updated docs
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</a>
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</p>
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<h3 align="center">
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Pytorch Lightning
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PyTorch Lightning
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</h3>
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<p align="center">
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The Keras for ML researchers using PyTorch. More control. Less boilerplate.
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@ -300,7 +300,7 @@ def tng_dataloader(self)
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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.
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##### Return
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Pytorch DataLoader
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PyTorch DataLoader
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**Example**
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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.
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##### Return
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Pytorch DataLoader
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PyTorch DataLoader
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**Example**
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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.
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##### Return
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Pytorch DataLoader
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PyTorch DataLoader
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**Example**
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| Param | description |
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|---|---|
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| weights_path | Path to a pytorch checkpoint |
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| weights_path | Path to a PyTorch checkpoint |
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| tags_csv | Path to meta_tags.csv file generated by the test-tube Experiment |
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| on_gpu | if True, puts model on GPU. Make sure to use transforms option if model devices have changed |
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| map_location | A dictionary mapping saved weight GPU devices to new GPU devices |
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@ -52,7 +52,7 @@ Trainer(experiment=exp)
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---
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### Tensorboard support
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The experiment object is a strict subclass of Pytorch SummaryWriter. However, this class
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The experiment object is a strict subclass of PyTorch SummaryWriter. However, this class
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also snapshots every detail about the experiment (data folder paths, code, hyperparams),
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and allows you to visualize it using tensorboard.
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``` {.python}
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@ -5,7 +5,7 @@ There are cases when you might want to do something different at different parts
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To enable a hook, simply override the method in your LightningModule and the trainer will call it at the correct time.
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**Contributing** If there's a hook you'd like to add, simply:
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1. Fork PytorchLightning.
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1. Fork PyTorchLightning.
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2. Add the hook [here](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/root_module/hooks.py).
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3. Add the correct place in the [Trainer](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/models/trainer.py) where it should be called.
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@ -64,7 +64,7 @@ But of course the fun is in all the advanced things it can do:
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- [Gradient Clipping](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping)
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- [Hooks](hooks)
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- [Learning rate scheduling](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
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- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/Pytorch-Lightning/LightningModule/#configure_optimizers)
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- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/PyTorch-Lightning/LightningModule/#configure_optimizers)
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- [Set how much of the training set to check (1-100%)](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check)
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**Validation loop**
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@ -71,7 +71,7 @@ one could be a seq-2-seq model, both (optionally) ran by the same trainer file.
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- [Gradient Clipping](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping)
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- [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
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- [Learning rate scheduling](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
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- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/Pytorch-Lightning/LightningModule/#configure_optimizers)
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- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/PyTorch-Lightning/LightningModule/#configure_optimizers)
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- [Set how much of the training set to check (1-100%)](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check)
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###### Validation loop
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site_name: Pytorch lightning Documentation
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site_name: PyTorch lightning Documentation
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theme:
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name: 'material'
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docs_dir: docs
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repo_url: https://github.com/williamFalcon/pytorch-lightning
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site_dir: 'site'
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site_description: 'Documentation for Pytorch LightningModule, the researcher version of keras.'
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site_description: 'Documentation for PyTorch LightningModule, the researcher version of keras.'
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dev_addr: '0.0.0.0:8000'
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#google_analytics: ['UA-aasd', 'sitename']
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# Pytorch-Lightning Tests
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# PyTorch-Lightning Tests
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## Running tests
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The automatic travis tests ONLY run CPU-based tests. Although these cover most of the use cases,
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