - -class QueueConsumer: - """Consume ResultQueues in a single daemon thread and emit results. - - Submit work via append_queue(queue, result_callback, user_data=None). - Items pulled from the provided `queue` will be forwarded to - `result_callback(item, user_data)`. - """ - - def __init__(self, - name: str, - stop_event: threading.Event, - queue_get_timeout: float = 1.0): - self._name = name - self._stop_event = stop_event - self._queue_get_timeout = queue_get_timeout - self._queue_depot = Queue() - self._queue_consumer_thread = threading.Thread(target=self.run, daemon=True) - self._queue_consumer_thread.start() - - def append_queue(self, - queue: Queue, - result_callback: Callable[[Any, Any], None], - user_data: Any = None): - if not callable(result_callback): - logger.error(f"Queue consumer {self._name} append_queue failed: result_callback is not callable") - return - result_queue = ResultQueue(queue, result_callback, user_data, self._stop_event) - self._queue_depot.put(result_queue) - - def run(self): - if self._stop_event is None: - logger.error(f"Queue consumer {self._name} failed to start, stopped event is None") - return - - logger.info(f"Queue consumer for {self._name} started") - while not self._stop_event.is_set(): - try: - q = self._queue_depot.get(timeout=self._queue_get_timeout) - except Empty: - continue - except Exception as e: - logger.exception(e) - continue - q.get_all() - - logger.info(f"Queue consumer for {self._name} stopped") diff --git a/tools/inference_builder/mcp.png b/tools/inference_builder/mcp.png deleted file mode 100644 index 7d0bb1cb124ef85a5316b5dcdbfb87c28f7e5ccb..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 150765 zcmc$`cT`l{vo*?5F#v)I0VNm^1QZZS62*ilIZKozIp@%zC?Jv~XHdymKyntL2?8R} zgeHiD7McdhLK9wX@Z8_`{`khYcZ~bSV{km(&F;O|UaMBstXZ?Rzr3vEak6t{BqSuq zr5@f>Bq1T4AR#%}cJwfOLK7xo3IF@YUQzN6Nq#5&47@pHEFmL7LQ)t)zNJSB?~mC$ zd}2>R!u$*Q?;mdiQhE}S-889t63Q+*bK9;lT8@e9yLFvesF_*RoJUrbqr*p8pt2?qIa+ zcd3QPj%tpCI&nqEOre_8s-8#()-JST-+jAIT!N5ecICsmPGYtA18r^X2Qad|pLZtG z!~_4n;jTD3@z38kS9w0W{QLQvKyJprub*GK9C+mK>vt{xjl*xw&d&0z`Aqb6cfWTK zvecy@ssE&)qH?8Y@*LmZd9DUa9h<;nv5}9`<^uj9iQ&)A$(gbdL|!95ETmVS@4bM3 z&d}+J{QLU;16r!TuWyH*Y(Dt+wY=f0#eWa^Zyk=zMsuxA_0ZoD_}K_0@=!F^(vm#? zAjN!Y?S5l+etuNr_(v{e#YsqF&M3phWKFdW?Y~$0=YM}p|HE7O|K)K0yhfKs?&j5h zC;NvX_rEJWI-0M{Zlo*ns%J)IUVh7^tgF>9m}n#?OifMA&czkY?C9*AlatdfW;i4` zSddm&s9;s5x#*(R(b2KiN6VVrl`%A=aVt@_lZ{22Czig)+0)Ungr3q5bz|>h^{Fvu zBNv-8t+<-SZt%UjbFm}#rsVQ;hmwp;5Q>i_)HE*chJo*NM|{R$v2iK@HebS zMfmvmvTAE}8lHZuor#!SCr&N87}EMC$QmnnFa4kAt>E4<^!v$5+_2p`eE4w0qqK-t z!!tn-2sv_OA?Osko6j=ezn4f$OS7vQ;i0&jrTl2oW!Nlb+(C%?=pkh_HDt6LXUGPf z`O@xPI@zRu!-$9O$|?U32@A+YxWmrZEx${0`RQMcmRn&XUf8>v=BJN*7M0izB|k!Y zFV5icJ)<@5CGP81C1qt;wPc(3@()*6SC@#}ifj53j~@p(EsW;XxUXkdR6HTB5qgqD z-Ft_JbLZ!+u7pcfXmP5jspX`nKS;lo=Ba#r#$m|qI;_b3V{LL@z=7@kH1S_yv0<

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z%ZXQ7I8=R~Wi|G_&f2QK^wILlj58g#Hdm|qTw0f2Jy_DO`W=1gvMEZ(F`=|l?|Qxg zO^C`rRU5|q7ZaN))8gG2K-M8**z@Chx2qE2nkA=AY!F|i56;=Fw7lwLPcz@;*iN(R uV|bf~Zy!Ii`tRIzyE0>}0fQm`XxY?v{Zaa0z diff --git a/tools/inference_builder/mcp/QUICK_NORMALIZATION_REFERENCE.md b/tools/inference_builder/mcp/QUICK_NORMALIZATION_REFERENCE.md deleted file mode 100644 index 7dd0504c..00000000 --- a/tools/inference_builder/mcp/QUICK_NORMALIZATION_REFERENCE.md +++ /dev/null @@ -1,211 +0,0 @@ -# Quick Normalization Reference - -## The Two Critical Parameters - -When generating DeepStream nvinfer configs, **BOTH** of these parameters must exactly match your model's training preprocessing: - -### 1. `net-scale-factor` -**Purpose**: Scales pixel values (typically from [0-255] range) - -**Common values**: -- `0.00392156862745098` (1/255) - Most common, scales to [0,1] -- `0.5` - Scales by half -- `1.0` - No scaling - -### 2. `offsets` (per-channel mean subtraction) -**Purpose**: Subtracts mean values from each color channel after scaling - -**Format**: `R;G;B` (Red;Green;Blue) - -**Common values**: -- **Not specified** or `0;0;0` - No mean subtraction -- `127.5;127.5;127.5` - For [-1,1] normalization -- `123.675;116.28;103.53` - ImageNet mean values - ---- - -## Quick Decision Tree - -### Step 1: Check your training code - -```python -# Example 1: Simple [0,1] scaling -image = image / 255.0 -``` -**Config**: `net_scale_factor: 0.00392156862745098`, **no offsets** - ---- - -```python -# Example 2: [-1,1] normalization -image = (image / 255.0 - 0.5) * 2 -# or: image = (image - 127.5) / 127.5 -``` -**Config**: `net_scale_factor: 0.00392156862745098`, `offsets: "127.5;127.5;127.5"` - ---- - -```python -# Example 3: ImageNet normalization -transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) -``` -**Config**: `net_scale_factor: 0.00392156862745098`, `offsets: "123.675;116.28;103.53"` -*(mean values × 255)* - ---- - -```python -# Example 4: Custom scaling -image = image * 0.5 -``` -**Config**: `net_scale_factor: 0.5`, **no offsets** - ---- - -```python -# Example 5: Custom scaling with mean subtraction -image = (image * 0.01) - custom_mean -``` -**Config**: `net_scale_factor: 0.01`, `offsets: "{R};{G};{B}"` *(your custom mean values)* - ---- - -```python -# Example 6a: Division by std (uniform std across channels) -image = image / 255.0 -image = (image - mean) / std # If std=0.229 for all channels -``` -**Config**: `net_scale_factor: 0.0171302428` *(1/(255×0.229))*, `offsets: "2.118;1.991;1.773"` *(mean/std)* - -⚠️ **Note**: This only works if std is the same for all channels. For per-channel std (e.g., ImageNet `std=[0.229, 0.224, 0.225]`), you need to bake normalization into the ONNX model or use a custom preprocessing plugin, as DeepStream only supports a single scale factor. - ---- - -```python -# Example 6b: Multiplication by scale factor d -image = image * d / 255.0 # Where d might be 1/std or a custom constant -``` -**Config**: `net_scale_factor: d/255` *(if d=0.5, then 0.5/255 = 0.00196078)* - -**Note**: The scale factor becomes `d/255` instead of `1/255`. This pattern is used when training applies a custom scale factor `d`. - ---- - -## The Formula - -DeepStream applies: -``` -output_pixel = (input_pixel × net_scale_factor) - offset -``` - -### Examples: - -**[0,1] normalization** (no offsets): -``` -output = pixel × 0.00392156862745098 -output = pixel / 255 -Result: [0, 1] range -``` - -**[-1,1] normalization** (with offsets): -``` -output = (pixel × 0.00392156862745098) - 0.5 -output = (pixel / 255) - 0.5 -Result: [-0.5, 0.5] range → typically then multiplied by 2 in the model -``` - -**ImageNet normalization**: -``` -For red channel: -output = (pixel × 0.00392156862745098) - 0.485 -output = (pixel / 255) - 0.485 -``` - ---- - -## Common Mistakes - -### ❌ Mistake 1: Using default values without checking training -```yaml -# Default assumes [0,1] normalization -net-scale-factor: 0.00392156862745098 -# No offsets -``` -**Problem**: If your model was trained with ImageNet normalization or [-1,1] range, this will give wrong results! - -### ❌ Mistake 2: Forgetting per-channel offsets -```python -# Training code used: -image = (image / 255) - 0.5 # [-1,1] normalization -``` -```yaml -# But config only sets scale factor: -net-scale-factor: 0.00392156862745098 -# Missing: offsets: "127.5;127.5;127.5" -``` -**Problem**: Mean subtraction is missing, results will be incorrect! - -### ❌ Mistake 3: Wrong offset values -```yaml -# Model trained with ImageNet mean -net-scale-factor: 0.00392156862745098 -offsets: "127.5;127.5;127.5" # WRONG! Should be ImageNet means -``` -**Problem**: Using wrong per-channel values will shift colors incorrectly! - ---- - -## Verification Checklist - -Before deploying your config: - -- [ ] I checked my training code for the exact preprocessing steps -- [ ] I verified the `net-scale-factor` matches the scaling in training -- [ ] I verified the `offsets` match any mean subtraction in training -- [ ] If training had NO mean subtraction, I did NOT set offsets -- [ ] If training had per-channel means, I set the correct RGB values -- [ ] I tested with a known input and compared results - ---- - -## When in Doubt - -1. **Test with known inputs**: Use images with known pixel values -2. **Compare outputs**: Run the same image through training and inference -3. **Check model documentation**: Look for preprocessing specifications -4. **Ask the model creator**: They should know the exact normalization used - ---- - -## Real-World Examples - -### YOLOv5 -```yaml -net-scale-factor: 0.00392156862745098 # Scales to [0,1] -# No offsets - training used simple /255 normalization -``` - -### ResNet-50 (ImageNet pre-trained) -```yaml -net-scale-factor: 0.00392156862745098 # Scales to [0,1] -offsets: "123.675;116.28;103.53" # ImageNet per-channel means -``` - -### TAO ChangeNet ([-1,1] range) -```yaml -net-scale-factor: 0.00392156862745098 # Scales to [0,1] -offsets: "127.5;127.5;127.5" # Subtracts 0.5 per channel -``` - ---- - -## Remember - -> **Both `net-scale-factor` AND `offsets` are equally critical!** -> -> Using the wrong scale factor OR wrong per-channel offsets will result in poor inference accuracy, regardless of how good your model is. - -For detailed information, see: -- [NORMALIZATION_GUIDE.md](NORMALIZATION_GUIDE.md) - Comprehensive guide -- [EXAMPLE_DEEPSTREAM_CONFIG.md](EXAMPLE_DEEPSTREAM_CONFIG.md) - Usage examples - diff --git a/tools/inference_builder/mcp/README-MCP.md b/tools/inference_builder/mcp/README-MCP.md deleted file mode 100644 index c4f16f15..00000000 --- a/tools/inference_builder/mcp/README-MCP.md +++ /dev/null @@ -1,217 +0,0 @@ -# MCP Server Internal Documentation - -This document provides detailed technical documentation for the Inference Builder MCP server. For getting started and basic usage, see the [main README](../README.md#mcp-integration). - -## Tool Reference - -### `generate_inference_pipeline` - -Generates an inference pipeline from a YAML configuration file. - -| Parameter | Required | Description | -|-----------|----------|-------------| -| `config_file` | Yes | Path to the YAML configuration file | -| `server_type` | No | Type of server to generate (`triton`, `fastapi`, `nim`, `serverless`) | -| `output_dir` | No | Output directory for generated code | -| `api_spec` | No | Path to OpenAPI specification file | -| `custom_modules` | No | List of custom Python module files | -| `exclude_lib` | No | Exclude common library from generated code | -| `tar_output` | No | Create a tar.gz archive of the output | -| `validation_dir` | No | Validation directory path to build validator | -| `no_docker` | No | Use local OpenAPI Generator instead of Docker | -| `test_cases_abs_path` | No | Use absolute paths in generated test_cases.yaml | - -### `build_docker_image` - -Builds a Docker image from a generated inference pipeline. - -| Parameter | Required | Description | -|-----------|----------|-------------| -| `image_name` | Yes | Name for the Docker image | -| `dockerfile` | Yes | Path to Dockerfile (must be placed in the output directory) | - -### `prepare_model_repository` - -Prepares a model repository by downloading models from NGC or Hugging Face and copying configuration files. - -| Parameter | Required | Description | -|-----------|----------|-------------| -| `model_configs` | Yes | List of model configurations to prepare | -| `config_dir` | No | Base directory for resolving relative config paths | - -### `docker_run_image` - -Runs a Docker image with optional model repository mounting and environment configuration. - -| Parameter | Required | Default | Description | -|-----------|----------|---------|-------------| -| `image_name` | Yes | - | Name of the Docker image to run | -| `model_repo_host` | No | - | Host path to model repository | -| `model_repo_container` | No | `/models` | Container path for model repository | -| `server_type` | No | `serverless` | Server type hint | -| `env` | No | - | Environment variables to set | -| `cmd` | No | - | Command-line arguments. For serverless flows, supply input values here using `-- ` flags. **Use hyphens, not underscores** in argument names (e.g., `media_url` → `--media-url`). Each flag and value is a separate list item: `["--media-url", "/path/to/video.mp4"]` | -| `gpus` | No | `all` | GPU devices to use | -| `timeout` | No | `300` | Timeout in seconds | - -### `generate_nvinfer_config` - -Generates a DeepStream nvinfer runtime configuration file (nvdsinfer_config.yaml). - -**IMPORTANT**: Before generating a new config file, first call `prepare_model_repository` to download the model and check for existing nvinfer configuration files. Models from NGC typically include a configuration file (YAML or TXT) with all required inference parameters - always prefer using this provided configuration when available. - -| Parameter | Required | Default | Description | -|-----------|----------|---------|-------------| -| `output_path` | Yes | - | Path where the generated config file should be saved | -| `onnx_file` | Yes | - | Name of the ONNX model file | -| `network_type` | Yes | - | Network type (0=detection, 1=classification, 2=segmentation, 3=instance_segmentation, 100=custom) | -| `input_dims` | Yes | - | Input dimensions in format `channel;height;width` (e.g., `3;224;224`) | -| `label_file` | Yes | - | Name of the label file | -| `precision_mode` | No | `2` | Precision mode (0=FP32, 1=INT8, 2=FP16) | -| `custom_lib_path` | No | - | Path to a C++ shared library (.so) for custom output parsing. Required for all models except classic ResNet when network_type is 0-3. For TAO models, build from https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps/tree/master/post_processor | -| `custom_parse_func` | No | - | Symbol name of the custom parsing function. (0) Detection: built-in assumes ResNet; (1) Classification: built-in treats as softmax; (2,3) Segmentation: required; (100) Custom: not required | -| `num_classes` | No | - | Number of detected classes | -| `gie_unique_id` | No | `1` | Unique ID for this GIE | -| `net_scale_factor` | No | `0.00392156862745098` | Scale factor for normalization | -| `offsets` | No | - | Mean subtraction values in format `R;G;B` | -| `classifier_threshold` | No | `0.0` | Confidence threshold for classification | -| `input_tensor_from_meta` | No | `0` | Whether to read input from metadata (0 or 1) | -| `output_tensor_meta` | No | `0` | Whether to output raw tensor format (0 or 1) | - -**Important**: The `net_scale_factor` and `offsets` parameters must match your model's training preprocessing. See: -- [QUICK_NORMALIZATION_REFERENCE.md](QUICK_NORMALIZATION_REFERENCE.md) - Quick reference guide -- [STD_NORMALIZATION_CALCULATOR.md](STD_NORMALIZATION_CALCULATOR.md) - For models with std normalization - -## Resource Navigation - -### Documentation Resources - -| URI | Description | -|-----|-------------| -| `docs://README.md` | Main project README with overview, getting started, and installation | -| `docs://mcp/README-MCP.md` | This document - MCP server internal documentation | - -### Schema Resources - -| URI | Description | -|-----|-------------| -| `schema://config.schema.json` | Main JSON Schema for configuration files | -| `schema://readme` | Comprehensive schema documentation | -| `schema://index.json` | Schema navigation index mapping backend types to parameter schemas | -| `schema://backends/{backend}.schema.json` | Backend-specific schemas (deepstream, triton, vllm, etc.) | -| `schema://backends/parameters/{backend}-parameters.schema.json` | Detailed parameter schemas | - -**Schema Navigation Flow:** -1. Read `schema://config.schema.json` for the main configuration structure -2. When a model specifies a `backend` type (e.g., `vllm`, `triton/python`), read `schema://index.json` to find the corresponding parameter schema path -3. Read the backend-specific parameter schema to get valid parameter options - -### Sample Resources - -Sample resources are dynamically discovered and categorized: - -| URI Pattern | Description | -|-------------|-------------| -| `samples://config/{path}` | Pipeline/application configuration YAML files | -| `samples://runtime_config/{path}` | DeepStream nvinfer runtime configuration files | -| `samples://runtime_preprocess/{path}` | DeepStream nvdspreprocess runtime configuration files | -| `samples://openapi/{path}` | OpenAPI server specification YAML files | -| `samples://dockerfile/{path}` | Sample Dockerfiles | -| `samples://processor/{path}` | Preprocessor/postprocessor Python modules | - -## Usage Examples - -### Generate a DeepStream Pipeline - -``` -Use the generate_inference_pipeline tool to create a DeepStream object detection pipeline using the ds_detect.yaml configuration with serverless output. -``` - -### Explore Available Samples - -``` -List the available MCP resources to see sample configurations, Dockerfiles, and processors. -``` - -### View Sample Configurations - -``` -Read the samples://config/ds_app/detection/ds_detect.yaml resource to see the DeepStream detection configuration. -``` - -### Build Docker Image - -``` -Build a Docker image from the generated deepstream-app pipeline with the name 'my-detector' using the build_docker_image tool. -``` - -## Workflow Integration - -### Typical Development Workflow - -1. **Explore Samples**: Browse `samples://config/*` resources to see available configurations -2. **Examine Configurations**: Read sample resources to understand configuration patterns -3. **Create Your Config**: Modify a sample configuration or create your own -4. **Generate Pipeline**: Use `generate_inference_pipeline` to create your code -5. **Build Container**: Use `build_docker_image` to create a deployable container - -### Integration with Version Control - -The generated code can be: -- Committed to version control -- Used as a starting point for custom modifications -- Shared with team members -- Deployed to production environments - -## Troubleshooting - -### Common Issues - -**MCP Server Not Found** -- Ensure the MCP server is running -- Check that the path in your MCP client configuration is correct -- Verify Python environment has required dependencies - -**Configuration Validation Errors** -- Check that required fields are present in your YAML -- Ensure model definitions are complete -- Verify backend specifications are correct - -**Pipeline Generation Failures** -- Check that the configuration file path is correct -- Ensure all required dependencies are installed -- Verify that the output directory is writable - -### Debug Mode - -To run the MCP server with debug output: - -```bash -python -u mcp_server.py -``` - -## Extending the MCP Server - -To add new tools or resources: - -1. **Add new tools** to the `InferenceBuilderMCPServer` class in `mcp_server.py` -2. **Update tool schemas** in the `list_tools` method -3. **Implement tool logic** in the `call_tool` method -4. **Add tests** for new functionality in `test_mcp_server.py` - -### Server Types - -The `server_type` parameter supports: - -| Type | Description | -|------|-------------| -| `fastapi` | RESTful API server with automatic OpenAPI generation | -| `serverless` | Standalone command-line application | - -### Custom Processors - -Custom preprocessors and postprocessors can be integrated: - -1. Create custom processor classes following the base interface -2. Reference them in your configuration file -3. Use the `custom_modules` parameter when generating pipelines diff --git a/tools/inference_builder/mcp/STD_NORMALIZATION_CALCULATOR.md b/tools/inference_builder/mcp/STD_NORMALIZATION_CALCULATOR.md deleted file mode 100644 index c20130b1..00000000 --- a/tools/inference_builder/mcp/STD_NORMALIZATION_CALCULATOR.md +++ /dev/null @@ -1,395 +0,0 @@ -# Standard Deviation Normalization Calculator - -## Understanding Std Normalization - -### Case 1: Division by Std (Most Common) - -When training uses standard deviation normalization by **dividing** by std: -```python -normalized = (pixel/255 - mean) / std -``` - -This can be rewritten as: -```python -normalized = pixel * (1/(255*std)) - (mean/std) -``` - -Which maps to DeepStream parameters: -- `net_scale_factor = 1/(255 * std)` -- `offsets = mean/std` - -**Example**: If `std = 0.229`, then `net_scale_factor = 1/(255 × 0.229) = 0.0171302428` - ---- - -### Case 2: Multiplication by Scale Factor (Alternative) - -If training uses a scaling factor `d` (where `d` might be related to std): -```python -normalized = pixel * d / 255 -``` - -This maps to DeepStream parameters: -- `net_scale_factor = d/255` -- `offsets` depend on any mean subtraction - -**Example**: If `d = 0.5`, then `net_scale_factor = 0.5/255 = 0.00196078431` - -**Note**: This is less common but may be used in custom preprocessing schemes where `d` could be `1/std` or another scaling factor. - -## The Challenge - -**DeepStream limitation**: Only supports a **single** `net_scale_factor` value, but many models use **per-channel** std values. - -**Example**: ImageNet uses `std=[0.229, 0.224, 0.225]` - different for each channel! - -## Solutions - -### Solution 1: Uniform Std (Best for DeepStream) - -If your model uses the **same std for all channels**, you can directly convert: - -#### Example: Uniform std = 0.229 -```python -# Training normalization -mean = [0.485, 0.456, 0.406] -std = [0.229, 0.229, 0.229] # Same for R, G, B -``` - -#### Calculation: -```python -net_scale_factor = 1 / (255 * 0.229) = 0.0171302428 - -offsets_R = 0.485 / 0.229 = 2.118122 -offsets_G = 0.456 / 0.229 = 1.991266 -offsets_B = 0.406 / 0.229 = 1.772926 -``` - -#### DeepStream Config: -```yaml -net-scale-factor: 0.0171302428 -offsets: 2.118122;1.991266;1.772926 -``` - ---- - -### Solution 2: Average Std (Approximation) - -If std values are close but not identical, use the average: - -#### Example: ImageNet std = [0.229, 0.224, 0.225] -```python -# Training normalization -mean = [0.485, 0.456, 0.406] -std = [0.229, 0.224, 0.225] # Different per channel -``` - -#### Calculation: -```python -avg_std = (0.229 + 0.224 + 0.225) / 3 = 0.226 - -net_scale_factor = 1 / (255 * 0.226) = 0.0173510648 - -offsets_R = 0.485 / 0.226 = 2.146018 -offsets_G = 0.456 / 0.226 = 2.017699 -offsets_B = 0.406 / 0.226 = 1.796460 -``` - -#### DeepStream Config: -```yaml -net-scale-factor: 0.0173510648 -offsets: 2.146018;2.017699;1.796460 -``` - -⚠️ **Trade-off**: This is an approximation. Accuracy may be slightly reduced because we're not using the exact per-channel std values. - ---- - -### Solution 3: Ignore Std (Mean-only, Less Accurate) - -Use only mean subtraction, ignore std division: - -#### Calculation: -```python -net_scale_factor = 1 / 255 = 0.00392156862745098 - -offsets_R = 0.485 * 255 = 123.675 -offsets_G = 0.456 * 255 = 116.28 -offsets_B = 0.406 * 255 = 103.53 -``` - -#### DeepStream Config: -```yaml -net-scale-factor: 0.00392156862745098 -offsets: 123.675;116.28;103.53 -``` - -⚠️ **Limitation**: This does NOT include std normalization, only mean subtraction. Results will be less accurate. - ---- - -### Solution 4: Bake into ONNX (Recommended for Per-Channel Std) - -The most accurate solution: Include preprocessing in the ONNX model itself. - -#### PyTorch Example: -```python -import torch -import torch.nn as nn - -class ModelWithPreprocessing(nn.Module): - def __init__(self, base_model, mean, std): - super().__init__() - self.base_model = base_model - # Register mean and std as buffers - self.register_buffer('mean', torch.tensor(mean).view(1, 3, 1, 1)) - self.register_buffer('std', torch.tensor(std).view(1, 3, 1, 1)) - - def forward(self, x): - # x is in [0, 255] range - x = x / 255.0 # Scale to [0, 1] - x = (x - self.mean) / self.std # Per-channel normalization - return self.base_model(x) - -# Wrap your model -mean = [0.485, 0.456, 0.406] -std = [0.229, 0.224, 0.225] -wrapped_model = ModelWithPreprocessing(original_model, mean, std) - -# Export to ONNX -torch.onnx.export(wrapped_model, dummy_input, "model_with_preprocessing.onnx") -``` - -#### DeepStream Config: -```yaml -# No normalization needed - it's in the model! -net-scale-factor: 1.0 -# No offsets -``` - -**Benefits**: -- ✅ Exact per-channel std normalization -- ✅ No accuracy loss -- ✅ Simpler DeepStream config - ---- - -## Quick Calculator - -### Method 1: Division by Std (Standard Approach) - -**Given Training Parameters**: -- **mean**: [mean_r, mean_g, mean_b] -- **std**: [std_r, std_g, std_b] - -**Training formula**: `(pixel/255 - mean) / std` - -#### If std is uniform (std_r = std_g = std_b = d): - -``` -net_scale_factor = 1 / (255 × d) -offset_r = mean_r / d -offset_g = mean_g / d -offset_b = mean_b / d -``` - -**Example**: If `d = 0.229`, `mean = [0.485, 0.456, 0.406]` -``` -net_scale_factor = 1 / (255 × 0.229) = 0.0171302428 -offsets = 2.118;1.991;1.773 -``` - -#### If std varies per channel (approximation): - -``` -avg_std = (std_r + std_g + std_b) / 3 -net_scale_factor = 1 / (255 × avg_std) -offset_r = mean_r / avg_std -offset_g = mean_g / avg_std -offset_b = mean_b / avg_std -``` - ---- - -### Method 2: Multiplication by Scale Factor - -**Given Training Parameters**: -- **scale_factor**: d (a custom scaling constant) - -**Training formula**: `pixel × d / 255` - -#### Calculation: - -``` -net_scale_factor = d / 255 -``` - -**Example**: If `d = 0.5` -``` -net_scale_factor = 0.5 / 255 = 0.00196078431 -``` - -**Example**: If `d = 1/0.229` (inverse of std) -``` -net_scale_factor = (1/0.229) / 255 = 4.366812 / 255 = 0.0171302428 -``` - -**Note**: The scale factor becomes `d/255` instead of `1/255`. - ---- - -## Common Datasets - -### ImageNet -``` -mean = [0.485, 0.456, 0.406] -std = [0.229, 0.224, 0.225] -``` - -**Recommended**: Bake into ONNX or use average std approximation - -### COCO (if normalized) -``` -mean = [0.485, 0.456, 0.406] -std = [0.229, 0.224, 0.225] -``` -Same as ImageNet - -### CIFAR-10 -``` -mean = [0.4914, 0.4822, 0.4465] -std = [0.2023, 0.1994, 0.2010] -``` - -**Average std**: 0.2009 - ---- - -## Decision Tree - -``` -Do you use std normalization in training? -│ -├─ No → Use simple config: -│ net_scale_factor: 1/255 -│ offsets: mean × 255 (if mean subtraction used) -│ -└─ Yes → Is std the same for all channels? - │ - ├─ Yes → Use combined config: - │ net_scale_factor: 1/(255×std) - │ offsets: mean/std - │ - └─ No → Choose approach: - │ - ├─ Approximation (easier): - │ net_scale_factor: 1/(255×avg_std) - │ offsets: mean/avg_std - │ ⚠️ Slight accuracy loss - │ - ├─ Mean-only (less accurate): - │ net_scale_factor: 1/255 - │ offsets: mean × 255 - │ ⚠️ Missing std normalization - │ - └─ Bake into ONNX (best): - Include preprocessing in model - ✅ Full accuracy, per-channel std -``` - ---- - -## Verification - -To verify your config is correct: - -1. **Test with known input**: - ```python - test_pixel = 128 # Middle gray - - # Training preprocessing: - train_value = (test_pixel/255 - mean) / std - - # DeepStream preprocessing: - ds_value = (test_pixel * net_scale_factor) - offset - - # These should match! - ``` - -2. **Compare outputs**: Run inference on the same image in training and DeepStream, compare results - -3. **Check accuracy**: Use validation dataset to measure accuracy drop - ---- - -## Python Helper Script - -```python -def calculate_deepstream_params(mean, std, use_average=False): - """ - Calculate DeepStream normalization parameters. - - Args: - mean: List of per-channel means [R, G, B] (in [0,1] range) - std: List of per-channel stds [R, G, B] - use_average: If True, use average std (approximation) - - Returns: - tuple: (net_scale_factor, offsets_string) - """ - mean_r, mean_g, mean_b = mean - std_r, std_g, std_b = std - - # Check if std is uniform - is_uniform = (std_r == std_g == std_b) - - if is_uniform: - # Exact calculation for uniform std - net_scale_factor = 1.0 / (255 * std_r) - offset_r = mean_r / std_r - offset_g = mean_g / std_g - offset_b = mean_b / std_b - print("✅ Uniform std detected - using exact calculation") - elif use_average: - # Approximation using average std - avg_std = (std_r + std_g + std_b) / 3 - net_scale_factor = 1.0 / (255 * avg_std) - offset_r = mean_r / avg_std - offset_g = mean_g / avg_std - offset_b = mean_b / avg_std - print(f"⚠️ Using average std approximation: {avg_std:.6f}") - else: - print("❌ Per-channel std detected. Consider baking into ONNX!") - print(" Using average std approximation...") - avg_std = (std_r + std_g + std_b) / 3 - net_scale_factor = 1.0 / (255 * avg_std) - offset_r = mean_r / avg_std - offset_g = mean_g / avg_std - offset_b = mean_b / avg_std - - offsets = f"{offset_r:.6f};{offset_g:.6f};{offset_b:.6f}" - - print(f"\nDeepStream Config:") - print(f" net-scale-factor: {net_scale_factor}") - print(f" offsets: {offsets}") - - return net_scale_factor, offsets - -# Example usage: -# ImageNet normalization -mean = [0.485, 0.456, 0.406] -std = [0.229, 0.224, 0.225] -calculate_deepstream_params(mean, std, use_average=True) -``` - ---- - -## Summary - -- **Best accuracy**: Bake std normalization into ONNX model -- **Good approximation**: Use average std (if stds are similar) -- **Uniform std**: Direct conversion works perfectly -- **Mean-only**: Simplest but least accurate (ignores std) - -Always verify your configuration with test data before deploying! - diff --git a/tools/inference_builder/mcp/examples.md b/tools/inference_builder/mcp/examples.md deleted file mode 100644 index 8406782a..00000000 --- a/tools/inference_builder/mcp/examples.md +++ /dev/null @@ -1,113 +0,0 @@ -# Using Inference Builder with AI Agents — Examples and Guidelines - -## General Guidelines - -1. **Start simple, then refine.** Begin with a short, clear prompt that states the core requirements. Use follow-up messages to add details or adjust behavior. - -2. **Ask for self-check and verification.** Tell the agent to validate its work (e.g., run a smoke test or sanity check) so the result is correct before you rely on it. - -3. **Request documentation and diagrams.** Ask the agent to produce a README and architecture diagrams. These make the solution easier to understand and easier to extend later. - -4. **Use helper scripts for operations.** Request small scripts for common tasks (e.g., “create a script to start, stop, rebuild, get status, and fetch logs for the microservice”). These simplify testing and debugging. - -5. **Correct course early.** If the agent goes off track, give a direct hint right away. For example: - - If no free GPU is available, tell it to stop and retry later. - - If it picks the wrong model, ask it to double-check the choice. - -## Sample Prompts - -### 1. Create a very basic Deepstream Application - -***Use deepstream inference builder tool to create an object detection pipeline with PeopleNet Transformer model from NGC and verify it with a smoke test.*** - -The Agent is expected to draft a plan based on the prompt and generates required files accordingly (file names may differ in each run unless explicitly specified): - -- peoplenet_transformer_pipeline.yaml (pipeline configuration yaml) -- Dockerfile -- nvdsinfer_config.yaml (nvinfer configuration yaml) -- peoplenet.tgz (python code for inference) -- models/peoplenet (folder for required model files) - -The Agent is expected to fix all the build and runtime errors while doing the smoke test. - -### 2. Create an advanced Deepstream Application Supporting Tracker and Multi-stream Inputs - -***Use deepstream inference builder tool to create an object detection pipeline with the PeopleNet transformer model from NGC; the pipeline should support 4 video inputs in parallel and track all the objects in the inputs using NVDCF tracker. Do a smoke test once done.*** - -The Agent is expected to draft a plan based on the prompt and generates required files accordingly (file names may differ in each run unless explicitly specified): - -- peoplenet_transformer_pipeline.yaml (pipeline configuration yaml) -- Dockerfile -- nvdsinfer_config.yaml (nvinfer configuration yaml) -- peoplenet.tgz (python code for inference) -- models/peoplenet (folder for required model files) - -The Agent is expected to fix all the build and runtime errors while doing the smoke test. - -### 3. Create a Deepstream Inference Application with Customized Logic - -***Use deepstream inferenc builder tool to create an object detection pipeline with PeopleNet transformer model from NGC; the pipeline accepts video url as input and output detected bounding boxes. Count the number of people in each frame and raise an alarm whenever the number exceeds 10 by writing it down to a file. Do a smoke test once done.*** - -The Agent is expected to draft a plan based on the prompt and generates required files accordingly (file names may differ in each run unless explicitly specified): - -- peoplenet_transformer_pipeline.yaml (pipeline configuration yaml) -- Dockerfile -- nvdsinfer_config.yaml (nvinfer configuration yaml) -- processor.py (Customized processor for counting and raising alarm) -- peoplenet.tgz (python code for inference) -- models/peoplenet (folder for required model files) - -The Agent is expected to fix all the build and runtime errors while doing the smoke test. - - -### 4. Create a Realtime Video Intelligence Microservice (rtvi) with vllm Backend and Deepstream Accelerated Decoder - -#### 4.1. Start with the Master Prompt as below - -***Locate the MCP tool deepstream-inference-builder and complete the task: Start a new project under name rtvi_vlm and create a video summarization/caption microservice there using Qwen3-VL-2B-Instruct model driven by vllm backend and Deepstream accelerated decoder. The microservice supports adding/deleting RTSP streams as assets and generating video caption for added streams. The given RTSP stream will be chunked to a batch of frames with a given interval for summarization. I also want to build a container image for the microservice to run on B200. Do a smoke test on the image once done.*** - -The Agent is expected to understand the prompt and creates a folder named “rtvi_vlm” and generates all the files there: -- config.yaml (pipeline configuration yaml) -- Dockerfile -- openapi.yaml (server specification) -- processors.py (customized code) -- rtvi_vlm.tgz (backbone python code) -- model-repo (folder for required model files) - -The Agent is responsible for the smoke testing and troubleshooting of any issues encountered with the generated container image. - -#### 4.2. Add Helper Scripts for Manual Testing with Follow-up Prompts - -***Generate a service management script to start, restart, stop, rebuild the service and check the service status, also fetch the service logs. Generate a client script to add/delete streams and trigger a summarization request on streams.*** - -With the generated server and client script, we can ask Cursor to intensively test stream summarization, and during this process Cursor will fix the generated processor code based on the container log. -We'll need to go through this a few times because Cursor has to figure out the best model setup by running it. - -You can ask Cursor to use your live stream: `rtsp://:/`. - -#### 4.3. New feature request - -New features can always be added through follow-up prompts: - -***Now let's add a new feature to the project. After the model generates summarizations, let's postprocess it for each chunk and send them to a Kafka server. Create an environment variable to toggle the feature.*** - -#### 4.4. Document the project for future development - -***Write a detailed documentation for the project to ensure seamless and consistent future work.*** - -Subsequently, the project remains open, allowing for continued adjustment, addition, or removal of features using the same methods. - -#### 4.5. YOLO26s Detection Deepstream Application - -***Download the YOLO26s detection model using the ultralytics library, then convert the model to ONNX model that supports dynamic batch, in Python virtual environment. Write a DeepStream custom parser for the model. Use deepstream inference builder tool to create an object detection pipeline with the model and custom parser. Do a smoke test once done.*** - -The Agent is expected to draft a plan based on the prompt and generates required files accordingly (file names may differ in each run unless explicitly specified): - -- yolo26s_pipeline.yaml (pipeline configuration yaml) -- Dockerfile -- nvdsinfer_config.yaml (nvinfer configuration yaml) -- yolo26s-detection.tgz (python code for inference) -- models/yolo26s (folder for required model files) -- custom_parser (DeepStream custom parser for the model) - -The Agent is expected to fix all the build and runtime errors while doing the smoke test. diff --git a/tools/inference_builder/mcp/mcp_server.py b/tools/inference_builder/mcp/mcp_server.py deleted file mode 100644 index 21a219ff..00000000 --- a/tools/inference_builder/mcp/mcp_server.py +++ /dev/null @@ -1,1994 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. -# All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -MCP (Model Context Protocol) Server for Inference Builder - -This module provides an MCP server that exposes Inference Builder functionality -to MCP-compatible clients like Cursor. -""" - -import asyncio -import os -import sys -from pathlib import Path -from typing import Any, Dict -from mcp.server import Server -from mcp.server.stdio import stdio_server -from mcp.types import ( - CallToolRequest, - CallToolResult, - ListToolsRequest, - ListToolsResult, - Tool, - TextContent, - Resource, -) -import subprocess -import logging -import time -import shutil -import uuid - -# Add the builder directory to Python path -sys.path.append(str(Path(__file__).parent.parent / "builder")) - - -def load_allowed_servers() -> list: - """Load allowed server types from the builder's allowed_servers.txt file. - - Returns: - List of allowed server type strings. - - Raises: - FileNotFoundError: If the configuration file is not found. - ValueError: If the file is empty or contains invalid entries. - """ - file_path = Path(__file__).parent.parent / "builder" / "allowed_servers.txt" - - if not file_path.exists(): - raise FileNotFoundError( - f"Allowed servers configuration file not found: {file_path}" - ) - - allowed_servers = [] - with open(file_path, 'r', encoding='utf-8') as f: - for line in f: - line = line.strip() - if line and not line.startswith('#'): - allowed_servers.append(line) - - if not allowed_servers: - raise ValueError(f"No valid server types found in {file_path}") - - return allowed_servers - - -# Load allowed server types from configuration file -ALLOWED_SERVERS = load_allowed_servers() - - -class InferenceBuilderMCPServer: - """MCP Server that exposes Inference Builder functionality""" - - def __init__(self): - self.server = Server("deepstream-inference-builder") - self.logger = logging.getLogger("deepstream-inference-builder") - - # Register handlers using decorators - self._setup_handlers() - - def _setup_handlers(self): - """Setup MCP server handlers using decorators""" - - @self.server.list_tools() - async def handle_list_tools(): - # The low-level server expects a list[Tool], not ListToolsResult - result = await self.list_tools(None) - return result.tools - - @self.server.call_tool() - async def handle_call_tool(name: str, arguments: Dict[str, Any]): - # Bridge to existing implementation which returns CallToolResult - from mcp.types import CallToolRequestParams - params = CallToolRequestParams(name=name, arguments=arguments) - request = CallToolRequest(params=params) - result = await self.call_tool(request) - if getattr(result, "isError", False): - # Let the low-level server wrap this as an error result - message = "" - for block in getattr(result, "content", []) or []: - if getattr(block, "type", "") == "text": - message = getattr(block, "text", "") - break - raise RuntimeError(message or "Tool execution error") - # Return unstructured content so low-level server wraps it properly - return getattr(result, "content", []) - - @self.server.list_resources() - async def handle_list_resources(): - """List available schema and sample resources""" - self.logger.info("list_resources handler called") - resources = [] - - # Add base schema resources - try: - self.logger.info("Creating base schema resources...") - base_resources = [ - # Schema resources - Resource( - uri="schema://config.schema.json", - name="Configuration Schema", - description=( - "JSON Schema for inference builder configuration files. " - "Defines required fields (name, model_repo, models), " - "tensor specifications, backend types, and server configuration." - ), - mimeType="application/json" - ), - Resource( - uri="schema://readme", - name="Schema Documentation", - description=( - "Comprehensive documentation for configuration schemas including " - "examples, backend-specific parameters, data types, " - "preprocessors/postprocessors, and server responder configuration." - ), - mimeType="text/markdown" - ), - Resource( - uri="schema://index.json", - name="Schema Index", - description=( - "Index mapping backend types to their parameter schemas. " - "Use this to find the correct parameter schema for a given backend " - "(e.g., 'vllm' -> 'backends/parameters/vllm-parameters.schema.json'). " - "Read this first to understand schema navigation." - ), - mimeType="application/json" - ), - # Documentation resources - Resource( - uri="docs://README.md", - name="Project README", - description=( - "Main project documentation for Inference Builder. " - "Includes overview, getting started guide, installation instructions, " - "and links to examples and detailed documentation." - ), - mimeType="text/markdown" - ), - Resource( - uri="docs://mcp/README-MCP.md", - name="MCP Integration Documentation", - description=( - "Detailed documentation for MCP server integration. " - "Includes tool reference with parameters, resource navigation guide, " - "usage examples, workflow integration, and troubleshooting." - ), - mimeType="text/markdown" - ), - Resource( - uri="docs://usage.md", - name="Usage Documentation", - description=( - "Comprehensive usage guide for Inference Builder. " - "Covers command line arguments, configuration file format, " - "model definitions, preprocessors/postprocessors, server configuration, " - "routing, and runtime environment variables." - ), - mimeType="text/markdown" - ), - Resource( - uri="docs://platform-guide.md", - name="Platform Guide", - description=( - "Hardware platform selection guide. " - "Maps target hardware (x86_64 datacenter, Jetson/Tegra, arm-sbsa servers " - "such as GB10/GB300/DGX Spark) to the correct Dockerfile template, " - "DeepStream base image, GPU architecture flags, PyTorch install method, " - "and CUDA version. **Read this first** when building a Docker image to " - "avoid runtime failures caused by mismatched base images or missing " - "platform libraries (e.g. libnvbufsurface on Tegra)." - ), - mimeType="text/markdown" - ), - ] - self.logger.info("Created %d base_resources objects", len(base_resources)) - for i, res in enumerate(base_resources): - self.logger.debug(" base_resource[%d]: uri=%s, name=%s", i, res.uri, res.name) - resources.extend(base_resources) - self.logger.info("After extend, resources has %d items", len(resources)) - except Exception as exc: - self.logger.exception("Error creating base resources: %s", exc) - - # Dynamically discover backend schema resources - try: - schema_dir = Path(__file__).parent.parent / "schemas" - backends_dir = schema_dir / "backends" - self.logger.debug("Schema dir: %s (exists: %s)", schema_dir, schema_dir.exists()) - self.logger.debug("Backends dir: %s (exists: %s)", backends_dir, backends_dir.exists()) - if backends_dir.exists(): - # Backend schemas (e.g., deepstream.schema.json, triton.schema.json) - for schema_file in sorted(backends_dir.glob("*.schema.json")): - backend_name = schema_file.stem.replace(".schema", "") - resources.append(Resource( - uri=f"schema://backends/{schema_file.name}", - name=f"Backend Schema: {backend_name}", - description=( - f"JSON Schema for {backend_name} backend configuration. " - f"Defines backend-specific parameters and settings." - ), - mimeType="application/json" - )) - - # Backend parameter schemas (e.g., deepstream-parameters.schema.json) - params_dir = backends_dir / "parameters" - if params_dir.exists(): - for param_file in sorted(params_dir.glob("*-parameters.schema.json")): - backend_name = param_file.stem.replace("-parameters.schema", "") - resources.append(Resource( - uri=f"schema://backends/parameters/{param_file.name}", - name=f"Backend Parameters: {backend_name}", - description=( - f"JSON Schema for {backend_name} backend parameters. " - f"Defines detailed parameter options for model configuration." - ), - mimeType="application/json" - )) - self.logger.info("After backend discovery, resources has %d items", len(resources)) - except Exception as exc: - self.logger.exception("Error discovering backend schemas: %s", exc) - - # Dynamically discover sample resources categorized by type - try: - samples_dir = Path(__file__).parent.parent / "builder" / "samples" - self.logger.debug("Samples dir: %s (exists: %s)", samples_dir, samples_dir.exists()) - if not samples_dir.exists(): - self.logger.warning("Samples directory not found: %s", samples_dir) - else: - for sample_dir in sorted(samples_dir.iterdir()): - if sample_dir.is_dir(): - sample_name = sample_dir.name - description = self._get_sample_description(sample_name) - - # Collect all files recursively from the sample directory - # This ensures deeply nested configs like nvdsinfer_config.yaml - # under ds_app/*/* are exposed as resources. - all_files = [ - f for f in sample_dir.rglob("*") if f.is_file() - ] - - # Category 1a: DeepStream nvinfer runtime configs (e.g., nvdsinfer_config.yaml) - # These are NOT pipeline definitions; they are runtime nvinfer config - # files that belong in the model repository when DeepStream backend is used. - runtime_yaml_files = [ - f - for f in all_files - if f.is_file() - and f.suffix == ".yaml" - and f.name == "nvdsinfer_config.yaml" - ] - for runtime_yaml in sorted(runtime_yaml_files): - rel_path = runtime_yaml.relative_to(samples_dir) - resources.append(Resource( - uri=f"samples://runtime_config/{rel_path}", - name=f"DeepStream Runtime Config for Inference: {rel_path}", - description=( - "DeepStream nvinfer runtime configuration file " - "(nvdsinfer_config.yaml). " - "This file should live in the model repository when Deepstream backend is used " - "and be referenced via 'infer_config_path' in the " - "DeepStream backend parameters, not used as a pipeline " - "configuration YAML." - ), - mimeType="application/x-yaml" - )) - - # Category 1b: DeepStream preprocess runtime configs (e.g., nvdspreprocess_config*.yaml) - # These are runtime configuration files for the nvdspreprocess plugin and - # should also live in the model repository when DeepStream backend is used. - preprocess_runtime_yaml_files = [ - f - for f in all_files - if f.is_file() - and f.suffix == ".yaml" - and f.name.startswith("nvdspreprocess_config") - ] - for preprocess_yaml in sorted(preprocess_runtime_yaml_files): - rel_path = preprocess_yaml.relative_to(samples_dir) - resources.append(Resource( - uri=f"samples://runtime_preprocess/{rel_path}", - name=f"DeepStream Runtime Config for Preprocessing: {rel_path}", - description=( - "DeepStream nvdspreprocess runtime configuration file " - "(nvdspreprocess_config*.yaml). " - "This file should live in the model repository when DeepStream " - "backend is used and be referenced via 'preprocess_config_path' " - "in the DeepStream backend parameters, not used as a pipeline " - "configuration YAML." - ), - mimeType="application/x-yaml" - )) - - # Category 1c: OpenAPI / server specification YAML files - # These define the HTTP API contract (e.g., for FastAPI, Triton, NIM) - # and should be treated as server configuration, not pipeline configs. - openapi_yaml_files = [ - f - for f in all_files - if f.is_file() - and f.suffix == ".yaml" - and ( - f.name in ("openapi.yaml", "openapi.yml") - or f.name.endswith("_openapi.yaml") - or f.name.endswith("_openapi.yml") - ) - ] - for openapi_yaml in sorted(openapi_yaml_files): - rel_path = openapi_yaml.relative_to(samples_dir) - resources.append(Resource( - uri=f"samples://openapi/{rel_path}", - name=f"OpenAPI Server Spec: {rel_path}", - description=( - "OpenAPI specification YAML describing the HTTP API for an " - "inference server (e.g., FastAPI, Triton, NIM). " - "Treat this as server configuration: it defines request/response " - "schemas and endpoints and is referenced via 'api_spec' when " - "generating a FastAPI/NIM/TRITON server, not as a model or " - "pipeline configuration." - ), - mimeType="application/x-yaml" - )) - - # Category 1d: Pipeline / application configuration YAML files - yaml_files = [ - f - for f in all_files - if f.is_file() - and f.suffix == ".yaml" - and f.name != "nvdsinfer_config.yaml" - and not f.name.startswith("nvdspreprocess_config") - and not ( - f.name in ("openapi.yaml", "openapi.yml") - or f.name.endswith("_openapi.yaml") - or f.name.endswith("_openapi.yml") - ) - ] - for yaml_file in sorted(yaml_files): - rel_path = yaml_file.relative_to(samples_dir) - resources.append(Resource( - uri=f"samples://config/{rel_path}", - name=f"Sample Config: {rel_path}", - description=( - f"Sample pipeline/application configuration YAML for " - f"{sample_name}. {description}" - ) if description else f"Sample configuration: {rel_path}", - mimeType="application/x-yaml" - )) - - # Category 2: Dockerfiles - dockerfiles = [f for f in all_files if f.is_file() and f.name.startswith("Dockerfile")] - for dockerfile in sorted(dockerfiles): - rel_path = dockerfile.relative_to(samples_dir) - resources.append(Resource( - uri=f"samples://dockerfile/{rel_path}", - name=f"Sample Dockerfile: {rel_path}", - description=( - f"Dockerfile for {sample_name}. " - f"Use as reference for container image builds." - ), - mimeType="text/plain" - )) - - # Category 3: Processor Python files (files with "processor" in name) - processor_files = [ - f for f in all_files - if f.is_file() and f.suffix == ".py" and "processor" in f.stem.lower() - ] - for processor in sorted(processor_files): - rel_path = processor.relative_to(samples_dir) - resources.append(Resource( - uri=f"samples://processor/{rel_path}", - name=f"Sample Processor: {rel_path}", - description=( - f"Sample preprocessor/postprocessor for {sample_name}. " - f"Python module with callable classes for pipeline processing." - ), - mimeType="text/x-python" - )) - - self.logger.info("After sample discovery, resources has %d items", len(resources)) - except Exception as exc: - self.logger.exception("Error discovering sample resources: %s", exc) - - self.logger.info("Listed %d MCP resources", len(resources)) - if len(resources) > 0: - self.logger.info("First resource URI: %s", resources[0].uri) - self.logger.info("Last resource URI: %s", resources[-1].uri) - else: - self.logger.warning("Resources list is EMPTY - this is the problem!") - - return resources - - @self.server.read_resource() - async def handle_read_resource(uri): - """Read a schema or sample resource by URI""" - # Convert AnyUrl to string for string operations - uri = str(uri) - schema_dir = Path(__file__).parent.parent / "schemas" - samples_dir = Path(__file__).parent.parent / "builder" / "samples" - - # Handle docs:// URIs for documentation files - if uri.startswith("docs://"): - project_root = Path(__file__).parent.parent - docs_mappings = { - "docs://README.md": project_root / "README.md", - "docs://mcp/README-MCP.md": project_root / "mcp" / "README-MCP.md", - "docs://usage.md": project_root / "doc" / "usage.md", - "docs://platform-guide.md": project_root / "doc" / "platform-guide.md", - } - if uri in docs_mappings: - file_path = docs_mappings[uri] - else: - raise ValueError( - f"Unknown docs resource URI: {uri}. " - f"Available: docs://README.md, docs://mcp/README-MCP.md, docs://usage.md" - ) - - # Handle schema:// URIs - elif uri.startswith("schema://"): - # Static schema mappings - uri_to_file = { - "schema://config.schema.json": schema_dir / "config.schema.json", - "schema://readme": schema_dir / "README.md", - "schema://index.json": schema_dir / "index.json", - } - - if uri in uri_to_file: - file_path = uri_to_file[uri] - elif uri.startswith("schema://backends/"): - # Handle dynamic backend schema URIs - rel_path = uri.replace("schema://backends/", "") - file_path = schema_dir / "backends" / rel_path - - # Security check: ensure path doesn't escape backends directory - backends_dir = schema_dir / "backends" - try: - file_path.resolve().relative_to(backends_dir.resolve()) - except ValueError as exc: - raise ValueError(f"Invalid backend schema path: {rel_path}") from exc - else: - raise ValueError( - f"Unknown schema resource URI: {uri}. " - f"Use schema://config.schema.json, schema://readme, " - f"schema://index.json, or schema://backends/*" - ) - - # Handle samples:// URIs (with category prefix: config/, dockerfile/, processor/, " - # runtime_config/, runtime_preprocess/, openapi/) - elif uri.startswith("samples://"): - rel_path = uri.replace("samples://", "") - - # If the URI is only a category prefix (e.g., samples://runtime_config), - # return a helpful error instead of trying to map it to a file. - category_labels = { - "config": "pipeline/application configuration YAML files", - "dockerfile": "sample Dockerfiles", - "processor": "sample pre/postprocessor Python modules", - "runtime_config": "DeepStream nvinfer runtime configuration files (nvdsinfer_config.yaml)", - "runtime_preprocess": "DeepStream nvdspreprocess runtime configuration files (nvdspreprocess_config*.yaml)", - "openapi": "OpenAPI server specification YAML files", - } - rel_path_stripped = rel_path.rstrip("/") - if rel_path_stripped in category_labels: - label = category_labels[rel_path_stripped] - raise ValueError( - f"'{uri}' is a category prefix for {label}, not a specific resource. " - f"Call list_resources to see the available 'samples://{rel_path_stripped}/...'" - " URIs, then use read_resource on one of those full URIs." - ) - - # Strip category prefix if present - for category in ( - "config/", - "dockerfile/", - "processor/", - "runtime_config/", - "runtime_preprocess/", - "openapi/", - ): - if rel_path.startswith(category): - rel_path = rel_path[len(category):] - break - file_path = samples_dir / rel_path - - # Security check: ensure path doesn't escape samples directory - try: - file_path.resolve().relative_to(samples_dir.resolve()) - except ValueError as exc: - raise ValueError(f"Invalid sample path: {rel_path}") from exc - - else: - raise ValueError( - f"Unknown resource URI scheme: {uri}. " - f"Supported schemes: docs://, schema://, samples://" - ) - - if not file_path.exists(): - raise FileNotFoundError(f"Resource file not found: {file_path}") - - # Check if the path is a directory - if file_path.is_dir(): - # List files in the directory to help the user - try: - files = [f.name for f in file_path.iterdir() if f.is_file()] - files_list = "\n - ".join(sorted(files)) if files else "(no files found)" - raise ValueError( - f"'{uri}' points to a directory, not a file. " - f"The MCP resource reader can only read individual files. " - f"Files in this directory:\n - {files_list}\n\n" - f"To read a specific file, use a URI like:\n" - f" {uri}/{{filename}}" - ) - except Exception as exc: - raise ValueError( - f"'{uri}' points to a directory, not a file. " - f"The MCP resource reader can only read individual files." - ) from exc - - with open(file_path, 'r', encoding='utf-8') as f: - content = f.read() - - return content - - def _sanitize_arguments(self, arguments: Dict[str, Any]) -> Dict[str, Any]: - """Redact sensitive values in tool arguments for logging.""" - if not isinstance(arguments, dict): - return {} - sensitive_keys = { - "api_key", "apikey", "token", "password", "secret", - "authorization", "auth", "bearer", "access_token", - } - redacted: Dict[str, Any] = {} - for key, value in arguments.items(): - if isinstance(key, str) and key.lower() in sensitive_keys: - redacted[key] = "" - else: - redacted[key] = value - return redacted - - async def list_tools(self, _request: ListToolsRequest) -> ListToolsResult: - """List available tools""" - return ListToolsResult( - tools=[ - Tool( - name="generate_inference_pipeline", - description=( - "Generate a deployable inference service from a YAML configuration file. " - "Outputs a complete project with model serving code, API endpoints, and deployment files. " - "Read 'samples://*' resources for example configurations, " - "or 'schema://config.schema.json' resource for the full configuration schema." - ), - inputSchema={ - "type": "object", - "properties": { - "config_file": { - "type": "string", - "description": ( - "Path to the YAML configuration file defining the pipeline. " - "If the user has not provided a config file, generate one based on " - "their requirements: read 'schema://config.schema.json' for the full schema, " - "'schema://index.json' to find backend-specific parameter schemas " - "(maps backend type to 'schema://backends/parameters/*'), " - "'schema://readme' for documentation, and 'samples://*' resources for " - "reference examples. Create the YAML file at a suitable path, then provide " - "that path here." - ) - }, - "server_type": { - "type": "string", - "enum": ALLOWED_SERVERS, - "default": "serverless", - "description": ( - "Type of server to generate. Use 'serverless' for standalone applications " - "designed for batch inference " - "(no API spec needed and no server section should appear in the config_file). " - "Use 'fastapi' for microservices that serve inference requests via HTTP endpoints " - " (requires api_spec and 'server' section in config_file)." - " If the user's intent is unclear, ask them to explicitly choose " - "between 'serverless' and 'fastapi' before proceeding." - ) - }, - "output_dir": { - "type": "string", - "description": ( - "The user's project directory where generated artifacts will be placed. " - "This directory will contain configurations, custom processors, Dockerfiles, " - "and other files for the inference project. A subdirectory named after the " - "pipeline (from config YAML) will be created here, or a tar.gz archive if " - "tar_output is enabled. If the project location is unclear, ask the user to " - "specify it, or prompt them to confirm after creating a new project directory." - ), - "default": "." - }, - "api_spec": { - "type": "string", - "description": ( - "Path to OpenAPI specification file (YAML/JSON). " - "Required for 'fastapi', 'triton', and 'nim' server types. " - "Not needed for 'serverless' type. If the user has not provided an " - "OpenAPI spec, attempt to generate one based on their requirements. " - "If the given information is insufficient to generate a valid spec " - "(e.g., missing endpoint definitions, request/response schemas), " - "ask the user to provide an OpenAPI specification file." - ) - }, - "custom_modules": { - "type": "array", - "items": {"type": "string"}, - "description": ( - "List of paths to custom Python module files for preprocessors and " - "postprocessors. Each module must define classes with a 'name' attribute " - "and '__call__' method to be registered in the pipeline. Determine if " - "processors are needed based on the user's requirements and pipeline " - "structure (e.g., input transformations, output formatting). If processors " - "are required, read 'samples://processor/*' resources for reference examples, " - "then generate the Python files accordingly, save them in the project " - "directory, and include their paths here." - ) - }, - "exclude_lib": { - "type": "boolean", - "default": False, - "description": ( - "If true, exclude the common utility library from generated code. " - "Use when deploying to an environment where the library is already installed." - ) - }, - "tar_output": { - "type": "boolean", - "default": False, - "description": ( - "If true, output a tar.gz archive instead of a directory. " - "This affects how the generated pipeline is copied into the container " - "image in the Dockerfile: use COPY with directory when false, or ADD " - "with tar.gz extraction when true. Set to true when transferring the " - "pipeline to a remote build system." - ) - }, - "validation_dir": { - "type": "string", - "description": ( - "Path to output directory for API validation/test artifacts. " - "When provided, generates OpenAPI client code and test cases " - "for validating the inference endpoints." - ) - }, - "no_docker": { - "type": "boolean", - "default": False, - "description": ( - "If true, use locally installed OpenAPI Generator CLI instead of Docker. " - "Requires 'openapi-generator-cli' to be installed and in PATH." - ) - }, - "test_cases_abs_path": { - "type": "boolean", - "default": False, - "description": ( - "If true, use absolute paths in generated test_cases.yaml. " - "Use when test files are located outside the validation directory." - ) - } - }, - "required": ["config_file"] - } - ), - Tool( - name="build_docker_image", - description=( - "Build a Docker image from a generated inference " - "pipeline. The Dockerfile is expected to consume the " - "tar.gz archive produced by 'generate_inference_pipeline' " - "when 'tar_output' is true (for example, using " - "'ADD .tar.gz /app') rather than copying a " - "loose directory of files. Agents should prefer " - "tar_output=true for Docker-based deployments." - ), - inputSchema={ - "type": "object", - "properties": { - "image_name": { - "type": "string", - "description": "Name for the Docker image" - }, - "dockerfile": { - "type": "string", - "description": ( - "Path to Dockerfile. Always generate the Dockerfile based on user " - "requirements and 'samples://dockerfile/*' resources. " - "**Before choosing a Dockerfile template, read 'docs://platform-guide.md' " - "to identify the correct base image and template for the target hardware** " - "(x86_64 datacenter, Jetson/Tegra, or arm-sbsa servers like GB10/GB300/DGX Spark). " - "Consider model backend (triton, vllm, tensorrt-llm, deepstream), server type " - "(serverless, fastapi), and hardware platform. " - "The Dockerfile MUST be placed in the output_dir where the generated " - "pipeline resides, as its parent directory becomes the Docker build " - "context for properly transferring the pipeline code into the container." - ) - } - }, - "required": ["image_name", "dockerfile"] - } - ), - Tool( - name="docker_run_image", - description=( - "Optionally run a built Docker image to help with testing and " - "troubleshooting. This is a lightweight helper around " - "'docker run' that can mount a prepared model repository " - "from the host into the container, set environment " - "variables, and pass command-line arguments. " - "The container is started with --ipc=host so that " - "PyTorch / TensorRT-LLM multiprocessing can share " - "tensors via /dev/shm without hitting the default " - "64 MB Docker limit. " - "Use this after 'prepare_model_repository' and " - "'build_docker_image' have completed." - ), - inputSchema={ - "type": "object", - "properties": { - "image_name": { - "type": "string", - "description": ( - "Name of the Docker image to run " - "(for example, 'my-inference-service:latest')." - ) - }, - "model_repo_host": { - "type": "string", - "description": ( - "Optional host path to a prepared model repository " - "directory (for example, produced by " - "'prepare_model_repository'). If provided, it will " - "be mounted into the container at " - "model_repo_container via a Docker volume." - ) - }, - "model_repo_container": { - "type": "string", - "description": ( - "Container path where the model repository should " - "be mounted (for example, '/models'). Used only " - "when model_repo_host is provided." - ), - "default": "/models" - }, - "server_type": { - "type": "string", - "description": ( - "High-level server type hint to choose sensible " - "defaults (for example, 'serverless' or 'fastapi'). " - "For non-serverless servers, the container will be " - "run on the host network to make HTTP endpoints " - "reachable from the host." - ), - "default": "serverless" - }, - "env": { - "type": "object", - "description": ( - "Optional environment variables to set inside the " - "container. Keys and values are serialized as " - "`-e KEY=VALUE`." - ), - "additionalProperties": { - "type": "string" - } - }, - "cmd": { - "type": "array", - "description": ( - "Optional list of command-line arguments or an " - "alternative command to pass to 'docker run' after " - "the image name. For serverless flows this is " - "typically the inference entrypoint arguments. " - "IMPORTANT: when server_type is 'serverless', the " - "user's input must be supplied via CLI arguments in " - "this list. Argument names use hyphens, not " - "underscores (e.g., the input named 'media_url' " - "becomes '--media-url '). Each flag and its " - "value should be separate items, for example: " - "[\"--media-url\", \"/path/to/video.mp4\"]." - ), - "items": { - "type": "string" - } - }, - "gpus": { - "type": "string", - "description": ( - "GPU devices to use for the container, passed as " - "`--gpus` (for example, 'all', 'device=0', " - "'device=0,1')." - ), - "default": "all" - }, - "timeout": { - "type": "integer", - "description": ( - "Maximum time in seconds to wait for the container " - "process to complete before timing out. For " - "non-serverless servers this is the time to wait " - "before collecting logs and stopping the container." - ), - "default": 300 - } - }, - "required": ["image_name"] - } - ), - Tool( - name="prepare_model_repository", - description=( - "Optionally prepare a model repository directory for deployment. " - "Given a model configuration list, this tool can download model artifacts " - "(for example from NGC or Hugging Face) and copy associated runtime " - "configuration files (such as DeepStream nvdsinfer_config.yaml and " - "nvdspreprocess_config*.yaml) into a model repository layout that " - "matches the generated pipeline and Dockerfile. Use this when the " - "user does not already have a model repository prepared." - "It is recommended to mount the model repository as a volume when running the Docker image." - ), - inputSchema={ - "type": "object", - "properties": { - "model_configs": { - "type": "array", - "description": ( - "List of model configurations to prepare, following the same " - "structure as the 'models' section in builder/tests/" - "test_docker_builds.py. Each item is an object with fields: " - "'name' (required, model name), 'source' (optional, 'NGC' or " - "'HF', default 'NGC'), 'target' (required, directory where the " - "prepared model repository should be created), 'path' (for NGC: " - "registry model path; for HF: repo id like " - "'Qwen/Qwen2.5-VL-3B-Instruct'), 'version' (for NGC), " - "'configs' (optional, path relative to config_dir of a directory " - "of runtime config files to copy into the final model repository), and " - "'post_script' (optional, shell command to execute after model download " - "for post-processing steps)." - ), - "items": { - "type": "object", - "properties": { - "name": { - "type": "string", - "description": "Model name (used as the final directory name).", - }, - "source": { - "type": "string", - "enum": ["NGC", "HF"], - "description": ( - "Model source: 'NGC' for NVIDIA NGC registry models, " - "or 'HF' for Hugging Face models." - ), - }, - "target": { - "type": "string", - "description": ( - "Target directory on the local filesystem where the prepared " - "model repository should be created." - ), - }, - "path": { - "type": "string", - "description": ( - "For NGC: registry model path (for example, " - "'nvidia/tao/grounding_dino'). For HF: model repo id " - "(for example, 'Qwen/Qwen2.5-VL-3B-Instruct')." - ), - }, - "version": { - "type": "string", - "description": ( - "For NGC models, the version/tag to download (for example, " - "'grounding_dino_swin_tiny_commercial_deployable_v1.0')." - ), - }, - "configs": { - "type": "string", - "description": ( - "Optional path (relative to config_dir) to a directory containing " - "runtime configuration files (for example, DeepStream " - "nvdsinfer_config.yaml, nvdspreprocess_config*.yaml, label files) " - "that should be copied into the final model repository." - ), - }, - "post_script": { - "type": "string", - "description": ( - "Optional shell command or script to execute after the model is downloaded. " - "This can be used for post-processing steps like installing additional " - "dependencies, converting model formats, or applying patches. " - "The script is executed with the model's target directory as the current " - "working directory." - ), - }, - }, - "required": ["name", "target"], - }, - }, - "config_dir": { - "type": "string", - "description": ( - "Base directory used to resolve relative 'configs' " - "paths in model_configs. Typically this is the " - "directory containing your pipeline/test " - "configuration files." - ), - "default": "." - } - }, - "required": ["model_configs"] - } - ), - Tool( - name="generate_nvinfer_config", - description=( - "Generate a DeepStream nvinfer runtime configuration file (nvdsinfer_config.yaml). " - "This configuration file is required by the DeepStream backend and must be placed " - "in the model repository. It defines inference parameters such as model file paths, " - "precision mode, network type, input dimensions, and custom parsers. " - "The generated file should be referenced via 'infer_config_path' in the DeepStream " - "backend parameters. See 'samples://runtime_config/*' resources for examples." - "**IMPORTANT: Before generating a new config file, first call 'prepare_model_repository' to download the model and check for existing nvinfer configuration files.** " - "Models from NGC typically include a configuration file (YAML or TXT) with all required inference parameters - always prefer using this provided configuration when available. "), - inputSchema={ - "type": "object", - "properties": { - "output_path": { - "type": "string", - "description": ( - "Path where the generated nvdsinfer_config.yaml file should be saved. " - "This should typically be in your model repository directory." - ) - }, - "onnx_file": { - "type": "string", - "description": ( - "Name of the ONNX model file (e.g., 'model.onnx'). " - "This file should exist in the same directory as the config file." - ) - }, - "precision_mode": { - "type": "integer", - "enum": [0, 1, 2], - "default": 2, - "description": ( - "Precision mode for inference: 0=FP32, 1=INT8, 2=FP16. " - "Default is FP16 (2) for optimal performance on modern GPUs." - ) - }, - "network_type": { - "type": "integer", - "enum": [0, 1, 2, 3, 100], - "description": ( - "Network type: 0=detection, 1=classification, 2=segmentation, " - "3=instance_segmentation. This determines how the model output is interpreted." - "100=custom is used for models that need to output raw tensors for custom downstream processing along with output-tensor-meta: 1." - ) - }, - "input_dims": { - "type": "string", - "description": ( - "Model input dimensions in format 'channel;height;width' " - "(e.g., '3;224;224' for a 224x224 RGB image). " - "Note: the format is C;H;W, not C;W;H." - ) - }, - "label_file": { - "type": "string", - "description": ( - "Name of the label file (e.g., 'labels.txt'). " - "This file should contain class names, one per line, and be placed " - "in the same directory as the config file." - ) - }, - "custom_lib_path": { - "type": "string", - "description": ( - "Path to a C++ shared library (.so) that provides custom output parsing functions. " - "Required for all models except classic ResNet when network_type is 0, 1, 2, or 3 (not required for network_type 100 which outputs raw tensors). " - "For TAO-trained models, build the library from https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps/tree/master/post_processor. " - "For other models, provide the path to your own custom parser library." - ) - }, - "custom_parse_func": { - "type": "string", - "description": ( - "Symbol name of the custom parsing function exported by custom_lib_path. " - "Behavior varies by network_type: " - "(0) Detection - if omitted, the built-in parser assumes a ResNet model with 'bbox' and 'cov' output layers. " - "(1) Classification - if omitted, the built-in parser treats model output as a softmax layer. " - "(2) Segmentation, (3) Instance segmentation - no built-in parser available, custom_parse_func is required. " - "(100) Custom - not required, raw tensors are output directly for downstream processing." - ) - }, - "num_classes": { - "type": "integer", - "description": ( - "Number of detected classes for the model. Required for detection and classification networks and can be deducted from the label file." - ) - }, - "gie_unique_id": { - "type": "integer", - "default": 1, - "description": ( - "Unique ID for this GIE (inference engine) in the pipeline. " - "Use different IDs if you have multiple inference engines in the same pipeline." - ) - }, - "net_scale_factor": { - "type": "number", - "default": 0.00392156862745098, - "description": ( - "Scale factor for input normalization. Default is 1/255 = 0.00392156862745098 " - "for models expecting [0,1] normalized inputs. IMPORTANT: This must match the " - "scaling factor used during model training. Common patterns: " - "(1) Simple scaling: 1/255; " - "(2) With uniform std: 1/(255*std) - dividing by std; " - "(3) With scale factor d: d/255 - multiplying by d; " - "(4) Custom: any value matching training. " - "For per-channel std normalization, consider baking into the ONNX model or using " - "a custom preprocessing plugin, as DeepStream only supports a single scale factor." - ) - }, - "offsets": { - "type": "string", - "description": ( - "Per-channel mean subtraction values in format 'R;G;B' " - "(e.g., '127.5;127.5;127.5' for [-1,1] normalization, " - "'123.675;116.28;103.53' for ImageNet models). " - "IMPORTANT: This must match the per-channel mean subtraction used during " - "model training. If training used mean subtraction, you MUST specify this " - "parameter. If no mean subtraction was used during training, omit this parameter " - "or use '0;0;0'. Incorrect offsets will result in poor inference accuracy." - ) - }, - "classifier_threshold": { - "type": "number", - "default": 0.0, - "description": ( - "Confidence threshold for classification results. " - "Only classifications above this threshold will be reported." - ) - }, - "input_tensor_from_meta": { - "type": "integer", - "enum": [0, 1], - "default": 0, - "description": ( - "Whether to read input tensor from metadata (1) or from frame buffer (0). " - "Set to 1 when using nvdspreprocess for custom preprocessing." - ) - }, - "output_tensor_meta": { - "type": "integer", - "enum": [0, 1], - "default": 0, - "description": ( - "Whether to output in DeepStream metadata format (0) or raw tensor format (1). " - "Set to 1 if raw tensors are expected as inference output, which allows downstream " - "components to access raw tensor data as a Dictionary." - "Set to 0 for TYPE_CUSTOM_DS_METADATA output type, where the custom parser library processes the raw tensors into detected objects." - ) - } - }, - "required": ["output_path", "onnx_file", "network_type", "input_dims", "label_file"] - } - ), - ] - ) - - async def call_tool(self, request: CallToolRequest) -> CallToolResult: - """Execute a tool""" - name = request.params.name - arguments = request.params.arguments - - start_time = time.perf_counter() - self.logger.info( - "tool_call_started name=%s args=%s", - name, - self._sanitize_arguments(arguments), - ) - - try: - if name == "generate_inference_pipeline": - result = await self._generate_pipeline(arguments) - elif name == "build_docker_image": - result = await self._build_docker_image(arguments) - elif name == "docker_run_image": - result = await self._docker_run_image(arguments) - elif name == "prepare_model_repository": - result = await self._prepare_model_repository(arguments) - elif name == "generate_nvinfer_config": - result = await self._generate_deepstream_nvinfer_config(arguments) - else: - raise ValueError(f"Unknown tool: {name}") - - duration_ms = (time.perf_counter() - start_time) * 1000.0 - self.logger.info( - "tool_call_completed name=%s error=%s duration_ms=%.2f", - name, - bool(getattr(result, "isError", False)), - duration_ms, - ) - return result - except Exception as e: - duration_ms = (time.perf_counter() - start_time) * 1000.0 - self.logger.exception( - "tool_call_failed name=%s duration_ms=%.2f error=%s", - name, - duration_ms, - str(e), - ) - return CallToolResult( - content=[TextContent(type="text", text=f"Error: {str(e)}")], - isError=True - ) - - async def _generate_pipeline( - self, arguments: Dict[str, Any] - ) -> CallToolResult: - """Generate inference pipeline""" - config_file = arguments["config_file"] - server_type = arguments.get("server_type", "serverless") - output_dir = arguments.get("output_dir", ".") - - # Build command - use the same Python executable as the parent process - cmd = [ - sys.executable, "builder/main.py", - "--server-type", server_type, - "-o", output_dir, - config_file - ] - - # Add optional arguments - if "api_spec" in arguments: - cmd.extend(["-a", arguments["api_spec"]]) - - if "custom_modules" in arguments: - for module in arguments["custom_modules"]: - cmd.extend(["-c", module]) - - if arguments.get("exclude_lib", False): - cmd.append("-x") - - if arguments.get("tar_output", False): - cmd.append("-t") - - if "validation_dir" in arguments: - cmd.extend(["--validation-dir", arguments["validation_dir"]]) - - if arguments.get("no_docker", False): - cmd.append("--no-docker") - - if arguments.get("test_cases_abs_path", False): - cmd.append("--test-cases-abs-path") - - # Execute the command - inherit the current process environment - - result = subprocess.run( - cmd, - capture_output=True, - text=True, - cwd=Path(__file__).parent.parent, - env=os.environ.copy(), # Inherit the current environment - check=False, - ) - - if result.returncode == 0: - return CallToolResult( - content=[ - TextContent( - type="text", - text=f"Successfully generated inference pipeline!\n\n" - f"Output directory: {output_dir}\n\n" - f"Command executed: {' '.join(cmd)}\n\n" - f"Output:\n{result.stdout}" - ) - ] - ) - else: - return CallToolResult( - content=[ - TextContent( - type="text", - text=( - "Failed to generate pipeline:\n\n" - f"Error:\n{result.stderr}\n\n" - f"Command: {' '.join(cmd)}" - ) - ) - ], - isError=True - ) - - def _get_sample_description(self, sample_name: str) -> str: - """Get description for a sample""" - descriptions = { - "ds_app": ( - "DeepStream applications for object detection and " - "segmentation" - ), - "qwen": "Qwen language models with various backends", - "changenet": "Change detection models", - "nvclip": "NVIDIA CLIP models for vision-language tasks", - "tao": "NVIDIA TAO toolkit models", - "dummy": "Dummy models for testing and development" - } - return descriptions.get(sample_name, "") - - async def _docker_run_image( - self, arguments: Dict[str, Any] - ) -> CallToolResult: - """Run a Docker image for testing/troubleshooting.""" - image_name = arguments["image_name"] - model_repo_host = arguments.get("model_repo_host") - model_repo_container = arguments.get("model_repo_container", "/models") - server_type = arguments.get("server_type", "serverless") - env_vars = arguments.get("env") or {} - cmd_args = arguments.get("cmd") or [] - gpus = arguments.get("gpus", "all") - timeout = int(arguments.get("timeout", 300)) - - # Base docker run command (no shell, argument list only) - # Use --ipc=host so the container shares the host's /dev/shm. - # Docker's default 64 MB /dev/shm is insufficient for - # PyTorch / TensorRT-LLM multiprocessing which shares tensors - # via shared memory, causing SIGBUS when /dev/shm is exhausted. - # Assign a deterministic name so we can force-remove the container - # on timeout — subprocess.TimeoutExpired kills the `docker run` - # client process but leaves the container running with GPUs held. - container_name = f"ib-run-{uuid.uuid4().hex[:12]}" - cmd: list[str] = [ - "docker", "run", "--rm", "--ipc=host", - "--name", container_name, - ] - - # GPU configuration - if gpus: - cmd.extend(["--gpus", str(gpus)]) - - # Network: for serverless we still use host network so local clients can connect - cmd.append("--network=host") - - # Volume for model repository if provided - if model_repo_host: - host_path = str(Path(model_repo_host).expanduser().resolve()) - volume_arg = f"{host_path}:{model_repo_container}" - cmd.extend(["-v", volume_arg]) - - # Environment variables - if isinstance(env_vars, dict): - for key, value in env_vars.items(): - cmd.extend(["-e", f"{key}={value}"]) - - # Image name and optional command/args - cmd.append(image_name) - if isinstance(cmd_args, list): - cmd.extend([str(a) for a in cmd_args]) - - self.logger.info( - "docker_run_invoked image=%s model_repo_host=%s cmd=%s", - image_name, - model_repo_host, - " ".join(cmd), - ) - - try: - result = subprocess.run( - cmd, - capture_output=True, - text=True, - timeout=timeout, - check=False, - ) - except FileNotFoundError: - return CallToolResult( - content=[ - TextContent( - type="text", - text=( - "Docker not found. Please ensure Docker is installed " - "and available in your PATH before using docker_run_image." - ), - ) - ], - isError=True, - ) - except subprocess.TimeoutExpired: - is_serverless = server_type == "serverless" - if is_serverless: - # Serverless containers should finish within the timeout. - # The `docker run` client is dead but the container keeps - # running (and holding GPU memory). Force-remove it. - cleanup_msg = "" - try: - subprocess.run( - ["docker", "rm", "-f", container_name], - capture_output=True, - timeout=30, - check=False, - ) - cleanup_msg = ( - f" The orphaned container '{container_name}' has " - "been force-removed." - ) - except Exception as cleanup_exc: - cleanup_msg = ( - f" WARNING: failed to remove orphaned container " - f"'{container_name}': {cleanup_exc}. " - "Please remove it manually with: " - f"docker rm -f {container_name}" - ) - self.logger.warning( - "Failed to remove timed-out container %s: %s", - container_name, - cleanup_exc, - ) - return CallToolResult( - content=[ - TextContent( - type="text", - text=( - f"Docker run timed out after {timeout} seconds " - f"for image '{image_name}'.{cleanup_msg} You may " - "want to increase the timeout or inspect the " - "image logs manually." - ), - ) - ], - isError=True, - ) - else: - # Non-serverless (persistent) servers are expected to keep - # running past the timeout — that means the server started - # successfully. Leave the container running. - return CallToolResult( - content=[ - TextContent( - type="text", - text=( - f"Container '{container_name}' is running " - f"(image '{image_name}'). The server appears to " - f"have started successfully (still alive after " - f"{timeout}s). To stop it later: " - f"docker rm -f {container_name}" - ), - ) - ], - isError=False, - ) - except Exception as exc: - return CallToolResult( - content=[ - TextContent( - type="text", - text=f"Exception while running Docker image '{image_name}': {exc}", - ) - ], - isError=True, - ) - - text = ( - f"docker run command: {' '.join(cmd)}\n\n" - f"exit code: {result.returncode}\n\n" - f"stdout:\n{result.stdout}\n\n" - f"stderr:\n{result.stderr}" - ) - - return CallToolResult( - content=[TextContent(type="text", text=text)], - isError=result.returncode != 0, - ) - - async def _prepare_model_repository( - self, arguments: Dict[str, Any] - ) -> CallToolResult: - """Prepare model repositories by downloading models and copying configs. - - This mirrors the 'models' handling in builder/tests/test_docker_builds.py, - but is exposed as an optional MCP tool so agents can help set up - model repositories before Docker image builds. - """ - # New interface: model_configs is a list of dicts (each includes 'name') - # Backward-compat: accept legacy 'models_config' dict keyed by model name. - model_configs = arguments.get("model_configs") - legacy_models_config = arguments.get("models_config") - config_dir_arg = arguments.get("config_dir", ".") - - if model_configs is None and legacy_models_config is not None: - # Convert dict[name -> config] into list[{name, ...config}] - if isinstance(legacy_models_config, dict): - converted: list[dict[str, Any]] = [] - for legacy_name, legacy_info in legacy_models_config.items(): - if isinstance(legacy_info, dict): - merged = dict(legacy_info) - merged["name"] = legacy_name - converted.append(merged) - model_configs = converted - else: - model_configs = [] - - if not isinstance(model_configs, list) or not model_configs: - return CallToolResult( - content=[ - TextContent( - type="text", - text=( - "No models specified in model_configs. " - "Nothing to prepare for model repositories." - ), - ) - ] - ) - - # Resolve config_dir relative to project root (repo root) - project_root = Path(__file__).resolve().parents[1] - config_dir = Path(config_dir_arg) - if not config_dir.is_absolute(): - config_dir = (project_root / config_dir).resolve() - - messages: list[str] = [] - - for model_info in model_configs: - try: - if not isinstance(model_info, dict): - messages.append( - "❌ Skipping model entry: config must be an object" - ) - continue - - model_name = model_info.get("name") - if not model_name or not isinstance(model_name, str): - messages.append( - "❌ Skipping model entry: missing required 'name'" - ) - continue - - source = str(model_info.get("source", "NGC")).upper() - target = model_info.get("target") - if not target: - messages.append( - f"❌ Skipping model '{model_name}': missing required 'target'" - ) - continue - - target_dir = Path(target).expanduser() - final_model_dir = target_dir / model_name - - # Create target parent directory - target_dir.mkdir(parents=True, exist_ok=True) - - if final_model_dir.exists(): - messages.append( - f"✅ Model '{model_name}' already exists at {final_model_dir}, " - f"skipping download" - ) - else: - if source == "HF": - # Hugging Face model download, based on DockerBuildTester.download_models - hf_repo = model_info.get("path") - if not hf_repo: - messages.append( - f"❌ Skipping HF model '{model_name}': missing 'path'" - ) - continue - - messages.append( - f"📥 Downloading Hugging Face model '{model_name}' " - f"({hf_repo}) to {final_model_dir}" - ) - - # Use huggingface_hub.snapshot_download instead of git clone - # so that actual binary weights are fetched via the HF CDN - # (git clone only retrieves LFS pointer stubs unless a full - # LFS pull is performed, which fails for gated repos without - # proper credential configuration). - try: - from huggingface_hub import snapshot_download - snapshot_download( - repo_id=hf_repo, - local_dir=str(final_model_dir), - ignore_patterns=["*.pt", "*.bin", "original/*"], - ) - except Exception as e: - messages.append( - f"❌ Failed to download HF model '{model_name}': {e}" - ) - continue - else: - # Default to NGC - ngc_path = model_info.get("path") - version = model_info.get("version") - if not ngc_path or not version: - messages.append( - f"❌ Skipping NGC model '{model_name}': " - f"both 'path' and 'version' are required" - ) - continue - - full_ngc_path = f"{ngc_path}:{version}" - messages.append( - f"📥 Downloading NGC model '{model_name}' " - f"({full_ngc_path}) to {final_model_dir}" - ) - - # Run NGC CLI to download the model version - download_cmd = [ - "ngc", - "registry", - "model", - "download-version", - full_ngc_path, - ] - result = subprocess.run( - download_cmd, - capture_output=True, - text=True, - timeout=900, - check=False, - ) - if result.returncode != 0: - messages.append( - f"❌ Failed to download NGC model '{model_name}': " - f"{result.stderr.strip()}" - ) - continue - - # Infer downloaded folder name as in test_docker_builds.py - model_base_name = ngc_path.split("/")[-1] - downloaded_folder = Path( - f"{model_base_name}_v{version}" - ) - if not downloaded_folder.exists(): - messages.append( - f"❌ Downloaded folder not found for model " - f"'{model_name}': {downloaded_folder}" - ) - continue - - try: - shutil.move(str(downloaded_folder), str(final_model_dir)) - except Exception as exc: - messages.append( - f"❌ Failed to move downloaded model '{model_name}' " - f"into {final_model_dir}: {exc}" - ) - continue - - # Copy configs into final_model_dir if specified - configs_rel = model_info.get("configs") - if configs_rel: - source_configs = (config_dir / configs_rel).resolve() - if source_configs.exists(): - messages.append( - f"📄 Copying runtime configs from {source_configs} " - f"to {final_model_dir}" - ) - try: - for item in source_configs.iterdir(): - dest = final_model_dir / item.name - if item.is_file(): - shutil.copy2(str(item), str(dest)) - elif item.is_dir(): - shutil.copytree( - str(item), str(dest), dirs_exist_ok=True - ) - except Exception as exc: - messages.append( - f"⚠️ Failed to copy configs for '{model_name}': {exc}" - ) - else: - messages.append( - f"⚠️ Config path not found for model '{model_name}': " - f"{source_configs}" - ) - - # Execute post-script if specified - post_script = model_info.get("post_script") - if post_script: - messages.append( - f"🔧 Executing post-script for '{model_name}': {post_script}" - ) - try: - script_result = subprocess.run( - post_script, - shell=True, - capture_output=True, - text=True, - cwd=str(final_model_dir), - timeout=600, - check=False, - ) - if script_result.returncode == 0: - messages.append( - f"✅ Post-script executed successfully for '{model_name}'" - ) - if script_result.stdout.strip(): - messages.append( - f" Output: {script_result.stdout.strip()}" - ) - else: - messages.append( - f"❌ Post-script failed for '{model_name}' " - f"(exit code {script_result.returncode}): " - f"{script_result.stderr.strip()}" - ) - except subprocess.TimeoutExpired: - messages.append( - f"❌ Post-script timed out for '{model_name}' after 600 seconds" - ) - except Exception as exc: - messages.append( - f"❌ Failed to execute post-script for '{model_name}': {exc}" - ) - - messages.append( - f"✅ Prepared model repository for '{model_name}' at " - f"{final_model_dir}" - ) - - except Exception as exc: # Catch-all per model - messages.append( - f"❌ Exception while preparing model '{model_name}': {exc}" - ) - - summary = "\n".join(messages) - return CallToolResult( - content=[ - TextContent( - type="text", - text=( - "Model repository preparation completed.\n\n" - f"{summary}" - ), - ) - ] - ) - - async def _build_docker_image( - self, arguments: Dict[str, Any] - ) -> CallToolResult: - """Build Docker image from generated pipeline""" - image_name = arguments["image_name"] - dockerfile = arguments["dockerfile"] - - # Validate Dockerfile exists - dockerfile_path = Path(dockerfile) - if not dockerfile_path.exists(): - return CallToolResult( - content=[ - TextContent( - type="text", - text=f"Dockerfile not found: {dockerfile}. Please " - f"provide a valid Dockerfile path." - ) - ], - isError=True - ) - - # Use Dockerfile's parent directory as build context - build_context = str(dockerfile_path.parent) - - # Build Docker image - cmd = [ - "docker", "build", - "-f", dockerfile, - "-t", image_name, - build_context - ] - self.logger.info( - "docker_build_invoked image=%s dockerfile=%s context=%s", - image_name, - dockerfile, - build_context, - ) - - try: - result = subprocess.run( - cmd, - capture_output=True, - text=True, - cwd=Path(__file__).parent.parent, - check=False, - ) - - if result.returncode == 0: - return CallToolResult( - content=[ - TextContent( - type="text", - text=( - f"Successfully built Docker image " - f"'{image_name}'!\n\n" - f"Command executed: {' '.join(cmd)}\n\n" - f"Output:\n{result.stdout}" - ) - ) - ] - ) - else: - return CallToolResult( - content=[ - TextContent( - type="text", - text=f"Failed to build Docker image:\n\n" - f"Error:\n{result.stderr}\n\n" - f"Command: {' '.join(cmd)}" - ) - ], - isError=True - ) - except FileNotFoundError: - return CallToolResult( - content=[ - TextContent( - type="text", - text=( - "Docker not found. Please ensure Docker is " - "installed and available in your PATH." - ) - ) - ], - isError=True - ) - - async def _generate_deepstream_nvinfer_config( - self, arguments: Dict[str, Any] - ) -> CallToolResult: - """Generate DeepStream nvinfer runtime configuration file""" - import yaml - - # Extract required parameters - output_path = arguments["output_path"] - onnx_file = arguments["onnx_file"] - network_type = arguments["network_type"] - input_dims = arguments["input_dims"] - label_file = arguments["label_file"] - - # Extract optional parameters with defaults - precision_mode = arguments.get("precision_mode", 2) # Default FP16 - custom_lib_path = arguments.get("custom_lib_path", "") - custom_parse_func = arguments.get("custom_parse_func", "") - num_classes = arguments.get("num_classes") - gie_unique_id = arguments.get("gie_unique_id", 1) - net_scale_factor = arguments.get("net_scale_factor", 0.00392156862745098) - offsets = arguments.get("offsets") - classifier_threshold = arguments.get("classifier_threshold", 0.0) - input_tensor_from_meta = arguments.get("input_tensor_from_meta", 0) - output_tensor_meta = arguments.get("output_tensor_meta", 0) - - # Validate network_type - network_type_names = { - 0: "detection", - 1: "classification", - 2: "segmentation", - 3: "instance_segmentation", - 100: "custom" - } - if network_type not in network_type_names: - return CallToolResult( - content=[ - TextContent( - type="text", - text=f"Invalid network_type: {network_type}. Must be 0 (detection), " - f"1 (classification), 2 (segmentation), 3 (instance_segmentation), " - f"or 100 (custom)." - ) - ], - isError=True - ) - - # Validate precision_mode - precision_names = {0: "FP32", 1: "INT8", 2: "FP16"} - if precision_mode not in precision_names: - return CallToolResult( - content=[ - TextContent( - type="text", - text=f"Invalid precision_mode: {precision_mode}. Must be 0 (FP32), 1 (INT8), or 2 (FP16)." - ) - ], - isError=True - ) - - # Validate input_dims format (should be channel;height;width) - dims_parts = input_dims.split(';') - if len(dims_parts) != 3: - return CallToolResult( - content=[ - TextContent( - type="text", - text=f"Invalid input_dims format: '{input_dims}'. " - f"Expected format: 'channel;height;width' (e.g., '3;224;224')" - ) - ], - isError=True - ) - - try: - # Validate dimensions are integers - for dim in dims_parts: - int(dim) - except ValueError: - return CallToolResult( - content=[ - TextContent( - type="text", - text=f"Invalid input_dims: '{input_dims}'. All dimensions must be integers." - ) - ], - isError=True - ) - - # Build the configuration - config = { - "property": { - "gie-unique-id": gie_unique_id, - "net-scale-factor": net_scale_factor, - "onnx-file": onnx_file, - "network-mode": precision_mode, - "network-type": network_type, - "infer-dims": input_dims, - "labelfile-path": label_file, - } - } - - # Add optional fields - if offsets: - config["property"]["offsets"] = offsets - - if num_classes is not None: - config["property"]["num-detected-classes"] = num_classes - - if input_tensor_from_meta: - config["property"]["input-tensor-from-meta"] = input_tensor_from_meta - - if output_tensor_meta: - config["property"]["output-tensor-meta"] = output_tensor_meta - - if network_type == 1: # classification - config["property"]["classifier-threshold"] = classifier_threshold - - if custom_lib_path: - config["property"]["custom-lib-path"] = custom_lib_path - - # Add custom parse function name based on network type - if custom_parse_func: - if network_type == 0: # detection - config["property"]["parse-bbox-func-name"] = custom_parse_func - elif network_type == 1: # classification - config["property"]["parse-classifier-func-name"] = custom_parse_func - elif network_type == 2: # segmentation - config["property"]["parse-segmentation-func-name"] = custom_parse_func - elif network_type == 3: # instance_segmentation - config["property"]["parse-bbox-instance-mask-func-name"] = custom_parse_func - # network_type 100 (custom) doesn't need a parse function name - - # Generate YAML content with header - header = """# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# IMPORTANT NORMALIZATION PARAMETERS: -# - net-scale-factor: Must match the scaling factor used during training -# - offsets: Must match any per-channel mean subtraction used during training -# Incorrect normalization will result in poor inference accuracy! - -""" - - yaml_content = yaml.dump(config, default_flow_style=False, sort_keys=False) - full_content = header + yaml_content - - # Write to file - try: - output_file = Path(output_path) - output_file.parent.mkdir(parents=True, exist_ok=True) - - with open(output_file, 'w', encoding='utf-8') as f: - f.write(full_content) - - return CallToolResult( - content=[ - TextContent( - type="text", - text=( - f"Successfully generated DeepStream nvinfer configuration!\n\n" - f"Output file: {output_path}\n\n" - f"Configuration summary:\n" - f" - Network type: {network_type_names[network_type]} ({network_type})\n" - f" - Precision mode: {precision_names[precision_mode]} ({precision_mode})\n" - f" - ONNX file: {onnx_file}\n" - f" - Input dimensions: {input_dims}\n" - f" - Label file: {label_file}\n" - f" - Scale factor: {net_scale_factor}\n" - + (f" - Per-channel offsets: {offsets}\n" if offsets else " - Per-channel offsets: NOT SET (no mean subtraction)\n") - + (f" - Number of classes: {num_classes}\n" if num_classes else "") - + (f" - Input from metadata: {bool(input_tensor_from_meta)}\n" if input_tensor_from_meta else "") - + (f" - Output as metadata: {bool(output_tensor_meta)} (raw tensors in DS META)\n" if output_tensor_meta else "") - + (f" - Custom library: {custom_lib_path}\n" if custom_lib_path else "") - + (f" - Custom parse function: {custom_parse_func}\n" if custom_parse_func else "") - + "\n⚠️ IMPORTANT: Verify that net-scale-factor and per-channel offsets match your model's training normalization!\n" - + " - net-scale-factor must match the scaling applied during training\n" - + " - offsets must match any per-channel mean subtraction used during training\n" - + " - If training used no mean subtraction, offsets should not be set (or set to 0;0;0)\n" - + f"\nGenerated content:\n\n{full_content}" - ) - ) - ] - ) - except Exception as e: - return CallToolResult( - content=[ - TextContent( - type="text", - text=f"Failed to write configuration file:\n\n{str(e)}" - ) - ], - isError=True - ) - - -async def main(): - """Main entry point for the MCP server""" - logging.basicConfig( - level=logging.DEBUG, - format=("%(asctime)s %(levelname)s %(name)s - %(message)s"), - ) - logger = logging.getLogger("deepstream-inference-builder") - logger.info("starting_mcp_server") - server = InferenceBuilderMCPServer() - logger.info("server_created") - - async with stdio_server() as (read_stream, write_stream): - logger.info("stdio_server_started") - init_options = server.server.create_initialization_options() - logger.info("initialization_options_created") - await server.server.run( - read_stream, - write_stream, - init_options, - ) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/tools/inference_builder/mcp/setup_mcp.py b/tools/inference_builder/mcp/setup_mcp.py deleted file mode 100644 index 27bfb593..00000000 --- a/tools/inference_builder/mcp/setup_mcp.py +++ /dev/null @@ -1,207 +0,0 @@ -#!/usr/bin/env python3 -""" -Setup script for Inference Builder MCP Integration - -This script helps install dependencies and configure the MCP integration -for use with MCP-compatible clients such as Cursor and Claude Code. - -Usage: - python setup_mcp.py [CONFIG_PATH] - -Arguments: - CONFIG_PATH Path to the MCP config file (default: ~/.cursor/mcp.json) - -Examples: - Cursor (global): python setup_mcp.py ~/.cursor/mcp.json - Cursor (project): python setup_mcp.py /path/to/project/.cursor/mcp.json - Claude Code: python setup_mcp.py ~/.claude/.mcp.json -""" - -import argparse -import subprocess -import sys -from pathlib import Path - -def run_command(cmd, description): - """Run a command and handle errors""" - print(f"Running: {description}") - print(f"Command: {' '.join(cmd)}") - - try: - result = subprocess.run(cmd, capture_output=True, text=True, check=True) - print(f"✓ {description} completed successfully") - if result.stdout: - print(f"Output: {result.stdout.strip()}") - return True - except subprocess.CalledProcessError as e: - print(f"✗ {description} failed") - print(f"Error: {e.stderr.strip()}") - return False - except FileNotFoundError: - print(f"✗ Command not found: {cmd[0]}") - return False - -def check_python_version(): - """Check if Python version is compatible""" - version = sys.version_info - if version.major < 3 or (version.major == 3 and version.minor < 8): - print(f"✗ Python 3.8+ required, found {version.major}.{version.minor}") - return False - print(f"✓ Python {version.major}.{version.minor}.{version.micro} is compatible") - return True - -def test_integration(): - """Test the MCP integration""" - print("\nTesting MCP integration...") - - if not run_command([sys.executable, "mcp/test_mcp_server.py"], "Running integration tests"): - print("Integration tests failed. Please check the errors above.") - return False - - return True - -def create_mcp_config(config_path=None): - """Create or update MCP configuration file - - Args: - config_path: Optional path to the MCP config file. - Defaults to ~/.cursor/mcp.json if not specified. - """ - import json - - print("\nSetting up MCP configuration...") - - if config_path: - config_file = Path(config_path).expanduser().resolve() - config_dir = config_file.parent - if not config_dir.exists(): - config_dir.mkdir(parents=True, exist_ok=True) - print(f"✓ Created directory: {config_dir}") - else: - default_dir = Path.home() / ".cursor" - if not default_dir.exists(): - default_dir.mkdir(parents=True, exist_ok=True) - print(f"✓ Created directory: {default_dir}") - config_file = default_dir / "mcp.json" - - # Define the server configuration for this project - server_name = "deepstream-inference-builder" - server_config = { - "command": str(Path(sys.executable)), - "args": [str(Path.cwd() / "mcp" / "mcp_server.py")], - "cwd": str(Path.cwd()), - "env": { - "PYTHONPATH": str(Path.cwd() / "mcp") - } - } - - # Load existing config or start with empty structure - existing_config = {"mcpServers": {}} - if config_file.exists(): - try: - with open(config_file, 'r') as f: - existing_config = json.load(f) - # Ensure mcpServers key exists - if "mcpServers" not in existing_config: - existing_config["mcpServers"] = {} - print(f"✓ Found existing config with {len(existing_config['mcpServers'])} server(s)") - except json.JSONDecodeError as e: - print(f"⚠️ Existing config is not valid JSON: {e}") - print("Creating new configuration (existing file will be overwritten)...") - existing_config = {"mcpServers": {}} - except Exception as e: - print(f"⚠️ Could not read existing config: {e}") - print("Creating new configuration...") - existing_config = {"mcpServers": {}} - - # Check if the server is already correctly configured - existing_server = existing_config["mcpServers"].get(server_name, {}) - if (existing_server.get("cwd") == server_config["cwd"] and - existing_server.get("command") == server_config["command"]): - print(f"✓ MCP server '{server_name}' already exists and is correct: {config_file}") - return True - - # Update or add the server configuration (preserving other servers) - if server_name in existing_config["mcpServers"]: - print(f"⚠️ Updating existing '{server_name}' server configuration...") - else: - print(f"Adding '{server_name}' server to configuration...") - - existing_config["mcpServers"][server_name] = server_config - - # Write the merged config back - try: - with open(config_file, 'w') as f: - json.dump(existing_config, f, indent=2) - print(f"✓ Updated MCP config: {config_file}") - print(f" Total servers configured: {len(existing_config['mcpServers'])}") - print("Note: You may need to restart your MCP client for the configuration to take effect.") - return True - except Exception as e: - print(f"✗ Failed to update MCP config: {e}") - print("Please manually update the MCP configuration file.") - return False - -def parse_args(): - """Parse command-line arguments""" - parser = argparse.ArgumentParser( - description="Setup script for Inference Builder MCP Integration", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Examples: - python setup_mcp.py # Default: ~/.cursor/mcp.json - python setup_mcp.py ~/.cursor/mcp.json # Cursor (global) - python setup_mcp.py .cursor/mcp.json # Cursor (project-specific) - python setup_mcp.py ~/.claude/.mcp.json # Claude Code (global) - python setup_mcp.py .mcp.json # Claude Code (project-specific) - """ - ) - parser.add_argument( - "config_path", - nargs="?", - default=None, - help="Path to the MCP config file (default: ~/.cursor/mcp.json)" - ) - return parser.parse_args() - - -def main(config_path=None): - """Main setup function - - Args: - config_path: Optional path to the MCP config file. - """ - print("Inference Builder MCP Integration Setup") - print("=" * 50) - - # Check Python version - if not check_python_version(): - print("\n❌ Setup failed: Python version incompatible") - return False - - # Test integration - if not test_integration(): - print("\n❌ Setup failed: Integration tests failed") - return False - - # Setup MCP config - create_mcp_config(config_path) - - print("\n" + "=" * 50) - print("🎉 Setup completed successfully!") - print("\nNext steps:") - print("1. Restart your MCP client (Cursor, Claude Code, etc.)") - print("2. Verify the 'deepstream-inference-builder' MCP server is connected") - print("3. Try using the tools:") - print(" - 'Show me what sample configurations are available from the inference builder?'") - print(" - 'Generate a DeepStream object detection pipeline using the inference builder with PeopleNet transformer model from NGC.'") - print("\nFor more information, see mcp/README-MCP.md") - - return True - - -if __name__ == "__main__": - args = parse_args() - success = main(args.config_path) - sys.exit(0 if success else 1) - diff --git a/tools/inference_builder/mcp/test_mcp_client.py b/tools/inference_builder/mcp/test_mcp_client.py deleted file mode 100644 index e19c3ab5..00000000 --- a/tools/inference_builder/mcp/test_mcp_client.py +++ /dev/null @@ -1,171 +0,0 @@ -#!/usr/bin/env python3 -""" -Simple MCP client to test direct communication with the inference-builder MCP server -""" - -import asyncio -import json -import subprocess -import sys -from pathlib import Path - - -class SimpleMCPClient: - def __init__(self, server_command, server_args, cwd=None): - self.server_command = server_command - self.server_args = server_args - self.cwd = cwd or Path.cwd() - self.process = None - - async def start_server(self): - """Start the MCP server process""" - cmd = [self.server_command] + self.server_args - print(f"🚀 Starting MCP server: {' '.join(cmd)}") - print(f"📁 Working directory: {self.cwd}") - - self.process = await asyncio.create_subprocess_exec( - *cmd, - cwd=self.cwd, - stdin=asyncio.subprocess.PIPE, - stdout=asyncio.subprocess.PIPE, - stderr=asyncio.subprocess.PIPE - ) - print("✅ MCP server started") - - async def send_request(self, request): - """Send a JSON-RPC request to the server""" - if not self.process: - raise RuntimeError("Server not started") - - request_str = json.dumps(request) + '\n' - print(f"📤 Sending request: {request}") - - self.process.stdin.write(request_str.encode()) - await self.process.stdin.drain() - - # Read response - response_line = await self.process.stdout.readline() - if not response_line: - stderr_output = await self.process.stderr.read() - raise RuntimeError(f"No response from server. stderr: {stderr_output.decode()}") - - response = json.loads(response_line.decode().strip()) - print(f"📥 Received response: {json.dumps(response, indent=2)}") - return response - - async def initialize(self): - """Initialize the MCP connection""" - init_request = { - "jsonrpc": "2.0", - "id": 1, - "method": "initialize", - "params": { - "protocolVersion": "2024-11-05", - "capabilities": { - "tools": {} - }, - "clientInfo": { - "name": "test-client", - "version": "1.0.0" - } - } - } - response = await self.send_request(init_request) - - # Send initialized notification - initialized_notification = { - "jsonrpc": "2.0", - "method": "notifications/initialized", - "params": {} - } - print(f"📤 Sending initialized notification: {initialized_notification}") - request_str = json.dumps(initialized_notification) + '\n' - self.process.stdin.write(request_str.encode()) - await self.process.stdin.drain() - - return response - - async def list_tools(self): - """List available tools""" - request = { - "jsonrpc": "2.0", - "id": 2, - "method": "tools/list", - "params": {} - } - return await self.send_request(request) - - async def call_tool(self, tool_name, arguments=None): - """Call a specific tool""" - request = { - "jsonrpc": "2.0", - "id": 3, - "method": "tools/call", - "params": { - "name": tool_name, - "arguments": arguments or {} - } - } - return await self.send_request(request) - - async def cleanup(self): - """Clean up the server process""" - if self.process: - self.process.terminate() - await self.process.wait() - print("🛑 MCP server stopped") - - -async def main(): - """Test the MCP server with a direct client""" - print("🧪 Testing MCP Server with Direct Client") - print("=" * 50) - - # Configuration matching the Cursor MCP setup - script_dir = Path(__file__).parent - server_command = str(Path.cwd() / ".venv" / "bin" / "python") - server_args = [str(script_dir / "mcp_server.py")] - cwd = Path.cwd() - - client = SimpleMCPClient(server_command, server_args, cwd) - - try: - # Start server - await client.start_server() - - # Initialize connection - print("\n📋 Step 1: Initialize MCP connection") - init_response = await client.initialize() - - # List tools - print("\n📋 Step 2: List available tools") - tools_response = await client.list_tools() - - if 'result' in tools_response: - tools = tools_response['result']['tools'] - print(f"✅ Found {len(tools)} tools:") - for tool in tools: - print(f" • {tool['name']}: {tool['description']}") - - # Test generate_inference_pipeline with missing config to verify error handling - print("\n📋 Step 3: Test generate_inference_pipeline error handling") - gen_response = await client.call_tool("generate_inference_pipeline", { - "config_file": "/nonexistent/path/config.yaml" - }) - - if 'error' in gen_response or ('result' in gen_response and gen_response['result'].get('isError')): - print("✅ generate_inference_pipeline correctly returned error for missing config") - else: - print(f"📋 generate_inference_pipeline response: {gen_response}") - - except Exception as e: - print(f"❌ Error: {e}") - import traceback - traceback.print_exc() - - finally: - await client.cleanup() - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/tools/inference_builder/mcp/test_mcp_server.py b/tools/inference_builder/mcp/test_mcp_server.py deleted file mode 100644 index 9847bb7b..00000000 --- a/tools/inference_builder/mcp/test_mcp_server.py +++ /dev/null @@ -1,144 +0,0 @@ -import unittest -import tempfile -import os -from pathlib import Path -from types import SimpleNamespace - -from mcp_server import InferenceBuilderMCPServer -from mcp.types import CallToolRequest, CallToolRequestParams - - -class MCPServerTests(unittest.IsolatedAsyncioTestCase): - async def asyncSetUp(self): - self.server = InferenceBuilderMCPServer() - - async def test_list_tools_contains_expected(self): - # Our server ignores the request payload; pass None - result = await self.server.list_tools(None) - tool_names = {t.name for t in result.tools} - self.assertIn("generate_inference_pipeline", tool_names) - self.assertIn("build_docker_image", tool_names) - self.assertIn("generate_nvinfer_config", tool_names) - - async def test_call_unknown_tool(self): - params = CallToolRequestParams(name="does_not_exist", arguments={}) - req = CallToolRequest(params=params) - res = await self.server.call_tool(req) - self.assertTrue(res.isError) - self.assertIn("Unknown tool", res.content[0].text) - - async def test_generate_deepstream_nvinfer_config(self): - """Test generating a DeepStream nvinfer config file""" - with tempfile.TemporaryDirectory() as tmpdir: - output_path = os.path.join(tmpdir, "nvdsinfer_config.yaml") - - params = CallToolRequestParams( - name="generate_nvinfer_config", - arguments={ - "output_path": output_path, - "onnx_file": "test_model.onnx", - "network_type": 0, # detection - "input_dims": "3;640;640", - "label_file": "labels.txt", - "precision_mode": 2, # FP16 - "num_classes": 80, - "custom_lib_path": "/opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser_tao.so" - } - ) - req = CallToolRequest(params=params) - res = await self.server.call_tool(req) - - # Check that the call was successful - self.assertFalse(res.isError, f"Tool call failed: {res.content[0].text if res.content else 'No content'}") - - # Check that the file was created - self.assertTrue(os.path.exists(output_path), f"Config file was not created at {output_path}") - - # Check file contents - with open(output_path, 'r') as f: - content = f.read() - self.assertIn("onnx-file: test_model.onnx", content) - self.assertIn("network-type: 0", content) - self.assertIn("network-mode: 2", content) - self.assertIn("infer-dims: 3;640;640", content) - self.assertIn("num-detected-classes: 80", content) - - async def test_generate_deepstream_nvinfer_config_invalid_network_type(self): - """Test that invalid network type is rejected""" - with tempfile.TemporaryDirectory() as tmpdir: - output_path = os.path.join(tmpdir, "nvdsinfer_config.yaml") - - params = CallToolRequestParams( - name="generate_nvinfer_config", - arguments={ - "output_path": output_path, - "onnx_file": "test_model.onnx", - "network_type": 99, # invalid - "input_dims": "3;640;640", - "label_file": "labels.txt" - } - ) - req = CallToolRequest(params=params) - res = await self.server.call_tool(req) - - # Check that the call failed - self.assertTrue(res.isError) - self.assertIn("Invalid network_type", res.content[0].text) - - async def test_generate_deepstream_nvinfer_config_invalid_dims(self): - """Test that invalid input dimensions are rejected""" - with tempfile.TemporaryDirectory() as tmpdir: - output_path = os.path.join(tmpdir, "nvdsinfer_config.yaml") - - params = CallToolRequestParams( - name="generate_nvinfer_config", - arguments={ - "output_path": output_path, - "onnx_file": "test_model.onnx", - "network_type": 0, - "input_dims": "3;640", # invalid - missing dimension - "label_file": "labels.txt" - } - ) - req = CallToolRequest(params=params) - res = await self.server.call_tool(req) - - # Check that the call failed - self.assertTrue(res.isError) - self.assertIn("Invalid input_dims format", res.content[0].text) - - async def test_generate_deepstream_nvinfer_config_with_output_tensor_meta(self): - """Test generating config with output-tensor-meta parameter""" - with tempfile.TemporaryDirectory() as tmpdir: - output_path = os.path.join(tmpdir, "nvdsinfer_config.yaml") - - params = CallToolRequestParams( - name="generate_nvinfer_config", - arguments={ - "output_path": output_path, - "onnx_file": "custom_model.onnx", - "network_type": 0, # detection - "input_dims": "3;640;640", - "label_file": "labels.txt", - "precision_mode": 2, - "num_classes": 80, - "output_tensor_meta": 1 # Output raw tensors in DS META format - } - ) - req = CallToolRequest(params=params) - res = await self.server.call_tool(req) - - # Check that the call was 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diff --git a/tools/inference_builder/schemas/ARCHITECTURE.md b/tools/inference_builder/schemas/ARCHITECTURE.md deleted file mode 100644 index e823c64b..00000000 --- a/tools/inference_builder/schemas/ARCHITECTURE.md +++ /dev/null @@ -1,244 +0,0 @@ -# Schema Architecture - -This document describes the unified schema architecture for the Inference Builder. - -## Design Principles - -1. **DRY (Don't Repeat Yourself)**: Common structures are defined once in base schemas -2. **Composition over Inheritance**: Use JSON Schema's `allOf` to compose schemas -3. **Separation of Concerns**: Backend logic is separate from parameter validation -4. **Extensibility**: Easy to add new backends without duplicating code - -## Schema Hierarchy - -``` -┌─────────────────────────────────────────────────────────────┐ -│ config.schema.json │ -│ (Top-level configuration) │ -│ ┌───────────────────────────────────────────────────────┐ │ -│ │ - name, model_repo │ │ -│ │ - input[], output[] │ │ -│ │ - server (responders) │ │ -│ │ - models[] ←──────────────────────────────────┐ │ │ -│ │ - routes │ │ │ -│ └────────────────────────────────────────────────│─────┘ │ -└─────────────────────────────────────────────────────────────┘ - │ - ▼ - ┌────────────────────────────────────────┐ - │ common/base-model.schema.json │ - │ (Base Model Structure) │ - │ ┌──────────────────────────────────┐ │ - │ │ - name │ │ - │ │ - backend │ │ - │ │ - max_batch_size │ │ - │ │ - input[] │ │ - │ │ - output[] │ │ - │ │ - parameters (generic) │ │ - │ │ - preprocessors[] │ │ - │ │ - postprocessors[] │ │ - │ └──────────────────────────────────┘ │ - └────────────────────────────────────────┘ - │ - ┌───────────────┴───────────────┐ - │ allOf Composition │ - │ │ - ┌───────────▼──────────┐ ┌────────────▼───────────┐ - │ Backend Schemas │ │ Parameter Schemas │ - │ (backends/*.json) │ │ (parameters/*.json) │ - │ │ │ │ - │ ┌──────────────────┐ │ │ ┌────────────────────┐ │ - │ │ deepstream.json ├─┼──────┼→│ deepstream-params │ │ - │ └──────────────────┘ │ │ └────────────────────┘ │ - │ │ │ │ - │ ┌──────────────────┐ │ │ ┌────────────────────┐ │ - │ │ triton.json ├─┼──────┼→│ triton-params │ │ - │ └──────────────────┘ │ │ └────────────────────┘ │ - │ │ │ │ - │ ┌──────────────────┐ │ │ ┌────────────────────┐ │ - │ │ vllm.json ├─┼──────┼→│ vllm-params │ │ - │ └──────────────────┘ │ │ └────────────────────┘ │ - │ │ │ │ - │ ┌──────────────────┐ │ │ ┌────────────────────┐ │ - │ │ tensorrtllm.json ├─┼──────┼→│ tensorrtllm-params │ │ - │ └──────────────────┘ │ │ └────────────────────┘ │ - │ │ │ │ - │ ┌──────────────────┐ │ │ ┌────────────────────┐ │ - │ │ polygraphy.json ├─┼──────┼→│ polygraphy-params │ │ - │ └──────────────────┘ │ │ └────────────────────┘ │ - │ │ │ │ - │ ┌──────────────────┐ │ │ ┌────────────────────┐ │ - │ │ dummy.json ├─┼──────┼→│ dummy-params │ │ - │ └──────────────────┘ │ │ └────────────────────┘ │ - └──────────────────────┘ └────────────────────────┘ -``` - -## Schema Composition Example - -### DeepStream Backend - -The DeepStream backend schema is composed as follows: - -```json -{ - "allOf": [ - { - "$ref": "../common/base-model.schema.json" // Inherit common structure - }, - { - "properties": { - "backend": { - "enum": ["deepstream/nvinfer"] // Restrict backend type - }, - "parameters": { - "$ref": "parameters/deepstream-parameters.schema.json" // Specific params - } - } - } - ] -} -``` - -This composition means: -- ✅ The model **must have** all fields from `base-model.schema.json` (name, backend, input, output, etc.) -- ✅ The `backend` field **must be** `"deepstream/nvinfer"` -- ✅ The `parameters` field **must validate** against `deepstream-parameters.schema.json` - -## Benefits of This Architecture - -### 1. Maintainability -- **Single source of truth**: Common fields defined once in `base-model.schema.json` -- **Easy updates**: Change base schema → all backends updated -- **Clear ownership**: Parameters are isolated in their own files - -### 2. Consistency -- **Guaranteed structure**: All backends share the same base structure -- **Type safety**: Common types defined in `definitions.schema.json` -- **Validation rules**: Consistent validation across all backends - -### 3. Extensibility -- **Add new backend**: Create 2 files (backend schema + parameters) -- **No duplication**: Reuse base model schema via `allOf` -- **Clear pattern**: Follow existing backend schemas - -### 4. Developer Experience -- **Better IDE support**: $ref resolution provides autocomplete -- **Clear documentation**: Each parameter schema documents its options -- **Validation messages**: Precise error messages point to exact issues - -## Adding a New Backend - -To add a new backend (e.g., `onnxruntime`): - -1. **Create parameter schema**: `backends/parameters/onnxruntime-parameters.schema.json` - ```json - { - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/parameters/onnxruntime-parameters.schema.json", - "title": "ONNX Runtime Backend Parameters", - "type": "object", - "properties": { - "execution_providers": { - "type": "array", - "items": {"type": "string"} - }, - "graph_optimization_level": { - "type": "string", - "enum": ["basic", "extended", "all"] - } - } - } - ``` - -2. **Create backend schema**: `backends/onnxruntime.schema.json` - ```json - { - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/onnxruntime.schema.json", - "title": "ONNX Runtime Backend Configuration", - "allOf": [ - {"$ref": "../common/base-model.schema.json"}, - { - "properties": { - "backend": { - "const": "onnxruntime" - }, - "parameters": { - "$ref": "parameters/onnxruntime-parameters.schema.json" - } - } - } - ] - } - ``` - -3. **Update definitions**: Add `"onnxruntime"` to `common/definitions.schema.json#/definitions/backendTypes` - -4. **Update index**: Add entry to `index.json` - -That's it! No need to duplicate input/output/preprocessor definitions. - -## Schema References - -### Internal References (within a schema) -```json -"$ref": "#/definitions/tensorSpec" -``` - -### External References (to other schemas) -```json -"$ref": "../common/base-model.schema.json" -"$ref": "parameters/deepstream-parameters.schema.json" -"$ref": "definitions.schema.json#/definitions/dataTypes" -``` - -### GitHub URL References (for public access) -```json -"$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/config.schema.json" -``` - -## Validation Flow - -``` -User YAML Config - ↓ -config.schema.json (validates top-level) - ↓ -models[] → base-model.schema.json (validates common fields) - ↓ -backend type check → specific backend schema - ↓ -parameters validation → backend-specific parameter schema - ↓ -✓ Valid Configuration -``` - -## Testing Schemas - -### Using the validation script: -```bash -python schemas/validate_config.py builder/samples/vllm/vllm_cosmos.yaml -``` - -### Using Python directly: -```python -import json -import yaml -from jsonschema import validate - -with open('schemas/config.schema.json') as f: - schema = json.load(f) - -with open('builder/samples/vllm/vllm_cosmos.yaml') as f: - config = yaml.safe_load(f) - -validate(instance=config, schema=schema) -``` - -## Future Enhancements - -1. **Conditional validation**: Use `if/then/else` to validate parameters based on backend type -2. **oneOf for backend selection**: Automatically select parameter schema based on backend -3. **Custom formats**: Define custom string formats for paths, URIs, etc. -4. **Schema versioning**: Support multiple schema versions for backward compatibility - diff --git a/tools/inference_builder/schemas/README.md b/tools/inference_builder/schemas/README.md deleted file mode 100644 index c482ac1a..00000000 --- a/tools/inference_builder/schemas/README.md +++ /dev/null @@ -1,686 +0,0 @@ -# Inference Builder JSON Schemas - -This directory contains JSON Schema definitions for NVIDIA Inference Builder YAML configurations. These schemas provide validation, autocompletion, and documentation for creating inference pipeline configurations. - -## Overview - -The Inference Builder allows you to create inference pipelines with various backends such as DeepStream, TensorRT, Triton, TensorRT-LLM, vLLM, etc. These schemas help validate your configuration files and provide IDE support. - -## Schema Organization - -``` -schemas/ -├── config.schema.json # Main configuration schema -├── backends/ # Backend-specific schemas -│ ├── deepstream.schema.json # DeepStream backend -│ ├── triton.schema.json # Triton backend -│ ├── vllm.schema.json # vLLM backend -│ ├── tensorrtllm.schema.json # TensorRT-LLM backend -│ ├── polygraphy.schema.json # Polygraphy backend -│ ├── dummy.schema.json # Dummy backend -│ └── parameters/ # Backend-specific parameters -│ ├── deepstream-parameters.schema.json -│ ├── triton-parameters.schema.json -│ ├── vllm-parameters.schema.json -│ ├── tensorrtllm-parameters.schema.json -│ ├── polygraphy-parameters.schema.json -│ └── dummy-parameters.schema.json -├── common/ # Common definitions -│ ├── base-model.schema.json # Base model schema (shared structure) -│ ├── definitions.schema.json # Shared type definitions -│ ├── preprocessors.schema.json # Preprocessor schemas -│ └── postprocessors.schema.json # Postprocessor schemas -└── README.md # This file -``` - -### Schema Composition - -The schemas use JSON Schema's composition features for better maintainability: - -- **Base Model Schema** (`common/base-model.schema.json`): Defines the common structure shared by all backends (name, backend, input, output, max_batch_size, preprocessors, postprocessors) -- **Backend Schemas** (`backends/*.schema.json`): Extend the base model using `allOf` and specify which backend types are valid -- **Parameter Schemas** (`backends/parameters/*.schema.json`): Define backend-specific parameters with validation rules - -This approach: -- ✅ Eliminates duplication across backend schemas -- ✅ Ensures consistency in common fields -- ✅ Makes it easy to add new backends -- ✅ Provides clear validation for backend-specific options - -## Main Configuration Schema - -The main schema (`config.schema.json`) defines the structure of inference builder configuration files: - -### Required Fields - -- `name`: String identifier for the microservice -- `model_repo`: Path to the directory containing model files -- `models`: Array of model specifications (at least one required) - -### Optional Fields - -- `input`: Array of input tensor specifications (required when pipeline includes multiple models or when inputs use custom types) -- `output`: Array of output tensor specifications (required when pipeline includes multiple models or when outputs use custom types) -- `server`: Server configuration including responders (not required for serverless) -- `routes`: Route map for tensor flow between models (required for multi-model pipelines) -- `postprocessors`: Top-level postprocessors (for consolidating outputs from multiple models) - -### Example Configuration - -```yaml -name: "my_service" -model_repo: "/workspace/model-repo" - -input: - - name: "text" - data_type: TYPE_STRING - dims: [-1] - - name: "images" - data_type: TYPE_CUSTOM_IMAGE_BASE64 - dims: [-1] - optional: true - -output: - - name: "output" - data_type: TYPE_FP32 - dims: [-1, -1, -1] - -models: - - name: "my_model" - backend: "triton/tensorrt" - max_batch_size: 1 - input: - - name: "input0" - data_type: TYPE_FP32 - dims: [3, 768, 768] - output: - - name: "output0" - data_type: TYPE_FP32 - dims: [10, 768, 768] -``` - -## Backend-Specific Schemas - -### DeepStream Backend - -**Files**: -- Schema: `backends/deepstream.schema.json` -- Parameters: `backends/parameters/deepstream-parameters.schema.json` - -Used for DeepStream-based inference pipelines. - -**Backend Type**: `deepstream/nvinfer` - -**Parameters** (defined in `deepstream-parameters.schema.json`): -- `infer_config_path`: Array of paths to DeepStream nvinfer runtime configuration files (required). The configuration file typically accompanies the ONNX model from NGC as a YAML or TXT file - always prefer using this provided configuration when available. If no configuration file is provided with the model, one must be created based on the model architecture, inference implementation details, and post-processing approaches. -- `preprocess_config_path`: Array of paths to nvdspreprocess configuration files (optional) -- `batch_timeout`: Timeout in microseconds for batching multiple inputs -- `inference_timeout`: Inference timeout in seconds -- `tracker_config`: Configuration for object tracking -- `msgbroker_config`: Configuration for message broker integration -- `render_config`: Configuration for rendering and display -- `perf_config`: Performance monitoring configuration -- `kitti_output_path`: Paths for KITTI format output dumps - -**Example**: -```yaml -models: - - name: tao - backend: deepstream/nvinfer - max_batch_size: 1 - parameters: - infer_config_path: - - nvdsinfer_config.yaml - preprocess_config_path: - - nvdspreprocess_config.yaml - batch_timeout: -1 - inference_timeout: 5 -``` - -### Triton Backend - -**Files**: -- Schema: `backends/triton.schema.json` -- Parameters: `backends/parameters/triton-parameters.schema.json` - -Used for Triton Inference Server with various frameworks. - -**Backend Types**: -- `triton/python` -- `triton/tensorrt` -- `triton/onnx` -- `triton/pytorch` - -**Parameters**: -Triton backend parameters are intentionally open and backend-specific. All parameters are converted to `string_value` in the generated `config.pbtxt` file as defined in Triton's ModelConfig protobuf schema ([model_config.proto](https://github.com/triton-inference-server/common/blob/main/protobuf/model_config.proto)). - -Common parameters include: -- `FORCE_CPU_ONLY_INPUT_TENSORS`: Controls whether input tensors are kept on GPU or copied to CPU (Triton Python backend). Set to `"no"` to keep tensors on GPU for better performance. -- Backend-specific parameters (e.g., TensorRT engine paths, ONNX model paths, etc.) - -For complete parameter documentation, refer to the [Triton Model Configuration documentation](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/model_configuration.html). - -**Example**: -```yaml -models: - - name: visual_changenet - backend: "triton/tensorrt" - max_batch_size: 1 - parameters: - FORCE_CPU_ONLY_INPUT_TENSORS: "no" -``` - -### vLLM Backend - -**File**: `backends/vllm.schema.json` - -Used for vLLM-based large language model inference. - -**Backend Type**: `vllm` - -**Key Parameters**: -- `max_num_tokens`: Maximum number of tokens -- `async_mode`: Enable asynchronous mode -- `gpu_memory_utilization`: GPU memory utilization (0.0 to 1.0) -- `tensor_parallel_size`: Number of GPUs for tensor parallelism - -**Example**: -```yaml -models: - - name: "Cosmos" - backend: "vllm" - parameters: - max_num_tokens: 19200 - async_mode: true -``` - -### TensorRT-LLM Backend - -**File**: `backends/tensorrtllm.schema.json` - -Used for TensorRT-LLM inference with optional PyTorch fallback. - -**Backend Types**: -- `tensorrtllm` -- `tensorrtllm/pytorch` - -**Key Parameters**: -- `max_num_tokens`: Maximum number of tokens -- `kv_cache_config`: KV cache configuration - - `enable_block_reuse`: Enable KV cache block reuse -- `decoding_config`: Decoding configuration - -**Example**: -```yaml -models: - - name: "Qwen2.5-VL" - backend: "tensorrtllm/pytorch" - parameters: - max_num_tokens: 19200 - kv_cache_config: - enable_block_reuse: false -``` - -### Polygraphy Backend - -**File**: `backends/polygraphy.schema.json` - -Used for TensorRT inference via Polygraphy. - -**Backend Type**: `polygraphy` - -**Key Parameters**: -- `tensorrt_engine`: Path to TensorRT engine file (required) -- `precision`: Inference precision (fp32, fp16, int8) - -**Example**: -```yaml -models: - - name: nvclip_text - backend: "polygraphy" - max_batch_size: 64 - parameters: - tensorrt_engine: "model.plan" -``` - -## Data Types - -The following data types are supported: - -### Numeric Types -- `TYPE_BOOL`, `TYPE_UINT8`, `TYPE_UINT16`, `TYPE_UINT32`, `TYPE_UINT64` -- `TYPE_INT8`, `TYPE_INT16`, `TYPE_INT32`, `TYPE_INT64` -- `TYPE_FP16`, `TYPE_FP32`, `TYPE_FP64` - -### Basic Types -- `TYPE_STRING`: String data - -### Custom Types -- `TYPE_CUSTOM_IMAGE_BASE64`: Base64-encoded JPEG/PNG image data as a data URI string. Each element must follow the format `data:image/{format};base64,{payload}` where `{format}` is `jpeg`, `jpg`, or `png`, and `{payload}` is the base64-encoded image bytes. This type is mapped to `TYPE_STRING` in Triton and arrives as a list of strings. When used as a top-level input, it creates an `ImageInputDataFlow` that parses the data URI prefix to determine the image format, decodes the base64 payload into raw bytes, and decodes the image using a hardware-accelerated DeepStream pipeline (`nvjpegdec`/`pngdec`) to produce RGB `uint8` image tensors in HWC layout `[H, W, 3]` on GPU at the original image resolution. These decoded tensors are then passed to downstream models. When used for an output, image tensors are encoded into a base64 string. -- `TYPE_CUSTOM_IMAGE_ASSETS`: Image asset identifiers (file paths, URLs, or asset IDs) referring to JPEG/PNG images. When used as a top-level input, it creates an `ImageInputDataFlow` that loads and decodes the referenced images into image tensors before they are passed to downstream models. -- `TYPE_CUSTOM_LONG_VIDEO_ASSETS`: Video asset identifiers for **long videos and live streams requiring temporal chunking**. Particularly suitable for: (1) **live streams** such as RTSP feeds that need continuous processing in chunks, (2) long video files that should be split into temporal segments, (3) scenarios where frames need to be delivered in multiple batches over time. Supports file paths, URLs, RTSP streams, or asset IDs, with query parameters such as `?frames=N&interval=INTERVAL_NSEC&chunks=M&scale=WIDTHxHEIGHT`. When used as a top-level input, it creates a `VideoInputDataFlow` that asynchronously extracts frames in chunks, with each chunk containing N frames sampled at the specified interval. Results are delivered via a queue as they become available, enabling streaming processing. Processing always starts from the beginning of the video or live stream. Use `TYPE_CUSTOM_VIDEO_CHUNK_ASSETS` instead for short videos that fit in a single batch without temporal chunking, or when you need to specify a start position. Supported query parameters: - - `frames`: (Required) Number of frames to sample per chunk. - - `interval`: (Required) Time interval between frames in nanoseconds (e.g., `100*1e6` for 100ms between frames). - - `chunks`: (Optional) Maximum number of chunks to yield before stopping. Useful for RTSP streams that don't have a natural end-of-stream. - - `scale`: (Optional) Target resolution for frame scaling in format `WIDTHxHEIGHT` (e.g., `scale=640x360`). When specified, all extracted frames are resized to the target resolution. This is useful for reducing memory usage and inference time when processing high-resolution video streams. All assets in a batch must use the same scale value. -- `TYPE_CUSTOM_VIDEO_CHUNK_ASSETS`: Video asset identifiers for **short videos requiring simple frame sampling without temporal chunking**. Use this type when: - 1. Processing short video clips that fit in memory as a single batch - 2. Extracting a fixed number of frames from a specific video segment - 3. Simple uniform frame sampling from a video duration is needed - - Supports file paths, URLs, or asset IDs, with query parameters such as `?frames=N&start=START_NSEC&duration=DURATION_NSEC`. When used as a top-level input, it creates a `VideoFrameSamplingDataFlow` that asynchronously extracts exactly N frames evenly distributed over the specified duration (interval is automatically calculated as duration/frames), decodes them, and produces a single frame batch delivered via queue. The result is one batch containing all requested frames. For long videos or live streams requiring temporal chunking and streaming delivery of multiple batches over time, use `TYPE_CUSTOM_LONG_VIDEO_ASSETS` instead. Supported query parameters: - - `frames`: (Required) Number of frames to sample. - - `start`: (Optional) Start timestamp in nanoseconds (default 0). - - `duration`: (Optional) Duration to sample from in nanoseconds (defaults to entire video). -- `TYPE_CUSTOM_BINARY_BASE64`: Base64-encoded binary data. For inputs, the strings are automatically decoded into uint8 tensors before being passed downstream. For outputs, binary tensors are base64-encoded into strings. -- `TYPE_CUSTOM_LIST`: Converts the input values into a list, which implies explicit batching of the input data. Use this for non-DS inputs that need list conversion (e.g., video URLs for VLM models). -- `TYPE_CUSTOM_DS_MEDIA_URL`: DeepStream media URL input. A string URL (file path or remote URL) pointing to an image or video for DeepStream pipeline processing. Unlike `TYPE_CUSTOM_LIST`, this type follows the implicit batch path and is recognized by the DeepStream backend as a media source. Must be paired with a `TYPE_CUSTOM_DS_MIME` input. -- `TYPE_CUSTOM_VLM_INPUT`: Vision-Language Model input in dictionary form, passed through to downstream models as-is. -- `TYPE_CUSTOM_OBJECT`: Generic custom object type for complex structured data. - -### DeepStream-Specific Types - -#### TYPE_CUSTOM_DS_IMAGE -DeepStream custom type for image buffers, usable as both input and output. - -**As Input:** A 1-D uint8 tensor (or list of tensors) containing encoded JPEG/PNG image bytes that can be passed directly into DeepStream backends without additional decoding in the Python runtime. **This input must be defined in the model configuration to enable the image decoder in the DeepStream pipeline.** - -**As Output:** Extracts the decoded image frames from the DeepStream pipeline and returns them as RGB uint8 image tensors in HWC (Height, Width, Channels) layout. Each tensor has shape `[H, W, 3]` where Channels is 3 (R, G, B), representing the decoded frame at its processed resolution. - -**Example Output Usage:** -```yaml -output: - - name: "decoded_frames" - data_type: TYPE_CUSTOM_DS_IMAGE - dims: [-1, -1, 3] -``` - -#### TYPE_CUSTOM_DS_METADATA -DeepStream custom type for metadata output, containing per-frame detection, classification, and segmentation results. The output is returned as a **dictionary** with the following structure (defined in `definitions/deepstreamMetadata`): - -**Output Structure:** -```python -{ - 'shape': [height, width], # Image dimensions in pixels - 'bboxes': [[left, top, right, bottom], ...], # Bounding boxes for detected objects - 'probs': [0.95, 0.87, ...], # Confidence scores (0.0-1.0) - 'labels': [["car"], ["person"], ...], # Classification labels (multi-label support) - 'seg_maps': [array([[...]]), ...], # Segmentation maps (2D arrays) - 'objects': [1, 2, 3, ...], # Tracking IDs (when tracker enabled) - 'timestamp': 1234567890 # Buffer PTS in nanoseconds -} -``` - -**Field Details:** -- **`shape`**: Array of 2 integers `[height, width]` representing frame dimensions -- **`bboxes`**: Array of bounding boxes, each with 4 integers `[left, top, right, bottom]` in pixel coordinates -- **`probs`**: Array of confidence scores (floats between 0.0 and 1.0) -- **`labels`**: Array of arrays of label strings, enabling multi-label classification (e.g., object type and attributes) -- **`seg_maps`**: Array of 2D segmentation maps for semantic/instance segmentation (numpy arrays or lists) -- **`objects`**: Array of tracking IDs (integers) assigned by DeepStream tracker (populated only when tracker is enabled) -- **`timestamp`**: Buffer presentation timestamp in nanoseconds for temporal synchronization - -**Important**: This type requires a low-level C++ postprocessor library (e.g., `libnvds_infercustomparser_tao.so`) to be available and copied into the container image. The library can be obtained from: -1. DeepStream SDK built-in parsers -2. TAO model parsers from https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git -3. Custom user-implemented parsers - -Without such a library, use regular tensor output types (`TYPE_FP32`, etc.) with Python postprocessors instead. - -**Example Usage in Response Templates:** -```yaml -responses: - InferenceResponse: > - { - "data": [ - {% for item in response.output %} { - "shape": {{item['shape']}}, - "bboxes": {{item['bboxes']}}, - "probs": {{item['probs']}}, - "labels": {{item['labels']|tojson}}, - "masks": {{item['seg_maps']}}, - "timestamp": {{item['timestamp']}} - } {% if not loop.last %}, {% endif %} {% endfor %} - ] - } -``` - -#### TYPE_CUSTOM_DS_MIME -DeepStream custom type for MIME type string (for example, `image/jpeg` or `image/png`) describing the associated DeepStream image or media buffers. Required alongside `TYPE_CUSTOM_DS_IMAGE` or `TYPE_CUSTOM_DS_MEDIA_URL` inputs so the DeepStream pipeline can select the appropriate decoder. - -#### TYPE_CUSTOM_DS_SOURCE_CONFIG -DeepStream custom type for path to a YAML source configuration file. When used as a model input, consider using it when sources such as live streams are dynamically added and removed. - -## Preprocessors and Postprocessors - -Processors transform data at different stages of the inference pipeline. Preprocessors operate before model inference, while postprocessors operate after. - -**Base Schema**: `common/base-processor.schema.json` -**Preprocessor Schema**: `common/preprocessors.schema.json` -**Postprocessor Schema**: `common/postprocessors.schema.json` - -### Data Flow and Processor Interface - -Processors follow a standard interface pattern where data flows through dictionaries: - -**Input Flow**: -1. Upstream data (from model outputs, top-level inputs, or previous processors) is provided as a dictionary -2. The processor's `input` field specifies keys to extract from this dictionary, in order -3. Extracted values are passed as positional arguments to the processor's `__call__` method - -**Output Flow**: -1. The processor's `__call__` method returns a tuple -2. The tuple length must match the length of the `output` field -3. Returned values are mapped to output names to form a dictionary for downstream consumption - -**Example Flow**: -```python -# Upstream data dictionary -upstream = { - 'image': , - 'text': , - 'metadata': -} - -# Processor configuration -processor = { - 'input': ['text', 'image'], # Extract in this order - 'output': ['tokens', 'pixels'] # Map results to these names -} - -# Processor invocation -result = processor.__call__(upstream['text'], upstream['image']) -# result = (, ) - -# Downstream receives -downstream = { - 'tokens': , - 'pixels': -} -``` - -### Preprocessors - -**File**: `common/preprocessors.schema.json` - -Preprocessors transform input data before model inference. They typically handle tasks like: -- Image normalization and resizing -- Text tokenization -- Data format conversions -- Feature extraction - -**Required Fields**: -- `kind`: Processor type (`"custom"` or `"builtin"`) -- `name`: Unique identifier for the processor; should match the `name` attribute defined within the processor class -- `input`: Array of input tensor names (extracted from upstream data) -- `output`: Array of output tensor names (mapped to returned tuple) -- `config`: Optional processor-specific configuration - -**Example**: -```yaml -preprocessors: - - kind: "custom" - name: "image-normalizer" - input: ["raw_image"] # Extract 'raw_image' from upstream - output: ["normalized_image"] # Return maps to 'normalized_image' - config: - mean: [0.485, 0.456, 0.406] - std: [0.229, 0.224, 0.225] -``` - -**Corresponding Python Implementation**: -```python -class ImageNormalizer: - name = "image-normalizer" - - def __init__(self, config): - self.mean = config['mean'] - self.std = config['std'] - - def __call__(self, raw_image): - # Process the input - normalized = (raw_image - self.mean) / self.std - # Return tuple matching output length - return (normalized,) -``` - -### Postprocessors - -**File**: `common/postprocessors.schema.json` - -Postprocessors transform model outputs after inference. They typically handle tasks like: -- Output decoding -- Masking and filtering -- Embedding normalization -- Result formatting - -**Required Fields**: Same as preprocessors - -**Example**: -```yaml -postprocessors: - - kind: "custom" - name: "detection-decoder" - input: ["boxes", "scores", "classes"] # Extract from model outputs - output: ["detections"] # Return as single output - config: - confidence_threshold: 0.5 - max_detections: 100 -``` - -**Corresponding Python Implementation**: -```python -class DetectionDecoder: - name = "detection-decoder" - - def __init__(self, config): - self.threshold = config['confidence_threshold'] - self.max_detections = config['max_detections'] - - def __call__(self, boxes, scores, classes): - # Process three inputs - mask = scores > self.threshold - filtered_boxes = boxes[mask][:self.max_detections] - filtered_scores = scores[mask][:self.max_detections] - filtered_classes = classes[mask][:self.max_detections] - - detections = { - 'boxes': filtered_boxes, - 'scores': filtered_scores, - 'classes': filtered_classes - } - # Return tuple with one element - return (detections,) -``` - -### Multi-Input, Multi-Output Example - -Processors can handle multiple inputs and outputs: - -```yaml -preprocessors: - - kind: "custom" - name: "dual-stream-processor" - input: ["reference_image", "test_image", "threshold"] - output: ["ref_features", "test_features"] - config: - feature_dim: 256 -``` - -```python -class DualStreamProcessor: - name = "dual-stream-processor" - - def __init__(self, config): - self.feature_dim = config['feature_dim'] - - def __call__(self, reference_image, test_image, threshold): - # Process inputs (receives 3 arguments in order) - ref_features = self.extract_features(reference_image) - test_features = self.extract_features(test_image) - - # Apply threshold - ref_features = ref_features * (ref_features > threshold) - test_features = test_features * (test_features > threshold) - - # Return tuple matching output length (2 elements) - return (ref_features, test_features) -``` - -## Server Configuration - -The server section defines how the inference service handles requests and responses. - -### Responders - -Responders map API operations to request/response transformations using Jinja2 templates. - -**Available Responder Keys** (must match templates in `templates/responder/`): - -| Responder | Description | Input | Output | -|-----------|-------------|-------|--------| -| `infer` | Primary inference endpoint. Handles model inference requests. Supports streaming responses with `Accept: application/x-ndjson` header. | Top-level pipeline inputs (as defined in config `input` section) | Top-level pipeline outputs (as defined in config `output` section) | -| `add_file` | File upload responder. Adds files to the asset store for later processing. | Multipart file upload (file object with `filename` and `content_type`) | Asset dict: `id`, `file_name`, `content_type`, `created_at` | -| `del_file` | File deletion responder. Removes files from the asset store. | `asset_id` (string, from path parameter) | `status` (boolean, deletion success) | -| `list_files` | File listing responder. Returns a list of files in the asset store. | None | `assets` (array of asset objects with `id`, `file_name`, `content_type`, `created_at`) | -| `add_live_stream` | Live stream addition responder. Registers a new RTSP or video stream for processing. | `url` (string, required), `description` (string, optional), `username` (string, optional), `password` (string, optional) | Asset dict: `id`, `url`, `description`, `created_at` | -| `del_live_stream` | Live stream removal responder. Stops and removes a registered live stream. | `asset_id` (string, from path parameter) | `status` (boolean, deletion success) | -| `list_live_streams` | Live stream listing responder. Returns all registered live streams. | None | `assets` (array of stream asset objects with `id`, `url`, `description`, `created_at`) | -| `healthy_ready` | Health check responder. Reports service health and readiness status. No request/response template mapping needed. | None | HTTP 200 with "Ready" or HTTP 503 with "Service Unavailable" | - -**Request/Response Template Mapping**: - -- **`requests`**: Jinja2 templates to map HTTP request body to responder inputs. Keys are **OpenAPI request schema/class names** (e.g., `InferenceRequest`, `AddLiveStreamRequest`), values are Jinja2 templates that transform the incoming `request` object into a JSON structure matching the responder's expected inputs. - -- **`responses`**: Jinja2 templates to map responder outputs to HTTP response body. Keys are **OpenAPI response schema/class names** (e.g., `InferenceResponse`, `AddFileResponse`), values are Jinja2 templates that transform the `response` object from the responder into the desired HTTP response JSON structure. - -**Common Operations** (values for the `operation` field): -- `inference`: General inference operation -- `create_chat_completion_v1_chat_completions_post`: Chat completion for LLMs -- `create_embedding`: Embedding generation -- `add_media_file`, `delete_media_file`, `list_media_files`: Media file management -- `add_live_stream`, `delete_live_stream`, `list_live_streams`: Live stream management -- `health_ready_v1_health_ready_get`: Health check - -See [RESPONDERS.md](RESPONDERS.md) for complete documentation. - -**Example**: -```yaml -server: - responders: - infer: - operation: inference - requests: - InferenceRequest: > - { - "images": [{{ request.input[0]|tojson }}] - } - responses: - InferenceResponse: > - { - "data": {{ response.output|tojson }}, - "model": "my-model" - } -``` - -## Routes - -Routes define the flow of tensors between models in multi-model pipelines. - -**Format**: `: : :` - -- Source/destination can be model names or `:` for top-level -- Tensor list is optional: `["tensor1", "tensor2"]` - -**Example**: -```yaml -routes: - ':["reference_image", "test_image"]': 'visual_changenet' - 'visual_changenet:["output_final"]': ':["output"]' -``` - -## Using the Schemas - -### In VS Code - -Add to your workspace settings (`.vscode/settings.json`): - -```json -{ - "yaml.schemas": { - "./schemas/config.schema.json": "*.yaml" - } -} -``` - -Or use the GitHub URL for the latest schema: - -```json -{ - "yaml.schemas": { - "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/config.schema.json": "*.yaml" - } -} -``` - -### With YAML Language Server - -Add to your `.yaml-language-server.json`: - -```json -{ - "yaml.schemas": { - "file:///path/to/schemas/config.schema.json": "/*.yaml" - } -} -``` - -### Validation with Python - -```python -import json -import yaml -from jsonschema import validate - -# Load schema -with open('schemas/config.schema.json') as f: - schema = json.load(f) - -# Load and validate config -with open('config.yaml') as f: - config = yaml.safe_load(f) - -validate(instance=config, schema=schema) -``` - -## Examples - -See the `builder/samples/` directory for complete examples of configurations for different backends: - -- `builder/samples/dummy/dummy.yaml` - Dummy backend example -- `builder/samples/vllm/vllm_cosmos.yaml` - vLLM backend example -- `builder/samples/qwen/trtllm_qwen.yaml` - TensorRT-LLM backend example -- `builder/samples/tao/ds_tao.yaml` - DeepStream backend example -- `builder/samples/changenet/trt_changenet.yaml` - Triton/TensorRT backend example -- `builder/samples/nvclip/tensorrt_nvclip.yaml` - Polygraphy backend example - -## References - -- [Inference Builder Documentation](../README.md) -- [JSON Schema Specification](https://json-schema.org/) -- [YAML Language Server](https://github.com/redhat-developer/yaml-language-server) - -## Contributing - -When adding new backends or features: - -1. Update the appropriate schema files -2. Add examples to this README -3. Test with sample configurations -4. Update the `common/definitions.schema.json` if adding new types - -## License - -SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -SPDX-License-Identifier: Apache-2.0 - diff --git a/tools/inference_builder/schemas/RESPONDERS.md b/tools/inference_builder/schemas/RESPONDERS.md deleted file mode 100644 index a8a61b8b..00000000 --- a/tools/inference_builder/schemas/RESPONDERS.md +++ /dev/null @@ -1,230 +0,0 @@ -# Available Responders - -This document lists the available responder templates that can be used in the `server.responders` section of the configuration. - -## Overview - -Responders map API endpoints to their implementation. Each responder corresponds to a template file in `templates/responder/` and handles a specific operation. - -## Available Responder Keys - -The following keys are valid for use in `server.responders`: - -| Key | Template File | Operation | Description | -|-----|--------------|-----------|-------------| -| `infer` | `infer.jinja.py` | `inference` or chat completion | Main inference endpoint for model predictions | -| `add_file` | `add_file.jinja.py` | `add_media_file` | Upload and register a media file as an asset | -| `del_file` | `del_file.jinja.py` | `delete_media_file` | Delete a registered media file | -| `list_files` | `list_files.jinja.py` | `list_media_files` | List all registered media files | -| `add_live_stream` | `add_live_stream.jinja.py` | `add_live_stream` | Add a live stream source | -| `del_live_stream` | `del_live_stream.jinja.py` | `delete_live_stream` | Delete a live stream source | -| `list_live_streams` | `list_live_streams.jinja.py` | `list_live_streams` | List all registered live streams | -| `healthy_ready` | `healthy_ready.jinja.py` | `health_ready_v1_health_ready_get` | Health check endpoint | - -## Schema Validation - -The schema restricts responder keys to the above list using `propertyNames.enum`. Invalid responder keys will cause validation to fail: - -```yaml -server: - responders: - infer: # ✅ Valid - operation: inference - - custom_op: # ❌ Error: invalid responder key - operation: custom -``` - -**Error message**: `Property name must be one of: infer, add_file, del_file, list_files, add_live_stream, del_live_stream, list_live_streams, healthy_ready` - -## Usage Examples - -### Basic Inference - -```yaml -server: - responders: - infer: - operation: inference - requests: - InferenceRequest: > - { - "images": {{ request.input|tojson }} - } - responses: - InferenceResponse: > - { - "data": {{ response.output|tojson }}, - "model": "my-model" - } -``` - -### LLM Chat Completion - -```yaml -server: - responders: - infer: - operation: create_chat_completion_v1_chat_completions_post - requests: - NIMLLMChatCompletionRequest: > - { - "messages": {{ request.messages|tojson }}, - "max_tokens": {{ request.max_tokens }} - } - responses: - NIMLLMChatCompletionResponse: > - { - "id": "{{ request._id }}", - "choices": [...] - } -``` - -### File Management - -```yaml -server: - responders: - add_file: - operation: add_media_file - responses: - AddFileResponse: > - { - "data": { - "id": {{response.id|tojson}}, - "path": {{response.path|tojson}}, - "contentType": {{response.mime_type|tojson}} - } - } - - list_files: - operation: list_media_files - responses: - ListFilesResponse: > - { - "data": [ - {% for item in response.assets %} - { - "id": {{item.id|tojson}}, - "path": {{item.path|tojson}} - } - {% if not loop.last %}, {% endif %} - {% endfor %} - ] - } -``` - -### Health Check - -```yaml -server: - responders: - healthy_ready: - operation: health_ready_v1_health_ready_get -``` - -## Responder Configuration - -Each responder has the following structure: - -| Field | Required | Type | Description | -|-------|----------|------|-------------| -| `operation` | ✅ Yes | string | Name of the operation (must match OpenAPI spec) | -| `requests` | ❌ No | object | Request transformation templates (Jinja2) | -| `responses` | ❌ No | object | Response transformation templates (Jinja2) | - -### Request Templates - -Transform incoming API requests to the internal inference format: - -```yaml -requests: - RequestTypeName: > - { - "field1": {{ request.some_field|tojson }}, - "field2": "{{ request.another_field }}" - } -``` - -- Keys can be any string (typically match OpenAPI schema names) -- Values are Jinja2 templates -- Access request data via `request.*` variables - -### Response Templates - -Transform internal inference results to API responses: - -```yaml -responses: - ResponseTypeName: > - { - "result": {{ response.output|tojson }}, - "metadata": { - "model": "my-model" - } - } -``` - -- Keys can be any string (typically match OpenAPI schema names) -- Values are Jinja2 templates -- Access response data via `response.*` variables - -## Common Operations - -### Inference Operations - -- `inference` - General inference endpoint -- `create_chat_completion_v1_chat_completions_post` - LLM chat completion - -### Media File Operations - -- `add_media_file` - Upload media file -- `delete_media_file` - Delete media file -- `list_media_files` - List media files - -### Live Stream Operations - -- `add_live_stream` - Add live stream -- `delete_live_stream` - Delete live stream -- `list_live_streams` - List live streams - -### System Operations - -- `health_ready_v1_health_ready_get` - Health check - -## Template Implementation - -Responder templates are located in `templates/responder/`. Each template: - -1. **Receives** the processed request data -2. **Calls** the inference pipeline -3. **Returns** the formatted response - -Templates use Jinja2 syntax and have access to: -- `request` - Incoming request data -- `response` - Inference pipeline output -- Various helper filters (e.g., `tojson`, `tojinja`) - -## Adding Custom Responders - -To add a new responder: - -1. Create template file: `templates/responder/my_responder.jinja.py` -2. Implement the responder logic -3. Add `"my_responder"` to the enum in: - - `schemas/config.schema.json` (propertyNames.enum) - - `schemas/common/definitions.schema.json` (responderNames) -4. Update this documentation - -## Related Documentation - -- [Usage Guide](../../doc/usage.md) - Server configuration details -- [Architecture](../ARCHITECTURE.md) - How responders fit into the system -- [OpenAPI Specification](../../builder/samples/*/openapi.yaml) - API definitions - -## References - -- Templates: `templates/responder/*.jinja.py` -- Schema: `schemas/config.schema.json#/definitions/serverConfig` -- Common Definitions: `schemas/common/definitions.schema.json#/definitions/responderNames` - diff --git a/tools/inference_builder/schemas/backends/deepstream.schema.json b/tools/inference_builder/schemas/backends/deepstream.schema.json deleted file mode 100644 index 920b5464..00000000 --- a/tools/inference_builder/schemas/backends/deepstream.schema.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/deepstream.schema.json", - "title": "DeepStream Backend Configuration", - "description": "Schema for DeepStream backend model configuration", - "allOf": [ - { - "$ref": "../common/base-model.schema.json" - }, - { - "properties": { - "backend": { - "type": "string", - "enum": ["deepstream/nvinfer", "deepstream/nvinferserver"], - "description": "DeepStream backend type" - }, - "parameters": { - "$ref": "parameters/deepstream-parameters.schema.json" - } - } - } - ], - "examples": [ - { - "name": "tao", - "backend": "deepstream/nvinfer", - "max_batch_size": 1, - "parameters": { - "infer_config_path": ["nvdsinfer_config.yaml"] - }, - "input": [ - { - "name": "image", - "data_type": "TYPE_CUSTOM_DS_IMAGE", - "dims": [-1, -1, 3] - } - ], - "output": [ - { - "name": "output", - "data_type": "TYPE_CUSTOM_DS_METADATA", - "dims": [-1] - } - ] - } - ] -} diff --git a/tools/inference_builder/schemas/backends/dummy.schema.json b/tools/inference_builder/schemas/backends/dummy.schema.json deleted file mode 100644 index 8b35c6df..00000000 --- a/tools/inference_builder/schemas/backends/dummy.schema.json +++ /dev/null @@ -1,45 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/dummy.schema.json", - "title": "Dummy Backend Configuration", - "description": "Schema for dummy backend model configuration (testing)", - "allOf": [ - { - "$ref": "../common/base-model.schema.json" - }, - { - "properties": { - "backend": { - "type": "string", - "const": "dummy", - "description": "Dummy backend for testing purposes" - }, - "parameters": { - "$ref": "parameters/dummy-parameters.schema.json" - } - } - } - ], - "examples": [ - { - "name": "dummy", - "backend": "dummy", - "max_batch_size": 1, - "parameters": {}, - "input": [ - { - "name": "images", - "data_type": "TYPE_FP32", - "dims": [3, 768, 768] - } - ], - "output": [ - { - "name": "output", - "data_type": "TYPE_FP32", - "dims": [10, 10] - } - ] - } - ] -} diff --git a/tools/inference_builder/schemas/backends/parameters/deepstream-parameters.schema.json b/tools/inference_builder/schemas/backends/parameters/deepstream-parameters.schema.json deleted file mode 100644 index 07f6a276..00000000 --- a/tools/inference_builder/schemas/backends/parameters/deepstream-parameters.schema.json +++ /dev/null @@ -1,207 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/parameters/deepstream-parameters.schema.json", - "title": "DeepStream Backend Parameters", - "description": "Parameter schema for DeepStream backend", - "type": "object", - "required": ["infer_config_path"], - "properties": { - "infer_config_path": { - "type": "array", - "description": "Paths (inside the model repository) to DeepStream nvinfer runtime configuration files (for example, nvdsinfer_config.yaml). These files are used by the DeepStream nvinfer plugin at runtime. **Creating or locating config files:** (1) The configuration file typically accompanies the ONNX model from the remote model repository (NGC) as a YAML or TXT file containing all required inference parameters - always prefer using this provided configuration when available. (2) If no configuration file is provided with the model, one must be created based on the model architecture, inference implementation details, and post-processing approaches specific to the model. (3) The config file must be copied into the model repository before deployment. Refer to https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvinfer.html#gst-nvinfer-file-configuration-specifications for the complete configuration specification.", - "items": { - "type": "string" - } - }, - "preprocess_config_path": { - "type": "array", - "description": "Paths (inside the model repository) to DeepStream nvdspreprocess runtime configuration files (for example, nvdspreprocess_config*.yaml). These files define preprocessing operations that run before inference and are required when non-image tensors must be injected into the pipeline. They must already be copied into the model repository before deployment. Refer to https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvdspreprocess.html for more details.", - "items": { - "type": "string" - } - }, - "batch_timeout": { - "type": "integer", - "minimum": -1, - "description": "Timeout in microseconds for batching multiple inputs. Set to -1 to wait infinitely for batch formation. Default is 1000 * max_batch_size microseconds." - }, - "inference_timeout": { - "type": "integer", - "minimum": 0, - "description": "Inference timeout in seconds, absence of this field means no timeout is enforced" - }, - "tracker_config": { - "type": "object", - "description": "Configuration for object tracking, absence of this field means no tracking is required", - "required": ["ll_lib_file", "ll_config_file"], - "properties": { - "ll_lib_file": { - "type": "string", - "description": "Path to the low-level tracker library file" - }, - "ll_config_file": { - "type": "string", - "description": "Path to the low-level tracker configuration file" - }, - "width": { - "type": "integer", - "minimum": 1, - "description": "Width of the tracker processing frame", - "default": 1920 - }, - "height": { - "type": "integer", - "minimum": 1, - "description": "Height of the tracker processing frame", - "default": 1088 - }, - "display_tracking_id": { - "type": "boolean", - "description": "Whether to display tracking IDs in the output", - "default": false - } - }, - "additionalProperties": false - }, - "msgbroker_config": { - "type": "object", - "description": "Configuration for message broker integration, absence of this field means no message broker is required", - "required": ["msgbroker_proto_lib_path", "msgbroker_conn_str", "msgbroker_topic", "msgconv_config_path"], - "properties": { - "msgbroker_proto_lib_path": { - "type": "string", - "description": "Path to the message broker protocol library" - }, - "msgbroker_conn_str": { - "type": "string", - "description": "Connection string for the message broker (e.g., 'localhost;9092' for Kafka)" - }, - "msgbroker_topic": { - "type": "string", - "description": "Topic name for publishing messages" - }, - "msgconv_config_path": { - "type": "string", - "description": "Path to the message conversion configuration file, refer to https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvmsgconv.html for more details" - }, - "msgconv_payload_type": { - "type": "integer", - "enum": [0, 1, 2], - "description": "Payload type for message conversion (0=DeepStream JSON, 1=DeepStream Minimal JSON, 2=DeepStream Protobuf)" - }, - "msgconv_msg2p_new_api": { - "type": "boolean", - "description": "Use new API which supports publishing multiple payloads using NvDsFrameMeta", - "default": false - }, - "msgconv_frame_interval": { - "type": "integer", - "minimum": 1, - "description": "Frame interval at which payload is generated (every Nth frame)", - "default": 1 - }, - "msgconv_msg2p_lib": { - "type": "string", - "description": "Path to the message converter library" - } - }, - "additionalProperties": false - }, - "render_config": { - "type": "object", - "description": "Configuration for rendering and display", - "properties": { - "enable_display": { - "type": "boolean", - "description": "Enable on-screen display", - "default": false - }, - "enable_osd": { - "type": "boolean", - "description": "Enable on-screen display overlays (bounding boxes, labels, etc.)", - "default": false - }, - "enable_stream": { - "type": "boolean", - "description": "Enable RTSP streaming output", - "default": false - }, - "rtsp_mount_point": { - "type": "string", - "description": "RTSP mount point path (e.g., '/ds_app'), not required if enable_stream is false", - "default": "/ds-test" - }, - "rtsp_port": { - "type": "integer", - "minimum": 1, - "maximum": 65535, - "description": "RTSP server port number, not required if enable_stream is false", - "default": 8554 - } - }, - "additionalProperties": false - }, - "perf_config": { - "type": "object", - "description": "Performance monitoring configuration", - "properties": { - "enable_fps_logs": { - "type": "boolean", - "description": "Enable FPS (frames per second) logging", - "default": false - }, - "enable_latency_logs": { - "type": "boolean", - "description": "Enable latency logging", - "default": false - } - }, - "additionalProperties": false - }, - "kitti_output_path": { - "type": "object", - "description": "Paths for KITTI format output dumps", - "properties": { - "infer": { - "type": "string", - "description": "Path for inference KITTI output" - }, - "tracker": { - "type": "string", - "description": "Path for tracker KITTI output" - } - }, - "additionalProperties": false - }, - "analytics_config": { - "type": "object", - "description": "Configuration for nvdsanalytics (ROI, line-crossing, overcrowding, direction). Absence means no analytics. Typically used after tracker. Refer to https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvdsanalytics.html", - "required": ["config_file_path"], - "properties": { - "config_file_path": { - "type": "string", - "description": "Path to the analytics configuration file (YAML or TXT). Path is relative to model repository unless absolute." - }, - "gpu_id": { - "type": "integer", - "minimum": 0, - "description": "GPU device id for nvdsanalytics", - "default": 0 - } - }, - "additionalProperties": false - }, - "resize_video": { - "type": "array", - "description": "Target resolution for input stream resizing in format [height, width]. When specified, the input stream will be resized to these dimensions before processing. When tracker is being enabled, it requires a specific resolution consistent with the tracker config. In such cases, the resize_video configuration is required.", - "items": { - "type": "integer", - "minimum": 1 - }, - "minItems": 2, - "maxItems": 2 - } - }, - "additionalProperties": false -} - diff --git a/tools/inference_builder/schemas/backends/parameters/dummy-parameters.schema.json b/tools/inference_builder/schemas/backends/parameters/dummy-parameters.schema.json deleted file mode 100644 index ebd75184..00000000 --- a/tools/inference_builder/schemas/backends/parameters/dummy-parameters.schema.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/parameters/dummy-parameters.schema.json", - "title": "Dummy Backend Parameters", - "description": "Parameter schema for dummy backend (testing)", - "type": "object", - "properties": {}, - "additionalProperties": true -} - diff --git a/tools/inference_builder/schemas/backends/parameters/polygraphy-parameters.schema.json b/tools/inference_builder/schemas/backends/parameters/polygraphy-parameters.schema.json deleted file mode 100644 index e8e432f7..00000000 --- a/tools/inference_builder/schemas/backends/parameters/polygraphy-parameters.schema.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/parameters/polygraphy-parameters.schema.json", - "title": "Polygraphy Backend Parameters", - "description": "Parameter schema for Polygraphy (TensorRT) backend", - "type": "object", - "required": ["tensorrt_engine"], - "properties": { - "tensorrt_engine": { - "type": "string", - "description": "Path to TensorRT engine file (typically model.plan) relative to the model home directory (typically folder named after model name under model repository)" - } - }, - "additionalProperties": false -} - diff --git a/tools/inference_builder/schemas/backends/parameters/pytorch-parameters.schema.json b/tools/inference_builder/schemas/backends/parameters/pytorch-parameters.schema.json deleted file mode 100644 index 1bd61ce1..00000000 --- a/tools/inference_builder/schemas/backends/parameters/pytorch-parameters.schema.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/parameters/pytorch-parameters.schema.json", - "title": "PyTorch Backend Parameters", - "description": "Parameter schema for PyTorch backend (Huggingface Transformers)", - "type": "object", - "properties": {}, - "additionalProperties": true -} - diff --git a/tools/inference_builder/schemas/backends/parameters/tensorrtllm-parameters.schema.json b/tools/inference_builder/schemas/backends/parameters/tensorrtllm-parameters.schema.json deleted file mode 100644 index 7718a901..00000000 --- a/tools/inference_builder/schemas/backends/parameters/tensorrtllm-parameters.schema.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/parameters/tensorrtllm-parameters.schema.json", - "title": "TensorRT-LLM Backend Parameters", - "description": "Parameter schema for TensorRT-LLM backend", - "type": "object", - "properties": {}, - "additionalProperties": true -} - diff --git a/tools/inference_builder/schemas/backends/parameters/triton-parameters.schema.json b/tools/inference_builder/schemas/backends/parameters/triton-parameters.schema.json deleted file mode 100644 index e774bd62..00000000 --- a/tools/inference_builder/schemas/backends/parameters/triton-parameters.schema.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/parameters/triton-parameters.schema.json", - "title": "Triton Backend Parameters", - "description": "Parameter schema for Triton Inference Server backend. Parameters are backend-specific and defined as map in model_config.proto (https://github.com/triton-inference-server/common/blob/main/protobuf/model_config.proto). All parameter values are converted to string_value in the generated config.pbtxt. Refer to Triton documentation for backend-specific parameters: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/model_configuration.html", - "type": "object", - "properties": {}, - "additionalProperties": true -} - diff --git a/tools/inference_builder/schemas/backends/parameters/vllm-parameters.schema.json b/tools/inference_builder/schemas/backends/parameters/vllm-parameters.schema.json deleted file mode 100644 index 6e5c21b4..00000000 --- a/tools/inference_builder/schemas/backends/parameters/vllm-parameters.schema.json +++ /dev/null @@ -1,55 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/parameters/vllm-parameters.schema.json", - "title": "vLLM Backend Parameters", - "description": "Parameter schema for vLLM backend", - "type": "object", - "properties": { - "max_num_tokens": { - "type": "integer", - "minimum": 1, - "description": "Maximum number of tokens" - }, - "async_mode": { - "type": "boolean", - "description": "Enable asynchronous mode" - }, - "dtype": { - "type": "string", - "enum": ["auto", "half", "float16", "bfloat16", "float", "float32"], - "description": "Data type for model weights and activations" - }, - "gpu_memory_utilization": { - "type": "number", - "minimum": 0.0, - "maximum": 1.0, - "description": "GPU memory utilization (0.0 to 1.0)" - }, - "max_model_len": { - "type": "integer", - "minimum": 1, - "description": "Maximum model context length" - }, - "tensor_parallel_size": { - "type": "integer", - "minimum": 1, - "description": "Number of GPUs to use for tensor parallelism" - }, - "pipeline_parallel_size": { - "type": "integer", - "minimum": 1, - "description": "Number of pipeline stages" - }, - "trust_remote_code": { - "type": "boolean", - "description": "Trust remote code from model hub" - }, - "max_num_seqs": { - "type": "integer", - "minimum": 1, - "description": "Maximum number of sequences per iteration" - } - }, - "additionalProperties": false -} - diff --git a/tools/inference_builder/schemas/backends/polygraphy.schema.json b/tools/inference_builder/schemas/backends/polygraphy.schema.json deleted file mode 100644 index 6f2d3346..00000000 --- a/tools/inference_builder/schemas/backends/polygraphy.schema.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/polygraphy.schema.json", - "title": "Polygraphy Backend Configuration", - "description": "Schema for Polygraphy (TensorRT) backend model configuration", - "allOf": [ - { - "$ref": "../common/base-model.schema.json" - }, - { - "properties": { - "backend": { - "type": "string", - "const": "polygraphy", - "description": "Polygraphy backend for TensorRT inference" - }, - "parameters": { - "$ref": "parameters/polygraphy-parameters.schema.json" - } - } - } - ], - "examples": [ - { - "name": "nvclip_text", - "backend": "polygraphy", - "max_batch_size": 64, - "parameters": { - "tensorrt_engine": "model.plan" - }, - "input": [ - { - "name": "TEXT", - "data_type": "TYPE_INT64", - "dims": [-1, -1] - } - ], - "output": [ - { - "name": "LOGITS_PER_TEXT", - "data_type": "TYPE_FP32", - "dims": [-1, -1] - } - ] - } - ] -} diff --git a/tools/inference_builder/schemas/backends/pytorch.schema.json b/tools/inference_builder/schemas/backends/pytorch.schema.json deleted file mode 100644 index e19995b9..00000000 --- a/tools/inference_builder/schemas/backends/pytorch.schema.json +++ /dev/null @@ -1,50 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/pytorch.schema.json", - "title": "PyTorch Backend Configuration", - "description": "Schema for PyTorch backend model configuration (Huggingface Transformers)", - "allOf": [ - { - "$ref": "../common/base-model.schema.json" - }, - { - "properties": { - "backend": { - "type": "string", - "const": "pytorch", - "description": "PyTorch backend for Huggingface Transformers models" - }, - "parameters": { - "$ref": "parameters/pytorch-parameters.schema.json" - } - } - } - ], - "examples": [ - { - "name": "llama_model", - "backend": "pytorch", - "parameters": { - "model_name_or_path": "meta-llama/Llama-2-7b-hf", - "device": "cuda", - "torch_dtype": "float16", - "trust_remote_code": true - }, - "input": [ - { - "name": "input_ids", - "data_type": "TYPE_INT64", - "dims": [-1, -1] - } - ], - "output": [ - { - "name": "logits", - "data_type": "TYPE_FP32", - "dims": [-1, -1, -1] - } - ] - } - ] -} - diff --git a/tools/inference_builder/schemas/backends/tensorrtllm.schema.json b/tools/inference_builder/schemas/backends/tensorrtllm.schema.json deleted file mode 100644 index 5441c014..00000000 --- a/tools/inference_builder/schemas/backends/tensorrtllm.schema.json +++ /dev/null @@ -1,49 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/tensorrtllm.schema.json", - "title": "TensorRT-LLM Backend Configuration", - "description": "Schema for TensorRT-LLM backend model configuration", - "allOf": [ - { - "$ref": "../common/base-model.schema.json" - }, - { - "properties": { - "backend": { - "type": "string", - "enum": ["tensorrtllm", "tensorrtllm/pytorch"], - "description": "TensorRT-LLM backend type with optional PyTorch fallback" - }, - "parameters": { - "$ref": "parameters/tensorrtllm-parameters.schema.json" - } - } - } - ], - "examples": [ - { - "name": "Qwen2.5-VL", - "backend": "tensorrtllm/pytorch", - "parameters": { - "max_num_tokens": 19200, - "kv_cache_config": { - "enable_block_reuse": false - } - }, - "input": [ - { - "name": "inputs", - "data_type": "TYPE_CUSTOM_VLM_INPUT", - "dims": [-1] - } - ], - "output": [ - { - "name": "outputs", - "data_type": "TYPE_STRING", - "dims": [-1] - } - ] - } - ] -} diff --git a/tools/inference_builder/schemas/backends/triton.schema.json b/tools/inference_builder/schemas/backends/triton.schema.json deleted file mode 100644 index 6825348e..00000000 --- a/tools/inference_builder/schemas/backends/triton.schema.json +++ /dev/null @@ -1,54 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/triton.schema.json", - "title": "Triton Backend Configuration", - "description": "Schema for Triton Inference Server backend model configuration", - "allOf": [ - { - "$ref": "../common/base-model.schema.json" - }, - { - "properties": { - "backend": { - "type": "string", - "enum": [ - "triton/python", - "triton/tensorrt", - "triton/onnx", - "triton/pytorch", - "triton/tensorflow", - "triton/python/tensorrtllm" - ], - "description": "Triton backend type with specific framework" - }, - "parameters": { - "$ref": "parameters/triton-parameters.schema.json" - } - } - } - ], - "examples": [ - { - "name": "visual_changenet", - "backend": "triton/tensorrt", - "max_batch_size": 1, - "parameters": { - "FORCE_CPU_ONLY_INPUT_TENSORS": "no" - }, - "input": [ - { - "name": "input0", - "data_type": "TYPE_FP32", - "dims": [3, 768, 768] - } - ], - "output": [ - { - "name": "output_final", - "data_type": "TYPE_FP32", - "dims": [10, 768, 768] - } - ] - } - ] -} diff --git a/tools/inference_builder/schemas/backends/vllm.schema.json b/tools/inference_builder/schemas/backends/vllm.schema.json deleted file mode 100644 index 10219ac8..00000000 --- a/tools/inference_builder/schemas/backends/vllm.schema.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/backends/vllm.schema.json", - "title": "vLLM Backend Configuration", - "description": "Schema for vLLM backend model configuration", - "allOf": [ - { - "$ref": "../common/base-model.schema.json" - }, - { - "properties": { - "backend": { - "type": "string", - "const": "vllm", - "description": "vLLM backend for large language models" - }, - "parameters": { - "$ref": "parameters/vllm-parameters.schema.json" - } - } - } - ], - "examples": [ - { - "name": "Cosmos", - "backend": "vllm", - "parameters": { - "max_num_tokens": 19200, - "async_mode": true - }, - "input": [ - { - "name": "inputs", - "data_type": "TYPE_CUSTOM_VLM_INPUT", - "dims": [-1] - } - ], - "output": [ - { - "name": "outputs", - "data_type": "TYPE_STRING", - "dims": [-1] - } - ] - } - ] -} diff --git a/tools/inference_builder/schemas/common/base-model.schema.json b/tools/inference_builder/schemas/common/base-model.schema.json deleted file mode 100644 index da387ae7..00000000 --- a/tools/inference_builder/schemas/common/base-model.schema.json +++ /dev/null @@ -1,96 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/common/base-model.schema.json", - "title": "Base Model Configuration", - "description": "Common base schema for all model configurations", - "type": "object", - "required": ["name", "backend"], - "additionalProperties": false, - "properties": { - "name": { - "type": "string", - "description": "Model name identifier" - }, - "backend": { - "$ref": "definitions.schema.json#/definitions/backendTypes" - }, - "max_batch_size": { - "oneOf": [ - { - "type": "integer", - "minimum": 1 - }, - { - "type": "string", - "pattern": "^\\$[A-Z_]+\\|[0-9]+$", - "description": "Environment variable with default value (e.g., $MAX_BATCH_SIZE|1)" - } - ], - "description": "Maximum batch size for the model", - "default": 1 - }, - "input": { - "type": "array", - "description": "Model input specification", - "items": { - "$ref": "#/definitions/tensorSpec" - } - }, - "output": { - "type": "array", - "description": "Model output specification", - "items": { - "$ref": "#/definitions/tensorSpec" - } - }, - "parameters": { - "type": "object", - "description": "Backend-specific parameters (see backend schemas for details)", - "additionalProperties": true - }, - "preprocessors": { - "type": "array", - "description": "Ordered list of preprocessors applied to this model's inputs before inference. Each preprocessor transforms or derives tensors from existing ones (for example, tokenization or image normalization) and is executed in sequence. Typically used to adapt data from top-level inputs or previous models into the format and shape expected by this model's inputs.", - "items": { - "$ref": "preprocessors.schema.json" - } - }, - "postprocessors": { - "type": "array", - "description": "Ordered list of postprocessors applied to this model's outputs after inference. Each postprocessor can transform, filter, or aggregate output tensors (for example, masking, decoding, or embedding processing) and is executed in sequence. Commonly used to convert raw model outputs into the tensors or objects expected by downstream models or top-level outputs.", - "items": { - "$ref": "postprocessors.schema.json" - } - } - }, - "definitions": { - "tensorSpec": { - "type": "object", - "required": ["name", "data_type", "dims"], - "additionalProperties": true, - "properties": { - "name": { - "type": "string", - "description": "Name of the tensor" - }, - "data_type": { - "$ref": "definitions.schema.json#/definitions/dataTypes" - }, - "dims": { - "type": "array", - "description": "Dimensions of the tensor (-1 for dynamic dimensions)", - "items": { - "type": "integer" - }, - "minItems": 1 - }, - "optional": { - "type": "boolean", - "description": "Whether this tensor is optional", - "default": false - } - } - } - } -} - diff --git a/tools/inference_builder/schemas/common/base-processor.schema.json b/tools/inference_builder/schemas/common/base-processor.schema.json deleted file mode 100644 index d1b5901c..00000000 --- a/tools/inference_builder/schemas/common/base-processor.schema.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/common/base-processor.schema.json", - "title": "Base Processor Configuration", - "description": "Common base schema for preprocessors and postprocessors", - "type": "object", - "required": ["kind", "name", "input", "output"], - "properties": { - "kind": { - "type": "string", - "enum": ["custom", "builtin"], - "description": "Type of processor: 'custom' for user-defined processors, 'builtin' for system-provided processors" - }, - "name": { - "type": "string", - "description": "Processor identifier used to locate the processor implementation" - }, - "input": { - "type": "array", - "description": "Ordered list of input tensor names that the processor's __call__ method receives as positional arguments. These names are used to extract values from the upstream data dictionary. For example, if the upstream provides {'image': img_data, 'text': txt_data, 'metadata': meta} and input is ['text', 'image'], the processor's __call__ method will be invoked as __call__(txt_data, img_data) - extracting values in the order specified by the input list.", - "items": { - "type": "string" - }, - "minItems": 1 - }, - "output": { - "type": "array", - "description": "Ordered list of output tensor names corresponding to the tuple returned by the processor's __call__ method. The processor must return a tuple with length equal to the output array length. The returned values are mapped to output names to form a dictionary for downstream consumption. For example, if output is ['embeddings', 'attention_mask'] and __call__ returns (emb_tensor, mask_tensor), the downstream receives {'embeddings': emb_tensor, 'attention_mask': mask_tensor}.", - "items": { - "type": "string" - }, - "minItems": 1 - } - } -} - diff --git a/tools/inference_builder/schemas/common/definitions.schema.json b/tools/inference_builder/schemas/common/definitions.schema.json deleted file mode 100644 index 5f84b8be..00000000 --- a/tools/inference_builder/schemas/common/definitions.schema.json +++ /dev/null @@ -1,111 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/common/definitions.schema.json", - "title": "Common Definitions", - "description": "Common definitions and reusable schemas for Inference Builder", - "definitions": { - "dataTypes": { - "type": "string", - "enum": [ - "TYPE_BOOL", - "TYPE_UINT8", - "TYPE_UINT16", - "TYPE_UINT32", - "TYPE_UINT64", - "TYPE_INT8", - "TYPE_INT16", - "TYPE_INT32", - "TYPE_INT64", - "TYPE_FP16", - "TYPE_FP32", - "TYPE_FP64", - "TYPE_STRING", - "TYPE_CUSTOM_IMAGE_BASE64", - "TYPE_CUSTOM_IMAGE_ASSETS", - "TYPE_CUSTOM_LONG_VIDEO_ASSETS", - "TYPE_CUSTOM_VIDEO_CHUNK_ASSETS", - "TYPE_CUSTOM_BINARY_BASE64", - "TYPE_CUSTOM_LIST", - "TYPE_CUSTOM_DS_MEDIA_URL", - "TYPE_CUSTOM_VLM_INPUT", - "TYPE_CUSTOM_OBJECT", - "TYPE_CUSTOM_DS_IMAGE", - "TYPE_CUSTOM_DS_METADATA", - "TYPE_CUSTOM_DS_MIME", - "TYPE_CUSTOM_DS_SOURCE_CONFIG" - ], - "description": "Supported data types for tensors" - }, - "backendTypes": { - "type": "string", - "enum": [ - "dummy", - "vllm", - "triton/python", - "triton/tensorrt", - "triton/onnx", - "triton/pytorch", - "triton/tensorflow", - "tensorrtllm", - "tensorrtllm/pytorch", - "deepstream/nvinfer", - "deepstream/nvinferserver", - "polygraphy", - "pytorch" - ], - "description": "Supported backend types" - }, - "operationTypes": { - "type": "string", - "enum": [ - "inference", - "create_chat_completion_v1_chat_completions_post", - "create_embedding", - "add_media_file", - "delete_media_file", - "list_media_files", - "add_live_stream", - "delete_live_stream", - "list_live_streams", - "health_ready_v1_health_ready_get" - ], - "description": "Supported operation types for responders" - }, - "processorKinds": { - "type": "string", - "enum": ["custom", "builtin"], - "description": "Types of processors" - }, - "commonProcessorNames": { - "type": "string", - "enum": [ - "dummy-preprocessor", - "dummy-tokenizer", - "qwen-vl-loader", - "qwen-vl-image-loader", - "qwen-vl-video-loader", - "openclip-tokenizer", - "nvclip-vision-preprocessor", - "nvclip-postprocessor", - "changenet-normalizer", - "changenet-masking" - ], - "description": "Common processor names" - }, - "responderNames": { - "type": "string", - "enum": [ - "infer", - "add_file", - "del_file", - "list_files", - "add_live_stream", - "del_live_stream", - "list_live_streams", - "healthy_ready" - ], - "description": "Available responder template names (must match templates/responder/*.jinja.py)" - } - } -} - diff --git a/tools/inference_builder/schemas/common/postprocessors.schema.json b/tools/inference_builder/schemas/common/postprocessors.schema.json deleted file mode 100644 index ed101e8f..00000000 --- a/tools/inference_builder/schemas/common/postprocessors.schema.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/common/postprocessors.schema.json", - "title": "Postprocessor Configurations", - "description": "Schema for custom postprocessor configurations", - "type": "object", - "$ref": "#/definitions/customPostprocessor", - "definitions": { - "customPostprocessor": { - "allOf": [ - { - "$ref": "base-processor.schema.json" - }, - { - "properties": { - "config": { - "type": "object", - "description": "Custom configuration for the postprocessor", - "additionalProperties": true - } - } - } - ] - } - } -} - diff --git a/tools/inference_builder/schemas/common/preprocessors.schema.json b/tools/inference_builder/schemas/common/preprocessors.schema.json deleted file mode 100644 index 0736d352..00000000 --- a/tools/inference_builder/schemas/common/preprocessors.schema.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/common/preprocessors.schema.json", - "title": "Preprocessor Configurations", - "description": "Schema for custom preprocessor configurations", - "type": "object", - "$ref": "#/definitions/customPreprocessor", - "definitions": { - "customPreprocessor": { - "allOf": [ - { - "$ref": "base-processor.schema.json" - }, - { - "properties": { - "config": { - "type": "object", - "description": "Custom configuration for the preprocessor", - "additionalProperties": true - } - } - } - ] - } - } -} - diff --git a/tools/inference_builder/schemas/config.schema.json b/tools/inference_builder/schemas/config.schema.json deleted file mode 100644 index 2653b5dd..00000000 --- a/tools/inference_builder/schemas/config.schema.json +++ /dev/null @@ -1,371 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/config.schema.json", - "title": "Inference Builder Configuration", - "description": "Schema for NVIDIA Inference Builder configuration files", - "type": "object", - "required": ["name", "model_repo", "models"], - "additionalProperties": false, - "properties": { - "name": { - "type": "string", - "description": "Unique project name. Used as the directory name for generated code and as the package name when bundling artifacts.", - "pattern": "^[a-zA-Z][a-zA-Z0-9_-]*$" - }, - "model_repo": { - "type": "string", - "description": "Root path to the directory containing the model files" - }, - "input": { - "type": "array", - "description": "Top level inference input specification. Required when pipeline includes multiple models or when inputs use custom types requiring preprocessing.", - "minItems": 1, - "items": { - "$ref": "#/definitions/tensorSpec" - } - }, - "output": { - "type": "array", - "description": "Top level inference output specification. Required when pipeline includes multiple models or when outputs use custom types requiring postprocessing.", - "minItems": 1, - "items": { - "$ref": "#/definitions/tensorSpec" - } - }, - "server": { - "$ref": "#/definitions/serverConfig", - "description": "Server configuration mapping endpoints to their implementation templates." - }, - "models": { - "type": "array", - "description": "List of model specifications for inference", - "minItems": 1, - "items": { - "$ref": "#/definitions/modelSpec" - } - }, - "routes": { - "type": "object", - "description": "Data-flow map between top-level inputs, models, and top-level outputs.", - "additionalProperties": { - "type": "string" - } - }, - "postprocessors": { - "type": "array", - "description": "Top-level postprocessors. Required when pipeline includes multiple models and outputs need to be consolidated.", - "items": { - "$ref": "#/definitions/processor" - } - } - }, - "definitions": { - "tensorSpec": { - "type": "object", - "required": ["name", "data_type", "dims"], - "additionalProperties": false, - "properties": { - "name": { - "type": "string", - "description": "Name of the tensor" - }, - "data_type": { - "description": "Data type of the tensor", - "oneOf": [ - { "const": "TYPE_BOOL", "description": "Tensor with bit type" }, - { "const": "TYPE_UINT8", "description": "Tensor with unsigned 8-bit integer type" }, - { "const": "TYPE_UINT16", "description": "Tensor with unsigned 16-bit integer type" }, - { "const": "TYPE_UINT32", "description": "Tensor with unsigned 32-bit integer type" }, - { "const": "TYPE_UINT64", "description": "Tensor with unsigned 64-bit integer type" }, - { "const": "TYPE_INT8", "description": "Tensor with signed 8-bit integer type" }, - { "const": "TYPE_INT16", "description": "Tensor with signed 16-bit integer type" }, - { "const": "TYPE_INT32", "description": "Tensor with signed 32-bit integer type" }, - { "const": "TYPE_INT64", "description": "Tensor with signed 64-bit integer type" }, - { "const": "TYPE_FP16", "description": "Tensor with 16-bit floating point (half precision) type" }, - { "const": "TYPE_FP32", "description": "Tensor with 32-bit floating point (single precision) type" }, - { "const": "TYPE_FP64", "description": "Tensor with 64-bit floating point (double precision) type" }, - { "const": "TYPE_STRING", "description": "Tensor with string type" }, - { "const": "TYPE_CUSTOM_IMAGE_BASE64", "description": "Base64-encoded JPEG/PNG image data as a data URI string. Each element must follow the format 'data:image/{format};base64,{payload}' where {format} is 'jpeg', 'jpg', or 'png', and {payload} is the base64-encoded image bytes. This type is mapped to TYPE_STRING in Triton and arrives as a list of strings. When used as a top-level input, an ImageInputDataFlow is created that (1) parses the data URI prefix to determine the image format, (2) decodes the base64 payload into raw bytes, and (3) decodes the image using a hardware-accelerated DeepStream pipeline (nvjpegdec/pngdec) to produce RGB uint8 image tensors in HWC layout [H, W, 3] on GPU at the original image resolution. These decoded tensors are then passed to downstream models." }, - { "const": "TYPE_CUSTOM_IMAGE_ASSETS", "description": "Image asset identifiers (file paths, URLs, or asset IDs) referring to JPEG/PNG images. When used as a top-level input, an ImageInputDataFlow will be created that loads and decodes the referenced images into image tensors before they are passed to the downstream models." }, - { "const": "TYPE_CUSTOM_LONG_VIDEO_ASSETS", "description": "Video asset identifiers for long videos and live streams requiring temporal chunking. Particularly suitable for: (1) live streams such as RTSP feeds that need continuous processing in chunks, (2) long video files that should be split into temporal segments, (3) scenarios where frames need to be delivered in multiple batches over time. Supports file paths, URLs, RTSP streams, or asset IDs, with required query parameters: ?frames=N (number of frames per chunk), &interval=INTERVAL_NSEC (time interval between frames in nanoseconds), and optional &chunks=M (maximum number of chunks to yield before stopping - useful for RTSP streams without natural end-of-stream), &scale=WIDTHxHEIGHT (target resolution for frame scaling, e.g., scale=640x360 - all assets in a batch must use the same scale value). When used as a top-level input, a VideoInputDataFlow will be created that asynchronously extracts frames in chunks, with each chunk containing N frames sampled at the specified interval. Results are delivered via a queue as they become available, enabling streaming processing. Processing always starts from the beginning of the video or live stream. For short videos that fit in a single batch without temporal chunking, or when you need to specify a start position, use TYPE_CUSTOM_VIDEO_CHUNK_ASSETS instead." }, - { "const": "TYPE_CUSTOM_VIDEO_CHUNK_ASSETS", "description": "Video asset identifiers for short videos requiring simple frame sampling without temporal chunking. Use this type when: (1) processing short video clips that fit in memory as a single batch, (2) extracting a fixed number of frames from a specific video segment, (3) simple uniform frame sampling from a video duration is needed. Supports file paths, URLs, or asset IDs, with required query parameter ?frames=N (number of frames to sample), and optional &start=START_NSEC (start time in nanoseconds, default 0), &duration=DURATION_NSEC (duration to sample from in nanoseconds, defaults to entire video). When used as a top-level input, a VideoFrameSamplingDataFlow will be created that asynchronously extracts exactly N frames evenly distributed over the specified duration (interval is automatically calculated as duration/frames), decodes them, and produces a single frame batch delivered via queue. The result is one batch containing all requested frames. For long videos or live streams requiring temporal chunking and streaming delivery of multiple batches over time, use TYPE_CUSTOM_LONG_VIDEO_ASSETS instead." }, - { "const": "TYPE_CUSTOM_BINARY_BASE64", "description": "Base64-encoded binary data. The input strings will be decoded into uint8 tensors before being passed to the downstream models." }, - { "const": "TYPE_CUSTOM_LIST", "description": "Converts the input values into a list, which implies explicit batching of the input data. Use for non-DS inputs that need list conversion (e.g., video URLs for VLM models)." }, - { "const": "TYPE_CUSTOM_DS_MEDIA_URL", "description": "DeepStream media URL input. A string URL (file path or remote URL) pointing to an image or video for DeepStream pipeline processing. Unlike TYPE_CUSTOM_LIST, this type follows the implicit batch path and is recognized by the DeepStream backend as a media source. Must be paired with a TYPE_CUSTOM_DS_MIME input." }, - { "const": "TYPE_CUSTOM_VLM_INPUT", "description": "Vision-Language Model input in dict format and will be passed to the downstream models as is." }, - { "const": "TYPE_CUSTOM_OBJECT", "description": "Custom object type for complex structured data" }, - { "const": "TYPE_CUSTOM_DS_IMAGE", "description": "DeepStream custom type for image buffers, usable as both input and output. As INPUT: a 1-D uint8 tensor (or list of tensors) containing encoded JPEG/PNG image bytes that can be passed directly into DeepStream backends without additional decoding in the Python runtime. This input must be defined in the model configuration to enable the image decoder in the DeepStream pipeline. As OUTPUT: extracts the decoded image frames from the DeepStream pipeline and returns them as RGB uint8 image tensors in HWC (Height, Width, Channels) layout, where Channels is 3 (R, G, B). Each tensor has shape [H, W, 3] representing the decoded frame at its processed resolution. Enable the image output only if raw image data is required for downstream models or postprocessors since it adds additional overhead to the pipeline." }, - { "const": "TYPE_CUSTOM_DS_METADATA", "description": "Deepstream custom type for metadata output. The output structure follows the deepstreamMetadata schema (see definitions/deepstreamMetadata), returned as a dictionary with flexible fields that vary based on the model type and parser implementation. This type requires a low-level C++ postprocessor library (e.g., libnvds_infercustomparser_tao.so) to be available and copied into the container image. The library can be obtained from: (1) TAO model parsers from https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git, or (2) custom user-implemented parsers. Without such a library, use regular tensor output types (TYPE_FP32, etc.) with Python postprocessors instead." }, - { "const": "TYPE_CUSTOM_DS_MIME", "description": "Deepstream custom type for MIME type string (for example, 'image/jpeg' or 'image/png') describing the associated DeepStream image or media buffers. Required alongside TYPE_CUSTOM_DS_IMAGE or TYPE_CUSTOM_DS_MEDIA_URL inputs so the DeepStream pipeline can select the appropriate decoder." }, - { "const": "TYPE_CUSTOM_DS_SOURCE_CONFIG", "description": "DeepStream custom type for path to a YAML source configuration file. Use this type when handling live cameras or video streams with DeepStream inference pipelines. The source config supports two deployment strategies: (1) STATIC CAMERA SETUP: For fixed scenarios with a known number of cameras (e.g., a retail store with 4 fixed cameras), set source-bin to 'nvurisrcbin' in the YAML config. All camera URIs are defined upfront in the config file and remain unchanged during runtime. This is simpler and recommended when camera topology is stable. (2) DYNAMIC CAMERA MANAGEMENT: For scenarios requiring runtime addition/removal of cameras (e.g., smart city deployments, fleet management), set source-bin to 'nvmultiurisrcbin' in the YAML config. This enables a built-in REST API server for dynamic stream management. Add streams via POST to /api/v1/stream/add with JSON payload containing camera_id, camera_url, and change='camera_add'. Remove streams via POST to /api/v1/stream/remove with change='camera_remove'. The REST API server endpoint (ip-address, port) is configured in the source config. Full API specification: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvmultiurisrcbin.html. Choose nvurisrcbin for static setups to reduce complexity; choose nvmultiurisrcbin when dynamic stream management is a requirement." } - ] - }, - "dims": { - "type": "array", - "description": "Dimensions of the tensor (-1 for dynamic dimensions)", - "items": { - "type": "integer" - }, - "minItems": 1 - }, - "optional": { - "type": "boolean", - "description": "Whether this tensor is optional", - "default": false - } - } - }, - "serverConfig": { - "type": "object", - "additionalProperties": true, - "properties": { - "responders": { - "type": "object", - "description": "Map of responder configurations. Keys must match available responder templates.", - "additionalProperties": false, - "properties": { - "infer": { - "description": "Primary inference endpoint responder. Handles model inference requests. Input: mapped from HTTP request body to top-level pipeline inputs (as defined in config 'input' section). Output: top-level pipeline outputs (as defined in config 'output' section). Supports streaming responses with 'Accept: application/x-ndjson' header.", - "$ref": "#/definitions/responder" - }, - "add_file": { - "description": "File upload responder. Adds files to the asset store for later processing. Input: multipart file upload (file object with filename and content_type). Output: asset dict with fields 'id', 'file_name', 'content_type', 'created_at'.", - "$ref": "#/definitions/responder" - }, - "del_file": { - "description": "File deletion responder. Removes files from the asset store. Input: 'asset_id' (string, from path parameter). Output: 'status' (boolean, deletion success).", - "$ref": "#/definitions/responder" - }, - "list_files": { - "description": "File listing responder. Returns a list of files in the asset store. Input: none. Output: 'assets' (array of asset objects with 'id', 'file_name', 'content_type', 'created_at').", - "$ref": "#/definitions/responder" - }, - "add_live_stream": { - "description": "Live stream addition responder. Registers a new RTSP or video stream for processing. Input: 'url' (string, required), 'description' (string, optional), 'username' (string, optional), 'password' (string, optional). Output: asset dict with fields 'id', 'url', 'description', 'created_at'.", - "$ref": "#/definitions/responder" - }, - "del_live_stream": { - "description": "Live stream removal responder. Stops and removes a registered live stream. Input: 'asset_id' (string, from path parameter). Output: 'status' (boolean, deletion success).", - "$ref": "#/definitions/responder" - }, - "list_live_streams": { - "description": "Live stream listing responder. Returns all registered live streams. Input: none. Output: 'assets' (array of stream asset objects with 'id', 'url', 'description', 'created_at').", - "$ref": "#/definitions/responder" - }, - "healthy_ready": { - "description": "Health check responder. Reports service health and readiness status. Input: none. Output: HTTP 200 with 'Ready' or HTTP 503 with 'Service Unavailable'. No request/response template mapping needed.", - "$ref": "#/definitions/responder" - } - } - } - } - }, - "responder": { - "type": "object", - "required": ["operation"], - "additionalProperties": false, - "properties": { - "operation": { - "type": "string", - "description": "Name of the operation this responder handles, which is defined in the openapi specification." - }, - "requests": { - "type": "object", - "description": "Request templates (Jinja2 templates) to map HTTP request body to responder inputs. Keys are OpenAPI request schema/class names (e.g., 'InferenceRequest', 'AddLiveStreamRequest'), values are Jinja2 templates that transform the incoming 'request' object into a JSON structure matching the responder's expected inputs.", - "additionalProperties": { - "type": "string" - } - }, - "responses": { - "type": "object", - "description": "Response templates (Jinja2 templates) to map responder outputs to HTTP response body. Keys are OpenAPI response schema/class names (e.g., 'InferenceResponse', 'AddFileResponse'), values are Jinja2 templates that transform the 'response' object from the responder into the desired HTTP response JSON structure.", - "additionalProperties": { - "type": "string" - } - } - } - }, - "modelSpec": { - "type": "object", - "required": ["name", "backend", "input", "output"], - "additionalProperties": false, - "properties": { - "name": { - "type": "string", - "description": "Model name identifier for locating the model in the model repository. Must be unique within the project." - }, - "backend": { - "type": "string", - "description": "Backend type for the model", - "enum": [ - "dummy", - "vllm", - "triton/python", - "triton/tensorrt", - "triton/onnx", - "triton/pytorch", - "triton/tensorflow", - "tensorrtllm", - "tensorrtllm/pytorch", - "deepstream/nvinfer", - "deepstream/nvinferserver", - "polygraphy", - "pytorch" - ] - }, - "max_batch_size": { - "oneOf": [ - { - "type": "integer", - "minimum": 1 - }, - { - "type": "string", - "pattern": "^\\$[A-Z_]+\\|[0-9]+$", - "description": "Environment variable with default value (e.g., $MAX_BATCH_SIZE|1)" - } - ], - "description": "Maximum batch size for the model", - "default": 1 - }, - "input": { - "type": "array", - "description": "Model input specification. Note on passthrough tensors: any keys present in the request after preprocessing that are NOT listed in this model's input spec are treated as passthrough (they are not sent to the backend). They are merged back into each backend result dict before this model's postprocessors run, so a model postprocessor can consume them by listing those names in its `input`. If you need passthrough values to propagate further downstream/top-level, ensure your postprocessor emits/mappings include them via configured outputs/routes.", - "items": { - "$ref": "#/definitions/tensorSpec" - } - }, - "output": { - "type": "array", - "description": "Model output specification", - "items": { - "$ref": "#/definitions/tensorSpec" - } - }, - "parameters": { - "type": "object", - "description": "Backend-specific parameters. The structure depends on the 'backend' field value. Read 'schema://index.json' to find the mapping from backend type to its parameter schema (e.g., backend 'vllm' -> 'schema://backends/parameters/vllm-parameters.schema.json').", - "additionalProperties": true - }, - "preprocessors": { - "type": "array", - "description": "Ordered list of preprocessors applied to this model's inputs before inference. Each processor transforms or derives tensors from existing ones (for example, tokenization or image normalization) and is executed in sequence. Typically used to adapt data from top-level inputs or previous models into the format and shape expected by this model's inputs.", - "items": { - "$ref": "#/definitions/processor" - } - }, - "postprocessors": { - "type": "array", - "description": "Ordered list of postprocessors applied to this model's outputs after inference, before passing results downstream. Each processor can transform, filter, or aggregate output tensors (for example, masking, decoding, or embedding processing) and is executed in sequence. This is also a primary extension point for adding custom logic, giving you room to expand pipeline functionality without changing the core runtime (e.g., adding new derived tensors, validation, formatting, or routing-friendly outputs).", - "items": { - "$ref": "#/definitions/processor" - } - } - } - }, - "processor": { - "type": "object", - "required": ["kind", "name", "input", "output"], - "additionalProperties": false, - "properties": { - "kind": { - "type": "string", - "description": "Type of processor", - "enum": ["custom", "auto"] - }, - "name": { - "type": "string", - "description": "Name/identifier of the processor" - }, - "input": { - "type": "array", - "description": "Input tensor names", - "items": { - "type": "string" - } - }, - "output": { - "type": "array", - "description": "Output tensor names", - "items": { - "type": "string" - } - }, - "config": { - "type": "object", - "description": "Processor-specific configuration", - "additionalProperties": true - } - } - }, - "deepstreamMetadata": { - "type": "object", - "description": "DeepStream metadata structure returned by TYPE_CUSTOM_DS_METADATA outputs. Contains per-frame detection, classification, and segmentation results extracted from DeepStream's internal metadata by C++ parser libraries. The structure is flexible and fields may be empty depending on the model type and parser implementation.", - "additionalProperties": true, - "properties": { - "shape": { - "type": "array", - "description": "Image or frame dimensions as [height, width] in pixels. Represents the spatial dimensions of the processed frame.", - "items": { - "type": "integer", - "minimum": 0 - }, - "minItems": 2, - "maxItems": 2 - }, - "bboxes": { - "type": "array", - "description": "Bounding boxes for detected objects in the format [left, top, right, bottom] coordinates. Each bounding box is an array of 4 integers representing pixel coordinates. Used by detection and instance segmentation models.", - "items": { - "type": "array", - "items": { - "type": "integer" - }, - "minItems": 4, - "maxItems": 4 - } - }, - "probs": { - "type": "array", - "description": "Confidence scores (probabilities) for each detected object or classification result. Values typically range from 0.0 to 1.0, with higher values indicating greater confidence.", - "items": { - "type": "number", - "minimum": 0.0, - "maximum": 1.0 - } - }, - "labels": { - "type": "array", - "description": "Classification labels as an array of arrays (list[list[str]]). The outer array has one entry per detected object (aligned by index with 'bboxes', 'probs', etc.). The inner array contains one or more label strings for that object, since a single object may have multiple labels (e.g., ['car', 'red', 'moving']). For single-label models, each inner array contains exactly one string (e.g., [['car'], ['person']]).", - "items": { - "type": "array", - "items": { - "type": "string" - } - } - }, - "seg_maps": { - "type": "array", - "description": "Segmentation maps containing pixel-wise class predictions or instance masks. Each element is a 2D array (represented as nested arrays or numpy arrays) where values correspond to class IDs or instance IDs. Used by semantic segmentation and instance segmentation models.", - "items": { - "type": "array" - } - }, - "objects": { - "type": "array", - "description": "Object tracking IDs assigned by the DeepStream tracker. Each integer uniquely identifies a tracked object across frames. Only populated when tracker is enabled in the pipeline configuration.", - "items": { - "type": "integer", - "minimum": 0 - } - }, - "timestamp": { - "type": "integer", - "description": "Buffer presentation timestamp (PTS) in nanoseconds. Represents the temporal position of the frame in the video stream. Useful for synchronization and temporal analysis.", - "minimum": 0 - } - } - } - } -} - diff --git a/tools/inference_builder/schemas/index.json b/tools/inference_builder/schemas/index.json deleted file mode 100644 index e6975c3a..00000000 --- a/tools/inference_builder/schemas/index.json +++ /dev/null @@ -1,105 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "$id": "https://raw.githubusercontent.com/NVIDIA-AI-IOT/inference_builder/main/schemas/index.json", - "title": "Inference Builder Schema Index", - "description": "Central index for all Inference Builder schemas", - "version": "1.0.0", - "schemas": { - "main": { - "id": "config.schema.json", - "description": "Main configuration schema for inference builder", - "path": "./config.schema.json" - }, - "backends": { - "deepstream": { - "id": "backends/deepstream.schema.json", - "description": "DeepStream backend configuration", - "path": "./backends/deepstream.schema.json", - "parameters": "./backends/parameters/deepstream-parameters.schema.json", - "backend_type": "deepstream/nvinfer" - }, - "triton": { - "id": "backends/triton.schema.json", - "description": "Triton Inference Server backend configuration", - "path": "./backends/triton.schema.json", - "parameters": "./backends/parameters/triton-parameters.schema.json", - "backend_types": [ - "triton/python", - "triton/tensorrt", - "triton/onnx", - "triton/pytorch" - ] - }, - "vllm": { - "id": "backends/vllm.schema.json", - "description": "vLLM backend configuration for large language models", - "path": "./backends/vllm.schema.json", - "parameters": "./backends/parameters/vllm-parameters.schema.json", - "backend_type": "vllm" - }, - "tensorrtllm": { - "id": "backends/tensorrtllm.schema.json", - "description": "TensorRT-LLM backend configuration", - "path": "./backends/tensorrtllm.schema.json", - "parameters": "./backends/parameters/tensorrtllm-parameters.schema.json", - "backend_types": [ - "tensorrtllm", - "tensorrtllm/pytorch" - ] - }, - "polygraphy": { - "id": "backends/polygraphy.schema.json", - "description": "Polygraphy (TensorRT) backend configuration", - "path": "./backends/polygraphy.schema.json", - "parameters": "./backends/parameters/polygraphy-parameters.schema.json", - "backend_type": "polygraphy" - }, - "dummy": { - "id": "backends/dummy.schema.json", - "description": "Dummy backend for testing", - "path": "./backends/dummy.schema.json", - "parameters": "./backends/parameters/dummy-parameters.schema.json", - "backend_type": "dummy" - }, - "pytorch": { - "id": "backends/pytorch.schema.json", - "description": "PyTorch backend configuration (Huggingface Transformers)", - "path": "./backends/pytorch.schema.json", - "parameters": "./backends/parameters/pytorch-parameters.schema.json", - "backend_type": "pytorch" - } - }, - "common": { - "base_model": { - "id": "common/base-model.schema.json", - "description": "Base model configuration (shared structure)", - "path": "./common/base-model.schema.json" - }, - "base_processor": { - "id": "common/base-processor.schema.json", - "description": "Base processor configuration (shared by preprocessors and postprocessors)", - "path": "./common/base-processor.schema.json" - }, - "definitions": { - "id": "common/definitions.schema.json", - "description": "Common definitions and reusable schemas", - "path": "./common/definitions.schema.json" - }, - "preprocessors": { - "id": "common/preprocessors.schema.json", - "description": "Preprocessor configurations", - "path": "./common/preprocessors.schema.json" - }, - "postprocessors": { - "id": "common/postprocessors.schema.json", - "description": "Postprocessor configurations", - "path": "./common/postprocessors.schema.json" - } - } - }, - "documentation": { - "readme": "./README.md", - "samples": "../builder/samples/" - } -} - diff --git a/tools/inference_builder/schemas/validate_config.py b/tools/inference_builder/schemas/validate_config.py deleted file mode 100644 index d813037e..00000000 --- a/tools/inference_builder/schemas/validate_config.py +++ /dev/null @@ -1,220 +0,0 @@ -#!/usr/bin/env python3 -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Validate Inference Builder configuration files against JSON schemas. - -Usage: - python validate_config.py config.yaml - python validate_config.py --schema custom_schema.json config.yaml -""" - -import argparse -import json -import sys -from pathlib import Path -from typing import Dict, Any - -try: - import yaml -except ImportError: - print("Error: PyYAML is required. Install with: pip install pyyaml") - sys.exit(1) - -try: - from jsonschema import validate, ValidationError, Draft7Validator -except ImportError: - print("Error: jsonschema is required. Install with: pip install jsonschema") - sys.exit(1) - - -def load_yaml(file_path: Path) -> Dict[Any, Any]: - """Load YAML configuration file. - - Args: - file_path: Path to YAML file - - Returns: - Parsed YAML as dictionary - """ - try: - with open(file_path, 'r') as f: - return yaml.safe_load(f) - except yaml.YAMLError as e: - print(f"Error parsing YAML file: {e}") - sys.exit(1) - except FileNotFoundError: - print(f"Error: File not found: {file_path}") - sys.exit(1) - - -def load_schema(schema_path: Path) -> Dict[Any, Any]: - """Load JSON schema file. - - Args: - schema_path: Path to schema file - - Returns: - Parsed schema as dictionary - """ - try: - with open(schema_path, 'r') as f: - return json.load(f) - except json.JSONDecodeError as e: - print(f"Error parsing schema file: {e}") - sys.exit(1) - except FileNotFoundError: - print(f"Error: Schema file not found: {schema_path}") - sys.exit(1) - - -def validate_config(config: Dict[Any, Any], schema: Dict[Any, Any], verbose: bool = False) -> bool: - """Validate configuration against schema. - - Args: - config: Configuration dictionary - schema: JSON schema dictionary - verbose: Enable verbose output - - Returns: - True if valid, False otherwise - """ - try: - # Create validator - validator = Draft7Validator(schema) - - # Check for errors - errors = list(validator.iter_errors(config)) - - if not errors: - print("✓ Configuration is valid!") - return True - - print(f"✗ Configuration validation failed with {len(errors)} error(s):\n") - - for i, error in enumerate(errors, 1): - print(f"Error {i}:") - print(f" Path: {'.'.join(str(p) for p in error.path)}") - print(f" Message: {error.message}") - - if verbose and error.context: - print(f" Context:") - for ctx_error in error.context: - print(f" - {ctx_error.message}") - - print() - - return False - - except Exception as e: - print(f"Error during validation: {e}") - return False - - -def get_backend_schema(config: Dict[Any, Any], schema_dir: Path) -> Path: - """Determine the appropriate backend schema based on config. - - Args: - config: Configuration dictionary - schema_dir: Directory containing schemas - - Returns: - Path to backend-specific schema or main schema - """ - if 'models' not in config or not config['models']: - return schema_dir / 'config.schema.json' - - # Get the backend from the first model - backend = config['models'][0].get('backend', '') - - backend_map = { - 'deepstream/nvinfer': 'deepstream.schema.json', - 'triton': 'triton.schema.json', - 'vllm': 'vllm.schema.json', - 'tensorrtllm': 'tensorrtllm.schema.json', - 'polygraphy': 'polygraphy.schema.json', - 'dummy': 'dummy.schema.json', - 'pytorch': 'pytorch.schema.json' - } - - for prefix, schema_file in backend_map.items(): - if backend.startswith(prefix): - backend_schema = schema_dir / 'backends' / schema_file - if backend_schema.exists(): - return backend_schema - - return schema_dir / 'config.schema.json' - - -def main(): - """Main entry point.""" - parser = argparse.ArgumentParser( - description='Validate Inference Builder configuration files' - ) - parser.add_argument( - 'config', - type=Path, - help='Path to configuration YAML file' - ) - parser.add_argument( - '-s', '--schema', - type=Path, - help='Path to JSON schema file (default: auto-detect from config)' - ) - parser.add_argument( - '-v', '--verbose', - action='store_true', - help='Enable verbose output' - ) - parser.add_argument( - '--schema-dir', - type=Path, - default=Path(__file__).parent, - help='Directory containing schema files' - ) - - args = parser.parse_args() - - # Load configuration - if args.verbose: - print(f"Loading configuration from: {args.config}") - config = load_yaml(args.config) - - # Determine schema to use - if args.schema: - schema_path = args.schema - else: - schema_path = get_backend_schema(config, args.schema_dir) - if args.verbose: - print(f"Auto-detected schema: {schema_path}") - - # Load schema - if args.verbose: - print(f"Loading schema from: {schema_path}") - schema = load_schema(schema_path) - - # Validate - if args.verbose: - print("\nValidating configuration...\n") - - is_valid = validate_config(config, schema, args.verbose) - - sys.exit(0 if is_valid else 1) - - -if __name__ == '__main__': - main() - diff --git a/tools/inference_builder/templates/api_server/fastapi/responder.jinja.py b/tools/inference_builder/templates/api_server/fastapi/responder.jinja.py deleted file mode 100644 index 9cda8a30..00000000 --- a/tools/inference_builder/templates/api_server/fastapi/responder.jinja.py +++ /dev/null @@ -1,88 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} -{{ license }} - -import json -from dataclasses import asdict -from config import global_config -from .model import GenericInference -from lib.utils import create_jinja2_env, convert_list -from lib.responder import ResponderBase -from lib.inference import Stop -from lib.errors import EnhancedError -import re -import numpy as np -import torch -from typing import Dict, Any -from fastapi.responses import StreamingResponse -import asyncio - -class Responder(ResponderBase): - def __init__(self): - super().__init__() - self._inference = GenericInference() - self._inference.initialize() - - # initialize the action map - {% for responder in responders %} - self._action_map["{{ responder.operation }}"] = self.{{ responder.name }} - {% endfor %} - - def process_request( - self, - responder : str, - request, - ): - # the helper function transforms the HTTP request to a dictionary that satisfies the inference input schema - return super().process_request(responder, request) - - def process_response(self, responder: str, request, response: Dict[str, Any]): - accept = request.headers.get("accept", "") - streaming = "application/x-ndjson" in accept - # transform numpy ndarray or tensor to universal value types for inference - if responder == "infer": - type_map = { i.name: i.data_type for i in global_config.output} - for name in response: - if not name in type_map: - self.logger.error(f"Unexpected output: {name}") - continue - expected_type = type_map[name] - value = response[name] - if isinstance(value, np.ndarray) or isinstance(value, torch.Tensor): - l = value.tolist() - if expected_type == "TYPE_STRING" and value.dtype != np.string_: - response[name] = convert_list(l, lambda i: i.decode("utf-8", "ignore")) - elif len(response[name].shape) == 1 and len(l) == 1: - response[name] = l[0] - else: - response[name] = l - - # the helper function transforms the inference output to a json string - json_string = super().process_response(responder, request, response) - self.logger.debug(f"Sending json payload: {json_string}") - - return (json.dumps(json.loads(json_string), separators=(',', ':')) + "\n") if streaming else json.loads(json_string) - - async def take_action(self, action_name:str, **kwargs): - action = self._action_map.get(action_name, None) - if not action: - raise ValueError(f"Unknown action: {action_name}") - return await action(**kwargs) - -{% for responder in responders %} -{{ responder.implementation }} -{% endfor %} diff --git a/tools/inference_builder/templates/api_server/fastapi/route/inference.jinja2 b/tools/inference_builder/templates/api_server/fastapi/route/inference.jinja2 deleted file mode 100644 index 65fca099..00000000 --- a/tools/inference_builder/templates/api_server/fastapi/route/inference.jinja2 +++ /dev/null @@ -1,76 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} - -from typing import Any -{{imports}} -from contextlib import asynccontextmanager -from fastapi import Request, HTTPException, FastAPI, APIRouter -import uvicorn -from .responder import Responder -from lib.utils import get_logger -import os - -class FastAPIInterface: - - def __init__(self, *args, **kwargs): - self.app = FastAPI() - self.router = APIRouter() - self.responder = Responder() - self.logger = get_logger(__name__) - self.register_routes() - self.app.include_router(self.router) - - def register_routes(self): - {% for operation in operations %} - self.router.{{operation.type}}("{{operation.path}}")(self.{{operation.function_name}}) - {% endfor %} - - - def serve(self, **kwargs): - http_port = int(os.getenv("HTTP_PORT", "8000")) - uvicorn.run(self.app, host="0.0.0.0", port=http_port) - - {% for operation in operations %} - async def {{operation.function_name}}(self, request: Request, {{operation.snake_case_arguments}}) -> {{operation.response}}: - self.logger.info("{{operation.function_name}} called") - kwargs = dict(locals()) - kwargs.pop("self") - status, response = await self.responder.take_action("{{operation.function_name}}", **kwargs) - if status != 200: - raise HTTPException(status_code=status, detail=response) - self.logger.debug(f"response generated as {response}") - return response - {% endfor %} - -def get_openapi_schema(file_dump=True, pprint=True): - """ - Utility function to get API schema in YAML format. - This is used after inference.py has been completed with custom user APIs. - """ - import yaml - interface_instance = Interface() - schema: dict = interface_instance.get_openapi_schema() - if pprint: - print(yaml.dump(schema, sort_keys=False)) - if file_dump: - with open("api_spec.yaml", "w+") as file: - yaml.dump(schema, file, sort_keys=False) - - -def main(): - interface = FastAPIInterface() - interface.serve() diff --git a/tools/inference_builder/templates/backend/deepstream.jinja.py b/tools/inference_builder/templates/backend/deepstream.jinja.py deleted file mode 100644 index 1358b319..00000000 --- a/tools/inference_builder/templates/backend/deepstream.jinja.py +++ /dev/null @@ -1,1178 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} - -from pyservicemaker import Pipeline, Flow, BufferProvider, Buffer, RenderMode, BufferRetriever, as_tensor, StateTransitionMessage, DynamicSourceMessage, SourceConfig, BatchMetadataOperator, Probe -from typing import Dict, List -from queue import Queue, Empty, Full -from dataclasses import dataclass -import base64 -import numpy as np -from abc import ABC, abstractmethod -import yaml -import tempfile -import os - -png_data = base64.b64decode("iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAIAAACQd1PeAAAAEElEQVR4nGK6HcwNCAAA//8DTgE8HuxwEQAAAABJRU5ErkJggg==") -jpg_data = base64.b64decode("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") - - -@dataclass -class PerfConfig: - enable_fps_logs: bool = False - enable_latency_logs: bool = False - -@dataclass -class RenderConfig: - enable_display: bool = False - enable_osd: bool = False - enable_stream: bool = False - rtsp_mount_point: str | None = "/ds-test" - rtsp_port: int | None = 8554 - seg_mask_config: dict | None = None - - def __bool__(self) -> bool: - """Check if render configuration is valid.""" - if self.enable_osd and not self.enable_display and not self.enable_stream: - logger.warning("RenderConfig: enable_osd is True but both enable_display and enable_stream are False. OSD requires an output method.") - return False - if self.enable_stream and (self.rtsp_mount_point is None or self.rtsp_port is None): - logger.warning("RenderConfig: enable_stream is True but rtsp_mount_point or rtsp_port is None.") - return False - return True - -@dataclass -class TrackerConfig: - config_path: str | None = None - lib_path: str | None = None - width: int = 1920 - height: int = 1088 - display_tracking_id: bool = False - - def __bool__(self) -> bool: - """Check if all required tracker configuration fields are set.""" - # Check if width/height is set than we need to have both, set default values if not set - if (self.width is not None and self.height is None) or (self.width is None and self.height is not None): - logger.warning("TrackerConfig: width and height must be set together, setting default values") - return self.config_path is not None and self.lib_path is not None - -@dataclass -class MessageBrokerConfig: - proto_lib_path: str | None = None - conn_str: str | None = None - topic: str | None = None - msgconv_config_path: str | None = None - msgconv_payload_type: int = 0 - msgconv_msg2p_new_api: bool = False - msgconv_frame_interval: int = 30 - msgconv_msg2p_lib: str | None = None - - def __bool__(self) -> bool: - """Check if all required message broker configuration fields are set.""" - if self.msgconv_msg2p_lib is not None and not os.path.exists(self.msgconv_msg2p_lib): - logger.warning(f"MessageBrokerConfig: msgconv_msg2p_lib does not exist: {self.msgconv_msg2p_lib}") - return False - if self.msgconv_config_path is not None and not os.path.exists(self.msgconv_config_path): - logger.warning(f"MessageBrokerConfig: msgconv_config_path does not exist: {self.msgconv_config_path}") - return False - if self.proto_lib_path is not None and not os.path.exists(self.proto_lib_path): - logger.warning(f"MessageBrokerConfig: proto_lib_path does not exist: {self.proto_lib_path}") - return False - return ( - self.proto_lib_path is not None and - self.msgconv_config_path is not None and - self.conn_str is not None and - self.topic is not None - ) - -@dataclass -class AnalyticsConfig: - """Configuration for nvdsanalytics (ROI, line-crossing, overcrowding, direction).""" - config_path: str | None = None - gpu_id: int = 0 - - def __bool__(self) -> bool: - """Check if analytics is configured (config_path set).""" - if self.config_path is None: - return False - if not os.path.exists(self.config_path): - logger.warning(f"AnalyticsConfig: config_path does not exist: {self.config_path}") - return False - return True - -@dataclass -class KittiConfig: - infer_kitti_output_dir: str | None = None - tracker_kitti_output_dir: str | None = None - - def __bool__(self) -> bool: - """Check if any required kitti configuration fields are set.""" - if self.infer_kitti_output_dir is None and self.tracker_kitti_output_dir is None: - logger.warning("KittiConfig: No kitti output directory specified") - return False - if self.infer_kitti_output_dir is not None and not os.path.exists(self.infer_kitti_output_dir): - logger.warning(f"KittiConfig: infer_kitti_output_dir does not exist: {self.infer_kitti_output_dir}") - return False - if self.tracker_kitti_output_dir is not None and not os.path.exists(self.tracker_kitti_output_dir): - logger.warning(f"KittiConfig: tracker_kitti_output_dir does not exist: {self.tracker_kitti_output_dir}") - return False - return True - -class ImageTensorInput(BufferProvider): - - def __init__(self, height, width, format): - super().__init__() - self.width = width - self.height = height - self.format = format - self.framerate = 1 - self.device = 'cpu' - self.queue = Queue(maxsize=10) - - def generate(self, size): - tensor = self.queue.get() - if isinstance(tensor, np.ndarray): - return Buffer(tensor.tolist()) - elif isinstance(tensor, Stop): - # EOS - return Buffer() - else: - logger.exception("Unexpected input tensor data") - return Buffer() - - def send(self, data): - self.queue.put(data) - -class GenericTensorInput(): - def __init__(self, device_id): - self.queue = Queue(maxsize=10) - self._device_id = device_id - - def generate(self, n): - try: - tensors = self.queue.get(timeout=1) - except Empty: - logger.warning("No tensor data to generate") - return dict() - result = {k: as_tensor(v, "").to_gpu(self._device_id) for k, v in tensors.items()} - return result - - def send(self, data): - self.queue.put(data) - -class StaticTensorInput(): - def __init__(self, device_id): - self._tensors = {} - self._device_id = device_id - - def generate(self, n): - result = {k: as_tensor(v, "").to_gpu(self._device_id) for k, v in self._tensors.items()} - return result - - def set(self, data): - self._tensors.update(data) - -class TensorInputPool(ABC): - @abstractmethod - def submit(self, data: List): - pass - @abstractmethod - def stop(self, reason: str): - pass - -class ImageTensorInputPool(TensorInputPool): - - def __init__(self, height, width, formats, batch_size, image_tensor_name, media_url_tensor_name, mime_tensor_name, device_id, require_extra_input): - self._image_inputs = [ImageTensorInput(width, height, format) for format in formats for _ in range(batch_size)] - self._media_url_tensor_name = media_url_tensor_name - self._mime_tensor_name = mime_tensor_name - self._image_tensor_name = image_tensor_name - self._generic_input = GenericTensorInput(device_id) if require_extra_input else None - self._batch_size = batch_size - - @property - def image_inputs(self): - return self._image_inputs - - @property - def generic_input(self): - return self._generic_input - - def submit(self, data: List): - indices = [] - for item in data: - mime_type = item.pop(self._mime_tensor_name, None) - if mime_type is None: - logger.error("MIME type is not specified") - continue - mime_type = mime_type.split('/'); - if mime_type[0] == 'image': - format = mime_type[1].upper() - # try find the free slot for the specific format - i, image_tensor_input = next(((i, x) for i,x in enumerate(self._image_inputs) if x.format == format and x.queue.empty()), (-1, None)) - if image_tensor_input is None: - i, image_tensor_input = next(((i, x) for i,x in enumerate(self._image_inputs) if x.format == format), (-1, None)) - if image_tensor_input is not None: - if self._image_tensor_name in item: - image_tensor = item.pop(self._image_tensor_name) - image_tensor_input.send(image_tensor) - indices.append(i) - elif self._media_url_tensor_name in item: - image_url = item.pop(self._media_url_tensor_name) - with open(image_url, 'rb') as f: - image_tensor = np.frombuffer(f.read(), dtype=np.uint8) - image_tensor_input.send(image_tensor) - indices.append(i) - else: - logger.error(f"image tensor or media url is missing: {item}") - else: - logger.error(f"Unable to find free slot for format {format}") - else: - logger.error(f"Unsupported MIME type {mime_type}") - continue - if self._generic_input: - data = [data[i:i + self._batch_size] for i in range(0, len(data), self._batch_size)] - for d in data: - self._generic_input.send(stack_tensors_in_dict(d)) - # batched indices for each input - return indices - - def stop(self, reason: str): - for input in self._image_inputs: - input.send(Stop(reason)) - if self._generic_input: - self._generic_input.send(Stop(reason)) - -class BulkVideoInputPool(TensorInputPool): - def __init__(self, - max_batch_size, - batch_timeout, - media_url_tensor_name, - source_tensor_name, - mime_tensor_name, - infer_config_paths, - preprocess_config_paths, - tracker_config: TrackerConfig, - analytics_config: AnalyticsConfig, - msgbroker_config: MessageBrokerConfig, - render_config: RenderConfig, - perf_config: PerfConfig, - kitti_config: KittiConfig, - output, - device_id, - require_extra_input, - engine_file_names, - dims, - label_file_path, - model_home - ): - self._batch_size = max_batch_size - self._batch_timeout = batch_timeout - self._media_url_tensor_name = media_url_tensor_name - self._source_tensor_name = source_tensor_name - self._mime_tensor_name = mime_tensor_name - self._infer_config_paths = infer_config_paths - self._engine_file_names = engine_file_names - self._preprocess_config_paths = preprocess_config_paths - self._pipeline = None - self._output = output - self._generic_input = StaticTensorInput(device_id) if require_extra_input else None - self._device_id = device_id - self._dims = dims - self._tracker_config = tracker_config - self._analytics_config = analytics_config - self._msgbroker_config = msgbroker_config - self._render_config = render_config - self._perf_config = perf_config - self._kitti_config = kitti_config - self._label_file_path = label_file_path - self._model_home = model_home - - def submit(self, data: List): - def on_message(message, total_streams, output): - # handle the dynamic source messages only in case the source config is set - if not isinstance(message, DynamicSourceMessage): - return - logger.info(f"Received source added/removed message: {message.source_added}") - if message.source_added: - total_streams += 1 - else: - total_streams -= 1 - if total_streams <= 0: - output.reset() - - url_list = [] - source_config_file = None - total_streams = 0 - for item in data: - if self._mime_tensor_name and self._mime_tensor_name in item: - item.pop(self._mime_tensor_name) - if self._media_url_tensor_name and self._media_url_tensor_name in item: - url_list.append(str(item.pop(self._media_url_tensor_name))) - elif self._source_tensor_name and self._source_tensor_name in item: - source_config_file = item.pop(self._source_tensor_name) - if not isinstance(source_config_file, str): - logger.error( - f"Source config value must be a string, got {type(source_config_file).__name__}: {source_config_file}" - ) - return [] - if not source_config_file.lower().endswith(('.yml', '.yaml')): - logger.error( - f"Source config file must be a YAML file: {source_config_file}" - ) - return [] - # Correct relative path to absolute path relative to model_home - if not os.path.isabs(source_config_file): - source_config_file = os.path.join(self._model_home, source_config_file) - break - else: - logger.error(f"Invalid input data: {data}") - continue - - if len(url_list) > self._batch_size: - logger.warning( - f"Number of media urls ({len(url_list)}) > " - f"batch size ({self._batch_size}), " - f"only the first {self._batch_size} will be used" - ) - url_list = url_list[:self._batch_size] - - pipeline = Pipeline(f"deepstream-video-batch") - if self._generic_input and data: - self._generic_input.set(stack_tensors_in_dict(data)) - - # Use appropriate batch_capture method based on input type - if source_config_file: - sc = SourceConfig() - sc.load(source_config_file) - total_streams = len(sc.sensor_list) - flow = Flow(pipeline).batch_capture( - input=source_config_file, - width=self._dims[1], height=self._dims[0], - batch_size=self._batch_size, - batched_push_timeout=self._batch_timeout - ) - else: - total_streams = len(url_list) - flow = Flow(pipeline).batch_capture( - url_list, - width=self._dims[1], - height=self._dims[0], - batched_push_timeout=self._batch_timeout) - - for config in self._preprocess_config_paths: - flow = flow.preprocess(config, None if not self._generic_input else self._generic_input.generate) - for config_path, engine_file in zip(self._infer_config_paths, self._engine_file_names): - if engine_file: - flow = flow.infer(config_path, batch_size=self._batch_size, model_engine_file=engine_file) - else: - flow = flow.infer(config_path, batch_size=self._batch_size) - if self._kitti_config.infer_kitti_output_dir: - flow = flow.attach(what="kitti_dump_probe", name="inference_kitti_dump", properties={"kitti-dir": self._kitti_config.infer_kitti_output_dir}) - if self._tracker_config: - flow = flow.track( - ll_config_file=self._tracker_config.config_path, - ll_lib_file=self._tracker_config.lib_path, - gpu_id=self._device_id, - tracker_width=self._tracker_config.width, - tracker_height=self._tracker_config.height, - display_tracking_id=self._tracker_config.display_tracking_id - ) - if self._kitti_config.tracker_kitti_output_dir: - flow = flow.attach(what="kitti_dump_probe", name="tracker_kitti_dump", properties={"tracker-kitti-output": True,"kitti-dir": self._kitti_config.tracker_kitti_output_dir}) - if self._analytics_config: - flow = flow.analyze( - self._analytics_config.config_path, - enable=1, - gpu_id=self._analytics_config.gpu_id - ) - - if self._perf_config.enable_fps_logs: - flow = flow.attach(what="measure_fps_probe", name="fps_probe") - if self._perf_config.enable_latency_logs: - flow = flow.attach(what="measure_latency_probe", name="latency_probe") - n_sink = 0 - if self._msgbroker_config: - flow = flow.attach( - what="add_message_meta_probe", - name="message_generator", - properties={ - "label-file": self._label_file_path if self._label_file_path else "", - "source-config": source_config_file if source_config_file else "" - } - ) - flow = flow.fork() - n_sink += 1 - publish_params = { - 'msg_broker_proto_lib': self._msgbroker_config.proto_lib_path, - 'msg_broker_conn_str': self._msgbroker_config.conn_str, - 'topic': self._msgbroker_config.topic, - 'msg_conv_config': self._msgbroker_config.msgconv_config_path, - 'msg_conv_payload_type': self._msgbroker_config.msgconv_payload_type, - 'msg_conv_msg2p_new_api': self._msgbroker_config.msgconv_msg2p_new_api, - 'msg_conv_frame_interval': self._msgbroker_config.msgconv_frame_interval, - 'sync': False - } - if self._msgbroker_config.msgconv_msg2p_lib: - publish_params['msg_conv_msg2p_lib'] = self._msgbroker_config.msgconv_msg2p_lib - - flow.publish(**publish_params) - if self._render_config.enable_stream: - if n_sink == 0: - flow = flow.fork() - n_sink += 1 - flow.render(RenderMode.STREAM, - enable_osd=self._render_config.enable_osd, - rtsp_mount_point=self._render_config.rtsp_mount_point, - rtsp_port=self._render_config.rtsp_port, - sync=False) - if self._render_config.enable_display: - if n_sink == 0: - flow = flow.fork() - n_sink += 1 - seg_mask_config = dict(self._render_config.seg_mask_config) if self._render_config.seg_mask_config else None - flow.render(RenderMode.DISPLAY, enable_osd=self._render_config.enable_osd, seg_mask_config=seg_mask_config, sync=False) - - flow = flow.attach(what=Probe("metadata_probe", self._output)).retrieve(self._output, **{'async': False}) - - if self._pipeline is not None: - self._pipeline.wait() - - if source_config_file: - # monitor the total streams and reset the output when the total streams is 0 - pipeline.start(on_message=lambda message: on_message(message, total_streams, self._output)) - else: - pipeline.start() - self._pipeline = pipeline - return list(range(len(url_list))) if url_list else list(range(self._batch_size)) - - def stop(self, reason: str): - if self._pipeline: - self._pipeline.stop() - self._pipeline.wait() - - -class BaseTensorOutput(BufferRetriever, BatchMetadataOperator): - """Base class for tensor outputs that can work as both BufferRetriever (appsink) and - BatchMetadataOperator (probe). - - Subclasses override _extract_metadata() to yield results. - Both consume() (appsink) and handle_metadata() (probe) iterate and deposit. - """ - def __init__(self, n_outputs, metadata_output_name: str = None, image_output_name: str = None): - """Initialize the base tensor output handler. - - Args: - n_outputs: Maximum number of output slots (typically max_batch_size - or max_batch_size * num_formats). Each slot corresponds to a - pad index in the DeepStream pipeline. - metadata_output_name: When set, collected results are wrapped in a - dict keyed by this name (e.g. {"output": }). Used by - MetadataOutput / PreprocessMetadataOutput for DS metadata - outputs (TYPE_CUSTOM_DS_METADATA). None for raw tensor outputs. - image_output_name: When set, indicates that decoded image frames - should be extracted from the DeepStream pipeline and returned - as RGB uint8 tensors in HWC layout (TYPE_CUSTOM_DS_IMAGE - output). The value is the output tensor name to use. - """ - BufferRetriever.__init__(self) - BatchMetadataOperator.__init__(self) - self._n_outputs = n_outputs - self._queue = Queue() - self._stashed = [] - self._metadata_output_name = metadata_output_name - self._image_output_name = image_output_name - self._image_queue = Queue() # separate queue for decoded image frames - self._image_stashed = [] # stashed image batches for cross-collect reuse - - def consume(self, buffer): - """Called when used as appsink retriever (BufferRetriever interface). - - When image output is enabled, extracts decoded RGB HWC frames from - the buffer and converts them to CPU numpy arrays via dlpack before - queuing. The dlpack conversion must happen here while the buffer's - GPU memory is still valid — once consume() returns, the buffer is - released and the GPU tensor data becomes stale. - - Obtains batch_meta from the buffer to get the batch_id → pad_index - mapping, then calls buffer.extract(batch_id) for each frame. - Note: batch_id is assigned by arrival order, not pad index — e.g. if - only pads 0, 2, 3 deliver frames, they get batch_ids 0, 1, 2. - """ - if self._image_output_name: - received = [None] * self._n_outputs - batch_meta = buffer.batch_meta - for frame_meta in batch_meta.frame_items: - frame = buffer.extract(frame_meta.batch_id) - if frame is not None: - # Clone via dlpack while buffer GPU memory is still valid; - # clone() copies to a new GPU allocation so the tensor - # survives after the buffer is released. - frame = torch.utils.dlpack.from_dlpack(frame).clone() - received[frame_meta.pad_index] = (frame, frame_meta.buffer_pts) - self._image_queue.put(received) - return 1 - - def handle_metadata(self, batch_meta): - """Called when used as probe (BatchMetadataOperator interface).""" - for metadata in self._extract_metadata(batch_meta): - self._deposit(metadata) - - def _extract_metadata(self, batch_meta): - """Override in subclasses to extract metadata. - - Yields a list (indexed by pad_index) where each non-None element is - a dict containing the extracted data. The dict should include a - "timestamp" key (buffer_pts) for synchronization with image frames - in collect(). DS metadata dicts already carry this; tensor outputs - insert it explicitly. - """ - return - yield # Make this a generator - - def collect(self, indices: List, timeout=None) -> List | None: - def move_data(data: list, collected: list): - for i, d in enumerate(data): - if d is not None: - if collected[i] is None: - collected[i] = d - data[i] = None - - def _drain(queue, stashed, collected, indices): - """Drain a queue into collected slots using stash for leftovers.""" - while stashed: - data = stashed.pop(0) - move_data(data, collected) - while not all(collected[i] is not None for i in indices): - try: - data = queue.get(timeout=timeout) - if data is None: - break - move_data(data, collected) - if any(d is not None for d in data): - stashed.append(data) - except Empty: - break - - def _drain_images(queue, stashed, collected, indices, target_pts): - """Drain image queue, discarding images older than metadata timestamps.""" - def process(data): - for i, d in enumerate(data): - if d is None: - continue - # Always discard stale images even if this pad is already collected - if i in target_pts: - _, img_pts = d - if img_pts < target_pts[i]: - data[i] = None - continue - if collected[i] is None: - collected[i] = d - data[i] = None - - remaining = [] - for data in stashed: - process(data) - if any(d is not None for d in data): - remaining.append(data) - stashed.clear() - stashed.extend(remaining) - - while not all(collected[i] is not None for i in indices): - try: - data = queue.get(timeout=timeout) - if data is None: - break - process(data) - if any(d is not None for d in data): - stashed.append(data) - except Empty: - break - - n_slots = max(indices) + 1 - - # Drain metadata queue first — metadata drives collection. - meta_collected = [None] * n_slots - _drain(self._queue, self._stashed, meta_collected, indices) - - if self._image_output_name: - # Build target timestamps from metadata for image synchronization. - # Images with pts older than the metadata are stale and discarded. - target_pts = {} - for i in indices: - meta = meta_collected[i] - if meta is not None and isinstance(meta, dict): - pts = meta.get("timestamp") - if pts is not None: - target_pts[i] = pts - - image_collected = [None] * n_slots - _drain_images(self._image_queue, self._image_stashed, image_collected, indices, target_pts) - - if all(i is None for i in meta_collected) and all(i is None for i in image_collected): - return None - - results = [] - for idx in indices: - result = {} - metadata = meta_collected[idx] - image_entry = image_collected[idx] - - if metadata is not None: - if self._metadata_output_name is not None: - result[self._metadata_output_name] = metadata - elif isinstance(metadata, dict): - result.update(metadata) - else: - result = metadata - - if image_entry is not None: - frame, _ = image_entry - if not isinstance(result, dict): - result = {} - result[self._image_output_name] = frame - - results.append(result if result else None) - return results - else: - # --- Metadata-only path --- - if all(i is None for i in meta_collected): - return None - collected = [meta_collected[i] for i in indices] - return collected if self._metadata_output_name is None else [ - {self._metadata_output_name: r} for r in collected - ] - - def reset(self): - # wake up the old queue first - self._queue.put(None) - self._queue = Queue(maxsize=self._queue.maxsize) - self._stashed = [] - self._image_queue = Queue() - self._image_stashed = [] - - def _deposit(self, received: list): - logger.debug("DeepstreamBackend: Depositing data: %s", received) - try: - self._queue.put(received) - except Full: - logger.warning(f"DeepstreamBackend: Queue is full, dropping data: {received}") - - -class TensorOutput(BaseTensorOutput): - def __init__(self, n_outputs, preprocess_config_path, image_output_name=None): - super().__init__(n_outputs, metadata_output_name=None, image_output_name=image_output_name) - self._preprocess_config_path = preprocess_config_path - - def _extract_metadata(self, batch_meta): - received = [None] * self._n_outputs - if self._preprocess_config_path: - for meta in batch_meta.preprocess_batch_items: - preprocess_batch = meta.as_preprocess_batch() - if not preprocess_batch: - continue - for roi in preprocess_batch.rois: - result = dict() - for user_meta in roi.tensor_items: - tensor_output = user_meta.as_tensor_output() - if tensor_output : - for n, tensor in tensor_output.get_layers().items(): - torch_tensor = torch.utils.dlpack.from_dlpack(tensor).to('cpu') - result[n] = torch_tensor - result["timestamp"] = roi.frame_meta.buffer_pts - received[roi.frame_meta.pad_index] = result - else: - for frame_meta in batch_meta.frame_items: - result = dict() - for user_meta in frame_meta.tensor_items: - tensor_output = user_meta.as_tensor_output() - if tensor_output : - for n, tensor in tensor_output.get_layers().items(): - torch_tensor = torch.utils.dlpack.from_dlpack(tensor).to('cpu') - result[n] = torch_tensor - result["timestamp"] = frame_meta.buffer_pts - received[frame_meta.pad_index] = result - yield received - -class PreprocessMetadataOutput(BaseTensorOutput): - """Output handler for preprocess metadata.""" - def __init__(self, n_outputs, output_name, dims, image_output_name=None): - super().__init__(n_outputs, metadata_output_name=output_name, image_output_name=image_output_name) - self._n_outputs = n_outputs - self._shape = dims - - def _extract_metadata(self, batch_meta): - for meta in batch_meta.preprocess_batch_items: - preprocess_batch = meta.as_preprocess_batch() - if not preprocess_batch: - continue - received = preprocess_batch.extract(self._n_outputs) - for roi in preprocess_batch.rois: - idx = roi.frame_meta.pad_index - if received[idx] is not None: - received[idx]["timestamp"] = roi.frame_meta.buffer_pts - yield received - -class MetadataOutput(BaseTensorOutput): - def __init__(self, n_outputs, output_name, dims, image_output_name=None): - super().__init__(n_outputs, metadata_output_name=output_name, image_output_name=image_output_name) - self._n_outputs = n_outputs - self._shape = dims - - def _extract_metadata(self, batch_meta): - # DS metadata from batch_meta.extract() already carries pts internally - yield batch_meta.extract(self._n_outputs) - -class DeepstreamBackend(ModelBackend): - """Deepstream backend using pyservicemaker""" - def __init__(self, model_config:Dict, model_home: str, device_id: int=0): - super().__init__(model_config, model_home, device_id) - self._max_batch_size = model_config["max_batch_size"] - self._model_name = model_config["name"] - self._output_names = [o['name'] for o in model_config['output']] - self._output_types = [o['data_type'] for o in model_config['output']] - self._image_tensor_name = None - self._media_url_tensor_name = None - self._mime_tensor_name = None - self._source_tensor_name = None - self._image_output_name = None - self._inference_timeout = model_config["parameters"].get("inference_timeout", None) - self._in_pools = {} - self._outputs = {} - self._pipelines = {} - - tensor_output = False if self._output_types[0] == "TYPE_CUSTOM_DS_METADATA" else True - for o in model_config['output']: - if o['data_type'] == 'TYPE_CUSTOM_DS_IMAGE': - self._image_output_name = o['name'] - break - dims = (0, 0) - - if "parameters" not in model_config or "infer_config_path" not in model_config["parameters"]: - raise Exception("Deepstream pipeline requires infer_config_path") - - infer_config_paths = self._correct_config_paths(model_config["parameters"]['infer_config_path']) - if not infer_config_paths: - raise Exception("Deepstream pipeline requires infer_config_path") - - preprocess_config_paths = [] - if "preprocess_config_path" in model_config["parameters"]: - preprocess_config_paths = self._correct_config_paths(model_config["parameters"]['preprocess_config_path']) - - if "tracker_config" in model_config["parameters"]: - tracker_config = TrackerConfig( - config_path=self._correct_config_paths( - [model_config["parameters"]["tracker_config"].get("ll_config_file")] - )[0] if model_config["parameters"]["tracker_config"].get("ll_config_file") else None, - lib_path=self._correct_config_paths( - [model_config["parameters"]["tracker_config"]["ll_lib_file"]] - )[0], - width=model_config["parameters"]["tracker_config"].get("width", 1920), - height=model_config["parameters"]["tracker_config"].get("height", 1088), - display_tracking_id=model_config["parameters"]["tracker_config"].get("display_tracking_id", False) - ) - if not tracker_config: - logger.warning("DeepstreamBackend: tracker_config is not properlyconfigured") - else: - tracker_config = TrackerConfig() - - if "msgbroker_config" in model_config["parameters"]: - msgbroker_config = MessageBrokerConfig( - proto_lib_path=self._correct_config_paths( - [model_config["parameters"]["msgbroker_config"]["msgbroker_proto_lib_path"]] - )[0], - conn_str=model_config["parameters"]["msgbroker_config"]["msgbroker_conn_str"], - topic=model_config["parameters"]["msgbroker_config"]["msgbroker_topic"], - msgconv_config_path=self._correct_config_paths( - [model_config["parameters"]["msgbroker_config"]["msgconv_config_path"]] - )[0] - ) - if "msgconv_payload_type" in model_config["parameters"]["msgbroker_config"]: - msgbroker_config.msgconv_payload_type = model_config["parameters"]["msgbroker_config"]["msgconv_payload_type"] - if "msgconv_msg2p_new_api" in model_config["parameters"]["msgbroker_config"]: - msgbroker_config.msgconv_msg2p_new_api = model_config["parameters"]["msgbroker_config"]["msgconv_msg2p_new_api"] - if "msgconv_frame_interval" in model_config["parameters"]["msgbroker_config"]: - msgbroker_config.msgconv_frame_interval = model_config["parameters"]["msgbroker_config"]["msgconv_frame_interval"] - if "msgconv_msg2p_lib" in model_config["parameters"]["msgbroker_config"]: - msgbroker_config.msgconv_msg2p_lib = self._correct_config_paths( - [model_config["parameters"]["msgbroker_config"]["msgconv_msg2p_lib"]] - )[0] - if not msgbroker_config: - logger.warning("DeepstreamBackend: msgbroker_config is not properly configured") - else: - msgbroker_config = MessageBrokerConfig() - - if "render_config" in model_config["parameters"]: - render_config = RenderConfig( - enable_display=model_config["parameters"]["render_config"].get("enable_display", False), - enable_osd=model_config["parameters"]["render_config"].get("enable_osd", False), - enable_stream=model_config["parameters"]["render_config"].get("enable_stream", False), - rtsp_mount_point=model_config["parameters"]["render_config"].get("rtsp_mount_point", "/ds-test"), - rtsp_port=model_config["parameters"]["render_config"].get("rtsp_port", 8554), - seg_mask_config=model_config["parameters"]["render_config"].get("seg_mask_visualization", None) - ) - else: - render_config = RenderConfig() - - if "perf_config" in model_config["parameters"]: - perf_config = PerfConfig( - enable_fps_logs=model_config["parameters"]["perf_config"].get("enable_fps_logs", False), - enable_latency_logs=model_config["parameters"]["perf_config"].get("enable_latency_logs", False) - ) - else: - perf_config = PerfConfig() - - if "kitti_output_path" in model_config["parameters"]: - kitti_config = KittiConfig( - infer_kitti_output_dir=model_config["parameters"]["kitti_output_path"].get("infer", None), - tracker_kitti_output_dir=model_config["parameters"]["kitti_output_path"].get("tracker", None) - ) - if not kitti_config: - logger.warning("DeepstreamBackend: kitti_config is not properly configured") - else: - kitti_config = KittiConfig() - - if "analytics_config" in model_config["parameters"]: - ac = model_config["parameters"]["analytics_config"] - config_path = self._correct_config_paths([ac["config_file_path"]])[0] if ac.get("config_file_path") else None - analytics_config = AnalyticsConfig( - config_path=config_path, - gpu_id=ac.get("gpu_id", device_id) - ) - if not analytics_config: - logger.warning("DeepstreamBackend: analytics_config is not properly configured") - else: - analytics_config = AnalyticsConfig() - - infer_element = model_config['backend'].split('/')[-1] - with_triton = infer_element == 'nvinferserver' - require_extra_input = False - warmup_data_0 = dict() - warmup_data_1 = dict() - for input in model_config['input']: - if input['data_type'] == 'TYPE_CUSTOM_DS_IMAGE': - self._image_tensor_name = input['name'] - elif input['data_type'] == 'TYPE_CUSTOM_DS_MEDIA_URL': - self._media_url_tensor_name = input['name'] - elif input['data_type'] == 'TYPE_CUSTOM_DS_MIME': - self._mime_tensor_name = input['name'] - elif input['data_type'] == 'TYPE_CUSTOM_DS_SOURCE_CONFIG': - self._source_tensor_name = input['name'] - elif not ('optional' in input and input['optional']): - tensor_name = input['name'] - require_extra_input = True - np_type = np_datatype_mapping[input['data_type']] - warmup_data_0[tensor_name] = np.random.rand(*input['dims']).astype(np_type) - warmup_data_1[tensor_name] = np.random.rand(*input['dims']).astype(np_type) - if (self._image_tensor_name is None and - self._media_url_tensor_name is None and - self._source_tensor_name is None): - raise ValueError( - "Deepstream pipeline requires at least one " - "TYPE_CUSTOM_DS_IMAGE or TYPE_CUSTOM_DS_MEDIA_URL input " - "or TYPE_CUSTOM_DS_SOURCE_CONFIG input" - ) - if ((self._image_tensor_name or self._media_url_tensor_name) and - self._mime_tensor_name is None): - raise ValueError("Deepstream pipeline requires TYPE_CUSTOM_DS_MIME input") - - primary_infer_config_property = {} - try: - primary_infer_config_path = infer_config_paths[0] - with open(primary_infer_config_path, 'r') as f: - primary_infer_config = yaml.safe_load(f) - if "property" in primary_infer_config: - primary_infer_config_property = primary_infer_config["property"] - except Exception as e: - raise RuntimeError(f"Failed to load primary inference config: {e}") from e - - if "resize_video" in model_config["parameters"] and len(model_config["parameters"]["resize_video"]) == 2: - resize_to = model_config["parameters"]["resize_video"] - dims = (resize_to[0], resize_to[1]) - logger.info(f"DeepstreamBackend: setting video size to {dims}") - else: - # video resized to network dimensions from the primary inference config by default - if "infer-dims" in primary_infer_config_property: - infer_dims = [int(dim) for dim in primary_infer_config_property["infer-dims"].split(";")] - if len(infer_dims) == 3: - if "network-input-order" in primary_infer_config_property and primary_infer_config_property["network-input-order"] == 1: - dims = (infer_dims[0], infer_dims[1]) - else: - dims = (infer_dims[1], infer_dims[2]) - logger.info(f"DeepstreamBackend: setting video size to network dimensions: {dims}") - - if dims[0] == 0 or dims[1] == 0: - raise ValueError( - "DeepstreamBackend: unable to find network dimensions: " - "infer-dims missing in the config?" - ) - - # get the label file path from the primary inference config - label_file_path = None - if "labelfile-path" in primary_infer_config_property: - label_path = primary_infer_config_property["labelfile-path"] - # Convert to absolute path if it's relative - if not os.path.isabs(label_path): - label_file_path = os.path.join(self._model_home, label_path) - else: - label_file_path = label_path - - # construct the input pools, outputs and pipelines - if self._image_tensor_name is not None: - # image input support - media = "image" - formats = ["JPEG", "PNG"] - in_pool = ImageTensorInputPool(dims[0], dims[1], formats, self._max_batch_size, self._image_tensor_name, self._media_url_tensor_name, self._mime_tensor_name, device_id, require_extra_input) - n_output = self._max_batch_size * len(formats) - if tensor_output: - output = TensorOutput(n_output, preprocess_config_paths, image_output_name=self._image_output_name) - elif preprocess_config_paths: - output = PreprocessMetadataOutput(n_output, self._output_names[0], dims, image_output_name=self._image_output_name) - else: - output = MetadataOutput(n_output, self._output_names[0], dims, image_output_name=self._image_output_name) - # create the pipeline - pipeline = Pipeline(f"deepstream-{self._model_name}-{media}") - - self._pipelines[media] = pipeline - self._in_pools[media] = in_pool - self._outputs[media] = output - - # build the inference flow - flow = Flow(pipeline) - batch_timeout = model_config["parameters"].get("batch_timeout", 1000 * self._max_batch_size) - flow = flow.inject(in_pool.image_inputs).decode().batch(batch_size=self._max_batch_size, batched_push_timeout=batch_timeout, live_source=False, width=dims[1], height=dims[0]) - for config_file in preprocess_config_paths: - input = self._in_pools[media].generic_input if require_extra_input else None - flow = flow.preprocess(config_file, None if not input else input.generate) - for config_file in infer_config_paths: - engine_file = self._generate_engine_name(config_file, device_id, self._max_batch_size) - if engine_file: - flow = flow.infer(config_file, with_triton, batch_size=self._max_batch_size, model_engine_file=engine_file) - else: - flow = flow.infer(config_file, with_triton, batch_size=self._max_batch_size) - - flow = flow.attach(what=Probe("metadata_probe", output)).retrieve(output, **{'async': False}) - - # warm up - warmup_data_0[self._image_tensor_name] = np.frombuffer(png_data, dtype=np.uint8) - warmup_data_0[self._mime_tensor_name] = "image/png" - warmup_data_1[self._image_tensor_name] = np.frombuffer(jpg_data, dtype=np.uint8) - warmup_data_1[self._mime_tensor_name] = "image/jpeg" - - logger.info("Image decoder warming up - pre-filling all queues") - # Submit BOTH PNG and JPEG warmup data BEFORE starting pipeline - # This ensures all 4 sources have data when streaming threads start - indices_0 = in_pool.submit([warmup_data_0.copy() for _ in range(self._max_batch_size)]) - indices_1 = in_pool.submit([warmup_data_1.copy() for _ in range(self._max_batch_size)]) - - # Now start pipeline - all queues have data - pipeline.start() - - # Collect all results together (PNG + JPEG) - all_indices = indices_0 + indices_1 - results = output.collect(all_indices) - output.reset() - logger.info(f"Warm up done: {results}") - - if (self._media_url_tensor_name is not None or - self._source_tensor_name is not None): - # video input support - media = "video" - if tensor_output: - output = TensorOutput(self._max_batch_size, preprocess_config_paths, image_output_name=self._image_output_name) - elif preprocess_config_paths: - output = PreprocessMetadataOutput(self._max_batch_size, self._output_names[0], dims, image_output_name=self._image_output_name) - else: - output = MetadataOutput(self._max_batch_size, self._output_names[0], dims, image_output_name=self._image_output_name) - engine_files = [ - self._generate_engine_name( - config_file, - device_id, - self._max_batch_size - ) - for config_file in infer_config_paths - ] - in_pool = BulkVideoInputPool( - max_batch_size=self._max_batch_size, - batch_timeout=model_config["parameters"].get("batch_timeout", 1000 * self._max_batch_size), - media_url_tensor_name=self._media_url_tensor_name, - source_tensor_name=self._source_tensor_name, - mime_tensor_name=self._mime_tensor_name, - infer_config_paths=infer_config_paths, - preprocess_config_paths=preprocess_config_paths, - tracker_config=tracker_config, - analytics_config=analytics_config, - msgbroker_config=msgbroker_config, - render_config=render_config, - perf_config=perf_config, - kitti_config=kitti_config, - output=output, - device_id=device_id, - require_extra_input=require_extra_input, - engine_file_names=engine_files, - dims=dims, - label_file_path=label_file_path, - model_home=self._model_home - ) - - # build tensorrt engine if not exist - if not all (os.path.exists(e) for e in engine_files): - with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as f: - f.write(jpg_data) - warmup_video_path = f.name - # Disable perf/render probes for warmup to avoid leaked FPS reporters - in_pool._perf_config = PerfConfig() - in_pool._render_config = RenderConfig() - try: - logger.info("Video decoder warming up - building TensorRT engine") - warmup_item = {self._media_url_tensor_name: warmup_video_path} - if self._mime_tensor_name: - warmup_item[self._mime_tensor_name] = "video/mp4" - indices = in_pool.submit([warmup_item]) - if indices: - results = output.collect(indices) - logger.info(f"Video warm up done: {results}") - output.reset() - in_pool.stop("warmup") - finally: - in_pool._perf_config = perf_config - in_pool._render_config = render_config - os.unlink(warmup_video_path) - - self._in_pools[media] = in_pool - self._outputs[media] = output - - logger.info( - f"DeepstreamBackend created for {self._model_name} to generate " - f"{self._output_names}, output tensor: {tensor_output}" - ) - - - def __call__(self, *args, **kwargs): - in_data_list = args if args else split_tensor_in_dict(kwargs) - media = None - explicit_batch = True if args else False - - # analyze the input batch - for data in in_data_list: - # convert numpy types to native Python types - for k, v in data.items(): - if isinstance(v, np.generic): - v = v.item() - if isinstance(v, bytes): - v = v.decode() - data[k] = v - # get the media type - if ((self._image_tensor_name in data or self._media_url_tensor_name in data) and - not self._mime_tensor_name in data): - logger.error(f"MIME type is not specified for input {data}") - return - if self._source_tensor_name and self._source_tensor_name in data: - media = "video" - explicit_batch = True - break # only video source is supported through source config - - current_media = data[self._mime_tensor_name].split('/')[0] - if media is None: - media = current_media - elif media != current_media: - logger.error( - f"Mixed media types are not supported in a single batch, " - f"got {media} and {current_media}" - ) - return - - # validate the media type - if media not in self._in_pools: - if media == "image": - logger.error("TYPE_CUSTOM_DS_IMAGE input must be added to the pipeline for image support") - elif media == "video": - logger.error("TYPE_CUSTOM_DS_MEDIA_URL input must be added to the pipeline for video support") - else: - logger.error("Unknown media type : ", media) - return - - # submit the data to the pipeline which supports the media type - indices = self._in_pools[media].submit(in_data_list) - # collect the results - while True: - # TODO: timeout should be runtime configurable - results = self._outputs[media].collect(indices, timeout=self._inference_timeout) - if not results: - logger.info("DeepstreamBackend: No more data from this batch") - break - out_data_list = [] - for result in results: - out_data = dict() - for o in self._output_names: - if o in result: - out_data[o] = result[o] - else: - out_data[o] = None - out_data_list.append(out_data) - yield out_data_list if explicit_batch else out_data_list[0] - # No consecutive inference results for image - if media == "image": - break - - def stop(self): - for input_pool in self._in_pools.values(): - input_pool.stop("Finalized") - for pipeline in self._pipelines.values(): - pipeline.stop() - pipeline.wait() - self._in_pools.clear() - self._pipelines.clear() - super().stop() - - def __del__(self): - self.stop() - - def _generate_engine_name(self, config_path: str, device_id: int, batch_size: int): - def network_mode_to_string(network_mode: int): - if network_mode == 0: - return "fp32" - elif network_mode == 1: - return "int8" - elif network_mode == 2: - return "fp16" - else: - return "" - network_mode = "fp16" - onnx_file = "model.onnx" - with open(config_path, 'r') as f: - config = yaml.safe_load(f) - if not "property" in config: - return None - property = config["property"] - if not "onnx-file" in property: - return None - onnx_file = property["onnx-file"] - if "network-mode" in property: - mode = network_mode_to_string(property["network-mode"]) - if mode: - network_mode = mode - engine_file = f"{onnx_file}_b{batch_size}_gpu{device_id}_{network_mode}.engine" - return os.path.join(self._model_home, engine_file) - - - def _correct_config_paths(self, config_paths: List[str]) -> List[str]: - if not config_paths: - return [] - for i, path in enumerate(config_paths): - if not os.path.isabs(path): - config_paths[i] = os.path.join(self._model_home, path) - return config_paths diff --git a/tools/inference_builder/templates/backend/dummy.jinja.py b/tools/inference_builder/templates/backend/dummy.jinja.py deleted file mode 100644 index b4ff1f1a..00000000 --- a/tools/inference_builder/templates/backend/dummy.jinja.py +++ /dev/null @@ -1,107 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} - -import numpy as np - -class DummyBackend(ModelBackend): - """Python TensorRT Backend from polygraph""" - def __init__(self, model_config:Dict, model_home: str, device_id: int=0): - super().__init__(model_config, model_home, device_id) - self._model_name = model_config["name"] - self._input_config = [i for i in model_config['input']] - self._output_config = [o for o in model_config['output']] - self._params = model_config["parameters"] if "parameters" in model_config else {} - n_workers = self._params.get("thread_concurrency", 0) - # enable thread pool for concurrent inference - self._thread_pool = ThreadPoolExecutor(max_workers=n_workers) if n_workers > 0 else None - logger.info(f"DummyBackend created for {self._model_name} to generate {[o['name'] for o in self._output_config]}") - - def __call__(self, *args, **kwargs): - def generate_sync(in_data): - for _ in in_data: - yield self._generate_dummy_data() - - def generate_async(in_data): - return [self._generate_dummy_data() for _ in in_data] - - in_data = list(args) if args else [kwargs] - if self._thread_pool is not None: - return self._thread_pool.submit(generate_async, in_data) - else: - return generate_sync(in_data) - - def _generate_dummy_data(self): - dummy_data = {} - for config in self._output_config: - name = config['name'] - dims = config['dims'] - data_type = config.get('data_type', 'TYPE_FP32') - - # Generate random data based on type with correct numpy dtype - if data_type == 'TYPE_BOOL': - data = np.random.choice([True, False], size=dims).astype(np.bool_) - elif data_type == 'TYPE_INT8': - data = np.random.randint(-128, 127, size=dims, dtype=np.int8) - elif data_type == 'TYPE_INT16': - data = np.random.randint(-32768, 32767, size=dims, dtype=np.int16) - elif data_type == 'TYPE_INT32': - data = np.random.randint(-100, 100, size=dims, dtype=np.int32) - elif data_type == 'TYPE_INT64': - data = np.random.randint(-100, 100, size=dims, dtype=np.int64) - elif data_type == 'TYPE_UINT8': - data = np.random.randint(0, 255, size=dims, dtype=np.uint8) - elif data_type == 'TYPE_UINT16': - data = np.random.randint(0, 65535, size=dims, dtype=np.uint16) - elif data_type == 'TYPE_UINT32': - data = np.random.randint(0, 100, size=dims, dtype=np.uint32) - elif data_type == 'TYPE_UINT64': - data = np.random.randint(0, 100, size=dims, dtype=np.uint64) - elif data_type == 'TYPE_FP16': - data = np.random.randn(*dims).astype(np.float16) - elif data_type == 'TYPE_FP32': - data = np.random.randn(*dims).astype(np.float32) - elif data_type == 'TYPE_FP64': - data = np.random.randn(*dims).astype(np.float64) - elif 'TYPE_STRING' in data_type or 'TYPE_BYTES' in data_type: - # Generate random strings - flat_size = np.prod(dims) if dims else 1 - data = np.array([''.join(np.random.choice(list('abcdefghijklmnopqrstuvwxyz0123456789'), size=10)) - for _ in range(flat_size)]).reshape(dims) if dims else np.array(['random_str']) - else: - # Default to FP32 for unknown types - data = np.random.randn(*dims).astype(np.float32) - - dummy_data[name] = data - return dummy_data - - def _valicate_inputs(self, data): - i_names = [i['name'] for i in self._input_config] - i_shapes = {i['name']: i['dims'] for i in self._input_config} - i_types = {i['name']: i['data_type'] for i in self._input_config} - for name, tensor in data.items(): - if name not in i_names: - raise ValueError(f"Input {name} not found in the input config") - if len(tensor.shape) != len(i_shapes[name]): - raise ValueError(f"Input {name} has invalid shape: {tensor.shape}") - for i, s in enumerate(tensor.shape): - - if i_shapes[name][i] != -1 and s != i_shapes[name][i]: - raise ValueError(f"Input {name} has invalid shape: {tensor.shape}") - if isinstance(tensor, np.ndarray) and tensor.dtype != np_datatype_mapping[i_types[name]]: - raise ValueError(f"Input {name} has invalid type: {tensor.dtype}") - if isinstance(tensor, torch.Tensor) and tensor.dtype != torch_datatype_mapping[i_types[name]]: - raise ValueError(f"Input {name} has invalid type: {tensor.dtype}") diff --git a/tools/inference_builder/templates/backend/polygraphy.jinja.py b/tools/inference_builder/templates/backend/polygraphy.jinja.py deleted file mode 100644 index 06bac46d..00000000 --- a/tools/inference_builder/templates/backend/polygraphy.jinja.py +++ /dev/null @@ -1,50 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} -from polygraphy.backend.common import BytesFromPath -from polygraphy.backend.trt import EngineFromBytes, TrtRunner - -class PolygraphBackend(ModelBackend): - """Python TensorRT Backend from polygraph""" - def __init__(self, model_config:Dict, model_home: str, device_id: int=0): - super().__init__(model_config, model_home, device_id) - self._model_name = model_config["name"] - self._output_names = [o['name'] for o in model_config['output']] - - logger.debug(f"PolygraphBackend created for {self._model_name} to generate {self._output_names}") - if "parameters" not in model_config or "tensorrt_engine" not in model_config["parameters"]: - raise("PolygraphBackend requires a path to tensorrt_engine") - engine_file = model_config["parameters"]["tensorrt_engine"] - if not os.path.isabs(engine_file): - engine_file = os.path.join(self._model_home, engine_file) - engine = EngineFromBytes(BytesFromPath(engine_file)) - self._trt_runner = TrtRunner(engine) - self._trt_runner.activate() - logger.info(f"TensorRT runtime created from {engine_file}") - - - def __call__(self, *args, **kwargs): - in_data = stack_tensors_in_dict(args) if args else dict(kwargs) - for k, v in in_data.items(): - if isinstance(v, np.ndarray): - in_data[k] = torch.from_numpy(v).to(self._device_id) - result = self._trt_runner.infer(in_data) - if not all([key in result for key in self._output_names]): - logger.error(f"Not all the expected output in {self._output_names} are not found in the result") - return - o_data = { o: result[o] for o in self._output_names} - yield o_data - diff --git a/tools/inference_builder/templates/backend/pytorch.jinja.py b/tools/inference_builder/templates/backend/pytorch.jinja.py deleted file mode 100644 index 62212919..00000000 --- a/tools/inference_builder/templates/backend/pytorch.jinja.py +++ /dev/null @@ -1,41 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} - -import importlib - -class PytorchBackend(ModelBackend): - """Pytorch Backend to run models from Huggingface""" - def __init__(self, model_config:Dict, model_home: str, device_id: int=0): - super().__init__(model_config, model_home, device_id) - self._model_name = model_config["name"] - self._device = f"cuda:{device_id}" - self._output_names = [o["name"] for o in model_config["output"]] - self._model_class = model_config["parameters"].get("model_class", "AutoModelForCausalLM") - self._module = importlib.import_module("transformers") - model_class = getattr(self._module, self._model_class) - logger.info(f"Loading pre-trained model {self._model_name} of type {self._model_class} from {self._model_home}") - self._model = model_class.from_pretrained(self._model_home, torch_dtype="auto", device_map="auto") - self._model.to(self._device) - logger.info(f"Model {self._model_name} loaded from {self._model_home}") - - def __call__(self, *args, **kwargs): - in_data_list = args if args else [kwargs] - for in_data in in_data_list: - result = self._model.generate(**in_data) - if len(self._output_names) != len(result): - raise ValueError(f"Number of output names ({len(self._output_names)}) does not match the number of output tensors ({len(result)})") - yield {self._output_names[i]: tensor for i, tensor in enumerate(result)} diff --git a/tools/inference_builder/templates/backend/tensorrtllm.jinja.py b/tools/inference_builder/templates/backend/tensorrtllm.jinja.py deleted file mode 100644 index 6cf8a661..00000000 --- a/tools/inference_builder/templates/backend/tensorrtllm.jinja.py +++ /dev/null @@ -1,199 +0,0 @@ -from typing import List, Dict -from lib.inference import ModelBackend -import tensorrt_llm -from tensorrt_llm.runtime import Session, TensorInfo -import tensorrt as trt -import numpy -import os -import json -def trt_dtype_to_torch(dtype): - ''' - Convert TRT data type to PyTorch data type - ''' - if dtype == trt.float16: - return torch.float16 - elif dtype == trt.float32: - return torch.float32 - elif dtype == trt.int32: - return torch.int32 - else: - raise TypeError("%s is not supported" % dtype) - -def get_trt_dtype(dtype): - if dtype == torch.float16: - return trt.float16 - elif dtype == torch.float32: - return trt.float32 - elif dtype == torch.int32: - return trt.int32 - else: - raise TypeError("%s is not supported" % dtype) - -class TensorRTSession: - def __init__(self, stream, device, engine_file, input_dtype): - self._stream = stream - self._device = device - self._input_dtype = input_dtype - torch.cuda.set_stream(self._stream) - logger.info(f"Loading TensorRT Engine from {engine_file}...") - with open(engine_file, 'rb') as f: - engine_buffer = f.read() - self._trt_session = Session.from_serialized_engine(engine_buffer) - logger.info("TensorRT Engine loaded") - - def infer(self, in_data: Dict): - tensor_infos = [] - for key in in_data: - tensor = in_data[key] - if isinstance(tensor, numpy.ndarray): - tensor = torch.from_numpy(tensor).to(self._device) - if not isinstance(tensor, torch.Tensor): - logger.error(f"Input tensor must be a numpy array or a torch tensor, but got {type(tensor)}") - return {} - dtype = self._input_dtype[key] - in_data[key] = tensor.to(dtype=dtype) - tensor_infos.append(TensorInfo(key, get_trt_dtype(dtype), tensor.shape)) - output_info = self._trt_session.infer_shapes(tensor_infos) - trt_out = { t.name: torch.empty(tuple(t.shape), dtype=trt_dtype_to_torch(t.dtype), device=tensor.device) for t in output_info } - ok = self._trt_session.run(in_data, trt_out, self._stream.cuda_stream) - assert ok, "Runtime execution failed for vision encoder session" - self._stream.synchronize() - # TODO associate trt output names with triton output names - return trt_out - - -class TensorRTLLMBackend(ModelBackend): - """Python TensorRT Backend""" - def __init__(self, model_config:Dict, model_home: str, device_id: int=0): - super().__init__(model_config, model_home, device_id) - self._model_name = model_config["name"] - self._output_names = [o['name'] for o in model_config['output']] - self._device = f"cuda:{device_id}" - self._stream = torch.cuda.Stream(self._device) - self._params = model_config["parameters"] if "parameters" in model_config else {} - self._inputs = model_config["input"] if "input" in model_config else [] - llm_backend = model_config["backend"].split("/")[-1] - - if not self._inputs: - raise ValueError("No input tensors found in model config") - - if llm_backend == "pytorch": - from tensorrt_llm import SamplingParams - from tensorrt_llm import LLM - from tensorrt_llm.llmapi import (DraftTargetDecodingConfig, EagleDecodingConfig, - KvCacheConfig, MTPDecodingConfig, - NGramDecodingConfig, TorchCompileConfig) - - in_names = [i['name'] for i in model_config['input']] - if "max_tokens" not in in_names or "temperature" not in in_names or "top_p" not in in_names or "top_k" not in in_names: - raise ValueError("max_tokens, temperature, top_p, and top_k must be provided for tensorrtllmpytorch backend") - self._trt_session = None - default_kv_cache_config = { - "enable_block_reuse": True - } - if "kv_cache_config" in self._params: - default_kv_cache_config.update(self._params.pop("kv_cache_config")) - kv_cache_config = KvCacheConfig(**default_kv_cache_config) - - spec_decode_algo = self._params.pop("spec_decode_algo", None) - if spec_decode_algo is not None: - spec_decode_algo = spec_decode_algo.upper() - - if spec_decode_algo == 'MTP': - spec_config = MTPDecodingConfig( - num_nextn_predict_layers=self._params.pop("spec_decode_nextn", 1), - use_relaxed_acceptance_for_thinking=self._params.pop("use_relaxed_acceptance_for_thinking", False), - relaxed_topk=self._params.pop("relaxed_topk", 1), - relaxed_delta=self._params.pop("relaxed_delta", 0.0)) - elif spec_decode_algo == "EAGLE3": - spec_config = EagleDecodingConfig( - max_draft_len=self._params.pop("spec_decode_nextn", 1), - pytorch_weights_path=self._params.pop("draft_model_dir", None), - eagle3_one_model=self._params.pop("use_one_model", None)) - elif spec_decode_algo == "DRAFT_TARGET": - spec_config = DraftTargetDecodingConfig( - max_draft_len=self._params.pop("spec_decode_nextn", 1), - pytorch_weights_path=self._params.pop("draft_model_dir", None)) - elif spec_decode_algo == "NGRAM": - spec_config = NGramDecodingConfig( - prompt_lookup_num_tokens=self._params.pop("spec_decode_nextn", 1), - max_matching_ngram_size=self._params.pop("max_matching_ngram_size", 1), - is_keep_all=True, - is_use_oldest=True, - is_public_pool=True, - ) - else: - spec_config = None - - use_torch_compile = self._params.pop("use_torch_compile", False) - torch_compile_config = TorchCompileConfig( - enable_fullgraph=True, - enable_inductor=True, - enable_piecewise_cuda_graph=self._params.pop("use_piecewise_cuda_graph", False) - ) if use_torch_compile else None - - self._llm = LLM( - model=self._model_home, - backend="pytorch", - kv_cache_config=kv_cache_config, - speculative_config=spec_config, - torch_compile_config=torch_compile_config, - **self._params - ) - self._sample_params_cls = SamplingParams - self._trtllm_input_name = next((i["name"] for i in self._inputs if i["data_type"] == "TYPE_CUSTOM_VLM_INPUT"), None) - if self._trtllm_input_name is None: - raise ValueError("TYPE_CUSTOM_VLM_INPUT must be provided for tensorrtllm/pytorch backend") - - logger.debug(f"TensorRTLLMBackend with pytorch created for {self._model_name} to generate {self._output_names}") - else: - self._llm = None - if "tensorrt_engine" not in model_config: - raise("TensorRTLLM backend requires a path to tensorrt_engine") - engine_file = model_config["parameters"]["tensorrt_engine"] - if not os.path.isabs(engine_file): - engine_file = os.path.join(self._model_home, engine_file) - input_dtype = { i['name']: torch_datatype_mapping[i['data_type']] for i in model_config['input']} - self._trt_session = TensorRTSession(self._stream, self._device, engine_file, input_dtype) - logger.debug(f"TensorRTBackend created for {self._model_name} to generate {self._output_names}") - - def __call__(self, *args, **kwargs): - if self._llm is not None: - params = args[0] if args else kwargs - sample_params = self._sample_params_cls( - max_tokens=params.get("max_tokens", 1024), - temperature=params.get("temperature", 0.7), - top_p=params.get("top_p", 0.95), - top_k=params.get("top_k", 0) - ) - inputs = [] - if args: - for i in args: - if self._trtllm_input_name in i: - input = i[self._trtllm_input_name] - inputs.extend(input) if isinstance(input, list) else inputs.append(input) - else: - logger.warning(f"TensorRTLLMBackend: Input {self._trtllm_input_name} not found in {i}") - elif kwargs: - if self._trtllm_input_name in kwargs: - inputs = kwargs[self._trtllm_input_name] - else: - logger.warning(f"TensorRTLLMBackend: Input {self._trtllm_input_name} not found in {kwargs}") - else: - logger.warning("TensorRTLLMBackend: No inputs provided") - if not inputs: - return - - results = self._llm.generate(inputs, sample_params) - yield [{"outputs": r} for r in results] - elif self._trt_session is not None: - # TensorRT LLM uses implicit batching, so we need to stack the input tensors - in_data = stack_tensors_in_dict(args) if args else dict(kwargs) - yield self._trt_session.infer(in_data) - else: - raise Exception("TensorRTLLM backend is not correctly initialized") - - - - - diff --git a/tools/inference_builder/templates/backend/triton.jinja.py b/tools/inference_builder/templates/backend/triton.jinja.py deleted file mode 100644 index 64c8a081..00000000 --- a/tools/inference_builder/templates/backend/triton.jinja.py +++ /dev/null @@ -1,180 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} - -{% if server_type == "triton" %} - -class TritonBackend(ModelBackend): - """Triton Python Backend""" - def __init__(self, model_config: Dict, model_home: str): - logger.debug(f"model_config: {model_config}, model_home: {model_home}") - super().__init__(model_config, model_home) - self._model_name = model_config["name"] - self._input_names = [i['name'] for i in model_config['input']] - self._output_names = [o['name'] for o in model_config['output']] - logger.debug(f"TritonBackend created for {self._model_name} with inputs {self._input_names} and outputs {self._output_names}") - - def __call__(self, *args, **kwargs): - in_data_list = args if args else [kwargs] - input_config = self._model_config["input"] - # to determine if we need to stack the input, TODO: below logic needs be more generic - need_stack = False - for in_data in in_data_list: - for key, value in in_data.items(): - input_config = next((i for i in self._model_config["input"] if i['name'] == key), None) - if input_config is None: - logger.error(f"Unexpected input: {key}") - continue - expected_dims = len(input_config["dims"]) - if expected_dims == len(value.shape) + 1: - need_stack = True - break - if need_stack: - in_data_list = [stack_tensors_in_dict(in_data_list)] - for in_data in in_data_list: - tensors = [] - for k in in_data: - tensor = in_data[k] - batched = "max_batch_size" in self._model_config and self._model_config["max_batch_size"] > 0 - if isinstance(tensor, np.ndarray): - tensor = pb_utils.Tensor(k, np.expand_dims(tensor, 0)) if batched else pb_utils.Tensor(k, tensor) - elif isinstance(tensor, torch.Tensor): - if batched: - tensor = tensor.unsqueeze(0) - tensor = pb_utils.Tensor.from_dlpack(k, torch.utils.dlpack.to_dlpack(tensor)) - elif hasattr(tensor, "__dlpack__") and hasattr(tensor, "__dlpack_device__"): - tensor = pb_utils.Tensor.from_dlpack(k, tensor) - else: - yield Error(message="Unsupported input tensor format") - return - tensors.append(tensor) - - llm_request = pb_utils.InferenceRequest( - model_name = self._model_name, - requested_output_names = self._output_names, - inputs = tensors - ) - finish_reason = "done" - for idx, response in enumerate(llm_request.exec(decoupled=True)): - expected = {n: None for n in self._output_names} - if response.has_error(): - yield Error(message=f"{response.error().message()}, stream_id={idx}") - if not response.output_tensors(): - continue - for name in expected: - config = next((c for c in self._model_config['output'] if c['name'] == name), None) - dims = config['dims'] - output = pb_utils.get_output_tensor_by_name(response, name) - if not output: - continue - if pb_utils.Tensor.is_cpu(output): - tensor = output.as_numpy() - if tensor.size != 0: - expected[name] = np.squeeze(tensor, 0) if len(tensor.shape) == (len(dims)+1) else tensor - else: - tensor = torch.utils.dlpack.from_dlpack(output.to_dlpack()) - if tensor.numel() != 0: - expected[name] = torch.squeeze(tensor, 0) if len(tensor.shape) == (len(dims)+1) else tensor - logger.debug(f"TritonBackend saved inference results to: {expected}") - if all([expected[k] is not None for k in expected]): - yield expected - logger.info(f"Infernece with TritonBackend {self._model_name} accomplished") - -{% else %} - -import tritonclient.http as httpclient -from tritonclient.utils import * - -class TritonBackend(ModelBackend): - """Triton Python Backend""" - def __init__(self, model_config: Dict, model_home: str): - logger.debug(f"model_config: {model_config}") - super().__init__(model_config, model_home) - self._model_name = model_config["name"] - self._input_names = [i['name'] for i in model_config['input']] - self._output_names = [o['name'] for o in model_config['output']] - - # Wait for Triton server to be ready - max_retries = 60 - retry_interval = 1 - for attempt in range(max_retries): - try: - with httpclient.InferenceServerClient("localhost:8000") as client: - if client.is_server_ready(): - logger.info(f"Triton server is ready after {attempt + 1} attempt(s)") - break - except Exception as e: - logger.debug(f"Attempt {attempt + 1}/{max_retries}: Triton server not ready yet - {e}") - if attempt < max_retries - 1: - import time - time.sleep(retry_interval) - else: - logger.error(f"Triton server did not become ready after {max_retries} attempts") - raise RuntimeError("Triton server is not ready") - - logger.debug(f"TritonBackend created for {self._model_name} with inputs {self._input_names} and outputs {self._output_names}") - - def __call__(self, *args, **kwargs): - in_data_list = args if args else [kwargs] - input_config = self._model_config["input"] - # to determine if we need to stack the input, TODO: below logic needs be more generic - need_stack = False - for in_data in in_data_list: - for key, value in in_data.items(): - input_config = next((i for i in self._model_config["input"] if i['name'] == key), None) - if input_config is None: - logger.error(f"Unexpected input: {key}") - continue - expected_dims = len(input_config["dims"]) - if expected_dims == len(value.shape) + 1: - need_stack = True - break - if need_stack: - in_data_list = [stack_tensors_in_dict(in_data_list)] - for in_data in in_data_list: - inputs = [] - for k in in_data: - tensor = in_data[k] - batched = "max_batch_size" in self._model_config and self._model_config["max_batch_size"] > 0 - if isinstance(tensor, torch.Tensor): - tensor = tensor.cpu().numpy() - if batched: - tensor = np.expand_dims(tensor, 0) - elif not isinstance(tensor, np.ndarray): - yield Error(message="Unsupported input tensor format") - return - input = httpclient.InferInput(k, tensor.shape, np_to_triton_dtype(tensor.dtype)) - input.set_data_from_numpy(tensor) - inputs.append(input) - outputs = [httpclient.InferRequestedOutput(n) for n in self._output_names] - with httpclient.InferenceServerClient("localhost:8000") as client: - response = client.infer(self._model_name, inputs, request_id=str(1), outputs=outputs) - - result = response.get_response() - expected = {n: None for n in self._output_names} -# if response.has_error(): -# yield Error(message=f"{response.error().message()}, stream_id={idx}") - for name in expected: - config = next((c for c in self._model_config['output'] if c['name'] == name), None) - dims = config['dims'] - output = response.as_numpy(name) - expected[name] = np.squeeze(output, 0) if len(output.shape) == (len(dims)+1) else output - logger.debug(f"TritonBackend saved inference results to: {expected}") - if all([expected[k] is not None for k in expected]): - yield expected - logger.info(f"Inference with TritonBackend {self._model_name} accomplished") - -{% endif %} \ No newline at end of file diff --git a/tools/inference_builder/templates/backend/vllm.jinja.py b/tools/inference_builder/templates/backend/vllm.jinja.py deleted file mode 100644 index 4652e0d5..00000000 --- a/tools/inference_builder/templates/backend/vllm.jinja.py +++ /dev/null @@ -1,202 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} - -import asyncio, threading, uuid, queue -from asyncio import run_coroutine_threadsafe -from typing import Callable, Any, Optional, List, Dict -from concurrent.futures import Future -from vllm import LLM, SamplingParams - -class VLLMSyncFacade: - """ - Wrapper around AsyncLLMEngine that provides a synchronous interface - for submitting inference requests that can be processed concurrently. - """ - def __init__(self, **engine_kwargs): - self.loop = asyncio.new_event_loop() - self.out_q = queue.Queue(maxsize=1000) - self._ready = threading.Event() - - def runner(): - from vllm.engine.async_llm_engine import AsyncLLMEngine - try: - from vllm.engine.async_llm_engine import AsyncEngineArgs - except ImportError: - from vllm.engine.arg_utils import AsyncEngineArgs - asyncio.set_event_loop(self.loop) - self.engine = AsyncLLMEngine.from_engine_args(AsyncEngineArgs(**engine_kwargs)) - self._ready.set() - self.loop.run_forever() - - threading.Thread(target=runner, daemon=True).start() - self._ready.wait() - logger.info("VLLMSyncFacade: AsyncLLMEngine initialized and ready") - - def submit(self, - prompts: List[Any], - sampling_params: SamplingParams, - result_filter: Callable[[Any], Dict[str, Any]], - job_id_prefix: Optional[str] = None) -> Future: - """ - Submit requests for inference. AsyncEngine processes one prompt at a time, - but can handle multiple concurrent requests with different request_ids. - - Args: - prompts: List of prompts (always a list from __call__) - sampling_params: SamplingParams object for this request - result_filter: Function that takes a result and returns a dict - job_id_prefix: Optional prefix for request IDs - Returns: - Future that resolves to list of filtered outputs (list of dicts) - """ - if not callable(result_filter): - raise ValueError("result_filter must be a callable") - - job_id_prefix = job_id_prefix or f"batch_{uuid.uuid4().hex[:8]}" - - async def _run_single(prompt, request_id): - """Process a single prompt with unique request_id""" - final_output = None - async for out in self.engine.generate(prompt, sampling_params, request_id): - final_output = out - # Apply result_filter to each output - return final_output - - async def _run_all(): - """Submit all prompts concurrently and collect results""" - # Create tasks for all prompts with unique request_ids - tasks = [] - for idx, prompt in enumerate(prompts): - request_id = f"{job_id_prefix}_req{idx}" - logger.debug(f"VLLMSyncFacade: Creating task for request {request_id}") - task = _run_single(prompt, request_id) - tasks.append(task) - - # Run all tasks concurrently and wait for all to complete - results = await asyncio.gather(*tasks, return_exceptions=True) - - # Extract outputs from RequestOutput objects and apply filter - all_outputs = [] - for idx, result in enumerate(results): - if isinstance(result, Exception): - logger.error(f"VLLMSyncFacade: Request {idx} failed: {result}") - # Add empty result for failed request - all_outputs.append({}) - elif result is not None: - all_outputs.append(result_filter(result)) - else: - logger.warning(f"VLLMSyncFacade: Request {idx} returned unexpected result: {result}") - - return all_outputs - - return run_coroutine_threadsafe(_run_all(), self.loop) - -class VLLMBackend(ModelBackend): - """VLLM Backend""" - def __init__(self, model_config:Dict, model_home: str, device_id: int=0): - super().__init__(model_config, model_home, device_id) - self._model_name = model_config["name"] - self._output_names = [o['name'] for o in model_config['output']] - self._params = model_config["parameters"] if "parameters" in model_config else {} - self._inputs = model_config["input"] if "input" in model_config else [] - self._vlm_input_name = next((i["name"] for i in self._inputs if i["data_type"] == "TYPE_CUSTOM_VLM_INPUT"), None) - self._async_mode = self._params.get("async_mode", False) - - llm_kwargs = {} - for key in [ - "tensor_parallel_size", - "gpu_memory_utilization", - "dtype", - "max_model_len", - "max_num_batched_tokens", - "max_num_seqs", - "kv_cache_dtype", - "pipeline_parallel_size", - "max_parallel_loading_workers", - "enforce_eager", - ]: - if key in self._params: - llm_kwargs[key] = self._params.pop(key) - - if self._async_mode: - self._llm = VLLMSyncFacade( - model=model_home, - **llm_kwargs - ) - else: - self._llm = LLM( - model=model_home, - **llm_kwargs - ) - logger.info(f"Model {self._model_name} loaded from {model_home}") - - def extract_outputs(self, result: Any) -> Dict[str, Any]: - """ - Extracts the outputs from the result based on the configured output names - Args: - result: The result object from vLLM generation - Returns: - Dictionary mapping output names to their values - """ - outputs = {n: None for n in self._output_names} - for name in self._output_names: - if hasattr(result, name): - outputs[name] = getattr(result, name) - else: - logger.warning(f"VLLMBackend: Output {name} not found in {result}") - return outputs - - def __call__(self, *args, **kwargs): - def assemble_input(vlm_input_name: str, vlm_input: List[Any], all_input: Dict[str, Any]) -> Any: - if vlm_input_name in all_input: - i = all_input.pop(vlm_input_name) - if isinstance(i, list): - vlm_input.extend(i) - else: - vlm_input.append(i) - else: - logger.warning(f"VLLMBackend: Input {vlm_input_name} not found in {all_input}") - - def generate_sync(inputs, sampling_params): - results = self._llm.generate(inputs, sampling_params) - for result in results: - yield self.extract_outputs(result) - - # Extract inputs and parameters - inputs = [] - params = {} - if args: - for i in args: - assemble_input(self._vlm_input_name, inputs, i) - params = args[0] - elif kwargs: - assemble_input(self._vlm_input_name, inputs, kwargs) - params = kwargs - - # Convert numpy scalars to Python scalars - for key, value in params.items(): - if isinstance(value, np.ndarray) and not value.shape: - params[key] = value.item() - - sampling_params = SamplingParams(**params) - - # Use async facade or sync LLM based on mode - if self._async_mode: - return self._llm.submit(inputs, sampling_params, self.extract_outputs) - else: - return generate_sync(inputs, sampling_params) - diff --git a/tools/inference_builder/templates/client/test_runner.jinja.py b/tools/inference_builder/templates/client/test_runner.jinja.py deleted file mode 100644 index e27c74d0..00000000 --- a/tools/inference_builder/templates/client/test_runner.jinja.py +++ /dev/null @@ -1,165 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} - -import unittest -import json -import yaml -import os -import sys -import logging -from pathlib import Path - -# Configure logging -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(levelname)s - %(message)s', - datefmt='%Y-%m-%d %H:%M:%S' -) -logger = logging.getLogger(__name__) - -# # Add builder/validator.py path to Python path -# VALIDATOR_DIR = Path("{{ validator_dir }}") -# if str(VALIDATOR_DIR) not in sys.path: -# sys.path.append(str(VALIDATOR_DIR)) - -# Will load validate.py in the same directory -import validate - -# Common paths relative to test runner location -GENERATED_CLIENT_DIR = Path(__file__).parent / validate.GENERATED_CLIENT_DIR -TEST_CASES_FILE = Path(__file__).parent / validate.TEST_CASES_FILE - -# Setup environment before imports -if not validate.check_client_exists(): - if not validate.setup_environment(GENERATED_CLIENT_DIR): - logger.error("Failed to setup client environment") - sys.exit(1) - -from openapi_client.api.nvidiametropolisinferenceapi_api import NVIDIAMETROPOLISINFERENCEAPIApi -from openapi_client.api_client import ApiClient -from openapi_client.configuration import Configuration -from openapi_client.models.inference_request import InferenceRequest - -class TestInference(unittest.TestCase): - @classmethod - def setUpClass(cls): - """Set up test environment once for all test methods. - - This is a class method because it sets up shared resources (host, api client) - that will be used by all test methods. It runs once before any tests. - """ - cls.host = os.getenv("TEST_HOST", "http://127.0.0.1:8800") - cls.tolerance = float(os.getenv("TEST_TOLERANCE", "1e-5")) - - logger.info("Setting up test environment") - logger.info(f"Server host: {cls.host}") - logger.info(f"Tolerance: {cls.tolerance}") - - with open(TEST_CASES_FILE) as f: - cls.test_cases = yaml.safe_load(f) - logger.info(f"Loaded {len(cls.test_cases)} test cases from {TEST_CASES_FILE}") - - config = Configuration(host=cls.host) - api_client = ApiClient(config) - cls.api = NVIDIAMETROPOLISINFERENCEAPIApi(api_client) - - def setUp(self): - """Set up test fixtures before each test method. - - Instance method because it's specific to each test run. - Creates a logger for the specific test instance. - """ - self.logger = logging.getLogger(f"{__name__}.{self.id()}") - self.logger.info(f"Starting test: {self._testMethodName}") - - def tearDown(self): - """Clean up after each test method. - - Instance method because it's specific to each test run. - """ - self.logger.info(f"Completed test: {self._testMethodName}") - - def test_health_check(self): - """Test health check endpoint""" - # self.logger.info("Running health check...") - # response = self.api.health_ready_v1_health_ready_get_with_http_info() - # self.assertEqual(response.status_code, 200) - # self.assertEqual(response.data["status"], "ready") - # self.logger.info("Health check successful") - self.logger.info(f"Skip health check for {self.host}") - - def test_inference(self): - """Test inference with pre-built requests""" - - for test in self.test_cases: - with self.subTest(test=test["name"]): - test_logger = logging.getLogger(f"{__name__}.{self.id()}.{test['name']}") - test_logger.info(f"Starting test case: {test['name']}") - try: - # Load pre-built request - request_path = Path(test["request"]) - test_logger.debug(f"Loading request from: {request_path}") - with open(request_path) as f: - request_data = json.load(f) - request = InferenceRequest.from_dict(request_data) - - # Load expected output - expected_path = Path(test["expected"]) - test_logger.debug(f"Loading expected output from: {expected_path}") - with open(expected_path) as f: - expected = json.load(f) - - # Run inference - test_logger.info("Executing inference request...") - response = self.api.inference(request) - actual = response.to_dict() - - # Compare results - if expected: - test_logger.info("Validating response...") - self.assertTrue( - # TODO: We can template below line for different validators - # validator = {{ my_validator }} - # validator.compare_responses(actual, expected, self.tolerance) - # or make abstract validator class and ask users to implement their own validator - validate.CvValidator.compare_responses(actual, expected, self.tolerance), - f"Test {test['name']}: Response doesn't match expected output under tolerance {self.tolerance}. " - f"Actual: {actual}, Expected: {expected}" - ) - else: - test_logger.info("No expected output, skipping validation") - test_logger.info(f"Test case '{test['name']}' passed successfully") - - except Exception as e: - test_logger.error(f"Test case '{test['name']}' failed: {str(e)}") - raise - -if __name__ == '__main__': - # Create a test runner that outputs both to console and file - runner = unittest.TextTestRunner( - verbosity=2, # Increased verbosity - stream=sys.stdout, # Output to console - ) - - # Create test suite - suite = unittest.TestLoader().loadTestsFromTestCase(TestInference) - - # Run tests - result = runner.run(suite) - - # Exit with appropriate code so the test framework detects failures - sys.exit(0 if result.wasSuccessful() else 1) \ No newline at end of file diff --git a/tools/inference_builder/templates/common/config.jinja.py b/tools/inference_builder/templates/common/config.jinja.py deleted file mode 100644 index 317bf6ff..00000000 --- a/tools/inference_builder/templates/common/config.jinja.py +++ /dev/null @@ -1,89 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} -{{ license }} - -import os -import re -from omegaconf import OmegaConf - - -def _resolve_env_vars(config_dict): - """ - Recursively resolve environment variable references in the config. - - Supports the format: $ENV_VAR|default_value - If ENV_VAR exists, uses its value. Otherwise, uses default_value. - - Type conversion is performed based on the default value: - - "true"/"false" (case-insensitive) -> bool - - numeric strings -> int or float - - everything else -> string - """ - env_pattern = re.compile(r'^\$([A-Za-z_][A-Za-z0-9_]*)\|(.*)$') - - def convert_value(value_str): - """Convert string value to appropriate type.""" - # Handle boolean strings - if value_str.lower() in ('true', 'false'): - return value_str.lower() == 'true' - - # Try to convert to int - try: - if '.' not in value_str: - return int(value_str) - except ValueError: - pass - - # Try to convert to float - try: - return float(value_str) - except ValueError: - pass - - # Return as string - return value_str - - def process_value(value): - """Process a single value for environment variable substitution.""" - if isinstance(value, str): - match = env_pattern.match(value) - if match: - env_var = match.group(1) - default_value = match.group(2) - - # Check if environment variable exists - if env_var in os.environ: - return convert_value(os.environ[env_var]) - else: - return convert_value(default_value) - elif isinstance(value, dict): - return {k: process_value(v) for k, v in value.items()} - elif isinstance(value, list): - return [process_value(item) for item in value] - - return value - - return process_value(config_dict) - - -# Load the base configuration -_base_config = OmegaConf.create("""{{ config }}""") - -# Convert to dict, resolve environment variables, and convert back to OmegaConf -_config_dict = OmegaConf.to_container(_base_config) -_resolved_config = _resolve_env_vars(_config_dict) -global_config = OmegaConf.create(_resolved_config) \ No newline at end of file diff --git a/tools/inference_builder/templates/common/custom.__init__.jinja.py b/tools/inference_builder/templates/common/custom.__init__.jinja.py deleted file mode 100644 index 2ff619e8..00000000 --- a/tools/inference_builder/templates/common/custom.__init__.jinja.py +++ /dev/null @@ -1,50 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} - -import importlib -from pathlib import Path -from lib.utils import get_logger - -logger = get_logger(__name__) - -custom_module_table = { - {% for item in classes %} - "{{ item.name }}" : { - "module": "{{ item.module }}", - "class": "{{ item.class_name }}" - }{% if not loop.last %},{% endif %} - {% endfor %} -} - -def create_instance(name:str, config): - if not name in custom_module_table: - logger.error(f"{name} not found") - return None - module_name = custom_module_table[name]["module"] - class_name = custom_module_table[name]["class"] - try: - # Dynamically import the module - module = importlib.import_module(f".{module_name}", __package__) - - # Get the class from the module - cls = getattr(module, class_name) - - # Instantiate and return the class object - return cls(config) - except Exception as e: - logger.error(f"Error creating class '{class_name}' from module '{module_name}': {e}") - return None \ No newline at end of file diff --git a/tools/inference_builder/templates/generic/model.jinja.py b/tools/inference_builder/templates/generic/model.jinja.py deleted file mode 100644 index 4098fc94..00000000 --- a/tools/inference_builder/templates/generic/model.jinja.py +++ /dev/null @@ -1,227 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} -{{ license }} - -from config import global_config -from lib.inference import * -from lib.utils import * -from lib.errors import ErrorFactory -from omegaconf import OmegaConf -import json -import os -from typing import List, Dict -import asyncio -import numpy as np -import torch -from pathlib import Path -import dataclasses - -logger = get_logger(__name__) - -{% for backend in backends %} -{{ backend }} -{% endfor %} - -class GenericInference(InferenceBase): - """The class that drives a generic inference flow""" - - def _create_backend(self, backend_spec: List[str], model_config: Dict, model_home: str): - if backend_spec[0] == 'triton': - backend_class = TritonBackend - elif backend_spec[0] == 'deepstream': - backend_class = DeepstreamBackend - elif backend_spec[0] == 'polygraphy': - backend_class = PolygraphBackend - elif backend_spec[0] == 'tensorrtllm': - backend_class = TensorRTLLMBackend - elif backend_spec[0] == 'dummy': - backend_class = DummyBackend - elif backend_spec[0] == 'pytorch': - backend_class = PytorchBackend - elif backend_spec[0] == 'vllm': - backend_class = VLLMBackend - else: - return None - return backend_class(model_config, model_home) - - async def execute(self, request): - """ execute a list of requests""" - if self._async_dispatcher is None: - self._async_dispatcher = AsyncDispatcher(self._collector, loop=asyncio.get_running_loop()) - - logger.debug(f"Received request {request}") - if not self._inject_tensors(request): - error = ErrorFactory.create( - "ERR_DF_006", - caller=self, - input_data={"request_keys": list(request.keys())}, - tensor_names=[n for i in self._inputs for n in i.in_names] - ) - error.log(logger) - InferenceBase._export_errors() - yield error - return - - async_queue = asyncio.Queue() - self._async_dispatcher.append_async_queue(async_queue) - # Wait for all the results from one inference request - while not self._stop_event.is_set(): - try: - logger.debug("Waiting for tensors from async queue") - result = await async_queue.get() - if isinstance(result, Stop): - logger.info(f"Got Stop: {result.reason}") - return - if isinstance(result, Error): - InferenceBase._export_errors() - else: - logger.debug(f"Got result: {result}") - # post-process the data - result = self._post_process(result) - # Yield control to allow other requests to be processed - await asyncio.sleep(0) - yield result - except Exception as e: - error = ErrorFactory.from_exception(e, caller=self) - error.log(logger) - yield error - - def exec_sync(self, request): - """ execute a list of requests synchronously""" - from queue import Empty - - logger.debug(f"Received request {request}") - if not self._inject_tensors(request): - error = ErrorFactory.create( - "ERR_DF_006", - caller=self, - input_data={"request_keys": list(request.keys())}, - tensor_names=[n for i in self._inputs for n in i.in_names] - ) - error.log(logger) - yield error - return - - # Wait for all the results from one inference request - while not self._stop_event.is_set(): - try: - logger.debug("Waiting for tensors from collector") - result = self._collector.collect() - if isinstance(result, Stop): - logger.info(f"Got Stop: {result.reason}") - return - if isinstance(result, Error): - InferenceBase._export_errors() - else: - logger.debug(f"Got result: {result}") - # post-process the data - result = self._post_process(result) - yield result - except Empty: - continue - except Exception as e: - error = ErrorFactory.from_exception(e, caller=self) - error.log(logger) - yield error - - def finalize(self): - self._stop_event.set() - super().finalize() - - def _inject_tensors(self, request: Dict) -> bool: - """Inject tensors into the input flows""" - injected = False - matched = [[n for n in i.in_names if n in request and request[n]] for i in self._inputs] - reshuffled = sorted(range(len(matched)), key=lambda x: len(matched[x]), reverse=True) - for i in reshuffled: - input_flow = self._inputs[i] - tensors = {} - # select and transform the tensors for the input - for n in matched[i]: - if n in request: - tensors[n] = np.array(request.pop(n)) - if tensors: - logger.debug(f"Injecting tensors {tensors}") - input_flow.put(tensors) - input_flow.put(Stop(reason="end")) - injected = True - return injected - - def _post_process(self, data: Dict): - processed = {k: v for k, v in data.items()} - data_type_names = { i['name']: i['data_type'] for i in self._output_config} - for processor in self._processors: - if not all([i in data for i in processor.input]): - logger.warning(f"Input settings invalid for the processor: {processor.name}") - continue - input = [processed.pop(i) for i in processor.input] - logger.debug(f"Post-processor invoked with given input {input}") - output = processor(*input) - logger.debug(f"Post-processor generated output {output}") - if len(output) != len(processor.output): - logger.warning(f"Number of postprocessing output doesn't match the configuration, expecting {len(processor.output)}, while getting {len(output)}") - continue - # update as processed - for key, value in zip(processor.output, output): - processed[key] = value - # correct data type - if key not in data_type_names: - logger.warning(f"Invalid output parsed: {key}") - continue - data_type = data_type_names[key] - if isinstance(value, np.ndarray): - data_type = np_datatype_mapping[data_type] - if value.dtype != data_type: - processed[key] = value.astype(data_type) - elif isinstance(value, torch.Tensor): - data_type = torch_datatype_mapping[data_type] - if value.dtype != data_type: - processed[key] = value.to(data_type) - else: - processed[key] = value - # convert numpy and torch tensors to list for server to process - def convert_to_list(v, is_string_type): - """Convert numpy/torch tensor to list with proper string decoding""" - if isinstance(v, np.ndarray): - if is_string_type and (np.issubdtype(v.dtype, np.bytes_) or np.issubdtype(v.dtype, np.str_) or v.dtype == np.object_): - # Decode string arrays - flat_list = v.flatten().tolist() - decoded = [i.decode("utf-8", "ignore") if isinstance(i, bytes) else str(i) for i in flat_list] - return np.array(decoded).reshape(v.shape).tolist() - else: - return v.tolist() - elif isinstance(v, torch.Tensor): - if v.dtype == torch.uint8 and is_string_type: - # Decode byte tensors as strings - flat_list = v.flatten().tolist() - decoded = [bytes([i]).decode("utf-8", "ignore") if isinstance(i, int) else str(i) for i in flat_list] - return decoded if v.dim() == 1 else np.array(decoded).reshape(v.shape).tolist() - else: - return v.tolist() - elif dataclasses.is_dataclass(v): - return dataclasses.asdict(v) - else: - return v - - for key, value in processed.items(): - is_string_type = key in data_type_names and 'TYPE_STRING' in data_type_names[key] - if isinstance(value, list): - # this is a batch of data - processed[key] = [convert_to_list(v, is_string_type) for v in value] - else: - processed[key] = convert_to_list(value, is_string_type) - return processed diff --git a/tools/inference_builder/templates/responder/add_file.jinja.py b/tools/inference_builder/templates/responder/add_file.jinja.py deleted file mode 100644 index 282cdf2d..00000000 --- a/tools/inference_builder/templates/responder/add_file.jinja.py +++ /dev/null @@ -1,27 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} - - async def {{ name }}(self, request, file): - if not file: - return 400, "No file provided" - try: - asset = self._asset_manager.save_file(file.file, file.filename, file.content_type) - except Exception as e: - return 500, str(e) - response = self.process_response("{{ name }}", request, asset.to_dict()) - return 200, response - diff --git a/tools/inference_builder/templates/responder/add_live_stream.jinja.py b/tools/inference_builder/templates/responder/add_live_stream.jinja.py deleted file mode 100644 index 2bfe8403..00000000 --- a/tools/inference_builder/templates/responder/add_live_stream.jinja.py +++ /dev/null @@ -1,32 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} - - async def {{ name }}(self, request, body, **kwargs): - stream_info = self.process_request("{{ name }}", body) - if "url" not in stream_info or not stream_info["url"]: - return 400, "No url provided" - try: - asset = self._asset_manager.add_live_stream( - stream_info["url"], - stream_info["description"], - stream_info["username"], - stream_info["password"]) - except Exception as e: - return 500, str(e) - response = self.process_response("{{ name }}", request, asset.to_dict()) - return 200, response - diff --git a/tools/inference_builder/templates/responder/del_file.jinja.py b/tools/inference_builder/templates/responder/del_file.jinja.py deleted file mode 100644 index 0ed61407..00000000 --- a/tools/inference_builder/templates/responder/del_file.jinja.py +++ /dev/null @@ -1,25 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} - - async def {{ name }}(self, request, **kwargs): - asset_id = list(kwargs.values())[0] - try: - status = self._asset_manager.delete_asset(asset_id) - except Exception as e: - return 500, str(e) - response = self.process_response("{{ name }}", request, {"status": status}) - return 200, response diff --git a/tools/inference_builder/templates/responder/del_live_stream.jinja.py b/tools/inference_builder/templates/responder/del_live_stream.jinja.py deleted file mode 100644 index 0ed61407..00000000 --- a/tools/inference_builder/templates/responder/del_live_stream.jinja.py +++ /dev/null @@ -1,25 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} - - async def {{ name }}(self, request, **kwargs): - asset_id = list(kwargs.values())[0] - try: - status = self._asset_manager.delete_asset(asset_id) - except Exception as e: - return 500, str(e) - response = self.process_response("{{ name }}", request, {"status": status}) - return 200, response diff --git a/tools/inference_builder/templates/responder/healthy_ready.jinja.py b/tools/inference_builder/templates/responder/healthy_ready.jinja.py deleted file mode 100644 index ef32a650..00000000 --- a/tools/inference_builder/templates/responder/healthy_ready.jinja.py +++ /dev/null @@ -1,22 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} - - async def {{ name }}(self, request, **kwargs): - if super().healthy_ready(): - return 200, "Ready" - else: - return 503, "Service Unavailable" diff --git a/tools/inference_builder/templates/responder/infer.jinja.py b/tools/inference_builder/templates/responder/infer.jinja.py deleted file mode 100644 index 2d1b5f4b..00000000 --- a/tools/inference_builder/templates/responder/infer.jinja.py +++ /dev/null @@ -1,64 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} - - async def {{ name }}(self, request, body, **kwargs): - accept = request.headers.get("accept", "") - streaming = "application/x-ndjson" in accept - try: - in_data = self.process_request("{{ name }}", body) - self.logger.debug(f"request processed as {in_data}") - except Exception as e: - self.logger.error(f"Request processing failed: {type(e).__name__}: {e}") - return 400, str(e) - - def get_error_response(err): - """Get structured error response for EnhancedError, or simple dict for Error.""" - if isinstance(err, EnhancedError): - return err.to_dict(include_stack_trace=False) - return {"error": str(err)} - - try: - if streaming: - async def generate_stream(): - async for result in self._inference.execute(in_data): - # Yield control to allow other requests to be processed - await asyncio.sleep(0) - if not result: - if isinstance(result, Stop): - return # Normal termination - self.logger.error(f"Inference error: {result}") - yield get_error_response(result) - return - yield self.process_response("infer", request, result) - - return 200, StreamingResponse(generate_stream(), media_type="application/x-ndjson") - else: - # If not streaming, process and return the last result - response = None - async for result in self._inference.execute(in_data): - # Yield control to allow other requests to be processed - await asyncio.sleep(0) - if not result: - if isinstance(result, Stop): - break # Normal termination - self.logger.error(f"Inference error: {result}") - return 500, get_error_response(result) - response = self.process_response("infer", request, result) - return (200, response) if response else (500, "No results generated from inference") - except Exception as e: - self.logger.error(f"Inference failed: {type(e).__name__}: {e}") - return 500, str(e) diff --git a/tools/inference_builder/templates/responder/list_files.jinja.py b/tools/inference_builder/templates/responder/list_files.jinja.py deleted file mode 100644 index 85da82ea..00000000 --- a/tools/inference_builder/templates/responder/list_files.jinja.py +++ /dev/null @@ -1,24 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} - - async def {{ name }}(self, request): - try: - assets = [asset for asset in self._asset_manager.list_assets() if asset.file_name] - except Exception as e: - return 500, str(e) - response = self.process_response("{{ name }}", request, {"assets": assets}) - return 200, response diff --git a/tools/inference_builder/templates/responder/list_live_streams.jinja.py b/tools/inference_builder/templates/responder/list_live_streams.jinja.py deleted file mode 100644 index d7416ddb..00000000 --- a/tools/inference_builder/templates/responder/list_live_streams.jinja.py +++ /dev/null @@ -1,24 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} - - async def {{ name }}(self, request): - try: - assets = [asset for asset in self._asset_manager.list_assets() if not asset.file_name] - except Exception as e: - return 500, str(e) - response = self.process_response("{{ name }}", request, {"assets": assets}) - return 200, response diff --git a/tools/inference_builder/templates/serverless/inference.jinja.py b/tools/inference_builder/templates/serverless/inference.jinja.py deleted file mode 100644 index 79c8cb25..00000000 --- a/tools/inference_builder/templates/serverless/inference.jinja.py +++ /dev/null @@ -1,137 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} -{{ license }} - -import argparse -from config import global_config -from typing import List, Optional -from lib.inference import py_datatype_mapping -from .model import GenericInference -import json -import sys -from lib.utils import NumpyFlatEncoder - - -def create_parser(inputs: List) -> argparse.ArgumentParser: - """Create and configure the argument parser for the application.""" - parser = argparse.ArgumentParser( - description='Command line interface for{{ service_name }}' - ) - for input_item in inputs: - input_name = input_item.name.replace("_", "-") - nargs = 1 - optional = input_item.optional if hasattr(input_item, "optional") else False - if len(input_item.dims) == 1: - if input_item.dims[0] == -1: - nargs = "+" - elif input_item.dims[0] == 1 and optional: - nargs = "?" - else: - nargs = input_item.dims[0] - parser.add_argument( - f'--{input_name}', - type=py_datatype_mapping[input_item.data_type], - nargs=nargs, - required=not optional - ) - parser.add_argument( - '-s', - '--save-to', - type=str, - nargs='?', - const='stdout', - help='Save results to a file, or use -s alone for stdout' - ) - return parser - - -def run_inference(args) -> Optional[int]: - """Run the inference service synchronously. - - Args: - args: Parsed command line arguments - - Returns: - Optional[int]: Exit code, 0 for success - """ - # Read and remove save_to argument before converting to dict - save_to = getattr(args, 'save_to', None) - if hasattr(args, 'save_to'): - delattr(args, 'save_to') - - # Convert remaining args to dictionary - inputs = vars(args) - - # Convert input names back to original format (replace '-' with '_') - inputs = {k.replace('-', '_'): v for k, v in inputs.items()} - - service = GenericInference() - service.initialize() - status = 0 - - # Determine output target: file, stdout, or None (no output) - output_file = None - close_file = False - if save_to: - if save_to in ('-', 'stdout'): - output_file = sys.stdout - else: - output_file = open(save_to, 'w', encoding='utf-8') - close_file = True - - try: - for result in service.exec_sync(inputs): - if not result: - status = -1 - break - # Unified logic: write JSON to output target if specified - if output_file: - json_str = json.dumps(result, indent=4, cls=NumpyFlatEncoder) - output_file.write(json_str) - output_file.write("\n") - if close_file: - output_file.flush() # Ensure data is written to disk - finally: - if close_file and output_file: - output_file.close() - - print("Inference completed.") - service.finalize() - return status - - -def main() -> int: - """Main entry point for the inference service. - - Returns: - Optional[int]: Exit code, None for success - """ - parser = create_parser(global_config.input) - args = parser.parse_args() - - try: - return run_inference(args) - except KeyboardInterrupt: - print("\nInterrupted by user") - return 130 - except Exception as e: - print(f"Error: {e}") - return 1 - - -if __name__ == "__main__": - exit(main()) diff --git a/tools/inference_builder/templates/triton/model.jinja.py b/tools/inference_builder/templates/triton/model.jinja.py deleted file mode 100644 index 2335740f..00000000 --- a/tools/inference_builder/templates/triton/model.jinja.py +++ /dev/null @@ -1,274 +0,0 @@ -{# - SPDX-FileCopyrightText: Copyright (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. - SPDX-License-Identifier: Apache-2.0 - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -#} - -from config import global_config -import triton_python_backend_utils as pb_utils -from lib.inference import * -from lib.utils import * -from omegaconf import OmegaConf -import json -import os -from typing import List, Dict -import asyncio -from concurrent.futures import ThreadPoolExecutor -import numpy as np -import torch -from pathlib import Path - -logger = get_logger(__name__) - -CHECKPOINTS_DIR = os.getenv("CHECKPOINTS_DIR", Path(__file__).resolve().parent.parent.parent) - -triton_input_type_map = { - "TYPE_CUSTOM_BINARY_BASE64": "TYPE_STRING", - "TYPE_CUSTOM_IMAGE_BASE64": "TYPE_STRING" -} - -{% for backend in backends %} -{{ backend }} -{% endfor %} - -{% if top_level %} - -class TritonPythonModel(InferenceBase): - """The top level python model that drives the inference flow""" - - @staticmethod - def auto_complete_config(auto_complete_model_config): - # create a minimum config - auto_complete_model_config.set_max_batch_size(1) - auto_complete_model_config.set_dynamic_batching() - auto_complete_model_config.set_model_transaction_policy({"decoupled": True}) - for input in global_config.input: - input_config = OmegaConf.to_container(input) - data_type = input_config["data_type"] - if data_type.startswith("TYPE_CUSTOM_"): - data_type = triton_input_type_map[data_type] if data_type in triton_input_type_map else None - if data_type is None: - raise Exception(f"Unsupported input type: {data_type}") - input_config["data_type"] = data_type - auto_complete_model_config.add_input(input_config) - for output in global_config.output: - output_config = OmegaConf.to_container(output) - auto_complete_model_config.add_output(output_config) - logger.info(f"Model configuration completed as {auto_complete_model_config.as_dict()}") - return auto_complete_model_config - - def _create_backend(self, backend_spec: List[str], model_config: Dict, model_home: str): - if backend_spec[0] == 'triton': - return TritonBackend(model_config, model_home) - return None - - async def execute(self, requests): - """ execute a list of requests""" - async def async_put(queue, item): - await queue.put(item) - def thread_to_async_bridge(thread_queue, async_queue, loop): - while True: - try: - item = thread_queue.get() - asyncio.run_coroutine_threadsafe(async_put(async_queue, item), loop) - if isinstance(item, Stop) or isinstance(item, Error): - break - except Empty: - continue - - - logger.info(f"Received {len(requests)} request(s)") - for request in requests: - response_sender = request.get_response_sender() - if request.is_cancelled(): - response_sender.send(pb_utils.InferenceResponse( - error=pb_utils.TritonError("request is cancelled", pb_utils.TritonError.CANCELLED)), - flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL) - continue - for input in self._inputs: - # select the tensors for the input - tensors = {n: pb_utils.get_input_tensor_by_name(request, n) for n in input.in_names} - # the tensors need to be transformed to generic type - for name in tensors: - tensor = tensors[name] - config = next((c for c in self._input_config if c['name'] == name), None) - if config is None: - logger.warning(f"Invalid input parsed: {name}") - continue - dims = config['dims'] - if pb_utils.Tensor.is_cpu(tensor): - tensor = tensor.as_numpy() - # auto reshape - if len(tensor.shape) == (len(dims)+1): - tensor = np.squeeze(tensor, 0) - else: - tensor = torch.utils.dlpack.from_dlpack(tensor.to_dlpack()) - # auto reshape - if len(tensor.shape) == (len(dims)+1): - tensor = torch.squeeze(tensor, 0) - tensors[name] = tensor - if tensors: - logger.debug(f"Injecting tensors {tensors}") - input.put(tensors) - input.put(Stop(reason="end")) - # fetch result - loop = asyncio.get_event_loop() - for a_output, output in zip(self._async_outputs, self._outputs): - logger.error(f"Submitting {output._timeout} to async executor") - self._async_executor.submit(thread_to_async_bridge, output, a_output, loop) - stop = False - while not stop: - try: - logger.debug("Waiting for tensors from async queue") - response_data = dict() - results = await asyncio.gather(*(ao.get() for ao in self._async_outputs)) - for data in results: - logger.debug(f"Got output data: {data}") - if isinstance(data, Error): - error = pb_utils.TritonError(f"steam llm_response, error received: {data.message}") - response_sender.send( - pb_utils.InferenceResponse(error=error), - flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL - ) - return - elif isinstance(data, Stop): - stop = True - continue - # collect the output - for k, v in data.items(): - response_data[k] = v - response_data = self._post_process(response_data) - # response with partial data - response_sender.send(pb_utils.InferenceResponse( - output_tensors=[ - pb_utils.Tensor(name, tensor) for name, tensor in response_data.items() - ] - )) - except Exception as e: - logger.exception(e) - - response_sender.send(flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL) - logger.debug(f"Finalizing the response for requests: {request}") - logger.debug(f"All request done") - - def finalize(self): - super().finalize() - - def _post_process(self, data: Dict): - processed = {k: v for k, v in data.items()} - for processor in self._processors: - if not all([i in data for i in processor.input]): - logger.warning(f"Input settings invalid for the processor: {processor}") - continue - input = [processed.pop(i) for i in processor.input] - logger.debug(f"Post-processor invoked with given input {input}") - output = processor(*input) - logger.debug(f"Post-processor generated output {output}") - if len(output) != len(processor.output): - logger.warning(f"Number of postprocessing output doesn't match the configuration, expecting {len(processor.output)}, while getting {len(output)}") - continue - # update as processed - for key, value in zip(processor.output, output): - processed[key] = value - return processed - -{% else %} -class TritonPythonModel: - def initialize(self, args): - """ - This function allows - the model to initialize any state associated with this model. - """ - model_name = args["model_name"] - model_home = os.path.join(global_config.model_repo, model_name, "1") - model_config = next((m for m in global_config.models if m.name == model_name), None) - if model_config is None: - raise Exception("Model config not found") - self._model_backend = None - self._model_config = OmegaConf.to_container(model_config) - self._in_config = {i["name"]: i for i in self._model_config["input"]} - self._out_config = {o["name"]: o for o in self._model_config["output"]} - self._model_repo = args["model_repository"] - self._device_id = int(json.loads(args["model_instance_device_id"])) - # default model path - engine_path = os.path.join(self._model_repo, "1", "model.plan") - if "tensorrt_engine" in self._model_config: - self._model_config["tensorrt_engine"] = os.path.join(CHECKPOINTS_DIR, self._model_config["tensorrt_engine"]) - else: - self._model_config["tensorrt_engine"] = engine_path - # create backend - BackendClass = None - backend_spec = model_config.backend.split('/') - if backend_spec[-1] == "tensorrtllm": - BackendClass = TensorRTLLMBackend - elif backend_spec[-1] == "polygraphy": - BackendClass = PolygraphBackend - elif backend_spec[-1] == "dummy": - BackendClass = DummyBackend - if BackendClass is not None: - self._model_backend = BackendClass(self._model_config, model_home,self._device_id) - else: - raise Exception(f"Backend not supported: {model_config.backend}") - - - def execute(self, requests): - """ - execute each request - """ - logger.info(f"Model {self._model_config['name']} received {len(requests)} requests") - - r_list = [] - responses = [] - for request in requests: - inputs = {k: None for k in self._in_config} - for name, config in self._in_config.items(): - dims = config['dims'] - pb_tensor = pb_utils.get_input_tensor_by_name(request, name) - if pb_utils.Tensor.is_cpu(pb_tensor): - tensor = pb_tensor.as_numpy() - # auto reshape - if len(dims) > 1 and len(tensor.shape) == (len(dims)+1): - tensor = np.squeeze(tensor, 0) - else: - tensor = torch.utils.dlpack.from_dlpack(pb_tensor.to_dlpack()) - # auto reshape - if len(dims) > 1 and len(tensor.shape) == (len(dims)+1): - tensor = torch.squeeze(tensor, 0) - inputs[name] = tensor - - for output in self._model_backend(**inputs): - r_list.append(output) - for r in r_list: - logger.info(f"Model {self._model_config['name']} generating responses {r}") - for k in r: - v = r[k] - force_cpu = True if "force_cpu" in self._out_config[k] and self._out_config[k]["force_cpu"] else False - if isinstance(v, torch.Tensor): - tensor = pb_utils.Tensor(k, v.cpu().numpy()) if force_cpu else pb_utils.Tensor.from_dlpack(k, torch.utils.dlpack.to_dlpack(v)) - elif isinstance(v, np.ndarray): - tensor = pb_utils.Tensor(k, v) - elif hasattr(v, "__dlpack__") and hasattr(v, "__dlpack_device__"): - tensor = pb_utils.Tensor(k, np.from_dlpack(v)) if force_cpu else pb_utils.Tensor.from_dlpack(k, v) - else: - responses.append( - pb_utils.InferenceResponse( - pb_utils.TritonError(f"Unsupported tensor format from the backend. {v}") - ) - ) - break - r[k] = tensor - responses.append(pb_utils.InferenceResponse(output_tensors=[r[k] for k in r])) - - return responses -{% endif %} \ No newline at end of file diff --git a/tools/inference_builder/tools/trt_build.py b/tools/inference_builder/tools/trt_build.py deleted file mode 100644 index 04598a1a..00000000 --- a/tools/inference_builder/tools/trt_build.py +++ /dev/null @@ -1,110 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import tensorrt as trt - -def build_engine(onnx_file_path, min_batch_size, opt_batch_size, max_batch_size, max_text_len): - # Create a TensorRT logger and builder - TRT_LOGGER = trt.Logger(trt.Logger.WARNING) - trt.init_libnvinfer_plugins(TRT_LOGGER, namespace="") - builder = trt.Builder(TRT_LOGGER) - network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) - parser = trt.OnnxParser(network, TRT_LOGGER) - - # Read the ONNX file - with open(onnx_file_path, "rb") as model: - if not parser.parse(model.read()): - print("Failed to parse the ONNX file!") - for error in range(parser.num_errors): - print(parser.get_error(error)) - return None - - inputs = [network.get_input(i) for i in range(network.num_inputs)] - outputs = [network.get_output(i) for i in range(network.num_outputs)] - # Set builder configurations - config = builder.create_builder_config() - config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 2 << 30) # 2GB of workspace - config.set_flag(trt.BuilderFlag.FP16) -# builder.max_batch_size = max_batch_size - profile = builder.create_optimization_profile() - for model_input in inputs: - print(f"Input {model_input.name} with shape {model_input.shape} and dtype {model_input.dtype}") - input_shape = model_input.shape - input_name = model_input.name - if input_name == 'inputs': - real_shape_min = (min_batch_size, *input_shape[1:]) - real_shape_opt = (opt_batch_size, *input_shape[1:]) - real_shape_max = (max_batch_size, *input_shape[1:]) - elif input_name == 'text_token_mask': - real_shape_min = (min_batch_size, max_text_len, max_text_len) - real_shape_opt = (opt_batch_size, max_text_len, max_text_len) - real_shape_max = (max_batch_size, max_text_len, max_text_len) - else: - real_shape_min = (min_batch_size, max_text_len) - real_shape_opt = (opt_batch_size, max_text_len) - real_shape_max = (max_batch_size, max_text_len) - - profile.set_shape(input=input_name, - min=real_shape_min, - opt=real_shape_opt, - max=real_shape_max) - - config.add_optimization_profile(profile) - - for output in outputs: - print(f"Output {output.name} with shape {output.shape} and dtype {output.dtype}") - - # Build and return the engine - print("Building the engine. This might take a while...") - engine = builder.build_serialized_network(network, config) - if engine: - print("Engine built successfully!") - return engine - -def save_engine(engine, file_name): - with open(file_name, "wb") as f: - f.write(engine) - -def main(args): - onnx_file_path = args.input # Your ONNX file path - engine_file_path = args.output # Output TensorRT engine file - - engine = build_engine(onnx_file_path, args.min_batch_size, args.opt_batch_size, args.max_batch_size, args.max_text_len) - if engine: - save_engine(engine, engine_file_path) - print(f"Engine saved to {engine_file_path}") - -if __name__ == "__main__": - parser = argparse.ArgumentParser("TRT Builder") - parser.add_argument("input", type=str, help="Onnx input file") - parser.add_argument("-o", "--output", type=str, help="TensorRT engine file", default="model.plan") - parser.add_argument('--max_batch_size', - type=int, - default=1, - help="Maximum batch size for input images") - parser.add_argument('--min-batch-size', - type=int, - default=1, - help="Minimum batch size for input images") - parser.add_argument('--opt-batch-size', - type=int, - default=1, - help="Optimal batch size for input images") - parser.add_argument('--max-text-len', - type=int, - default=256, - help="Maximum text length for input prompt") - main(parser.parse_args())