Microsoft Machine Learning for Apache Spark

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MMLSpark is an ecosystem of tools aimed towards expanding the distributed computing framework Apache Spark in several new directions. MMLSpark adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK), LightGBM and OpenCV. These tools enable powerful and highly-scalable predictive and analytical models for a variety of datasources.

MMLSpark 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. In this vein, MMLSpark provides easy to use SparkML transformers for a wide variety of Microsoft Cognitive Services. For production grade deployment, the Spark Serving project enables high throughput, sub-millisecond latency web services, backed by your Spark cluster.

MMLSpark requires Scala 2.11, Spark 2.3+, and either Python 2.7 or Python 3.5+. See the API documentation for Scala and for PySpark.

Table of Contents


Vowpal Wabbit on Spark The Cognitive Services on Spark 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 CNTK on Spark Lime on Spark Spark Binding Autogeneration
An Integration Between Spark and the HTTP Protocol, enabling Distributed Microservice Orchestration Distributed Deep Learning with the Microsoft Cognitive Toolkit Distributed, Model Agnostic, Interpretations for Classifiers Automatically Generate Spark bindings for PySpark and SparklyR


  • Create a deep image classifier with transfer learning (example 9)
  • Fit a LightGBM classification or regression model on a biochemical dataset (example 3), to learn more check out the LightGBM documentation page.
  • Deploy a deep network as a distributed web service with MMLSpark Serving
  • Use web services in Spark with HTTP on Apache Spark
  • Use Bi-directional LSTMs from Keras for medical entity extraction (example 8)
  • Create a text analytics system on Amazon book reviews (example 4)
  • Perform distributed hyperparameter tuning to identify Breast Cancer (example 5)
  • Easily ingest images from HDFS into Spark DataFrame (example 6)
  • Use OpenCV on Spark to manipulate images (example 7)
  • Train classification and regression models easily via implicit featurization of data (example 1)
  • Train and evaluate a flight delay prediction system (example 2)

See our notebooks for all examples.

A short example

Below is an excerpt from a simple example of using a pre-trained CNN to classify images in the CIFAR-10 dataset. View the whole source code in notebook example 9.

import mmlspark
# Initialize CNTKModel and define input and output columns
cntkModel = mmlspark.cntk.CNTKModel() \
  .setInputCol("images").setOutputCol("output") \
# Train on dataset with internal spark pipeline
scoredImages = cntkModel.transform(imagesWithLabels)

See other sample notebooks as well as the MMLSpark documentation for Scala and PySpark.

Setup and installation

Spark package

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

spark-shell --packages
pyspark --packages
spark-submit --packages MyApp.jar

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


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

For the coordinates use: Ensure this library is attached to all clusters you create.

Finally, ensure that your Spark cluster has at least Spark 2.1 and Scala 2.11.

You can use MMLSpark in both your Scala and PySpark notebooks. To get started with our example notebooks import the following databricks archive:


The easiest way to evaluate MMLSpark is via our pre-built Docker container. To do so, run the following command:

docker run -it -p 8888:8888 -e ACCEPT_EULA=yes

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 eula

GPU VM Setup

MMLSpark 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.


To try out MMLSpark 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", "") \
import mmlspark


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

libraryDependencies += "" %% "mmlspark" % "0.18.1"

Building from source

MMLSpark has recently transitioned to a new build infrastructure. For detailed developer docs please see the Developer Readme

If you are an existing mmlspark 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!

R (Beta)

To try out MMLSpark using the R autogenerated wrappers see our instructions. Note: This feature is still under development and some necessary custom wrappers may be missing.

Learn More

Contributing & feedback

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

See for contribution guidelines.

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

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