datasets/v2.2.2/en/beam.html

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<h1 class="relative group"><a id="beam-datasets" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#beam-datasets"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Beam Datasets
	</span></h1>

<p>Some datasets are too large to be processed on a single machine. Instead, you can process them with <a href="https://beam.apache.org/" rel="nofollow">Apache Beam</a>, a library for parallel data processing. The processing pipeline is executed on a distributed processing backend such as <a href="https://flink.apache.org/" rel="nofollow">Apache Flink</a>, <a href="https://spark.apache.org/" rel="nofollow">Apache Spark</a>, or <a href="https://cloud.google.com/dataflow" rel="nofollow">Google Cloud Dataflow</a>.</p>
<p>We have already created Beam pipelines for some of the larger datasets like <a href="https://huggingface.co/datasets/wikipedia" rel="nofollow">wikipedia</a>, and <a href="https://huggingface.co/datasets/wiki40b" rel="nofollow">wiki40b</a>. You can load these normally with <a href="/docs/datasets/v2.2.2/en/package_reference/loading_methods#datasets.load_dataset">load_dataset()</a>. But if you want to run your own Beam pipeline with Dataflow, here is how:</p>
<ol><li>Specify the dataset and configuration you want to process:</li></ol>

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	<pre><!-- HTML_TAG_START --><span class="hljs-attr">DATASET_NAME</span>=your_dataset_name  <span class="hljs-comment"># ex: wikipedia</span>
<span class="hljs-attr">CONFIG_NAME</span>=your_config_name    <span class="hljs-comment"># ex: 20220301.en</span><!-- HTML_TAG_END --></pre></div>
<ol start="2"><li>Input your Google Cloud Platform information:</li></ol>

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	<pre><!-- HTML_TAG_START --><span class="hljs-attribute">PROJECT</span><span class="hljs-operator">=</span>your_project
<span class="hljs-attribute">BUCKET</span><span class="hljs-operator">=</span>your_bucket
<span class="hljs-attribute">REGION</span><span class="hljs-operator">=</span>your_region<!-- HTML_TAG_END --></pre></div>
<ol start="3"><li>Specify your Python requirements:</li></ol>

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	<pre><!-- HTML_TAG_START -->echo <span class="hljs-string">&quot;datasets&quot;</span> &gt; <span class="hljs-regexp">/tmp/</span>beam_requirements.txt
echo <span class="hljs-string">&quot;apache_beam&quot;</span> &gt;&gt; <span class="hljs-regexp">/tmp/</span>beam_requirements.txt<!-- HTML_TAG_END --></pre></div>
<ol start="4"><li>Run the pipeline:</li></ol>

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	<pre><!-- HTML_TAG_START -->datasets-cli run_beam datasets/<span class="hljs-variable">$DATASET_NAME</span> \
--name <span class="hljs-variable">$CONFIG_NAME</span> \
--save_infos \
--cache_dir gs://<span class="hljs-variable">$BUCKET</span>/cache/datasets \
--beam_pipeline_options=\
<span class="hljs-string">&quot;runner=DataflowRunner,project=<span class="hljs-variable">$PROJECT</span>,job_name=<span class="hljs-variable">$DATASET_NAME</span>-gen,&quot;</span>\
<span class="hljs-string">&quot;staging_location=gs://<span class="hljs-variable">$BUCKET</span>/binaries,temp_location=gs://<span class="hljs-variable">$BUCKET</span>/temp,&quot;</span>\
<span class="hljs-string">&quot;region=<span class="hljs-variable">$REGION</span>,requirements_file=/tmp/beam_requirements.txt&quot;</span><!-- HTML_TAG_END --></pre></div>


<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>When you run your pipeline, you can adjust the parameters to change the runner (Flink or Spark), output location (S3 bucket or HDFS), and the number of workers.</p></div>


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