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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
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class="hidden-sm hidden xs"><ul><li><a href="#">Deploying an Engine</a><span
class="spacer">></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'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-<value></code>' class='header-anchors' ><code>--input
<value></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-<value></code>' class='header-anchors' ><code>--output
<value></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-<value></code>' class='header-anchors'
><code>--query-partitions <value></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's
<code>textFile</code> (probably the number of cores available on the local
machine)</p><h3 id='<code>--engine-instance-id-<value></code>'
class='header-anchors' ><code>--engine-instance-id <
;value></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'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'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="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
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class="hidden-sm hidden xs"><ul><li><a href="#">Deploying an Engine</a><span
class="spacer">></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'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 req
uirement 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-<value></code>' class='header-anchors' ><code>--input
<value></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-<value></code>' class='header-anchors' ><code>--output
<value></code></h3><p>Path to file to receive results; a multi-object
JSON file with one object per line, the predictio
n + 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-<value></code>' class='header-anchors'
><code>--query-partitions <value></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's
<code>textFile</code> (probably the number of cores available on the local
machine)</p><h3 id='<code>--engine-instance-id-<value></code>'
class='header-anchors' ><code>--engine-instance-id <value></code></h
3><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'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'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>
> batchpredict-output-all.json
-</pre></td></tr></tbody></table> </div> </div></div></div></div><footer><div
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