# SynapseML **Repository Path**: mirrors_microsoft/SynapseML ## Basic Information - **Project Name**: SynapseML - **Description**: Simple and Distributed Machine Learning - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-08-25 - **Last Updated**: 2026-09-26 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ![SynapseML](https://mmlspark.blob.core.windows.net/icons/mmlspark.svg) # Synapse Machine Learning 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](https://spark.apache.org/) and shares the same API as the [SparkML/MLLib library](https://spark.apache.org/mllib/), 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](#setup-and-installation) before selecting a Maven coordinate. | Topics | Links | | :------ | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Build | [![Build Status](https://msdata.visualstudio.com/A365/_apis/build/status/microsoft.SynapseML?branchName=master)](https://msdata.visualstudio.com/A365/_build/latest?definitionId=17563&branchName=master) [![codecov](https://codecov.io/gh/Microsoft/SynapseML/branch/master/graph/badge.svg)](https://codecov.io/gh/Microsoft/SynapseML) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) | | Version | [![Version](https://img.shields.io/badge/version-1.1.3-blue)](https://github.com/Microsoft/SynapseML/releases) [![Release Notes](https://img.shields.io/badge/release-notes-blue)](https://github.com/Microsoft/SynapseML/releases) [![Snapshot Version](https://mmlspark.blob.core.windows.net/icons/badges/master_version3.svg)](#sbt) | | Docs | [![Website](https://img.shields.io/badge/SynapseML-Website-blue)](https://aka.ms/spark) [![Scala Docs](https://img.shields.io/static/v1?label=api%20docs&message=scala&color=blue&logo=scala)](https://mmlspark.blob.core.windows.net/docs/1.1.3/scala/index.html#package) [![PySpark Docs](https://img.shields.io/static/v1?label=api%20docs&message=python&color=blue&logo=python)](https://mmlspark.blob.core.windows.net/docs/1.1.3/pyspark/index.html) [![Academic Paper](https://img.shields.io/badge/academic-paper-7fdcf7)](https://arxiv.org/abs/1810.08744) | | Support | [![Gitter](https://badges.gitter.im/Microsoft/MMLSpark.svg)](https://gitter.im/Microsoft/MMLSpark?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) [![Mail](https://img.shields.io/badge/mail-synapseml--support-brightgreen)](mailto:synapseml-support@microsoft.com) | | Binder | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/microsoft/SynapseML/v1.1.3?labpath=notebooks%2Ffeatures) | | Usage | [![Downloads](https://static.pepy.tech/badge/synapseml)](https://pepy.tech/project/synapseml) |
Table of Contents - [Synapse Machine Learning](#synapse-machine-learning) - [Features](#features) - [Documentation and Examples](#documentation-and-examples) - [Setup and installation](#setup-and-installation) - [Microsoft Fabric](#microsoft-fabric) - [Synapse Analytics](#synapse-analytics) - [Databricks](#databricks) - [Python Standalone](#python-standalone) - [Spark Submit](#spark-submit) - [SBT](#sbt) - [Apache Livy and HDInsight](#apache-livy-and-hdinsight) - [Docker](#docker) - [R](#r) - [Building from source](#building-from-source) - [Papers](#papers) - [Learn More](#learn-more) - [Contributing \& feedback](#contributing--feedback) - [Other relevant projects](#other-relevant-projects)
## Features | | | | | | :----------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------: | | [**Vowpal Wabbit on Spark**](https://microsoft.github.io/SynapseML/docs/Explore%20Algorithms/Vowpal%20Wabbit/Overview/) | [**The Cognitive Services for Big Data**](https://microsoft.github.io/SynapseML/docs/Explore%20Algorithms/AI%20Services/Overview/) | [**LightGBM on Spark**](https://microsoft.github.io/SynapseML/docs/Explore%20Algorithms/LightGBM/Overview/) | [**Spark Serving**](https://microsoft.github.io/SynapseML/docs/Deploy%20Models/Overview/) | | 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 | | | | | | | :----------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------: |:-----------------------------------------------------------------------------------------------------------------------:| | [**HTTP on Spark**](https://microsoft.github.io/SynapseML/docs/Explore%20Algorithms/AI%20Services/Overview/#arbitrary-web-apis) | [**ONNX on Spark**](https://microsoft.github.io/SynapseML/docs/Explore%20Algorithms/Deep%20Learning/ONNX/) | [**Responsible AI**](https://microsoft.github.io/SynapseML/docs/Explore%20Algorithms/Responsible%20AI/Interpreting%20Model%20Predictions/) | [**Spark Binding Autogeneration**](https://microsoft.github.io/SynapseML/docs/Reference/Developer%20Setup/#packagepython) | | 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 | | | | | | :-----------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------: | | [**Isolation Forest on Spark**](https://microsoft.github.io/SynapseML/docs/Explore%20Algorithms/Anomaly%20Detection/Quickstart%20-%20Isolation%20Forests/) | [**CyberML**](https://microsoft.github.io/SynapseML/docs/Explore%20Algorithms/Other%20Algorithms/Cyber%20ML/) | [**Conditional KNN**](https://microsoft.github.io/SynapseML/docs/Explore%20Algorithms/Other%20Algorithms/Quickstart%20-%20Exploring%20Art%20Across%20Cultures/) | | Distributed Nonlinear Outlier Detection | Machine Learning Tools for Cyber Security | Scalable KNN Models with Conditional Queries | ## Documentation and Examples For quickstarts, documentation, demos, and examples please see our [website](https://aka.ms/spark). ## Setup and installation 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`](https://github.com/microsoft/SynapseML/tree/master) | Spark 3.5.x | 2.12 | Python 3.11 | [`v1.1.3`](https://github.com/microsoft/SynapseML/tree/v1.1.3) | `synapseml==1.1.3` | `com.microsoft.azure:synapseml_2.12:1.1.3` | | [`spark4.0`](https://github.com/microsoft/SynapseML/tree/spark4.0) | Spark 4.0.1+ (`<4.1`) | 2.13 | Python 3.12 | [`v1.1.3-spark4.0`](https://github.com/microsoft/SynapseML/tree/v1.1.3-spark4.0) | `synapseml==1.1.3` | `com.microsoft.azure:synapseml_2.13:1.1.3-spark4.0` | | [`spark4.1`](https://github.com/microsoft/SynapseML/tree/spark4.1) | Spark 4.1.x | 2.13 | Python 3.13 | [`v1.1.3-spark4.1`](https://github.com/microsoft/SynapseML/tree/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. ### Latest master snapshot 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: ```bash 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: - [Synapse Machine Learning](#synapse-machine-learning) - [Features](#features) - [Documentation and Examples](#documentation-and-examples) - [Setup and installation](#setup-and-installation) - [Latest master snapshot](#latest-master-snapshot) - [Microsoft Fabric](#microsoft-fabric) - [Synapse Analytics](#synapse-analytics) - [Databricks](#databricks) - [Python Standalone](#python-standalone) - [Spark Submit](#spark-submit) - [SBT](#sbt) - [Apache Livy and HDInsight](#apache-livy-and-hdinsight) - [Docker](#docker) - [R](#r) - [Building from source](#building-from-source) - [Papers](#papers) - [Learn More](#learn-more) - [Contributing \& feedback](#contributing--feedback) - [Other relevant projects](#other-relevant-projects) ### Microsoft Fabric In Microsoft Fabric notebooks SynapseML is already installed. The following copy-ready override targets a Spark 4.1 / Scala 2.13 runtime: ```bash %%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" } } ``` ### Synapse Analytics Current Azure Synapse pools use Spark 3.5. Place the following in the first cell of your notebook: ```bash %%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](https://techcommunity.microsoft.com/t5/azure-synapse-analytics-blog/how-to-set-spark-pyspark-custom-configs-in-synapse-workspace/ba-p/2114434). ### Databricks To install SynapseML on the [Databricks cloud](http://community.cloud.databricks.com), create a new [library from Maven coordinates](https://docs.databricks.com/user-guide/libraries.html#libraries-from-maven-pypi-or-spark-packages) 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` ### Python Standalone Choose exactly one complete runtime variant below, then start Spark with that variant's JVM artifact. **Spark 4.1 / Python 3.13** ```bash python -m pip install "synapseml==1.1.3" "pyspark>=4.1,<4.2" ``` **Spark 4.0 / Python 3.12** ```bash python -m pip install "synapseml==1.1.3" "pyspark>=4.0.1,<4.1" ``` **Spark 3.5 / Python 3.11** ```bash python -m pip install "synapseml==1.1.3" "pyspark>=3.5,<3.6" ``` ```python 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.ml ``` ### Spark Submit SynapseML can be conveniently installed on existing Spark clusters via the `--packages` option. Each example below is independently copyable. ```bash # Spark 4.1 pyspark --repositories "https://mmlspark.blob.core.windows.net/maven" \ --packages "com.microsoft.azure:synapseml_2.13:1.1.3-spark4.1" ``` ```bash # Spark 4.0 pyspark --repositories "https://mmlspark.blob.core.windows.net/maven" \ --packages "com.microsoft.azure:synapseml_2.13:1.1.3-spark4.0" ``` ```bash # Spark 3.5 pyspark --repositories "https://mmlspark.blob.core.windows.net/maven" \ --packages "com.microsoft.azure:synapseml_2.12:1.1.3" ``` ### SBT Choose the dependency matching your Spark runtime. **Spark 4.1** ```scala 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** ```scala 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** ```scala resolvers += "SynapseML" at "https://mmlspark.blob.core.windows.net/maven" libraryDependencies += "com.microsoft.azure" % "synapseml_2.12" % "1.1.3" ``` ### Apache Livy and HDInsight 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. ```bash %%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" } } ``` ### Docker The easiest way to evaluate SynapseML is via our pre-built Docker container. To do so, run the following command: ```bash docker run -it -p 8888:8888 -e ACCEPT_EULA=yes mcr.microsoft.com/mmlspark/release jupyter notebook ``` Navigate to in your web browser to run the sample notebooks. See the [documentation](https://microsoft.github.io/SynapseML/docs/Reference/Docker%20Setup/) 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` ### R To try out SynapseML using the R autogenerated wrappers [see our instructions](https://microsoft.github.io/SynapseML/docs/Reference/R%20Setup/). Note: This feature is still under development and some necessary custom wrappers may be missing. ### Building from source SynapseML has recently transitioned to a new build infrastructure. For detailed developer docs, please see the [Developer Readme](https://microsoft.github.io/SynapseML/docs/Reference/Developer%20Setup/). If you are an existing SynapseML developer, 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! ## Papers - [Large Scale Intelligent Microservices](https://arxiv.org/abs/2009.08044) - [Conditional Image Retrieval](https://arxiv.org/abs/2007.07177) - [MMLSpark: Unifying Machine Learning Ecosystems at Massive Scales](https://arxiv.org/abs/1810.08744) - [Flexible and Scalable Deep Learning with SynapseML](https://arxiv.org/abs/1804.04031) - [Large-Scale Automatic Audiobook Creation](https://arxiv.org/abs/2309.03926) ## Learn More - Visit our [website]. - 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]. - See how SynapseML is used to [help endangered species]. - Explore generative adversarial artwork in [our collaboration with The MET and MIT]. - Explore [our collaboration with Apache Spark] on image analysis. [website]: https://microsoft.github.io/SynapseML/ "aka.ms/spark" [full installation guide]: https://microsoft.github.io/SynapseML/docs/Get%20Started/Install%20SynapseML/ [build the matching wheel from source]: docs/Reference/Developer%20Setup.md [the Spark+AI Summit 2018]: https://databricks.com/sparkaisummit/north-america/spark-summit-2018-keynotes#Intelligent-cloud "Developing for the Intelligent Cloud and Intelligent Edge" [the Spark+AI Summit 2019]: https://youtu.be/T_fs4C0aqD0?t=425 [the Spark+AI European Summit 2018]: https://youtu.be/N3ozCZXeOeU?t=472 [help endangered species]: https://www.microsoft.com/en-us/ai/ai-lab-stories?activetab=pivot1:primaryr3 "Identifying snow leopards with AI" [our collaboration with The MET and MIT]: https://www.microsoft.com/en-us/ai/ai-lab-stories?activetab=pivot1:primaryr4 "Generative art at the MET" [our collaboration with Apache Spark]: https://blogs.technet.microsoft.com/machinelearning/2018/03/05/image-data-support-in-apache-spark/ "Image Data Support in Apache Spark" [SynapseML at the Spark Summit]: https://databricks.com/session/mmlspark-lessons-from-building-a-sparkml-compatible-machine-learning-library-for-apache-spark "MMLSpark: Lessons from Building a SparkML-Compatible Machine Learning Library for Apache Spark" ## Contributing & feedback 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](mailto:opencode@microsoft.com) with any additional questions or comments. [Microsoft Open Source Code of Conduct]: https://opensource.microsoft.com/codeofconduct/ [Code of Conduct FAQ]: https://opensource.microsoft.com/codeofconduct/faq/ See [CONTRIBUTING.md](CONTRIBUTING.md) for contribution guidelines. To give feedback and/or report an issue, open a [GitHub Issue](https://help.github.com/articles/creating-an-issue/). ## Other relevant projects - [Vowpal Wabbit](https://github.com/VowpalWabbit/vowpal_wabbit) - [LightGBM](https://github.com/lightgbm-org/LightGBM) - [DMTK: Microsoft Distributed Machine Learning Toolkit](https://github.com/Microsoft/DMTK) - [Recommenders](https://github.com/recommenders-team/Recommenders) - [JPMML-SparkML plugin for converting SynapseML LightGBM models to PMML](https://github.com/alipay/jpmml-sparkml-lightgbm) - [Microsoft Cognitive Toolkit](https://github.com/Microsoft/CNTK) _Apache®, Apache Spark, and Spark® are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries._