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<section id="customization">
<h1>Customization<a class="headerlink" href="#customization" title="Permalink to this heading">¶</a></h1>
<p>This section describes how to customize TorchElastic to fit your needs.</p>
<section id="launcher">
<h2>Launcher<a class="headerlink" href="#launcher" title="Permalink to this heading">¶</a></h2>
<p>The launcher program that ships with TorchElastic
should be sufficient for most use-cases (see <a class="reference internal" href="run.html#launcher-api"><span class="std std-ref">torchrun (Elastic Launch)</span></a>).
You can implement a custom launcher by
programmatically creating an agent and passing it specs for your workers as
shown below.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># my_launcher.py</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__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">rdzv_handler</span> <span class="o">=</span> <span class="n">RendezvousHandler</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="n">spec</span> <span class="o">=</span> <span class="n">WorkerSpec</span><span class="p">(</span>
<span class="n">local_world_size</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">nproc_per_node</span><span class="p">,</span>
<span class="n">fn</span><span class="o">=</span><span class="n">trainer_entrypoint_fn</span><span class="p">,</span>
<span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">trainer_entrypoint_fn</span> <span class="n">args</span><span class="o">.</span><span class="n">fn_args</span><span class="p">,</span><span class="o">...</span><span class="p">),</span>
<span class="n">rdzv_handler</span><span class="o">=</span><span class="n">rdzv_handler</span><span class="p">,</span>
<span class="n">max_restarts</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">max_restarts</span><span class="p">,</span>
<span class="n">monitor_interval</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">monitor_interval</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">agent</span> <span class="o">=</span> <span class="n">LocalElasticAgent</span><span class="p">(</span><span class="n">spec</span><span class="p">,</span> <span class="n">start_method</span><span class="o">=</span><span class="s2">"spawn"</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">run_result</span> <span class="o">=</span> <span class="n">agent</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
<span class="k">if</span> <span class="n">run_result</span><span class="o">.</span><span class="n">is_failed</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"worker 0 failed with: run_result.failures[0]"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"worker 0 return value is: run_result.return_values[0]"</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="n">ex</span><span class="p">:</span>
<span class="c1"># handle exception</span>
</pre></div>
</div>
</section>
<section id="rendezvous-handler">
<h2>Rendezvous Handler<a class="headerlink" href="#rendezvous-handler" title="Permalink to this heading">¶</a></h2>
<p>To implement your own rendezvous, extend <code class="docutils literal notranslate"><span class="pre">torch.distributed.elastic.rendezvous.RendezvousHandler</span></code>
and implement its methods.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Rendezvous handlers are tricky to implement. Before you begin
make sure you completely understand the properties of rendezvous.
Please refer to <a class="reference internal" href="rendezvous.html#rendezvous-api"><span class="std std-ref">Rendezvous</span></a> for more information.</p>
</div>
<p>Once implemented you can pass your custom rendezvous handler to the worker
spec when creating the agent.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">spec</span> <span class="o">=</span> <span class="n">WorkerSpec</span><span class="p">(</span>
<span class="n">rdzv_handler</span><span class="o">=</span><span class="n">MyRendezvousHandler</span><span class="p">(</span><span class="n">params</span><span class="p">),</span>
<span class="o">...</span>
<span class="p">)</span>
<span class="n">elastic_agent</span> <span class="o">=</span> <span class="n">LocalElasticAgent</span><span class="p">(</span><span class="n">spec</span><span class="p">,</span> <span class="n">start_method</span><span class="o">=</span><span class="n">start_method</span><span class="p">)</span>
<span class="n">elastic_agent</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">spec</span><span class="o">.</span><span class="n">role</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="metric-handler">
<h2>Metric Handler<a class="headerlink" href="#metric-handler" title="Permalink to this heading">¶</a></h2>
<p>TorchElastic emits platform level metrics (see <a class="reference internal" href="metrics.html#metrics-api"><span class="std std-ref">Metrics</span></a>).
By default metrics are emitted to <cite>/dev/null</cite> so you will not see them.
To have the metrics pushed to a metric handling service in your infrastructure,
implement a <cite>torch.distributed.elastic.metrics.MetricHandler</cite> and <cite>configure</cite> it in your
custom launcher.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># my_launcher.py</span>
<span class="kn">import</span> <span class="nn">torch.distributed.elastic.metrics</span> <span class="k">as</span> <span class="nn">metrics</span>
<span class="k">class</span> <span class="nc">MyMetricHandler</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">MetricHandler</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">emit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">metric_data</span><span class="p">:</span> <span class="n">metrics</span><span class="o">.</span><span class="n">MetricData</span><span class="p">):</span>
<span class="c1"># push metric_data to your metric sink</span>
<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="n">metrics</span><span class="o">.</span><span class="n">configure</span><span class="p">(</span><span class="n">MyMetricHandler</span><span class="p">())</span>
<span class="n">spec</span> <span class="o">=</span> <span class="n">WorkerSpec</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="n">agent</span> <span class="o">=</span> <span class="n">LocalElasticAgent</span><span class="p">(</span><span class="n">spec</span><span class="p">)</span>
<span class="n">agent</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
</pre></div>
</div>
</section>
<section id="events-handler">
<h2>Events Handler<a class="headerlink" href="#events-handler" title="Permalink to this heading">¶</a></h2>
<p>TorchElastic supports events recording (see <a class="reference internal" href="events.html#events-api"><span class="std std-ref">Events</span></a>).
The events module defines API that allows you to record events and
implement custom EventHandler. EventHandler is used for publishing events
produced during torchelastic execution to different sources, e.g. AWS CloudWatch.
By default it uses <cite>torch.distributed.elastic.events.NullEventHandler</cite> that ignores
events. To configure custom events handler you need to implement
<cite>torch.distributed.elastic.events.EventHandler</cite> interface and <cite>configure</cite> it
in your custom launcher.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># my_launcher.py</span>
<span class="kn">import</span> <span class="nn">torch.distributed.elastic.events</span> <span class="k">as</span> <span class="nn">events</span>
<span class="k">class</span> <span class="nc">MyEventHandler</span><span class="p">(</span><span class="n">events</span><span class="o">.</span><span class="n">EventHandler</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">record</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">event</span><span class="p">:</span> <span class="n">events</span><span class="o">.</span><span class="n">Event</span><span class="p">):</span>
<span class="c1"># process event</span>
<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="n">events</span><span class="o">.</span><span class="n">configure</span><span class="p">(</span><span class="n">MyEventHandler</span><span class="p">())</span>
<span class="n">spec</span> <span class="o">=</span> <span class="n">WorkerSpec</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="n">agent</span> <span class="o">=</span> <span class="n">LocalElasticAgent</span><span class="p">(</span><span class="n">spec</span><span class="p">)</span>
<span class="n">agent</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
</pre></div>
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