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 class="col-md-9 col-sm-12"><div class="content-header hidden-md 
hidden-lg"><div id="bre
 adcrumbs" class="hidden-sm hidden xs"><ul><li><a href="#">Deploying an 
Engine</a><span class="spacer">&gt;</span></li><li><span class="last">Batch 
Predictions</span></li></ul></div><div id="page-title"><h1>Batch 
Predictions</h1></div></div><div id="table-of-content-wrapper"><h5>On this 
page</h5><aside id="table-of-contents"><ul> <li> <a 
href="#overview">Overview</a> </li> <li> <a 
href="#compatibility">Compatibility</a> </li> <li> <a href="#usage">Usage</a> 
</li> <li> <a href="#example">Example</a> </li> </ul> </aside><hr/><a 
id="edit-page-link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/batchpredict/index.html.md";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" 
class="hidden-sm hidden xs"><ul><li><a href="#">Deploying an Engine</a><span 
class="spacer">&gt;</span></li><li><span class="last">Batch 
Predictions</span></li></ul></div><div id="page-t
 itle"><h1>Batch Predictions</h1></div></div><div class="content"> <h2 
id='overview' class='header-anchors'>Overview</h2><p>Process predictions for 
many queries using efficient parallelization through Spark. Useful for mass 
auditing of predictions and for generating predictions to push into other 
systems.</p><p>Batch predict reads and writes multi-object JSON files similar 
to the <a href="/datacollection/batchimport/">batch import</a> format. JSON 
objects are separated by newlines and cannot themselves contain unencoded 
newlines.</p><h2 id='compatibility' 
class='header-anchors'>Compatibility</h2><p><code>pio batchpredict</code> loads 
the engine and processes queries exactly like <code>pio deploy</code>. There is 
only one additional requirement for engines to utilize batch predict:</p><div 
class="alert-message warning"><p>All algorithm classes used in the engine must 
be <a 
href="https://www.scala-lang.org/api/2.11.8/index.html#scala.Serializable";>serializable</a>.
 <strong>This is alre
 ady true for PredictionIO&#39;s base algorithm classes</strong>, but may be 
broken by including non-serializable fields in their constructor. Using the <a 
href="http://fdahms.com/2015/10/14/scala-and-the-transient-lazy-val-pattern/";><code>@transient</code>
 annotation</a> may help in these cases.</p></div><p>This requirement is due to 
processing the input queries as a <a 
href="https://spark.apache.org/docs/latest/rdd-programming-guide.html#resilient-distributed-datasets-rdds";>Spark
 RDD</a> which enables high-performance parallelization, even on a single 
machine.</p><h2 id='usage' class='header-anchors'>Usage</h2><h3 
id='<code>pio-batchpredict</code>' class='header-anchors' ><code>pio 
batchpredict</code></h3><p>Command to process bulk predictions. Takes the same 
options as <code>pio deploy</code> plus:</p><h3 
id='<code>--input-&lt;value&gt;</code>' class='header-anchors' ><code>--input 
&lt;value&gt;</code></h3><p>Path to file containing queries; a multi-object 
JSON file with one query
  object per line. Accepts any valid Hadoop file URL.</p><p>Default: 
<code>batchpredict-input.json</code></p><h3 
id='<code>--output-&lt;value&gt;</code>' class='header-anchors' ><code>--output 
&lt;value&gt;</code></h3><p>Path to file to receive results; a multi-object 
JSON file with one object per line, the prediction + original query. Accepts 
any valid Hadoop file URL. Actual output will be written as Hadoop partition 
files in a directory with the output name.</p><p>Default: 
<code>batchpredict-output.json</code></p><h3 
id='<code>--query-partitions-&lt;value&gt;</code>' class='header-anchors' 
><code>--query-partitions &lt;value&gt;</code></h3><p>Configure the concurrency 
of predictions by setting the number of partitions used internally for the RDD 
of queries. This will directly effect the number of resulting 
<code>part-*</code> output files. While setting to <code>1</code> may seem 
appealing to get a single output file, this will remove parallelization for the 
batch process, reducin
 g performance and possibly exhausting memory.</p><p>Default: number created by 
Spark context&#39;s <code>textFile</code> (probably the number of cores 
available on the local machine)</p><h3 
id='<code>--engine-instance-id-&lt;value&gt;</code>' class='header-anchors' 
><code>--engine-instance-id &lt;value&gt;</code></h3><p>Identifier for the 
trained instance to use for batch predict.</p><p>Default: the latest trained 
instance.</p><h2 id='example' class='header-anchors'>Example</h2><h3 id='input' 
class='header-anchors'>Input</h3><p>A multi-object JSON file of queries as they 
would be sent to the engine&#39;s HTTP Queries API.</p><div 
class="alert-message note"><p>Read via <a 
href="https://spark.apache.org/docs/latest/rdd-programming-guide.html#external-datasets";>SparkContext&#39;s
 <code>textFile</code></a> and so may be a single file or any supported Hadoop 
format.</p></div><p>File: <code>batchpredict-input.json</code></p><div 
class="highlight json"><table style="border-spacing: 0"><tbo
 dy><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
+2
+3
+4
+5</pre></td><td class="code"><pre><span class="p">{</span><span 
class="s2">"user"</span><span class="p">:</span><span 
class="s2">"1"</span><span class="p">}</span><span class="w">
+</span><span class="p">{</span><span class="s2">"user"</span><span 
class="p">:</span><span class="s2">"2"</span><span class="p">}</span><span 
class="w">
+</span><span class="p">{</span><span class="s2">"user"</span><span 
class="p">:</span><span class="s2">"3"</span><span class="p">}</span><span 
class="w">
+</span><span class="p">{</span><span class="s2">"user"</span><span 
class="p">:</span><span class="s2">"4"</span><span class="p">}</span><span 
class="w">
+</span><span class="p">{</span><span class="s2">"user"</span><span 
class="p">:</span><span class="s2">"5"</span><span class="p">}</span><span 
class="w">
+</span></pre></td></tr></tbody></table> </div> <h3 id='execute' 
class='header-anchors'>Execute</h3><div class="highlight shell"><table 
style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: 
right"><pre class="lineno">1
+2
+3</pre></td><td class="code"><pre>pio batchpredict <span class="se">\</span>
+  --input batchpredict-input.json <span class="se">\</span>
+  --output batchpredict-output.json
+</pre></td></tr></tbody></table> </div> <p>This command will run to 
completion, aborting if any errors are encountered.</p><h3 id='output' 
class='header-anchors'>Output</h3><p>A multi-object JSON file of predictions + 
original queries. The predictions are JSON objects as they would be returned 
from the engine&#39;s HTTP Queries API.</p><div class="alert-message 
note"><p>Results are written via Spark RDD&#39;s <code>saveAsTextFile</code> so 
each partition will be written to its own <code>part-*</code> file. See <a 
href="#post-processing-results">post-processing results</a>.</p></div><p>File 
1: <code>batchpredict-output.json/part-00000</code></p><div class="highlight 
json"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" 
style="text-align: right"><pre class="lineno">1
+2
+3</pre></td><td class="code"><pre><span class="p">{</span><span 
class="s2">"query"</span><span class="p">:{</span><span 
class="s2">"user"</span><span class="p">:</span><span 
class="s2">"1"</span><span class="p">},</span><span 
class="s2">"prediction"</span><span class="p">:{</span><span 
class="s2">"itemScores"</span><span class="p">:[{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"1"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">33</span><span class="p">},{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"2"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">32</span><span class="p">}]}}</span><span class="w">
+</span><span class="p">{</span><span class="s2">"query"</span><span 
class="p">:{</span><span class="s2">"user"</span><span class="p">:</span><span 
class="s2">"3"</span><span class="p">},</span><span 
class="s2">"prediction"</span><span class="p">:{</span><span 
class="s2">"itemScores"</span><span class="p">:[{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"2"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">16</span><span class="p">},{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"3"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">12</span><span class="p">}]}}</span><span class="w">
+</span><span class="p">{</span><span class="s2">"query"</span><span 
class="p">:{</span><span class="s2">"user"</span><span class="p">:</span><span 
class="s2">"4"</span><span class="p">},</span><span 
class="s2">"prediction"</span><span class="p">:{</span><span 
class="s2">"itemScores"</span><span class="p">:[{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"3"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">19</span><span class="p">},{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"1"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">18</span><span class="p">}]}}</span><span class="w">
+</span></pre></td></tr></tbody></table> </div> <p>File 2: 
<code>batchpredict-output.json/part-00001</code></p><div class="highlight 
json"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" 
style="text-align: right"><pre class="lineno">1
+2</pre></td><td class="code"><pre><span class="p">{</span><span 
class="s2">"query"</span><span class="p">:{</span><span 
class="s2">"user"</span><span class="p">:</span><span 
class="s2">"2"</span><span class="p">},</span><span 
class="s2">"prediction"</span><span class="p">:{</span><span 
class="s2">"itemScores"</span><span class="p">:[{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"5"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">55</span><span class="p">},{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"3"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">28</span><span class="p">}]}}</span><span class="w">
+</span><span class="p">{</span><span class="s2">"query"</span><span 
class="p">:{</span><span class="s2">"user"</span><span class="p">:</span><span 
class="s2">"5"</span><span class="p">},</span><span 
class="s2">"prediction"</span><span class="p">:{</span><span 
class="s2">"itemScores"</span><span class="p">:[{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"1"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">24</span><span class="p">},{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"4"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">14</span><span class="p">}]}}</span><span class="w">
+</span></pre></td></tr></tbody></table> </div> <h3 
id='post-processing-results' class='header-anchors'>Post-processing 
Results</h3><p>After the process exits successfully, the parts may be 
concatenated into a single output file using a command like:</p><div 
class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td 
class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td 
class="code"><pre>cat batchpredict-output.json/part-<span class="k">*</span> 
&gt; batchpredict-output-all.json
+</pre></td></tr></tbody></table> </div> </div></div></div></div><footer><div 
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class="content-header hidden-sm hidden-xs"><div id="page-title"><h1>Command 
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class='header-anchors'>Overview</h2><p>Interaction with Apache PredictionIO 
(incubating) is done through the command line interface. It follows the format 
of:</p><p><code>pio &lt;command&gt; [options] &lt;args&gt;...</code></p><p>You 
can run <code>pio help</code> to see a list of all available com
 mands and <code>pio help &lt;command&gt;</code> to see details of the 
command.</p><p>Apache PredictionIO (incubating) commands can be separated into 
the following three categories.</p><h2 id='general-commands' 
class='header-anchors'>General Commands</h2><p><code>pio help</code> Display 
usage summary. <code>pio help &lt;command&gt;</code> to read about a specific 
subcommand.</p><p><code>pio version</code> Displays the version of the 
installed PredictionIO.</p><p><code>pio status</code> Displays install path and 
running status of PredictionIO system and its dependencies.</p><h2 
id='event-server-commands' class='header-anchors'>Event Server 
Commands</h2><p><code>pio eventserver</code> Launch the Event 
Server.</p><p><code>pio app</code> Manage apps that are used by the Event 
Server.</p><p><code>pio app data-delete &lt;name&gt;</code> deletes all data 
associated with the app.</p><p><code>pio app delete &lt;name&gt;</code> deletes 
the app and its data.</p><p> <code>--ip &lt;value&gt;</cod
 e> IP to bind to. Default to localhost.</p><p> <code>--port 
&lt;value&gt;</code> Port to bind to. Default to 7070.</p><p><code>pio 
accesskey</code> Manage app access keys.</p><h2 id='engine-commands' 
class='header-anchors'>Engine Commands</h2><p>Engine commands need to be run 
from the directory that contains the engine project. <code>--debug</code> and 
<code>--verbose</code> flags will provide debug and third-party informational 
messages.</p><p><code>pio build</code> Build the engine at the current 
directory.</p><p><code>pio train</code> Kick off a training using an 
engine.</p><p><code>pio deploy</code> Deploy an engine as an engine 
server.</p><p><code>pio batchpredict</code> Process bulk predictions using an 
engine.</p><p>For <code>deploy</code> &amp; <code>batchpredict</code>, if 
<code>--engine-instance-id</code> is not specified, it will use the latest 
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for Users</span></a></li><li class="level-3"><a class="final" 
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Code</h1></div></div><div id="table-of-content-wrapper"><h5>On this 
page</h5><aside id="table-of-contents"><ul> <li> <a 
href="#areas-in-need-of-help">Areas in Need of Help</a> </li> <li> <a 
href="#how-to-report-an-issue">How to Report an Issue</a> </li> <li> <a 
href="#how-to-help-resolve-existing-issues">How to Help Resolve Existing 
Issues</a> </li> <li> <a href="#how-to-add-propose-a-new-feature">How to Add / 
Propose a New Feature</a> </li> <li> <a href="#how-to-issue-a-pull-request">How 
to Issue a Pull Request</a> </li> <li> <a href="#getting-started">Getting 
Started</a> </li> <li> <a 
href="#anatomy-of-apache-predictionio-incubating-code-tree">Anatomy of Apache 
PredictionIO (incubating) Code Tree</a> </li> </ul> </aside><hr/><a 
id="edit-page-link" href="ht
 
tps://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/community/contribute-code.html.md"><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" 
class="hidden-sm hidden xs"><ul><li><a href="/community/">Getting 
Involved</a><span class="spacer">&gt;</span></li><li><span 
class="last">Contribute Code</span></li></ul></div><div 
id="page-title"><h1>Contribute Code</h1></div></div><div class="content"> 
<p>Thank you for your interest in contributing to Apache PredictionIO 
(incubating). Our mission is to enable developers to build scalable machine 
learning applications easily. Here is how you can help with the project 
development. If you have any question regarding development at anytime, please 
free to <a 
href="mailto:[email protected]";>subscribe</a> and 
post to the <a 
href="mailto:[email protected]";>Development Mailin
 g List</a>.</p><h2 id='areas-in-need-of-help' class='header-anchors'>Areas in 
Need of Help</h2><p>We accept contributions of all kinds at any time. We are 
compiling this list to show features that are highly sought after by the 
community.</p> <ul> <li>Tests and CI</li> <li>Engine template, tutorials, and 
samples</li> <li>Client SDKs</li> <li>Building engines in Java (updating the 
Java controller API)</li> <li>Code clean up and refactoring</li> <li>Code and 
data pipeline optimization</li> <li>Developer experience (UX) improvement</li> 
</ul> <h2 id='how-to-report-an-issue' class='header-anchors'>How to Report an 
Issue</h2><p>If you wish to report an issue you found, you can do so on <a 
href="https://issues.apache.org/jira/browse/PIO";>Apache PredictionIO 
(incubating) JIRA</a>.</p><h2 id='how-to-help-resolve-existing-issues' 
class='header-anchors'>How to Help Resolve Existing Issues</h2><p>In general, 
bug fixes should be done the same way as new features, but critical bug fixes 
will fol
 low a different path.</p><h2 id='how-to-add-/-propose-a-new-feature' 
class='header-anchors'>How to Add / Propose a New Feature</h2><p>Before adding 
new features into JIRA, please check that the feature does not currently exist 
in JIRA.</p> <ol> <li>To propose a new feature, simply <a 
href="mailto:[email protected]";>subscribe</a> and 
post your proposal to <a 
href="mailto:[email protected]";>Apache PredictionIO 
(incubating) Development Mailing List</a>.</li> <li>Discuss with the community 
and the core development team on what needs to be done, and lay down concrete 
plans on deliverables.</li> <li>Once solid plans are made, start creating 
tickets in the <a href="https://issues.apache.org/jira/browse/PIO";>issue 
tracker</a>.</li> <li>Work side by side with other developers using Apache 
PredictionIO (incubating) Development Mailing List as primary mode of 
communication. You never know if someone else has a better idea. ;)</li> </ol> 
<h3 id=
 'adding-ticket-to-jira' class='header-anchors'>Adding ticket to JIRA</h3> <ol> 
<li>Add a descriptive Summary and a detailed description</li> <li>Set Issue 
Type to Bug, Improvement, New Feature, Test or Wish</li> <li>Set Priority to 
Blocker, Critical, Major, Minor or Trivial</li> <li>Fill out Affects Version 
with the version of PredictionIO you are currently using</li> <li>Fill out 
Environment if needed for description of your bug / feature</li> <li>Please 
leave other fields blank</li> </ol> <h3 id='triaging-jira' 
class='header-anchors'>Triaging JIRA</h3><p>Tickets will be triaged by 
PredictionIO committers.</p> <ul> <li><p><strong>Target Version</strong>: 
Either a particular version or <code>Future</code> if to be done later</p> <ul> 
<li>Once a fix has been committed, the Fix Version will filled in with the 
appropriate release</li> </ul></li> <li><p><strong>Component</strong>: Each 
ticket will be annotated with one or more of the following Components</p> <ul> 
<li><strong>Core</stron
 g>: affects the main code branch / will be part of a release</li> 
<li><strong>Documentation</strong>: affects the documents / will be pushed to 
livedoc branch</li> <li><strong>Templates</strong>: affects one of the separate 
github repositories for a template</li> </ul></li> </ul> <h2 
id='how-to-issue-a-pull-request' class='header-anchors'>How to Issue a Pull 
Request</h2><p>When you have finished your code, you can <a 
href="https://help.github.com/articles/creating-a-pull-request/";>create a pull 
request</a> against the <strong>develop</strong> branch.</p> <ul> <li>The title 
must contain a tag associating with an existing JIRA ticket. You must create a 
ticket so that the infrastructure can correctly track issues across Apache JIRA 
and GitHub. If your ticket is <code>PIO-789</code>, your title must look 
something like <code>[PIO-789] Some short description</code>.</li> <li>Please 
also, in your commit message summary, include the JIRA ticket number similar to 
above.</li> <li>Make sure t
 he title and description are clear and concise. For more details on writing a 
good commit message, check out <a 
href="http://tbaggery.com/2008/04/19/a-note-about-git-commit-messages.html";>this
 guide</a>.</li> <li>If the change is visual, make sure to include a screenshot 
or GIF.</li> <li>Make sure it is being opened into the right branch.</li> 
<li>Make sure it has been rebased on top of that branch.</li> </ul> <div 
class="alert-message note"><p>When it is close to a release, and if there are 
major development ongoing, a release branch will be forked from the develop 
branch to stabilize the code for binary release. Please refer to the <em>git 
flow</em> methodology page for more information.</p></div><h2 
id='getting-started' class='header-anchors'>Getting Started</h2><p>Apache 
PredictionIO (incubating) relies heavily on the <a 
href="http://nvie.com/posts/a-successful-git-branching-model/";>git flow 
methodology</a>. Please make sure you read and understand it before you start 
your devel
 opment. By default, cloning Apache PredictionIO (incubating) will put you in 
the <em>develop</em> branch, which in most cases is where all the latest 
development go to.</p><div class="alert-message note"><p>For core development, 
please follow the <a href="http://docs.scala-lang.org/style/";>Scala Style 
Guide</a>.</p></div><h3 
id='create-a-fork-of-the-apache-predictionio-(incubating)-repository' 
class='header-anchors'>Create a Fork of the Apache PredictionIO (incubating) 
Repository</h3> <ol> <li>Start by creating a GitHub account if you do not 
already have one.</li> <li>Go to <a 
href="https://github.com/PredictionIO/PredictionIO";>Apache PredictionIO 
(incubating)’s GitHub mirror</a> and fork it to your own account.</li> 
<li>Clone your fork to your local machine.</li> </ol> <p>If you need additional 
help, please refer to <a 
href="https://help.github.com/articles/fork-a-repo/";>https://help.github.com/articles/fork-a-repo/</a>.</p><h3
 id='building-apache-predictionio-(incubating)-from-s
 ource' class='header-anchors'>Building Apache PredictionIO (incubating) from 
Source</h3><p>After the previous section, you should have a copy of Apache 
PredictionIO (incubating) in your local machine ready to be built.</p> <ol> 
<li>Make sure you are on the <em>develop</em> branch. You can double check by 
<code>git status</code> or simply <code>git checkout develop</code>.</li> 
<li>At the root of the repository, do <code>./make-distribution.sh</code> to 
build PredictionIO.</li> </ol> <h3 id='setting-up-the-environment' 
class='header-anchors'>Setting Up the Environment</h3><p>Apache PredictionIO 
(incubating) relies on 3rd party software to perform its tasks. To set them up, 
simply follow this <a 
href="http://predictionio.incubator.apache.org/install/install-sourcecode/#installing-dependencies";>documentation</a>.</p><h3
 id='start-hacking' class='header-anchors'>Start Hacking</h3><p>You should have 
a Apache PredictionIO (incubating) development environment by now. Happy 
hacking!</p><h2 
 id='anatomy-of-apache-predictionio-(incubating)-code-tree' 
class='header-anchors'>Anatomy of Apache PredictionIO (incubating) Code 
Tree</h2><p>The following describes each directory’s purpose.</p><h3 id='bin' 
class='header-anchors'>bin</h3><p>Shell scripts and any relevant components to 
go into the binary distribution. Utility shell scripts can also be included 
here.</p><h3 id='conf' class='header-anchors'>conf</h3><p>Configuration files 
that are used by both a source tree and binary distribution.</p><h3 id='core' 
class='header-anchors'>core</h3><p>Core Apache PredictionIO (incubating) code 
that provides the DASE controller API, core data structures, and workflow 
creation and management code.</p><h3 id='data' 
class='header-anchors'>data</h3><p>Apache PredictionIO (incubating) Event 
Server, and backend-agnostic storage layer for event store and metadata 
store.</p><h3 id='docs' class='header-anchors'>docs</h3><p>Source code for <a 
href="http://predictionio.incubator.apache.org";>http
 ://predictionio.incubator.apache.org</a> site, and any other documentation 
support files.</p><h3 id='examples' 
class='header-anchors'>examples</h3><p>Complete code examples showing Apache 
PredictionIO (incubating)&#39;s application.</p><h3 id='sbt' 
class='header-anchors'>sbt</h3><p>Embedded SBT (Simple Build Tool) 
launcher.</p><h3 id='storage' class='header-anchors'>storage</h3><p>Storage 
implementations.</p><h3 id='tools' class='header-anchors'>tools</h3><p>Tools 
for running Apache PredictionIO (incubating). Contains primarily the CLI 
(command-line interface) and its supporting code, and the experimental 
evaluation dashboard.</p></div></div></div></div><footer><div 
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 cs</a></li><li><a href="//github.com/apache/incubator-predictionio" 
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href="mailto:[email protected]"; 
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target="blank">Stackoverflow</a></li></ul></div></div><div class="col-md-6 
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 rojects/predictionio.html"><img alt="Apache Incubator" 
src="/images/logos/apache_incubator-6954bd16.png"/></a><span>Apache 
PredictionIO is an effort undergoing incubation at The Apache Software 
Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of 
all newly accepted projects until a further review indicates that the 
infrastructure, communications, and decision making process have stabilized in 
a manner consistent with other successful ASF projects. While incubation status 
is not necessarily a reflection of the completeness or stability of the code, 
it does indicate that the project has yet to be fully endorsed by the 
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