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MLlib | Apache Spark
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<li><a href="/news/spark-2-0-0-released.html">Spark 2.0.0 released</a>
<span class="small">(Jul 26, 2016)</span></li>
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Built-in Libraries:
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<li><a href="/sql/">SQL and DataFrames</a></li>
<li><a href="/streaming/">Spark Streaming</a></li>
<li><a href="/mllib/">MLlib (machine learning)</a></li>
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<b>MLlib</b> is Apache Spark's scalable machine learning library.
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<h2>Ease of Use</h2>
<p class="lead">
Usable in Java, Scala, Python, and R.
</p>
<p>
MLlib fits into <a href="/">Spark</a>'s
APIs and interoperates with <a href="http://www.numpy.org">NumPy</a>
in Python (as of Spark 0.9) and R libraries (as of Spark 1.5).
You can use any Hadoop data source (e.g. HDFS, HBase, or local files), making it
easy to plug into Hadoop workflows.
</p>
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<div style="margin-top: 15px; text-align: left; display: inline-block;">
<div class="code">
data = spark.read.format(<span class="string">"libsvm"</span>)\<br />
.load(<span class="string">"hdfs://..."</span>)<br />
<br />
model = <span class="sparkop">KMeans</span>(data, k=10)
</div>
<div class="caption">Calling MLlib in Python</div>
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<h2>Performance</h2>
<p class="lead">
High-quality algorithms, 100x faster than MapReduce.
</p>
<p>
Spark excels at iterative computation, enabling MLlib to run fast.
At the same time, we care about algorithmic performance:
MLlib contains high-quality algorithms that leverage iteration, and
can yield better results than the one-pass approximations sometimes used on MapReduce.
</p>
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<div class="caption" style="min-width: 272px;">Logistic regression in Hadoop and Spark</div>
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<h2>Easy to Deploy</h2>
<p class="lead">
Runs on existing Hadoop clusters and data.
</p>
<p>
If you have a Hadoop 2 cluster, you can run Spark and MLlib without any pre-installation.
Otherwise, Spark is easy to run <a href="/docs/latest/spark-standalone.html">standalone</a>
or on <a href="/docs/latest/ec2-scripts.html">EC2</a> or <a href="http://mesos.apache.org">Mesos</a>.
You can read from <a href="http://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsUserGuide.html">HDFS</a>, <a href="http://hbase.apache.org">HBase</a>, or any Hadoop data source.
</p>
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<h3>Algorithms</h3>
<p>
MLlib contains many algorithms and utilities, including:
</p>
<ul class="list-narrow">
<li>Classification: logistic regression, naive Bayes,...</li>
<li>Regression: generalized linear regression, isotonic regression,...</li>
<li>Decision trees, random forests, and gradient-boosted trees</li>
<li>Recommendation: alternating least squares (ALS)</li>
<li>Clustering: K-means, Gaussian mixtures (GMMs),...</li>
<li>Topic modeling: latent Dirichlet allocation (LDA)</li>
<li>Feature transformations: standardization, normalization, hashing,...</li>
<li>Model evaluation and hyper-parameter tuning</li>
<li>ML Pipeline construction</li>
<li>ML persistence: saving and loading models and Pipelines</li>
<li>Survival analysis: accelerated failure time model</li>
<li>Frequent itemset and sequential pattern mining: FP-growth, association rules, PrefixSpan</li>
<li>Distributed linear algebra: singular value decomposition (SVD), principal component analysis (PCA),...</li>
<li>Statistics: summary statistics, hypothesis testing,...</li>
</ul>
<p>Refer to the <a href="/docs/latest/mllib-guide.html">MLlib guide</a> for usage examples.</p>
</div>
<div class="col-md-4 col-padded">
<h3>Community</h3>
<p>
MLlib is developed as part of the Apache Spark project. It thus gets
tested and updated with each Spark release.
</p>
<p>
If you have questions about the library, ask on the
<a href="/community.html#mailing-lists">Spark mailing lists</a>.
</p>
<p>
MLlib is still a rapidly growing project and welcomes contributions. If you'd like to submit an algorithm to MLlib,
read <a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">how to
contribute to Spark</a> and send us a patch!
</p>
</div>
<div class="col-md-4 col-padded">
<h3>Getting Started</h3>
<p>
To get started with MLlib:
</p>
<ul class="list-narrow">
<li><a href="/downloads.html">Download Spark</a>. MLlib is included as a module.</li>
<li>Read the <a href="/docs/latest/mllib-guide.html">MLlib guide</a>, which includes
various usage examples.</li>
<li>Learn how to <a href="/docs/latest/#launching-on-a-cluster">deploy</a> Spark on a cluster
if you'd like to run in distributed mode. You can also run locally on a multicore machine
without any setup.
</li>
</ul>
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