SystemML Documentation - SystemML 0.12.0
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Apache SystemML™ (incubating)
0.12.0
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Overview
GitHub
Documentation
Running SystemML:
SystemML GitHub README
Spark MLContext
Spark Batch Mode
Hadoop Batch Mode
Standalone Guide
Java Machine Learning Connector (JMLC)
Language Guides:
DML Language Reference
Beginner's Guide to DML and PyDML
Beginner's Guide for Python Users
Reference Guide for Python Users
ML Algorithms:
Algorithms Reference
Tools:
Debugger Guide
IDE Guide
Other:
Contributing to SystemML
Engine Developer Guide
Troubleshooting Guide
Release Process
API Docs
Javadoc
Issues
JIRA:
SystemML JIRA
SystemML Documentation
SystemML is now an Apache Incubator project! Please see the Apache SystemML
website for more information.
SystemML is a flexible, scalable machine learning system.
SystemML’s distinguishing characteristics are:
Algorithm customizability via R-like and Python-like languages.
Multiple execution modes, including Spark MLContext, Spark Batch, Hadoop Batch, Standalone, and JMLC.
Automatic optimization based on data and cluster characteristics to ensure both efficiency and scalability.
The SystemML GitHub README describes
building, testing, and running SystemML. Please read Contributing to SystemML
to find out how to help make SystemML even better!
To download SystemML, visit the downloads page.
Running SystemML
Beginner’s Guide For Python Users - Beginner’s Guide for Python users.
Spark MLContext - Spark MLContext is a programmatic API
for running SystemML from Spark via Scala, Python, or Java.
Spark Shell Example (Scala)
Jupyter Notebook Example (PySpark)
Spark Batch - Algorithms are automatically optimized to run across Spark clusters.
See Invoking SystemML in Spark Batch Mode for detailed information.
Hadoop Batch - Algorithms are automatically optimized when distributed across Hadoop clusters.
See Invoking SystemML in Hadoop Batch Mode for detailed information.
Standalone - Standalone mode allows data scientists to rapidly prototype algorithms on a single
machine in R-like and Python-like declarative languages.
The Standalone Guide provides examples of algorithm execution
in Standalone Mode.
JMLC - Java Machine Learning Connector.
See Java Machine Learning Connector (JMLC) for more information.
Language Guides
Python API Reference - API Reference Guide for Python users.
DML Language Reference -
DML is a high-level R-like declarative language for machine learning.
PyDML Language Reference (Coming Soon) -
PyDML is a high-level Python-like declarative language for machine learning.
Beginner’s Guide to DML and PyDML -
An introduction to the basics of DML and PyDML.
ML Algorithms
Algorithms Reference - The Algorithms Reference describes the
machine learning algorithms included with SystemML in detail.
Tools
Debugger Guide - SystemML supports DML script-level debugging through a
command-line interface.
IDE Guide - Useful IDE Guide for Developing SystemML.
Other
Contributing to SystemML - Describes ways to contribute to SystemML.
Engine Developer Guide - Guide for internal SystemML engine development.
Troubleshooting Guide - Troubleshoot various issues related to SystemML.
Release Process - Description of the SystemML release process.