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| <span class="version">1.1.0</span> |
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| <h1 class="title">SystemML Documentation</h1> |
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| <p>SystemML is a flexible, scalable machine learning system. |
| SystemML’s distinguishing characteristics are:</p> |
| |
| <ol> |
| <li><strong>Algorithm customizability via R-like and Python-like languages</strong>.</li> |
| <li><strong>Multiple execution modes</strong>, including Spark MLContext, Spark Batch, Hadoop Batch, Standalone, and JMLC.</li> |
| <li><strong>Automatic optimization</strong> based on data and cluster characteristics to ensure both efficiency and scalability.</li> |
| </ol> |
| |
| <p>The <a href="https://github.com/apache/systemml">SystemML GitHub README</a> describes |
| building, testing, and running SystemML. Please read <a href="contributing-to-systemml">Contributing to SystemML</a> |
| to find out how to help make SystemML even better!</p> |
| |
| <p>To download SystemML, visit the <a href="http://systemml.apache.org/download">downloads</a> page.</p> |
| |
| <p>This version of SystemML supports: Java 8+, Scala 2.11+, Python 2.7/3.5+, Hadoop 2.6+, and Spark 2.1+.</p> |
| |
| <h2 id="running-systemml">Running SystemML</h2> |
| |
| <ul> |
| <li><a href="beginners-guide-python">Beginner’s Guide For Python Users</a> - Beginner’s Guide for Python users.</li> |
| <li><a href="spark-mlcontext-programming-guide">Spark MLContext</a> - Spark MLContext is a programmatic API |
| for running SystemML from Spark via Scala, Python, or Java. |
| <ul> |
| <li><a href="spark-mlcontext-programming-guide#spark-shell-example">Spark Shell Example (Scala)</a></li> |
| <li><a href="spark-mlcontext-programming-guide#jupyter-pyspark-notebook-example---poisson-nonnegative-matrix-factorization">Jupyter Notebook Example (PySpark)</a></li> |
| </ul> |
| </li> |
| <li><a href="spark-batch-mode">Spark Batch</a> - Algorithms are automatically optimized to run across Spark clusters.</li> |
| <li><a href="hadoop-batch-mode">Hadoop Batch</a> - Algorithms are automatically optimized when distributed across Hadoop clusters.</li> |
| <li><a href="standalone-guide">Standalone</a> - Standalone mode allows data scientists to rapidly prototype algorithms on a single |
| machine in R-like and Python-like declarative languages.</li> |
| <li><a href="jmlc">JMLC</a> - Java Machine Learning Connector.</li> |
| <li><a href="deep-learning">Deep Learning with SystemML</a> |
| <ul> |
| <li><em>Experimental</em> Caffe2DML API for Deep Learning (<a href="beginners-guide-caffe2dml">beginner’s guide</a>, <a href="reference-guide-caffe2dml">reference guide</a>) - Converts a Caffe specification to DML.</li> |
| <li><em>Experimental</em> <a href="beginners-guide-keras2dml">Keras2DML API</a> for Deep Learning.</li> |
| </ul> |
| </li> |
| </ul> |
| |
| <h2 id="language-guides">Language Guides</h2> |
| |
| <ul> |
| <li><a href="python-reference">Python API Reference</a> - API Reference Guide for Python users.</li> |
| <li><a href="dml-language-reference">DML Language Reference</a> - |
| DML is a high-level R-like declarative language for machine learning.</li> |
| <li><strong>PyDML Language Reference</strong> - |
| PyDML is a high-level Python-like declarative language for machine learning.</li> |
| <li><a href="beginners-guide-to-dml-and-pydml">Beginner’s Guide to DML and PyDML</a> - |
| An introduction to the basics of DML and PyDML.</li> |
| </ul> |
| |
| <h2 id="ml-algorithms">ML Algorithms</h2> |
| |
| <ul> |
| <li><a href="algorithms-reference">Algorithms Reference</a> - The Algorithms Reference describes the |
| machine learning algorithms included with SystemML in detail.</li> |
| </ul> |
| |
| <h2 id="tools">Tools</h2> |
| |
| <ul> |
| <li><a href="debugger-guide">Debugger Guide</a> - SystemML supports DML script-level debugging through a |
| command-line interface.</li> |
| <li><a href="developer-tools-systemml">IDE Guide</a> - Useful IDE Guide for Developing SystemML.</li> |
| </ul> |
| |
| <h2 id="other">Other</h2> |
| |
| <ul> |
| <li><a href="contributing-to-systemml">Contributing to SystemML</a> - Describes ways to contribute to SystemML.</li> |
| <li><a href="engine-dev-guide">Engine Developer Guide</a> - Guide for internal SystemML engine development.</li> |
| <li><a href="troubleshooting-guide">Troubleshooting Guide</a> - Troubleshoot various issues related to SystemML.</li> |
| <li><a href="release-process">Release Process</a> - Description of the SystemML release process.</li> |
| <li><a href="native-backend">Using Native BLAS</a> in SystemML.</li> |
| </ul> |
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