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<span class="version">2.0.0</span>
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<h1 class="title">SystemDS Documentation</h1>
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<p>SystemDS is a flexible, scalable machine learning system.
SystemDS&#8217;s distinguishing characteristics are:</p>
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<li><strong>Algorithm customizability via R-like and Python-like languages</strong>.</li>
<li><strong>Multiple execution modes</strong>, including Spark MLContext, Spark Batch, Standalone, and JMLC.</li>
<li><strong>Automatic optimization</strong> based on data and cluster characteristics to ensure both efficiency and scalability.</li>
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<p>This version of SystemDS supports: Java 8+, Python 3.5+, Hadoop 2.6+ (Not 3.X), and Spark 2.1+ (Not 3.X) Nvidia CUDA 10.2
(CuDNN 7.x) Intel MKL (&lt;=2019.x).</p>
<h2 id="links">Links</h2>
<p>Various forms of documentation for SystemDS are available.</p>
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<li>a <a href="./site/dml-language-reference">DML Language Reference</a> for an list of operations possible inside SystemDS.</li>
<li><a href="./site/builtins-reference">Builtin Functions</a> contains a collection of builtin functions providing an high level abstraction on complex machine learning algorithms.</li>
<li><a href="./site/algorithms-reference">Algorithm Reference</a> contains specifics on algorithms supported in systemds.</li>
<li><a href="./site/entity-resolution">Entity Resolution</a> provides a collection of customizable entity resolution primitives and pipelines.</li>
<li><a href="./site/run">Run SystemDS</a> contains an Helloworld example along with an environment setup guide.</li>
<li>Instructions on python can be found at <a href="./api/python/index">Python Documentation</a></li>
<li>The <a href="./api/java/index">JavaDOC</a> contains internal documentation of the system source code.</li>
<li><a href="./site/install">Install from Source</a> guides through setup from git download to running system.</li>
<li>If you want to contribute take a look at <a href="https://github.com/apache/systemds/blob/master/CONTRIBUTING.md">Contributing</a></li>
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