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<section id="quickstart">
<h1>Quickstart<a class="headerlink" href="#quickstart" title="Permalink to this heading">¶</a></h1>
<p>To launch a <strong>fault-tolerant</strong> job, run the following on all nodes.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>torchrun
--nnodes<span class="o">=</span>NUM_NODES
--nproc_per_node<span class="o">=</span>TRAINERS_PER_NODE
--max_restarts<span class="o">=</span>NUM_ALLOWED_FAILURES
--rdzv_id<span class="o">=</span>JOB_ID
--rdzv_backend<span class="o">=</span>c10d
--rdzv_endpoint<span class="o">=</span>HOST_NODE_ADDR
YOUR_TRAINING_SCRIPT.py <span class="o">(</span>--arg1 ... train script args...<span class="o">)</span>
</pre></div>
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<p>To launch an <strong>elastic</strong> job, run the following on at least <code class="docutils literal notranslate"><span class="pre">MIN_SIZE</span></code> nodes
and at most <code class="docutils literal notranslate"><span class="pre">MAX_SIZE</span></code> nodes.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>torchrun
--nnodes<span class="o">=</span>MIN_SIZE:MAX_SIZE
--nproc_per_node<span class="o">=</span>TRAINERS_PER_NODE
--max_restarts<span class="o">=</span>NUM_ALLOWED_FAILURES_OR_MEMBERSHIP_CHANGES
--rdzv_id<span class="o">=</span>JOB_ID
--rdzv_backend<span class="o">=</span>c10d
--rdzv_endpoint<span class="o">=</span>HOST_NODE_ADDR
YOUR_TRAINING_SCRIPT.py <span class="o">(</span>--arg1 ... train script args...<span class="o">)</span>
</pre></div>
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<div class="admonition note">
<p class="admonition-title">Note</p>
<p>TorchElastic models failures as membership changes. When a node fails,
this is treated as a “scale down” event. When the failed node is replaced by
the scheduler, it is a “scale up” event. Hence for both fault tolerant
and elastic jobs, <code class="docutils literal notranslate"><span class="pre">--max_restarts</span></code> is used to control the total number of
restarts before giving up, regardless of whether the restart was caused
due to a failure or a scaling event.</p>
</div>
<p><code class="docutils literal notranslate"><span class="pre">HOST_NODE_ADDR</span></code>, in form <host>[:<port>] (e.g. node1.example.com:29400),
specifies the node and the port on which the C10d rendezvous backend should be
instantiated and hosted. It can be any node in your training cluster, but
ideally you should pick a node that has a high bandwidth.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>If no port number is specified <code class="docutils literal notranslate"><span class="pre">HOST_NODE_ADDR</span></code> defaults to 29400.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The <code class="docutils literal notranslate"><span class="pre">--standalone</span></code> option can be passed to launch a single node job with a
sidecar rendezvous backend. You don’t have to pass <code class="docutils literal notranslate"><span class="pre">--rdzv_id</span></code>,
<code class="docutils literal notranslate"><span class="pre">--rdzv_endpoint</span></code>, and <code class="docutils literal notranslate"><span class="pre">--rdzv_backend</span></code> when the <code class="docutils literal notranslate"><span class="pre">--standalone</span></code> option
is used.</p>
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<p class="admonition-title">Note</p>
<p>Learn more about writing your distributed training script
<a class="reference external" href="train_script.html">here</a>.</p>
</div>
<p>If <code class="docutils literal notranslate"><span class="pre">torchrun</span></code> does not meet your requirements you may use our APIs directly
for more powerful customization. Start by taking a look at the
<a class="reference external" href="agent.html">elastic agent</a> API.</p>
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