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| <h1 class="title">Invoking SystemML in Spark Batch Mode</h1> |
| |
| |
| <!-- |
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| --> |
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| <ul id="markdown-toc"> |
| <li><a href="#overview" id="markdown-toc-overview">Overview</a></li> |
| <li><a href="#spark-batch-mode-invocation-syntax" id="markdown-toc-spark-batch-mode-invocation-syntax">Spark Batch Mode Invocation Syntax</a></li> |
| <li><a href="#execution-modes" id="markdown-toc-execution-modes">Execution modes</a></li> |
| <li><a href="#recommended-spark-configuration-settings" id="markdown-toc-recommended-spark-configuration-settings">Recommended Spark Configuration Settings</a></li> |
| <li><a href="#examples" id="markdown-toc-examples">Examples</a></li> |
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| <p><br /></p> |
| |
| <h1 id="overview">Overview</h1> |
| |
| <p>Given that a primary purpose of SystemML is to perform machine learning on large distributed data |
| sets, one of the most important ways to invoke SystemML is Spark Batch. Here, we will look at this |
| mode in more depth.</p> |
| |
| <p><strong>NOTE:</strong> For a programmatic API to run and interact with SystemML via Scala or Python, please see the |
| <a href="spark-mlcontext-programming-guide">Spark MLContext Programming Guide</a>.</p> |
| |
| <hr /> |
| |
| <h1 id="spark-batch-mode-invocation-syntax">Spark Batch Mode Invocation Syntax</h1> |
| |
| <p>SystemML can be invoked in Spark Batch mode using the following syntax:</p> |
| |
| <pre><code>spark-submit SystemML.jar [-? | -help | -f <filename>] (-config <config_filename>) ([-args | -nvargs] <args-list>) |
| </code></pre> |
| |
| <p>The DML script to invoke is specified after the <code>-f</code> argument. Configuration settings can be passed to SystemML |
| using the optional <code>-config </code> argument. DML scripts can optionally take named arguments (<code>-nvargs</code>) or positional |
| arguments (<code>-args</code>). Named arguments are preferred over positional arguments. Positional arguments are considered |
| to be deprecated. All the primary algorithm scripts included with SystemML use named arguments.</p> |
| |
| <p><strong>Example #1: DML Invocation with Named Arguments</strong></p> |
| |
| <pre><code>spark-submit SystemML.jar -f scripts/algorithms/Kmeans.dml -nvargs X=X.mtx k=5 |
| </code></pre> |
| |
| <p><strong>Example #2: DML Invocation with Positional Arguments</strong></p> |
| |
| <pre><code>spark-submit SystemML.jar -f src/test/scripts/applications/linear_regression/LinearRegression.dml -args "v" "y" 0.00000001 "w" |
| </code></pre> |
| |
| <h1 id="execution-modes">Execution modes</h1> |
| |
| <p>SystemML works seamlessly with all Spark execution modes, including <em>local</em> (<code>--master local[*]</code>), |
| <em>yarn client</em> (<code>--master yarn --deploy-mode client</code>), <em>yarn cluster</em> (<code>--master yarn --deploy-mode cluster</code>), <em>etc</em>. More |
| information on Spark cluster execution modes can be found on the |
| <a href="https://spark.apache.org/docs/latest/cluster-overview.html">official Spark cluster deployment documentation</a>. |
| <em>Note</em> that Spark can be easily run on a laptop in local mode using the <code>--master local[*]</code> described |
| above, which SystemML supports.</p> |
| |
| <h1 id="recommended-spark-configuration-settings">Recommended Spark Configuration Settings</h1> |
| |
| <p>For best performance, we recommend setting the following configuration value when running SystemML with Spark: |
| <code>--conf spark.driver.maxResultSize=0</code>.</p> |
| |
| <h1 id="examples">Examples</h1> |
| |
| <p>Please see the MNIST examples in the included |
| <a href="https://github.com/apache/systemml/tree/master/scripts/nn">SystemML-NN</a> |
| library for examples of Spark Batch mode execution with SystemML to train MNIST classifiers:</p> |
| |
| <ul> |
| <li><a href="https://github.com/apache/systemml/blob/master/scripts/nn/examples/mnist_softmax-train.dml">MNIST Softmax Classifier</a></li> |
| <li><a href="https://github.com/apache/systemml/blob/master/scripts/nn/examples/mnist_lenet-train.dml">MNIST LeNet ConvNet</a></li> |
| </ul> |
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