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<li class="toctree-l1"><a class="reference internal" href="../notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
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<div class="section" id="train-script">
<span id="elastic-train-script"></span><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">torchrun</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> can 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>). The default
<code class="docutils literal notranslate"><span class="pre">rdzv_backend</span></code> creates a non-elastic rendezvous where <code class="docutils literal notranslate"><span class="pre">rdzv_endpoint</span></code> holds
the master address.</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 any number of
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="run.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">int(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 ensures 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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