Training neural networks in TensorFlow 2.0 with 5x less memory
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Updated
Feb 21, 2022 - Python
Training neural networks in TensorFlow 2.0 with 5x less memory
A Toolkit for Training, Tracking, Saving Models and Syncing Results
A memory profiler for NVIDIA GPUs to explore memory inefficiencies in GPU-accelerated applications.
Demonstration of generating mini-batches in Tensorlfow from GPU memory.
Dynamic GPU Layer Swapping: Train large models on consumer GPUs with intelligent memory management
A CLI tool for estimating GPU VRAM requirements for Hugging Face models, supporting various data types, parallelization strategies, and fine-tuning scenarios like LoRA.
Research harness for evaluating query-time bounded elimination of reconstructable KV-cache witnesses in long-context transformer inference workloads. Related provisional filing: IN 202641062451.
Deadline-aware KV-cache scheduling for protecting decode-critical request-state under long-context LLM inference pressure.
📊 A command line monitoring tool (graph) for NVIDIA GPUs
Tiered GPU memory architecture for consumer AI inference. VRAM as execution cache, system RAM as passive staging layer.
GPU memory-efficient training for PyTorch - 90%+ memory savings through gradient compression
Hardware Control GateKeeper Kernels for AI inference within frameworks.
Kernel panic prevention for MLX on Apple Silicon. Five pre-flight safety checks before model loading — because Metal doesn't warn you, it just reboots.
Production-grade PyTorch training monitor. Wraps your loop in one context manager to track loss, gradients, LR, GPU memory and throughput, with real-time alerts for NaN, explosions, plateaus, and OOM.
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