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William Falcon
2019-12-12 13:35:41 -05:00
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@@ -38,7 +38,14 @@ pip install pytorch-lightning
[Copy and run this COLAB!](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=HOk9c4_35FKg)
## What is it?
Lightning is a very lightweight wrapper on PyTorch. This means you don't have to learn a new library. To use Lightning, simply refactor your research code into the [LightningModule](https://github.com/williamFalcon/pytorch-lightning#how-do-i-do-use-it) format and Lightning will automate the rest. Lightning guarantees tested, correct, modern best practices for the automated parts.
Lightning is a very lightweight wrapper on PyTorch that decouples the science from the engineering. This means you don't have to learn a new library. To use Lightning, simply refactor your research code into the [LightningModule](https://github.com/williamFalcon/pytorch-lightning#how-do-i-do-use-it) format (the science) and Lightning will automate the rest (the engineering). Lightning guarantees tested, correct, modern best practices for the automated parts.
## What does lightning control for me?
Everything in Blue!
This is how lightning separates the science (red) from the engineering (blue).
![Overview](docs/source/_static/images/pl.gif)
## How much effort is it to convert?
You're probably tired of switching frameworks at this point. But it is a very quick process to refactor into the Lightning format. [Check out this tutorial](https://towardsdatascience.com/how-to-refactor-your-pytorch-code-to-get-these-42-benefits-of-pytorch-lighting-6fdd0dc97538)
@@ -179,29 +186,6 @@ When you're all done you can even run the test set separately.
trainer.test()
```
## What does lightning control for me?
Everything in Blue!
This is how lightning separates the science (red) from the engineering (blue).
![Overview](docs/source/_static/images/pl.gif)
```python
# what to do in the training loop
def training_step(self, batch, batch_idx):
# what to do in the validation loop
def validation_step(self, batch, batch_idx):
# how to aggregate validation_step outputs
def validation_end(self, outputs):
# and your dataloaders
def train_dataloader():
def val_dataloader():
def test_dataloader():
```
**Could be as complex as seq-2-seq + attention**
```python