447 lines
20 KiB
Markdown
447 lines
20 KiB
Markdown
<div align="center">
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# PyTorch Lightning
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**The lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate.**
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[](https://badge.fury.io/py/pytorch-lightning)
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[](https://pepy.tech/project/pytorch-lightning)
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[](https://codecov.io/gh/PyTorchLightning/pytorch-lightning)
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[](https://www.codefactor.io/repository/github/pytorchlightning/pytorch-lightning)
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[](https://pytorch-lightning.readthedocs.io/en/stable/)
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[](https://join.slack.com/t/pytorch-lightning/shared_invite/enQtODU5ODIyNTUzODQwLTFkMDg5Mzc1MDBmNjEzMDgxOTVmYTdhYjA1MDdmODUyOTg2OGQ1ZWZkYTQzODhhNzdhZDA3YmNhMDhlMDY4YzQ)
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[](https://github.com/PytorchLightning/pytorch-lightning/blob/master/LICENSE)
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[](https://shields.io/)
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<!--
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removed until codecov badge isn't empy. likely a config error showing nothing on master.
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[](https://codecov.io/gh/Borda/pytorch-lightning)
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-->
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</div>
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---
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## Continuous Integration
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<center>
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| System / PyTorch ver. | 1.3 (min. reg) | 1.4 | 1.5 (latest) |
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| :---: | :---: | :---: | :---: |
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| Linux py3.6 [CPU] | [](https://circleci.com/gh/PyTorchLightning/pytorch-lightning) | [](https://circleci.com/gh/PyTorchLightning/pytorch-lightning) | [](https://circleci.com/gh/PyTorchLightning/pytorch-lightning) |
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| Linux py3.7 [GPU] | - | - | [](http://35.192.60.23/PyTorchLightning/pytorch-lightning) |
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| Linux py3.6 / py3.7 / py3.8 | [](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | - | [](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) |
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| OSX py3.6 / py3.7 | - | [](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | [](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) |
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| Windows py3.6 / py3.7 / py3.8 | [](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) |[](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | - |
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</center>
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Simple installation from PyPI
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```bash
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pip install pytorch-lightning
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```
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## Docs
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- [master](https://pytorch-lightning.readthedocs.io/en/latest)
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- [0.7.6](https://pytorch-lightning.readthedocs.io/en/0.7.6/)
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- [0.7.5](https://pytorch-lightning.readthedocs.io/en/0.7.5/)
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- [0.7.3](https://pytorch-lightning.readthedocs.io/en/0.7.3/)
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- [0.7.1](https://pytorch-lightning.readthedocs.io/en/0.7.1/)
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- [0.6.0](https://pytorch-lightning.readthedocs.io/en/0.6.0/)
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- [0.5.3.2](https://pytorch-lightning.readthedocs.io/en/0.5.3.2/)
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## Refactoring your PyTorch code + benefits + full walk-through
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[](https://www.youtube.com/watch?v=QHww1JH7IDU)
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## Demo
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Here's a minimal example without a validation or test loop.
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```python
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# this is just a plain nn.Module with some structure
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class LitClassifier(pl.LightningModule):
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def __init__(self):
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super().__init__()
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self.l1 = torch.nn.Linear(28 * 28, 10)
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def forward(self, x):
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return torch.relu(self.l1(x.view(x.size(0), -1)))
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def training_step(self, batch, batch_nb):
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x, y = batch
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loss = F.cross_entropy(self(x), y)
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tensorboard_logs = {'train_loss': loss}
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return {'loss': loss, 'log': tensorboard_logs}
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def configure_optimizers(self):
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return torch.optim.Adam(self.parameters(), lr=0.02)
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# train!
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train_loader = DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
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model = LitClassifier()
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trainer = pl.Trainer(gpus=8, precision=16)
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trainer.fit(model, train_loader)
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```
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Other examples:
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[GAN](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=P0bSmCw57aV5)
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[BERT](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=7uQVI-xv9Ddj)
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[DQN](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=NWvMLBDySQI5)
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[MNIST on TPUs](https://colab.research.google.com/drive/1-_LKx4HwAxl5M6xPJmqAAu444LTDQoa3)
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## What is it?
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[READ THIS QUICK START PAGE](https://pytorch-lightning.readthedocs.io/en/stable/new-project.html)
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Lightning is a way to organize your PyTorch code to decouple the science code from the engineering.
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It's more of a PyTorch style-guide than a framework.
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In Lightning, you organize your code into 3 distinct categories:
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1. Research code (goes in the LightningModule).
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2. Engineering code (you delete, and is handled by the Trainer).
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3. Non-essential research code (logging, etc... this goes in Callbacks).
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Here's an example of how to refactor your research code into a [LightningModule](https://pytorch-lightning.readthedocs.io/en/latest/lightning-module.html).
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The rest of the code is automated by the [Trainer](https://pytorch-lightning.readthedocs.io/en/latest/trainer.html)!
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## Testing Rigour
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All the automated code by the Trainer is [tested rigorously with every new PR](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/tests).
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In fact, we also train a few models using a vanilla PyTorch loop and compare with the same model trained using the Trainer to make sure we achieve the EXACT same results. [Check out the parity tests here](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/benchmarks).
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Overall, Lightning guarantees rigorously tested, correct, modern best practices for the automated parts.
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## How flexible is it?
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As you see, you're just organizing your PyTorch code - there's no abstraction.
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And for the stuff that the Trainer abstracts out, you can [override any part](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html#extensibility) you want to do things like implement your own distributed training, 16-bit precision, or even a custom backward pass.
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For example, here you could do your own backward pass
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```python
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class LitModel(LightningModule):
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def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,
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second_order_closure=None):
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optimizer.step()
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optimizer.zero_grad()
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```
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For anything else you might need, we have an extensive [callback system](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html#callbacks) you can use to add arbitrary functionality not implemented by our team in the Trainer.
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## Who is Lightning for?
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- Professional researchers
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- Ph.D. students
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- Corporate production teams
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If you're just getting into deep learning, we recommend you learn PyTorch first! Once you've implemented a few models, come back and use all the advanced features of Lightning :)
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## What does lightning control for me?
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Everything in Blue!
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This is how lightning separates the science (red) from engineering (blue).
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## How much effort is it to convert?
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If your code is not a huge mess you should be able to organize it into a LightningModule in less than 1 hour.
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If your code IS a mess, then you needed to clean up anyhow ;)
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[Check out this step-by-step guide](https://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09).
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[Or watch this video](https://www.youtube.com/watch?v=QHww1JH7IDU).
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## Starting a new project?
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[Use our seed-project aimed at reproducibility!](https://github.com/PytorchLightning/pytorch-lightning-conference-seed)
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## Why do I want to use lightning?
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Although your research/production project might start simple, once you add things like GPU AND TPU training, 16-bit precision, etc, you end up spending more time engineering than researching. Lightning automates AND rigorously tests those parts for you.
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## Support
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- [8 core contributors](https://pytorch-lightning.readthedocs.io/en/latest/governance.html) who are all a mix of professional engineers, Research Scientists, Ph.D. students from top AI labs.
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- 100+ community contributors.
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Lightning is also part of the [PyTorch ecosystem](https://pytorch.org/ecosystem/) which requires projects to have solid testing, documentation and support.
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---
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## README Table of Contents
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- [How do I use it](https://github.com/PytorchLightning/pytorch-lightning#how-do-i-do-use-it)
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- [What lightning automates](https://github.com/PytorchLightning/pytorch-lightning#what-does-lightning-control-for-me)
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- [Tensorboard integration](https://github.com/PytorchLightning/pytorch-lightning#tensorboard)
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- [Lightning features](https://github.com/PytorchLightning/pytorch-lightning#lightning-automates-all-of-the-following-each-is-also-configurable)
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- [Examples](https://github.com/PytorchLightning/pytorch-lightning#examples)
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- [Tutorials](https://github.com/PytorchLightning/pytorch-lightning#tutorials)
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- [Asking for help](https://github.com/PytorchLightning/pytorch-lightning#asking-for-help)
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- [Contributing](https://github.com/PytorchLightning/pytorch-lightning/blob/master/.github/CONTRIBUTING.md)
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- [Bleeding edge install](https://github.com/PytorchLightning/pytorch-lightning#bleeding-edge)
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- [Lightning Design Principles](https://github.com/PytorchLightning/pytorch-lightning#lightning-design-principles)
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- [Lightning team](https://github.com/PytorchLightning/pytorch-lightning#lightning-team)
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- [FAQ](https://github.com/PytorchLightning/pytorch-lightning#faq)
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---
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## Realistic example
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Here's how you would organize a realistic PyTorch project into Lightning.
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The LightningModule defines a *system* such as seq-2-seq, GAN, etc...
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It can ALSO define a simple classifier.
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In summary, you:
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1. Define a [LightningModule](https://pytorch-lightning.rtfd.io/en/latest/lightning-module.html)
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```python
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class LitSystem(pl.LightningModule):
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def __init__(self):
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super().__init__()
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# not the best model...
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self.l1 = torch.nn.Linear(28 * 28, 10)
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def forward(self, x):
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return torch.relu(self.l1(x.view(x.size(0), -1)))
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def training_step(self, batch, batch_idx):
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...
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```
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2. Fit it with a [Trainer](https://pytorch-lightning.rtfd.io/en/latest/pytorch_lightning.trainer.html)
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```python
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from pytorch_lightning import Trainer
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model = LitSystem()
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# most basic trainer, uses good defaults
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trainer = Trainer()
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trainer.fit(model)
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```
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[Check out the COLAB demo here](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=HOk9c4_35FKg)
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## What types of research works?
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Anything! Remember, that this is just organized PyTorch code.
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The Training step defines the core complexity found in the training loop.
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#### Could be as complex as a seq2seq
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```python
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# define what happens for training here
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def training_step(self, batch, batch_idx):
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x, y = batch
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# define your own forward and loss calculation
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hidden_states = self.encoder(x)
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# even as complex as a seq-2-seq + attn model
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# (this is just a toy, non-working example to illustrate)
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start_token = '<SOS>'
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last_hidden = torch.zeros(...)
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loss = 0
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for step in range(max_seq_len):
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attn_context = self.attention_nn(hidden_states, start_token)
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pred = self.decoder(start_token, attn_context, last_hidden)
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last_hidden = pred
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pred = self.predict_nn(pred)
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loss += self.loss(last_hidden, y[step])
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#toy example as well
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loss = loss / max_seq_len
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return {'loss': loss}
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```
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#### Or as basic as CNN image classification
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```python
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# define what happens for validation here
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def validation_step(self, batch, batch_idx):
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x, y = batch
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# or as basic as a CNN classification
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out = self(x)
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loss = my_loss(out, y)
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return {'loss': loss}
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```
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And without changing a single line of code, you could run on CPUs
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```python
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trainer = Trainer(max_epochs=1)
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```
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Or GPUs
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```python
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# 8 GPUs
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trainer = Trainer(max_epochs=1, gpus=8)
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# 256 GPUs
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trainer = Trainer(max_epochs=1, gpus=8, num_nodes=32)
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```
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Or TPUs
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```python
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# Distributes TPU core training
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trainer = Trainer(tpu_cores=8)
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# Single TPU core training
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trainer = Trainer(tpu_cores=[1])
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```
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When you're done training, run the test accuracy
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```python
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trainer.test()
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```
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## Visualization
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Lightning has out-of-the-box integration with the popular logging/visualizing frameworks
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- [Tensorboard](https://pytorch.org/docs/stable/tensorboard.html)
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- [MLFlow](https://mlflow.org/)
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- [Neptune.ai](https://neptune.ai/)
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- [Comet.ml](https://www.comet.ml/site/)
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- [Wandb](https://www.wandb.com/)
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- [Trains](https://github.com/allegroai/trains)
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- ...
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## Lightning automates 40+ parts of DL/ML research
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- GPU training
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- Distributed GPU (cluster) training
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- TPU training
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- EarlyStopping
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- Logging/Visualizing
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- Checkpointing
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- Experiment management
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- [Full list here](https://pytorch-lightning.readthedocs.io/en/latest/#common-use-cases)
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## Examples
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Check out this awesome list of research papers and implementations done with Lightning.
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- [Contextual Emotion Detection (DoubleDistilBert)](https://github.com/PyTorchLightning/emotion_transformer)
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- [Generative Adversarial Network](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=TyYOdg8g77P0)
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- [Hyperparameter optimization with Optuna](https://github.com/optuna/optuna/blob/master/examples/pytorch_lightning_simple.py)
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- [Image Inpainting using Partial Convolutions](https://github.com/ryanwongsa/Image-Inpainting)
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- [MNIST on TPU](https://colab.research.google.com/drive/1-_LKx4HwAxl5M6xPJmqAAu444LTDQoa3#scrollTo=BHBz1_AnamN_)
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- [NER (transformers, TPU, huggingface)](https://colab.research.google.com/drive/1dBN-wwYUngLYVt985wGs_OKPlK_ANB9D)
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- [NeuralTexture (CVPR)](https://github.com/PyTorchLightning/neuraltexture)
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- [Recurrent Attentive Neural Process](https://github.com/PyTorchLightning/attentive-neural-processes)
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- [Siamese Nets for One-shot Image Recognition](https://github.com/PyTorchLightning/Siamese-Neural-Networks)
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- [Speech Transformers](https://github.com/PyTorchLightning/speech-transformer-pytorch_lightning)
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- [Transformers transfer learning (Huggingface)](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=yr7eaxkF-djf)
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- [Transformers text classification](https://github.com/ricardorei/lightning-text-classification)
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- [VAE Library of over 18+ VAE flavors](https://github.com/AntixK/PyTorch-VAE)
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## Tutorials
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Check out our [introduction guide](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html) to get started.
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Or jump straight into [our tutorials](https://pytorch-lightning.readthedocs.io/en/latest/#tutorials).
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---
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## Asking for help
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Welcome to the Lightning community!
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If you have any questions, feel free to:
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1. [read the docs](https://pytorch-lightning.rtfd.io/en/latest/).
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2. [Search through the issues](https://github.com/PytorchLightning/pytorch-lightning/issues?utf8=%E2%9C%93&q=my++question).
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3. [Ask on stackoverflow](https://stackoverflow.com/questions/ask?guided=false) with the tag pytorch-lightning.
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4. [Join our slack](https://join.slack.com/t/pytorch-lightning/shared_invite/enQtODU5ODIyNTUzODQwLTFkMDg5Mzc1MDBmNjEzMDgxOTVmYTdhYjA1MDdmODUyOTg2OGQ1ZWZkYTQzODhhNzdhZDA3YmNhMDhlMDY4YzQ).
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---
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## FAQ
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**How do I use Lightning for rapid research?**
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[Here's a walk-through](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html)
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**Why was Lightning created?**
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Lightning has 3 goals in mind:
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1. Maximal flexibility while abstracting out the common boilerplate across research projects.
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2. Reproducibility. If all projects use the LightningModule template, it will be much much easier to understand what's going on and where to look! It will also mean every implementation follows a standard format.
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3. Democratizing PyTorch power-user features. Distributed training? 16-bit? know you need them but don't want to take the time to implement? All good... these come built into Lightning.
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**How does Lightning compare with Ignite and fast.ai?**
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[Here's a thorough comparison](https://medium.com/@_willfalcon/pytorch-lightning-vs-pytorch-ignite-vs-fast-ai-61dc7480ad8a).
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**Is this another library I have to learn?**
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Nope! We use pure Pytorch everywhere and don't add unnecessary abstractions!
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**Are there plans to support Python 2?**
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Nope.
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**Are there plans to support virtualenv?**
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Nope. Please use anaconda or miniconda.
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```bash
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conda activate my_env
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pip install pytorch-lightning
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```
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## Custom installation
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### Bleeding edge
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If you can't wait for the next release, install the most up to date code with:
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* using GIT (locally clone whole repo with full history)
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```bash
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pip install git+https://github.com/PytorchLightning/pytorch-lightning.git@master --upgrade
|
|
```
|
|
* using instant zip (last state of the repo without git history)
|
|
```bash
|
|
pip install https://github.com/PytorchLightning/pytorch-lightning/archive/master.zip --upgrade
|
|
```
|
|
|
|
### Any release installation
|
|
|
|
You can also install any past release `0.X.Y` from this repository:
|
|
```bash
|
|
pip install https://github.com/PytorchLightning/pytorch-lightning/archive/0.X.Y.zip --upgrade
|
|
```
|
|
|
|
### Lightning team
|
|
|
|
#### Leads
|
|
- William Falcon [(williamFalcon)](https://github.com/williamFalcon) (Lightning founder)
|
|
- Jirka Borovec [(Borda)](https://github.com/Borda) (ghost :)
|
|
- Ethan Harris [(ethanwharris)](https://github.com/ethanwharris) (Torchbearer founder)
|
|
- Matthew Painter [(MattPainter01)](https://github.com/MattPainter01) (Torchbearer founder)
|
|
- Justus Schock [(justusschock)](https://github.com/justusschock) (Former Core Member PyTorch Ignite)
|
|
|
|
#### Core Maintainers
|
|
|
|
- Nick Eggert [(neggert)](https://github.com/neggert)
|
|
- Jeff Ling [(jeffling)](https://github.com/jeffling)
|
|
- Jeremy Jordan [(jeremyjordan)](https://github.com/jeremyjordan)
|
|
- Tullie Murrell [(tullie)](https://github.com/tullie)
|
|
- Adrian Wälchli [(awaelchli)](https://github.com/awaelchli)
|
|
- Nicki Skafte [(skaftenicki)](https://github.com/SkafteNicki)
|
|
|
|
#### Funding
|
|
Building open-source software with only a few part-time people is hard! We've secured funding to make sure we can
|
|
hire a full-time staff, attend conferences, and move faster through implementing features you request.
|
|
|
|
Our goal is to build an incredible research platform and a big supportive community. Many open-source projects
|
|
have gone on to fund operations through things like support and special help for big corporations!
|
|
|
|
If you are one of these corporations, please feel free to reach out to will@pytorchlightning.ai!
|
|
|
|
## Bibtex
|
|
If you want to cite the framework feel free to use this (but only if you loved it 😊):
|
|
|
|
```bibtex
|
|
@article{falcon2019pytorch,
|
|
title={PyTorch Lightning},
|
|
author={Falcon, WA},
|
|
journal={GitHub. Note: https://github. com/williamFalcon/pytorch-lightning Cited by},
|
|
volume={3},
|
|
year={2019}
|
|
}
|
|
```
|