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<a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/Trainer/debugging.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
<h1>Debugging</h1>
<p>These flags are useful to help debug a model.</p>
<hr />
<h4 id="fast-dev-run">Fast dev run</h4>
<p>This flag is meant for debugging a full train/val/test loop. It'll activate callbacks, everything but only with 1 training and 1 validation batch.
Use this to debug a full run of your program quickly</p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">fast_dev_run</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
</pre></div>
</td></tr></table>
<hr />
<h4 id="inspect-gradient-norms">Inspect gradient norms</h4>
<p>Looking at grad norms can help you figure out where training might be going wrong.</p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
2
3
4
5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT (-1 doesn&#39;t track norms)</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">track_grad_norm</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># track the LP norm (P=2 here)</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">track_grad_norm</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
</pre></div>
</td></tr></table>
<hr />
<h4 id="make-model-overfit-on-subset-of-data">Make model overfit on subset of data</h4>
<p>A useful debugging trick is to make your model overfit a tiny fraction of the data.</p>
<p>setting <code>overfit_pct &gt; 0</code> will overwrite train_percent_check, val_percent_check, test_percent_check</p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
2
3
4
5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT don&#39;t overfit (ie: normal training)</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">overfit_pct</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
<span class="c1"># overfit on 1% of data </span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">overfit_pct</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
</pre></div>
</td></tr></table>
<hr />
<h4 id="print-the-parameter-count-by-layer">Print the parameter count by layer</h4>
<p>By default lightning prints a list of parameters <em>and submodules</em> when it starts training.</p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
2
3
4
5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT print a full list of all submodules and their parameters.</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">weights_summary</span><span class="o">=</span><span class="s1">&#39;full&#39;</span><span class="p">)</span>
<span class="c1"># only print the top-level modules (i.e. the children of LightningModule).</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">weights_summary</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">)</span>
</pre></div>
</td></tr></table>
<hr />
<h4 id="print-which-gradients-are-nan">Print which gradients are nan</h4>
<p>This option prints a list of tensors with nan gradients.</p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">print_nan_grads</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
</pre></div>
</td></tr></table>
<hr />
<h4 id="log-gpu-usage">Log GPU usage</h4>
<p>Lightning automatically logs gpu usage to the test tube logs. It'll only do it at the metric logging interval, so it doesn't slow down training.</p>
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