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@ -68,6 +68,22 @@ conda install pytorch-lightning -c conda-forge
- [0.8.1](https://pytorch-lightning.readthedocs.io/en/0.8.1/)
- [0.7.6](https://pytorch-lightning.readthedocs.io/en/0.7.6/)
## PyTorch Lightning is just organized PyTorch
![PT to PL](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/docs/source/_images/general/fast_2.gif)
Lightning is a way to organize your PyTorch code to decouple the science code from the engineering.
It's more of a PyTorch style-guide than a framework.
In Lightning, you organize your code into 3 distinct categories:
1. Research code (goes in the LightningModule).
2. Engineering code (you delete, and is handled by the Trainer).
3. Non-essential research code (logging, etc... this goes in Callbacks).
Once you do this, you can train on multiple-GPUs, TPUs, CPUs and even in 16-bit precision without changing your code!
Get started with our [QUICK START PAGE](https://pytorch-lightning.readthedocs.io/en/stable/new-project.html)
## Refactoring your PyTorch code + benefits + full walk-through
[![Watch the video](docs/source/_images/general/tutorial_cover.jpg)](https://www.youtube.com/watch?v=QHww1JH7IDU)
@ -103,28 +119,12 @@ trainer = pl.Trainer(gpus=8, precision=16)
trainer.fit(model, train_loader)
```
Other examples:
[MNIST hello world](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=gEulmrbxwaYL)
[GAN](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=P0bSmCw57aV5)
[BERT](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=7uQVI-xv9Ddj)
[DQN](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=NWvMLBDySQI5)
[MNIST on TPUs](https://colab.research.google.com/drive/1-_LKx4HwAxl5M6xPJmqAAu444LTDQoa3)
## What is it?
[READ THIS QUICK START PAGE](https://pytorch-lightning.readthedocs.io/en/stable/new-project.html)
Lightning is a way to organize your PyTorch code to decouple the science code from the engineering.
It's more of a PyTorch style-guide than a framework.
In Lightning, you organize your code into 3 distinct categories:
1. Research code (goes in the LightningModule).
2. Engineering code (you delete, and is handled by the Trainer).
3. Non-essential research code (logging, etc... this goes in Callbacks).
Here's an example of how to refactor your research code into a [LightningModule](https://pytorch-lightning.readthedocs.io/en/latest/lightning-module.html).
![PT to PL](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/docs/source/_images/general/fast_2.gif)
Other examples:
[MNIST hello world](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=gEulmrbxwaYL)
[GAN](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=P0bSmCw57aV5)
[BERT](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=7uQVI-xv9Ddj)
[DQN](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=NWvMLBDySQI5)
[MNIST on TPUs](https://colab.research.google.com/drive/1-_LKx4HwAxl5M6xPJmqAAu444LTDQoa3)
## Testing Rigour
All the automated code by the Trainer is [tested rigorously with every new PR](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/tests).