SynapseML (previously known as MMLSpark), is an open-source library that simplifies the creation of massively scalable machine learning (ML) pipelines. SynapseML provides simple, composable, and distributed APIs for a wide variety of different machine learning tasks such as text analytics, vision, anomaly detection, and many others. SynapseML is built on the Apache Spark distributed computing framework and shares the same API as the SparkML/MLLib library, allowing you to seamlessly embed SynapseML models into existing Apache Spark workflows.
With SynapseML, you can build scalable and intelligent systems to solve challenges in domains such as anomaly detection, computer vision, deep learning, text analytics, and others. SynapseML can train and evaluate models on single-node, multi-node, and elastically resizable clusters of computers. This lets you scale your work without wasting resources. SynapseML is usable across Python, R, Scala, Java, and .NET. Furthermore, its API abstracts over a wide variety of databases, file systems, and cloud data stores to simplify experiments no matter where data is located.
SynapseML publishes runtime-specific JVM artifacts: Spark 3.5 uses Scala 2.12, while Spark 4.0 and 4.1 use Scala 2.13. See the installation matrix before selecting a Maven coordinate.
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| Docs |     |
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Table of Contents
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|---|---|---|---|
| Vowpal Wabbit on Spark | The Cognitive Services for Big Data | LightGBM on Spark | Spark Serving |
| Fast, Sparse, and Effective Text Analytics | Leverage the Microsoft Cognitive Services at Unprecedented Scales in your existing SparkML pipelines | Train Gradient Boosted Machines with LightGBM | Serve any Spark Computation as a Web Service with Sub-Millisecond Latency |
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| HTTP on Spark | ONNX on Spark | Responsible AI | Spark Binding Autogeneration |
| An Integration Between Spark and the HTTP Protocol, enabling Distributed Microservice Orchestration | Distributed and Hardware Accelerated Model Inference on Spark | Understand Opaque-box Models and Measure Dataset Biases | Automatically Generate Spark bindings for PySpark and SparklyR |
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| Isolation Forest on Spark | CyberML | Conditional KNN |
| Distributed Nonlinear Outlier Detection | Machine Learning Tools for Cyber Security | Scalable KNN Models with Conditional Queries |
For quickstarts, documentation, demos, and examples please see our website.
SynapseML installation has two parts: the language wrapper and the JVM
artifacts loaded by Spark. Installing synapseml from PyPI does not add the
JVM artifacts. A Python wrapper can import successfully while its JVM class is
missing; using a _2.12 artifact with Spark 4 can produce errors such as
LightGBMClassifier does not exist in the JVM.
Choose one complete published build from the Spark runtime. master is the
canonical Spark 3.5 development line; Spark 4.0 and Spark 4.1 are maintained on
their corresponding branches.
| Code line | Spark runtime | Scala | Python baseline | Release tag | Python package | Maven coordinate |
|---|---|---|---|---|---|---|
master |
Spark 3.5.x | 2.12 | Python 3.11 | v1.1.3 |
synapseml==1.1.3 |
com.microsoft.azure:synapseml_2.12:1.1.3 |
spark4.0 |
Spark 4.0.1+ (<4.1) |
2.13 | Python 3.12 | v1.1.3-spark4.0 |
synapseml==1.1.3 |
com.microsoft.azure:synapseml_2.13:1.1.3-spark4.0 |
spark4.1 |
Spark 4.1.x | 2.13 | Python 3.13 | v1.1.3-spark4.1 |
synapseml==1.1.3 |
com.microsoft.azure:synapseml_2.13:1.1.3-spark4.1 |
Always configure
https://mmlspark.blob.core.windows.net/maven, where the Spark 4 artifacts are
published. See the full installation guide for platform-specific details.
The latest successful master build targets Spark 3.5 and Scala 2.12. This
copy-ready command reads the current snapshot version published by CI and starts
Spark with that exact JVM build:
MASTER_VERSION="$(
curl -fsSL https://mmlspark.blob.core.windows.net/icons/badges/master_version3.svg |
sed -n 's/.*aria-label="master version: \([^"]*\)".*/\1/p'
)"
test -n "$MASTER_VERSION"
spark-shell \
--repositories "https://mmlspark.blob.core.windows.net/maven" \
--packages "com.microsoft.azure:synapseml_2.12:${MASTER_VERSION}"The PyPI package contains released Python wrappers. If you need Python APIs
that are new on master, build the matching wheel from source.
First select the correct platform that you are installing SynapseML into:
In Microsoft Fabric notebooks SynapseML is already installed. The following copy-ready override targets a Spark 4.1 / Scala 2.13 runtime:
%%configure -f
{
"name": "synapseml",
"conf": {
"spark.jars.packages": "com.microsoft.azure:synapseml_2.13:1.1.3-spark4.1",
"spark.jars.repositories": "https://mmlspark.blob.core.windows.net/maven",
"spark.jars.excludes": "org.scala-lang:scala-reflect,org.apache.spark:spark-tags_2.13,org.scalactic:scalactic_2.13,org.scalatest:scalatest_2.13,com.fasterxml.jackson.core:jackson-databind",
"spark.yarn.user.classpath.first": "true",
"spark.sql.parquet.enableVectorizedReader": "false"
}
}Current Azure Synapse pools use Spark 3.5. Place the following in the first cell of your notebook:
%%configure -f
{
"name": "synapseml",
"conf": {
"spark.jars.packages": "com.microsoft.azure:synapseml_2.12:1.1.3",
"spark.jars.repositories": "https://mmlspark.blob.core.windows.net/maven",
"spark.jars.excludes": "org.scala-lang:scala-reflect,org.apache.spark:spark-tags_2.12,org.scalactic:scalactic_2.12,org.scalatest:scalatest_2.12,com.fasterxml.jackson.core:jackson-databind",
"spark.yarn.user.classpath.first": "true",
"spark.sql.parquet.enableVectorizedReader": "false"
}
}To install at the pool level instead of the notebook level add the spark properties listed above to the pool configuration.
To install SynapseML on the Databricks cloud, create a new library from Maven coordinates in your workspace.
Use one of these exact Maven coordinates:
- Spark 4.1 / Scala 2.13:
com.microsoft.azure:synapseml_2.13:1.1.3-spark4.1 - Spark 4.0 / Scala 2.13:
com.microsoft.azure:synapseml_2.13:1.1.3-spark4.0 - Spark 3.5 / Scala 2.12:
com.microsoft.azure:synapseml_2.12:1.1.3
Add the resolver https://mmlspark.blob.core.windows.net/maven, attach the
library to the target cluster, and restart it before importing synapse.ml.
You can use SynapseML in both your Scala and PySpark notebooks. To get started with our example notebooks import the following databricks archive:
https://mmlspark.blob.core.windows.net/dbcs/SynapseMLExamplesv1.1.3.dbc
Choose exactly one complete runtime variant below, then start Spark with that variant's JVM artifact.
Spark 4.1 / Python 3.13
python -m pip install "synapseml==1.1.3" "pyspark>=4.1,<4.2"Spark 4.0 / Python 3.12
python -m pip install "synapseml==1.1.3" "pyspark>=4.0.1,<4.1"Spark 3.5 / Python 3.11
python -m pip install "synapseml==1.1.3" "pyspark>=3.5,<3.6"from pyspark.sql import SparkSession
# Spark 4.1. Select the coordinate matching the PySpark command used above.
synapseml_coordinate = "com.microsoft.azure:synapseml_2.13:1.1.3-spark4.1"
# Spark 4.0:
# synapseml_coordinate = "com.microsoft.azure:synapseml_2.13:1.1.3-spark4.0"
# Spark 3.5:
# synapseml_coordinate = "com.microsoft.azure:synapseml_2.12:1.1.3"
spark = (
SparkSession.builder.appName("MyApp")
.config("spark.jars.packages", synapseml_coordinate)
.config(
"spark.jars.repositories",
"https://mmlspark.blob.core.windows.net/maven",
)
.getOrCreate()
)
import synapse.mlSynapseML can be conveniently installed on existing Spark clusters via the
--packages option. Each example below is independently copyable.
# Spark 4.1
pyspark --repositories "https://mmlspark.blob.core.windows.net/maven" \
--packages "com.microsoft.azure:synapseml_2.13:1.1.3-spark4.1"# Spark 4.0
pyspark --repositories "https://mmlspark.blob.core.windows.net/maven" \
--packages "com.microsoft.azure:synapseml_2.13:1.1.3-spark4.0"# Spark 3.5
pyspark --repositories "https://mmlspark.blob.core.windows.net/maven" \
--packages "com.microsoft.azure:synapseml_2.12:1.1.3"Choose the dependency matching your Spark runtime.
Spark 4.1
resolvers += "SynapseML" at "https://mmlspark.blob.core.windows.net/maven"
libraryDependencies +=
"com.microsoft.azure" % "synapseml_2.13" % "1.1.3-spark4.1"Spark 4.0
resolvers += "SynapseML" at "https://mmlspark.blob.core.windows.net/maven"
libraryDependencies +=
"com.microsoft.azure" % "synapseml_2.13" % "1.1.3-spark4.0"Spark 3.5
resolvers += "SynapseML" at "https://mmlspark.blob.core.windows.net/maven"
libraryDependencies +=
"com.microsoft.azure" % "synapseml_2.12" % "1.1.3"To install SynapseML from within a Jupyter notebook served by Apache Livy, the following Spark 3.5 / Scala 2.12 configure magic can be used. You will need to start a new session after this configure cell is executed.
Excluding certain packages from the library may be necessary due to current issues with Livy 0.5.
%%configure -f
{
"name": "synapseml",
"conf": {
"spark.jars.packages": "com.microsoft.azure:synapseml_2.12:1.1.3",
"spark.jars.repositories": "https://mmlspark.blob.core.windows.net/maven",
"spark.jars.excludes": "org.scala-lang:scala-reflect,org.apache.spark:spark-tags_2.12,org.scalactic:scalactic_2.12,org.scalatest:scalatest_2.12,com.fasterxml.jackson.core:jackson-databind"
}
}The easiest way to evaluate SynapseML is via our pre-built Docker container. To do so, run the following command:
docker run -it -p 8888:8888 -e ACCEPT_EULA=yes mcr.microsoft.com/mmlspark/release jupyter notebookNavigate to http://localhost:8888/ in your web browser to run the sample notebooks. See the documentation for more on Docker use.
To read the EULA for using the docker image, run
docker run -it -p 8888:8888 mcr.microsoft.com/mmlspark/release eula
To try out SynapseML using the R autogenerated wrappers see our instructions. Note: This feature is still under development and some necessary custom wrappers may be missing.
SynapseML has recently transitioned to a new build infrastructure. For detailed developer docs please see the Developer Readme
If you are an existing synapsemldeveloper, you will need to reconfigure your development setup. We now support platform independent development and better integrate with intellij and SBT. If you encounter issues please reach out to our support email!
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Visit our website.
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Watch our keynote demos at the Spark+AI Summit 2019, the Spark+AI European Summit 2018, the Spark+AI Summit 2018 and SynapseML at the Spark Summit.
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See how SynapseML is used to help endangered species.
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Explore generative adversarial artwork in our collaboration with The MET and MIT.
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Explore our collaboration with Apache Spark on image analysis.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
See CONTRIBUTING.md for contribution guidelines.
To give feedback and/or report an issue, open a GitHub Issue.
Apache®, Apache Spark, and Spark® are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.