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William Falcon
2019-08-01 09:11:56 -05:00
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<title>Pytorch lightning Documentation</title>
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LightningModule/RequiredTrainerInterface/index.html
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<title>Lightning Module interface - Pytorch lightning Documentation</title>
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<p>Called by lightning during training loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.</p>
<h5 id="return_3">Return</h5>
<p>Pytorch DataLoader</p>
<p>PyTorch DataLoader</p>
<p><strong>Example</strong></p>
<pre><code class="python">@ptl.data_loader
def tng_dataloader(self):
@@ -1253,7 +1253,7 @@ def tng_dataloader(self)
<p>Called by lightning during validation loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.</p>
<h5 id="return_4">Return</h5>
<p>Pytorch DataLoader</p>
<p>PyTorch DataLoader</p>
<p><strong>Example</strong></p>
<pre><code class="python">@ptl.data_loader
def val_dataloader(self):
@@ -1276,7 +1276,7 @@ def test_dataloader(self)
<p>Called by lightning during test loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.</p>
<h5 id="return_5">Return</h5>
<p>Pytorch DataLoader</p>
<p>PyTorch DataLoader</p>
<p><strong>Example</strong></p>
<pre><code class="python">@ptl.data_loader
def test_dataloader(self):
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<tbody>
<tr>
<td>weights_path</td>
<td>Path to a pytorch checkpoint</td>
<td>Path to a PyTorch checkpoint</td>
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<tr>
<td>tags_csv</td>
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<hr />
<h3 id="tensorboard-support">Tensorboard support</h3>
<p>The experiment object is a strict subclass of Pytorch SummaryWriter. However, this class
<p>The experiment object is a strict subclass of PyTorch SummaryWriter. However, this class
also snapshots every detail about the experiment (data folder paths, code, hyperparams),
and allows you to visualize it using tensorboard.</p>
<pre><code class="python">from test_tube import Experiment, HyperOptArgumentParser
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<p>There are cases when you might want to do something different at different parts of the training/validation loop.
To enable a hook, simply override the method in your LightningModule and the trainer will call it at the correct time.</p>
<p><strong>Contributing</strong> If there's a hook you'd like to add, simply: <br />
1. Fork PytorchLightning. <br />
1. Fork PyTorchLightning. <br />
2. Add the hook <a href="https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/root_module/hooks.py">here</a>. <br />
3. Add the correct place in the <a href="https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/models/trainer.py">Trainer</a> where it should be called. </p>
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PyTorch lightning Documentation
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Pytorch lightning Documentation
PyTorch lightning Documentation
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<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping">Gradient Clipping</a></li>
<li><a href="hooks">Hooks</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers">Learning rate scheduling</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Pytorch-Lightning/LightningModule/#configure_optimizers">Use multiple optimizers (like GANs)</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/PyTorch-Lightning/LightningModule/#configure_optimizers">Use multiple optimizers (like GANs)</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/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>
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<meta name="description" content="Documentation for Pytorch LightningModule, the researcher version of keras.">
<meta name="description" content="Documentation for PyTorch LightningModule, the researcher version of keras.">
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<title>Examples - PyTorch lightning Documentation</title>
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Pytorch lightning Documentation
PyTorch lightning Documentation
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Pytorch lightning Documentation
PyTorch lightning Documentation
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<meta name="viewport" content="width=device-width,initial-scale=1">
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<meta name="description" content="Documentation for Pytorch LightningModule, the researcher version of keras.">
<meta name="description" content="Documentation for PyTorch LightningModule, the researcher version of keras.">
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Pytorch lightning Documentation
PyTorch lightning Documentation
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Pytorch lightning Documentation
PyTorch lightning Documentation
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@@ -728,7 +728,7 @@ one could be a seq-2-seq model, both (optionally) ran by the same trainer file.<
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping">Gradient Clipping</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/">Hooks</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers">Learning rate scheduling</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Pytorch-Lightning/LightningModule/#configure_optimizers">Use multiple optimizers (like GANs)</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/PyTorch-Lightning/LightningModule/#configure_optimizers">Use multiple optimizers (like GANs)</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/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>
<h6 id="validation-loop">Validation loop</h6>
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