Synapse Machine Learning

SynapseML (previously MMLSpark) is an open source library to simplify the creation of scalable machine learning pipelines. SynapseML builds on Apache Spark and SparkML to enable new kinds of machine learning, analytics, and model deployment workflows. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with the Open Neural Network Exchange (ONNX), LightGBM, The Cognitive Services, Vowpal Wabbit, and OpenCV. These tools enable powerful and highly-scalable predictive and analytical models for a variety of datasources.

SynapseML also brings new networking capabilities to the Spark Ecosystem. With the HTTP on Spark project, users can embed any web service into their SparkML models. For production grade deployment, the Spark Serving project enables high throughput, sub-millisecond latency web services, backed by your Spark cluster.

SynapseML requires Scala 2.12, Spark 3.0+, and Python 3.6+. See the API documentation for Scala and for PySpark.

Features

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

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

Isolation Forest on Spark	CyberML	Conditional KNN
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.

Setup and installation

Python

To try out SynapseML on a Python (or Conda) installation you can get Spark installed via pip with pip install pyspark. You can then use pyspark as in the above example, or from python:

import pyspark
spark = pyspark.sql.SparkSession.builder.appName("MyApp") \
            .config("spark.jars.packages", "com.microsoft.azure:synapseml_2.12:0.9.4") \
            .config("spark.jars.repositories", "https://mmlspark.azureedge.net/maven") \
            .getOrCreate()
import synapse.ml

SBT

If you are building a Spark application in Scala, add the following lines to your build.sbt:

resolvers += "SynapseML" at "https://mmlspark.azureedge.net/maven"
libraryDependencies += "com.microsoft.azure" % "synapseml_2.12" % "0.9.4"

Spark package

SynapseML can be conveniently installed on existing Spark clusters via the --packages option, examples:

spark-shell --packages com.microsoft.azure:synapseml_2.12:0.9.4 --conf spark.jars.repositories=https://mmlspark.azureedge.net/maven
pyspark --packages com.microsoft.azure:synapseml_2.12:0.9.4 --conf spark.jars.repositories=https://mmlspark.azureedge.net/maven
spark-submit --packages com.microsoft.azure:synapseml_2.12:0.9.4 MyApp.jar --conf spark.jars.repositories=https://mmlspark.azureedge.net/maven

This can be used in other Spark contexts too. For example, you can use SynapseML in AZTK by adding it to the .aztk/spark-defaults.conf file.

Databricks

To install SynapseML on the Databricks cloud, create a new library from Maven coordinates in your workspace.

For the coordinates use: com.microsoft.azure:synapseml_2.12:0.9.4 with the resolver: https://mmlspark.azureedge.net/maven. Ensure this library is attached to your target cluster(s).

Finally, ensure that your Spark cluster has at least Spark 3.12 and Scala 2.12.

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/SynapseMLExamplesv0.9.4.dbc

Apache Livy and HDInsight

To install SynapseML from within a Jupyter notebook served by Apache Livy the following 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_2.12:0.9.4",
        "spark.jars.repositories": "https://mmlspark.azureedge.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"
    }
}

In Azure Synapse, "spark.yarn.user.classpath.first" should be set to "true" to override the existing SynapseML packages

%%configure -f
{
    "name": "synapseml",
    "conf": {
        "spark.jars.packages": "com.microsoft.azure:synapseml_2.12:0.9.4",
        "spark.jars.repositories": "https://mmlspark.azureedge.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",
        "spark.yarn.user.classpath.first": "true"
    }
}

Docker

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

Navigate 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

GPU VM Setup

SynapseML can be used to train deep learning models on GPU nodes from a Spark application. See the instructions for setting up an Azure GPU VM.

Building from source

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!

R (Beta)

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.

Papers

• Large Scale Intelligent Microservices

• Conditional Image Retrieval

• MMLSpark: Unifying Machine Learning Ecosystems at Massive Scales

• Flexible and Scalable Deep Learning with SynapseML

Learn More

• Visit our website.

• Watch our keynote demos at the Spark+AI Summit 2019, the Spark+AI European Summit 2018, and the Spark+AI Summit 2018.

• 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.

Contributing & feedback

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

See CONTRIBUTING.md for contribution guidelines.

To give feedback and/or report an issue, open a GitHub Issue.

Other relevant projects

• Vowpal Wabbit

• LightGBM

• DMTK: Microsoft Distributed Machine Learning Toolkit

• Recommenders

• JPMML-SparkML plugin for converting SynapseML LightGBM models to PMML

• Microsoft Cognitive Toolkit

Apache®, Apache Spark, and Spark® are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.