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hidden-lg"><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-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 hr
 ef="#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-title"><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 n
 ewlines 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 already 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-rdd
 s">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>Defaul
 t: <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, reducing 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'>Exampl
 e</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"><tbody><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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id="page-title"><h1>Command Line</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 
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Interface</span></li></ul></div><div id="page-title"><h1>Command 
Line</h1></div></div><div class="content"> <h2 id='overview' 
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 commands 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 
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 >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;</code> IP to 
 >bind to. Default to localhost.</p><p> <code>--port &lt;value&gt;</code> Por
 t 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 
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Libraries</span></a></li><li class="level-2"><a class="final" href="/algorith
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href="/algorithm/custom/"><span>Adding Your Own 
Algorithms</span></a></li></ul></li><li class="level-1"><a class="expandible" 
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class="final" href="/evaluation/"><span>Overview</span></a></li><li 
class="level-2"><a class="final" 
href="/evaluation/paramtuning/"><span>Hyperparameter Tuning</span></a></li><li 
class="level-2"><a class="final" 
href="/evaluation/evaluationdashboard/"><span>Evaluation 
Dashboard</span></a></li><li class="level-2"><a class="final" 
href="/evaluation/metricchoose/"><span>Choosing Evaluation 
Metrics</span></a></li><li class="level-2"><a class="final" 
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Metrics</span></a></li></ul></li><li class="level-1"><a class="expand
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class="final" href="/system/"><span>Architecture Overview</span></a></li><li 
class="level-2"><a class="final" href="/system/anotherdatastore/"><span>Using 
Another Data Store</span></a></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>PredictionIO Official 
Templates</span></a><ul><li class="level-2"><a class="final" 
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href="/templates/recommendation/reading-custom-events/"><span>Read Custom 
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Preparator</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/customize-serving/"><span>Customize 
Serving</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/training-with-implicit-preference/"><span>Train 
with Implicit Preference</span></a></li><li class="level-3"><a class="final" 
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Items by Blacklist in Query</span></a></li><li class="level-3"><a class="final" 
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Evaluator</span></a></li></ul></li><li class="level-2"><a class="expandible" 
href="#"><span>E-Commerce Recommendation</span></a><ul><li class="level-3"><a 
class="final" href="/
 templates/ecommercerecommendation/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/how-to/"><span>How-To</span></a></li><li
 class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/train-with-rate-event/"><span>Train 
with Rate Event</span></a></li><li class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/adjust-score/"><span>Adjust 
Score</span></a></li></ul></li><li class="level-2"><a class="expandible" 
href="#"><span>Similar Product</span></a><ul><li class="level-3"><a 
class="final" href="/templates/similarproduct/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/similarproduct/how-to/"><span>How-To</span></a></li
 ><li class="level-3"><a class="final" 
 >href="/templates/similarproduct/multi-events-multi-algos/"><span>Multiple 
 >Events and Multiple Algorithms</span></a></li><li class="level-3"><a 
 >class="final" 
 >href="/templates/similarproduct/return-item-properties/"><span>Returns Item 
 >Properties</span></a></li><li class="level-3"><a class="final" 
 >href="/templates/similarproduct/train-with-rate-event/"><span>Train with Rate 
 >Event</span></a></li><li class="level-3"><a class="final" 
 >href="/templates/similarproduct/rid-user-set-event/"><span>Get Rid of Events 
 >for Users</span></a></li><li class="level-3"><a class="final" 
 >href="/templates/similarproduct/recommended-user/"><span>Recommend 
 >Users</span></a></li></ul></li><li class="level-2"><a class="expandible" 
 >href="#"><span>Classification</span></a><ul><li class="level-3"><a 
 >class="final" href="/templates/classification/quickstart/"><span>Quick 
 >Start</span></a></li><li class="level-3"><a class="final" 
 >href="/templates/classification/dase/"><span>DASE</s
 pan></a></li><li class="level-3"><a class="final" 
href="/templates/classification/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/classification/add-algorithm/"><span>Use Alternative 
Algorithm</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/reading-custom-properties/"><span>Read Custom 
Properties</span></a></li></ul></li></ul></li><li class="level-1"><a 
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class="level-2"><a class="final" 
href="/gallery/template-gallery/"><span>Browse</span></a></li><li 
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Template</span></a></li></ul></li><li class="level-1"><a class="expandible" 
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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 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="https://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 
Mailing 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 op
 timization</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 follow 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) De
 velopment 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 field
 s 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</strong>: 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-r
 equest/">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 the 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 releas
 e, 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 development. 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) Rep
 ository</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-source' 
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 tr
 ee 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 runn
 ing 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 class="container"><div 
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PredictionIO is an effort undergoing incubation at The Apache Software 
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all newly accepted projects until a further review indicates that the 
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