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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="tab
 le-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/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 pr
 edict 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 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 requir
 ement 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, 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'>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"><tbody><tr><td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
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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 href="#example">Example</a> </li> </ul> </aside><hr/><a 
id="edit-page-link" 
href="https://github.com/apache/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 predi
 ct 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 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 requireme
 nt 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 + or
 iginal 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, 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>I
 dentifier 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"><tbody><tr><td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
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 > class='header-anchors'>Overview</h2><p>Interaction with Apache PredictionIO 
 > 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 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 
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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 
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<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 
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REST/SDKs</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/eventmodel/"><span>Events Modeling</span></a></li><li 
class="level-2"><a class="final" 
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Webhooks</span></a></li><li class="level-2"><a class="final" 
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class="level-2"><a class="final" 
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Batch</span></a></li><li class="level-2"><a class="final" 
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 i><li class="level-2"><a class="expandible" href="#"><span>E-Commerce 
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Start</span></a></li><li class="level-3"><a class="final" 
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with Rate Event</span></a></li><li class="level-3"><a class="final" 
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Score</span></a></li></ul></li><li class="level-2"><a class="expandible" 
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class="final" href="/templates/similarproduct/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarpro
 duct/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" 
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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" 
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for Users</span></a></li><li class="level-3"><a class="final" 
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IDEA</span></a></li><li class="level-2"><a class="final" 
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 vel-2"><a class="final" href="https://www.apache.org/";><span>Apache 
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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 href="#general-commands">General 
Commands</a> </li> <li> <a href="#event-server-commands">Event Server 
Commands</a> </li> <li> <a href="#engine-commands">Engine Commands</a> </li> 
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class="spacer">&gt;</span></li><li><span class="last">Command-line 
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 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 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 it
 s data.</p><p> <code>--ip &lt;value&gt;</code> 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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