docs/index.md

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title: SystemDS Documentation
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SystemDS is a flexible, scalable machine learning system.
SystemDS's distinguishing characteristics are:

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

This version of SystemDS supports: Java 17,  Python 3.5+, Hadoop 3.3.x, and Spark 3.5.x, Nvidia CUDA 10.2
 (CuDNN 7.x) Intel MKL (<=2019.x).

## Links

Various forms of documentation for SystemDS are available.

- a [DML Language Reference](./site/dml-language-reference) for an list of operations possible inside SystemDS.
- [Builtin Functions](./site/builtins-reference) contains a collection of builtin functions providing an high level abstraction on complex machine learning algorithms.
- [Algorithm Reference](./site/algorithms-reference) contains specifics on algorithms supported in systemds.
- [Entity Resolution](./site/entity-resolution) provides a collection of customizable entity resolution primitives and pipelines.
- [Run SystemDS](./site/run) contains an Helloworld example along with an environment setup guide.
- Instructions on python can be found at [Python Documentation](./api/python/index)
- The [JavaDOC](./api/java/index) contains internal documentation of the system source code.
- [Install from Source](./site/install) guides through setup from git download to running system.
- If you want to contribute take a look at [Contributing](https://github.com/apache/systemds/blob/main/CONTRIBUTING.md)
- [R to DML](./site/dml-vs-r-guide) walks through the basics of converting a script from R to dml.