---
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displayTitle: SystemML Documentation
title: SystemML Documentation
description: SystemML Documentation
---
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SystemML is a flexible, scalable machine learning system.
SystemML's distinguishing characteristics are:

  1. **Algorithm customizability via R-like and Python-like languages**.
  2. **Multiple execution modes**, including Spark MLContext, Spark Batch, Hadoop Batch, Standalone, and JMLC.
  3. **Automatic optimization** based on data and cluster characteristics to ensure both efficiency and scalability.

The [SystemML GitHub README](https://github.com/apache/systemml) describes
building, testing, and running SystemML. Please read [Contributing to SystemML](contributing-to-systemml)
to find out how to help make SystemML even better!

To download SystemML, visit the [downloads](http://systemml.apache.org/download) page.

This version of SystemML supports: Java 8+, Scala 2.11+, Python 2.7/3.5+, Hadoop 2.6+, and Spark 2.1+.

## Running SystemML

* [Beginner's Guide For Python Users](beginners-guide-python) - Beginner's Guide for Python users.
* [Spark MLContext](spark-mlcontext-programming-guide) - Spark MLContext is a programmatic API
for running SystemML from Spark via Scala, Python, or Java.
  * [Spark Shell Example (Scala)](spark-mlcontext-programming-guide#spark-shell-example)
  * [Jupyter Notebook Example (PySpark)](spark-mlcontext-programming-guide#jupyter-pyspark-notebook-example---poisson-nonnegative-matrix-factorization)
* [Spark Batch](spark-batch-mode) - Algorithms are automatically optimized to run across Spark clusters.
* [Hadoop Batch](hadoop-batch-mode) - Algorithms are automatically optimized when distributed across Hadoop clusters.
* [Standalone](standalone-guide) - Standalone mode allows data scientists to rapidly prototype algorithms on a single
machine in R-like and Python-like declarative languages.
* [JMLC](jmlc) - Java Machine Learning Connector.
* [Deep Learning with SystemML](deep-learning)
  * *Experimental* Caffe2DML API for Deep Learning ([beginner's guide](beginners-guide-caffe2dml), [reference guide](reference-guide-caffe2dml)) - Converts a Caffe specification to DML.
  * *Experimental* [Keras2DML API](beginners-guide-keras2dml) for Deep Learning.

## Language Guides

* [Python API Reference](python-reference) - API Reference Guide for Python users.
* [DML Language Reference](dml-language-reference) -
DML is a high-level R-like declarative language for machine learning.
* **PyDML Language Reference** -
PyDML is a high-level Python-like declarative language for machine learning.
* [Beginner's Guide to DML and PyDML](beginners-guide-to-dml-and-pydml) -
An introduction to the basics of DML and PyDML.

## ML Algorithms

* [Algorithms Reference](algorithms-reference) - The Algorithms Reference describes the
machine learning algorithms included with SystemML in detail.

## Tools

* [Debugger Guide](debugger-guide) - SystemML supports DML script-level debugging through a
command-line interface.
* [IDE Guide](developer-tools-systemml) - Useful IDE Guide for Developing SystemML.

## Other

* [Contributing to SystemML](contributing-to-systemml) - Describes ways to contribute to SystemML.
* [Engine Developer Guide](engine-dev-guide) - Guide for internal SystemML engine development.
* [Troubleshooting Guide](troubleshooting-guide) - Troubleshoot various issues related to SystemML.
* [Release Process](release-process) - Description of the SystemML release process.
* [Using Native BLAS](native-backend) in SystemML.
* [Using GPU backend](gpu) in SystemML.
