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<h1 class="relative group"><a id="distributed-training-with-optimum-habana" 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="#distributed-training-with-optimum-habana"><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>Distributed training with Optimum Habana
	</span></h1>

<p>As models get bigger, parallelism has emerged as a strategy for training larger models on limited hardware and accelerating training speed by several orders of magnitude.</p>
<p>All the <a href="https://github.com/huggingface/optimum-habana/tree/main/examples" rel="nofollow">PyTorch examples</a> and the <a href="https://huggingface.co/docs/optimum/habana/package_reference/trainer" rel="nofollow"><code>GaudiTrainer</code></a> script work out of the box with distributed training.
There are two ways of launching them:</p>
<ol><li>Using the <a href="https://github.com/huggingface/optimum-habana/blob/main/examples/gaudi_spawn.py" rel="nofollow">gaudi_spawn.py</a> script:</li></ol>

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	<pre><!-- HTML_TAG_START -->python gaudi_spawn.py \
    --world_size number_of_hpu_you_have --use_mpi \
    path_to_script.py --args1 --args2 ... --argsN<!-- HTML_TAG_END --></pre></div>
<p>where <code>--argX</code> is an argument of the script to run in a distributed way.
Examples are given for question answering <a href="https://github.com/huggingface/optimum-habana/blob/main/examples/question-answering/README.md#multi-card-training" rel="nofollow">here</a> and for text classification <a href="https://github.com/huggingface/optimum-habana/tree/main/examples/text-classification#multi-card-training" rel="nofollow">here</a>.</p>
<ol start="2"><li>Using the <a href="https://huggingface.co/docs/optimum/habana/package_reference/distributed_runner" rel="nofollow"><code>DistributedRunner</code></a> directly in code:</li></ol>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> optimum.habana.distributed <span class="hljs-keyword">import</span> DistributedRunner
<span class="hljs-keyword">from</span> optimum.utils <span class="hljs-keyword">import</span> logging

world_size=<span class="hljs-number">8</span> <span class="hljs-comment"># Number of HPUs to use (1 or 8)</span>

<span class="hljs-comment"># define distributed runner</span>
distributed_runner = DistributedRunner(
    command_list=[<span class="hljs-string">&quot;scripts/train.py --args1 --args2 ... --argsN&quot;</span>],
    world_size=world_size,
    use_mpi=<span class="hljs-literal">True</span>,
    multi_hls=<span class="hljs-literal">False</span>,
)

<span class="hljs-comment"># start job</span>
ret_code = distributed_runner.run()<!-- HTML_TAG_END --></pre></div>


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