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Pytorch-Lightning/LightningModule/index.html
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<h4 id="display-metrics-in-progress-bar">Display metrics in progress bar</h4>
<pre><code class="python"># DEFAULT
trainer = Trainer(progress_bar=True)
</code></pre>
<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>
<pre><code class="python"># DEFAULT
trainer = Trainer(print_nan_grads=False)
</code></pre>
<hr />
<h4 id="process-position">Process position</h4>
<p>When running multiple models on the same machine we want to decide which progress bar to use.
Lightning will stack progress bars according to this value. </p>
<pre><code class="python"># DEFAULT
trainer = Trainer(process_position=0)
# if this is the second model on the node, show the second progress bar below
trainer = Trainer(process_position=1)
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Trainer/SLURM Managed Cluster/index.html
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<li class="toctree-l3"><a href="#anneal-learning-rate">Anneal Learning rate</a></li>
<li class="toctree-l3"><a href="#check-gpu-usage">Check GPU usage</a></li>
<li class="toctree-l3"><a href="#check-which-gradients-are-nan">Check which gradients are nan</a></li>
<li class="toctree-l3"><a href="#display-metrics-in-progress-bar">Display metrics in progress bar</a></li>
<li class="toctree-l3"><a href="#display-the-parameter-count-by-layer">Display the parameter count by layer</a></li>
<li class="toctree-l3"><a href="#fast-dev-run">Fast dev run</a></li>
<li class="toctree-l3"><a href="#force-training-for-min-or-max-epochs">Force training for min or max epochs</a></li>
@@ -119,12 +108,6 @@
<li class="toctree-l3"><a href="#inspect-gradient-norms">Inspect gradient norms</a></li>
<li class="toctree-l3"><a href="#make-model-overfit-on-subset-of-data">Make model overfit on subset of data</a></li>
<li class="toctree-l3"><a href="#process-position">Process position</a></li>
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@@ -136,6 +119,10 @@
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@@ -199,33 +186,6 @@ trainer = Trainer(lr_scheduler_milestones=None)
trainer = Trainer(lr_scheduler_milestones=[100, 200, 300])
</code></pre>
<hr />
<h4 id="check-gpu-usage">Check 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>
<hr />
<h4 id="check-which-gradients-are-nan">Check which gradients are nan</h4>
<p>This option prints a list of tensors with nan gradients.</p>
<pre><code class="python"># DEFAULT
trainer = Trainer(print_nan_grads=False)
</code></pre>
<hr />
<h4 id="display-metrics-in-progress-bar">Display metrics in progress bar</h4>
<pre><code class="python"># DEFAULT
trainer = Trainer(progress_bar=True)
</code></pre>
<hr />
<h4 id="display-the-parameter-count-by-layer">Display the parameter count by layer</h4>
<p>By default lightning prints a list of parameters <em>and submodules</em> when it starts training.</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>
<pre><code class="python"># DEFAULT
trainer = Trainer(fast_dev_run=False)
</code></pre>
<hr />
<h4 id="force-training-for-min-or-max-epochs">Force training for min or max epochs</h4>
<p>It can be useful to force training for a minimum number of epochs or limit to a max number</p>
@@ -250,27 +210,6 @@ trainer = Trainer(track_grad_norm=-1)
trainer = Trainer(track_grad_norm=2)
</code></pre>
<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>
<pre><code class="python"># DEFAULT don't overfit (ie: normal training)
trainer = Trainer(overfit_pct=0.0)
# overfit on 1% of data
trainer = Trainer(overfit_pct=0.01)
</code></pre>
<hr />
<h4 id="process-position">Process position</h4>
<p>When running multiple models on the same machine we want to decide which progress bar to use.
Lightning will stack progress bars according to this value. </p>
<pre><code class="python"># DEFAULT
trainer = Trainer(process_position=0)
# if this is the second model on the node, show the second progress bar below
trainer = Trainer(process_position=1)
</code></pre>
<hr />
<h4 id="set-how-much-of-the-training-set-to-check">Set how much of the training set to check</h4>
<p>If you don't want to check 100% of the training set (for debugging or if it's huge), set this flag</p>
Trainer/Validation loop/index.html
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@@ -112,6 +116,10 @@
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@@ -210,7 +218,7 @@ trainer = Trainer(nb_sanity_val_steps=5)
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@@ -244,7 +252,7 @@ trainer = Trainer(nb_sanity_val_steps=5)
<span><a href="../Training Loop/" style="color: #fcfcfc;">&laquo; Previous</a></span>
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<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>
<pre><code class="python"># DEFAULT
trainer = Trainer(fast_dev_run=False)
</code></pre>
<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>
<pre><code class="python"># DEFAULT (-1 doesn't track norms)
trainer = Trainer(track_grad_norm=-1)
# track the LP norm (P=2 here)
trainer = Trainer(track_grad_norm=2)
</code></pre>
<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>
<pre><code class="python"># DEFAULT don't overfit (ie: normal training)
trainer = Trainer(overfit_pct=0.0)
# overfit on 1% of data
trainer = Trainer(overfit_pct=0.01)
</code></pre>
<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>
<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>
<pre><code class="python"># DEFAULT
trainer = Trainer(print_nan_grads=False)
</code></pre>
<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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@@ -100,6 +104,10 @@
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@@ -161,17 +169,9 @@ trainer.fit(model)
<ul>
<li><a href="Training%20Loop/#accumulated-gradients">Accumulate gradients</a></li>
<li><a href="Training%20Loop/#anneal-learning-rate">Anneal Learning rate</a></li>
<li><a href="Training%20Loop/#Check-gpu-usage">Check GPU usage</a></li>
<li><a href="Training%20Loop/#check-which-gradients-are-nan">Check which gradients are nan</a></li>
<li><a href="Training%20Loop/#display-metrics-in-progress-bar">Display metrics in progress bar</a></li>
<li><a href="Training%20Loop/#display-the-parameter-count-by-layer">Display the parameter count by layer</a></li>
<li><a href="Training%20Loop/#fast-dev-run">Fast dev run</a></li>
<li><a href="Training%20Loop/#force-training-for-min-or-max-epochs">Force training for min or max epochs</a></li>
<li><a href="Training%20Loop/#force-disable-early-stop">Force disable early stop</a></li>
<li><a href="Training%20Loop/#inspect-gradient-norms">Inspect gradient norms</a></li>
<li><a href="Training%20Loop/#make-model-overfit-on-subset-of-data">Make model overfit on subset of data</a></li>
<li><a href="../Pytorch-lightning/LightningModule/#configure_optimizers">Use multiple optimizers (like GANs)</a></li>
<li><a href="Training%20Loop/#process-position">Process position</a></li>
<li><a href="Training%20Loop/#set-how-much-of-the-training-set-to-check">Set how much of the training set to check (1-100%)</a></li>
</ul>
<p><strong>Validation loop</strong> </p>
@@ -181,14 +181,30 @@ trainer.fit(model)
<li><a href="Validation%20Loop/#set-how-much-of-the-test-set-to-check">Set how much of the test set to check</a></li>
<li><a href="Validation%20Loop/#set-validation-check-frequency-within-1-training-epoch">Set validation check frequency within 1 training epoch</a></li>
<li><a href="Validation%20Loop/#set-the-number-of-validation-sanity-steps">Set the number of validation sanity steps</a></li>
<li><a href="Validation%20Loop/#check-validation-every-n-epochs">Check validation every n epochs</a></li>
</ul>
<p><strong>Debugging</strong> </p>
<ul>
<li><a href="Debugging/#fast-dev-run">Fast dev run</a></li>
<li><a href="Debugging/#inspect-gradient-norms">Inspect gradient norms</a></li>
<li><a href="Debugging/#Log-gpu-usage">Log GPU usage</a></li>
<li><a href="Debugging/#make-model-overfit-on-subset-of-data">Make model overfit on subset of data</a></li>
<li><a href="Debugging/#print-the-parameter-count-by-layer">Print the parameter count by layer</a></li>
<li><a href="Debugging/#print-which-gradients-are-nan">Pring which gradients are nan</a></li>
</ul>
<p><strong>Experiment Logging</strong> </p>
<ul>
<li><a href="Logging/#display-metrics-in-progress-bar">Display metrics in progress bar</a></li>
<li>Log arbitrary metrics</li>
<li><a href="Logging/#process-position">Process position</a></li>
<li>Save a snapshot of all hyperparameters</li>
<li>Save a snapshot of the code for a particular model run</li>
</ul>
<p><strong>Distributed training</strong> </p>
<ul>
<li>16-bit mixed precision</li>
<li>Single-gpu </li>
<li>Multi-gpu </li>
<li>Multi-node </li>
<li>16-bit mixed precision</li>
</ul>
<p><strong>Checkpointing</strong> </p>
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