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Trainer/Distributed training/index.html

@@ -813,7 +813,7 @@ In this setting, the model will run on all 8 GPUs at once using DataParallel und
 <p>Multi-node training is easily done by specifying these flags.</p>
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 2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># train on 12*8 GPUs</span>
-<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">gpus</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">nb_gpu_nodes</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">gpus</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">nb_gpu_nodes</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">distributed_backend</span><span class="o">=</span><span class="s1">&#39;ddp&#39;</span><span class="p">)</span>
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@@ -868,6 +868,8 @@ In this setting, the model will run on all 8 GPUs at once using DataParallel und
 </pre></div>
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+<p><strong>NOTE:</strong> When running in DDP mode, any errors in your code will show up as an NCCL issue.
+Set the <code>NCCL_DEBUG=INFO</code> flag to see the ACTUAL error.</p>
 <p>Finally, make sure to add a distributed sampler to your dataset. The distributed sampler copies a 
 portion of your dataset onto each GPU. (World_size = gpus_per_node * nb_nodes).   </p>
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