Python库 | pytorch-lightning-0.8.1.tar.gz

<div align="center">  # PyTorch Lightning **The lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate.** [](https://badge.fury.io/py/pytorch-lightning) [](https://pepy.tech/project/pytorch-lightning) [](https://codecov.io/gh/PyTorchLightning/pytorch-lightning) [](https://pytorch-lightning.readthedocs.io/en/stable/) [](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-f6bl2l0l-JYMK3tbAgAmGRrlNr00f1A) [](https://github.com/PytorchLightning/pytorch-lightning/blob/master/LICENSE) [](https://shields.io/) <!-- [](https://www.codefactor.io/repository/github/pytorchlightning/pytorch-lightning) --> </div> --- ## Trending contributors [](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/links/0)[](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/links/1)[](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/links/2)[](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/links/3)[](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/links/4)[](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/links/5)[](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/links/6)[](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/links/7) --- ## Continuous Integration <center> | System / PyTorch ver. | 1.3 (min. req.) | 1.4 | 1.5 (latest) | | :---: | :---: | :---: | :---: | | 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) | | Linux py3.7 [GPU] | - | - | [](http://35.192.60.23/PyTorchLightning/pytorch-lightning) | | 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) | | 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) | | 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) | - | </center> Simple installation from PyPI ```bash pip install pytorch-lightning ``` From Conda ```bash conda install pytorch-lightning -c conda-forge ``` ## Docs - [master](https://pytorch-lightning.readthedocs.io/en/latest) - [stable](https://pytorch-lightning.readthedocs.io/en/stable) - [0.8.0](https://pytorch-lightning.readthedocs.io/en/0.8.0/) - [0.7.6](https://pytorch-lightning.readthedocs.io/en/0.7.6/) - [0.7.5](https://pytorch-lightning.readthedocs.io/en/0.7.5/) - [0.7.3](https://pytorch-lightning.readthedocs.io/en/0.7.3/) - [0.7.1](https://pytorch-lightning.readthedocs.io/en/0.7.1/) - [0.6.0](https://pytorch-lightning.readthedocs.io/en/0.6.0/) - [0.5.3.2](https://pytorch-lightning.readthedocs.io/en/0.5.3.2/) ## Refactoring your PyTorch code + benefits + full walk-through [](https://www.youtube.com/watch?v=QHww1JH7IDU) ## Demo Here's a minimal example without a validation or test loop. ```python # this is just a plain nn.Module with some structure class LitClassifier(pl.LightningModule): def __init__(self): super().__init__() self.l1 = torch.nn.Linear(28 * 28, 10) def forward(self, x): return torch.relu(self.l1(x.view(x.size(0), -1))) def training_step(self, batch, batch_nb): x, y = batch loss = F.cross_entropy(self(x), y) tensorboard_logs = {'train_loss': loss} return {'loss': loss, 'log': tensorboard_logs} def configure_optimizers(self): return torch.optim.Adam(self.parameters(), lr=0.02) # train! train_loader = DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32) model = LitClassifier() 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). ![