layout: base title: SystemDS Documentation
SystemDS is a flexible, scalable machine learning system. SystemDS's distinguishing characteristics are:
- Algorithm customizability via R-like and Python-like languages.
- Multiple execution modes, including Spark MLContext, Spark Batch, Standalone, and JMLC.
- Automatic optimization based on data and cluster characteristics to ensure both efficiency and scalability.
This version of SystemDS supports: Java 8+, Python 3.5+, Hadoop 2.6+ (Not 3.X), and Spark 2.1+ (Not 3.X).
Links
Various forms of documentation for SystemDS are available.
- a DML language reference for an list of operations possible inside SystemDS.
- builtin functions contains a collection of builtin functions providing an high level abstraction on complex machine learning algorithms.
- Entity Resolution provides a collection of customizable entity resolution primitives and pipelines.
- Run SystemDS contains an Helloworld example along with an environment setup guide.
- Instructions on python can be found at Python Documentation
- The javadoc API contains internal documentation of the system source code.
- Install from Source guides through setup from git download to running system.
- If you want to contribute take a look at Contributing