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<div class="section" id="train-script">
<h1>Train script<a class="headerlink" href="#train-script" title="Permalink to this headline">¶</a></h1>
<p>If your train script works with <code class="docutils literal notranslate"><span class="pre">torch.distributed.launch</span></code> it will continue
working with <code class="docutils literal notranslate"><span class="pre">torch.distributed.run</span></code> with these differences:</p>
<ol class="arabic simple">
<li><p>No need to manually pass <code class="docutils literal notranslate"><span class="pre">RANK</span></code>, <code class="docutils literal notranslate"><span class="pre">WORLD_SIZE</span></code>,
<code class="docutils literal notranslate"><span class="pre">MASTER_ADDR</span></code>, and <code class="docutils literal notranslate"><span class="pre">MASTER_PORT</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">rdzv_backend</span></code> and <code class="docutils literal notranslate"><span class="pre">rdzv_endpoint</span></code> must be provided. For most users
this will be set to <code class="docutils literal notranslate"><span class="pre">c10d</span></code> (see <a class="reference external" href="rendezvous.html">rendezvous</a>).</p></li>
<li><p>Make sure you have a <code class="docutils literal notranslate"><span class="pre">load_checkpoint(path)</span></code> and
<code class="docutils literal notranslate"><span class="pre">save_checkpoint(path)</span></code> logic in your script. When workers fail
we restart all the workers with the same program arguments so you will
lose progress up to the most recent checkpoint
(see <a class="reference external" href="distributed.html">elastic launch</a>).</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">use_env</span></code> flag has been removed. If you were parsing local rank by parsing
the <code class="docutils literal notranslate"><span class="pre">--local_rank</span></code> option, you need to get the local rank from the
environment variable <code class="docutils literal notranslate"><span class="pre">LOCAL_RANK</span></code> (e.g. <code class="docutils literal notranslate"><span class="pre">os.environ["LOCAL_RANK"]</span></code>).</p></li>
</ol>
<p>Below is an expository example of a training script that checkpoints on each
epoch, hence the worst-case progress lost on failure is one full epoch worth
of training.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="n">args</span> <span class="o">=</span> <span class="n">parse_args</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
<span class="n">state</span> <span class="o">=</span> <span class="n">load_checkpoint</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">checkpoint_path</span><span class="p">)</span>
<span class="n">initialize</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
<span class="c1"># torch.distributed.run ensure that this will work</span>
<span class="c1"># by exporting all the env vars needed to initialize the process group</span>
<span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">init_process_group</span><span class="p">(</span><span class="n">backend</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">backend</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">state</span><span class="o">.</span><span class="n">epoch</span><span class="p">,</span> <span class="n">state</span><span class="o">.</span><span class="n">total_num_epochs</span><span class="p">)</span>
<span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">iter</span><span class="p">(</span><span class="n">state</span><span class="o">.</span><span class="n">dataset</span><span class="p">)</span>
<span class="n">train</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">state</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
<span class="n">state</span><span class="o">.</span><span class="n">epoch</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">save_checkpoint</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
</pre></div>
</div>
<p>For concrete examples of torchelastic-compliant train scripts, visit
our <a class="reference external" href="examples.html">examples</a> page.</p>
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