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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/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 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>@transie
 nt</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 res
 ults; 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>' clas
 s='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><as
 ide 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-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 sy
 stems.</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 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-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 l
 ine, 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;v
 alue&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
 2
 3
 4
@@ -21,7 +21,7 @@
 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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