Machine Learning Library (MLlib) Guide

MLlib is Spark’s scalable machine learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives, as outlined below:

MLlib is under active development. The APIs marked Experimental/DeveloperApi may change in future releases, and the migration guide below will explain all changes between releases.

spark.ml: high-level APIs for ML pipelines

Spark 1.2 introduced a new package called spark.ml, which aims to provide a uniform set of high-level APIs that help users create and tune practical machine learning pipelines. It is currently an alpha component, and we would like to hear back from the community about how it fits real-world use cases and how it could be improved.

Note that we will keep supporting and adding features to spark.mllib along with the development of spark.ml. Users should be comfortable using spark.mllib features and expect more features coming. Developers should contribute new algorithms to spark.mllib and can optionally contribute to spark.ml.

See the spark.ml programming guide for more information on this package.

Dependencies

MLlib uses the linear algebra package Breeze, which depends on netlib-java for optimised numerical processing. If natives are not available at runtime, you will see a warning message and a pure JVM implementation will be used instead.

To learn more about the benefits and background of system optimised natives, you may wish to watch Sam Halliday’s ScalaX talk on High Performance Linear Algebra in Scala).

Due to licensing issues with runtime proprietary binaries, we do not include netlib-java’s native proxies by default. To configure netlib-java / Breeze to use system optimised binaries, include com.github.fommil.netlib:all:1.1.2 (or build Spark with -Pnetlib-lgpl) as a dependency of your project and read the netlib-java documentation for your platform’s additional installation instructions.

To use MLlib in Python, you will need NumPy version 1.4 or newer.


Migration Guide

For the spark.ml package, please see the spark.ml Migration Guide.

From 1.2 to 1.3

In the spark.mllib package, there were several breaking changes. The first change (in ALS) is the only one in a component not marked as Alpha or Experimental.

Previous Spark Versions

Earlier migration guides are archived on this page.