**The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.** ______________________________________________________________________

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###### \*Codecov is > 90%+ but build delays may show less ______________________________________________________________________ ## PyTorch Lightning is just organized PyTorch Lightning disentangles PyTorch code to decouple the science from the engineering. ![PT to PL](https://lightning.ai/docs/pytorch/stable/_static/images/general/pl_quick_start_full_compressed.gif) ______________________________________________________________________ ## Lightning Design Philosophy Lightning structures PyTorch code with these principles:
Lightning forces the following structure to your code which makes it reusable and shareable: - Research code (the LightningModule). - Engineering code (you delete, and is handled by the Trainer). - Non-essential research code (logging, etc... this goes in Callbacks). - Data (use PyTorch DataLoaders or organize them into a LightningDataModule). Once you do this, you can train on multiple-GPUs, TPUs, CPUs, HPUs and even in 16-bit precision without changing your code! [Get started in just 15 minutes](https://lightning.ai/docs/pytorch/latest/starter/introduction.html) ______________________________________________________________________ ## Continuous Integration Lightning is rigorously tested across multiple CPUs, GPUs and TPUs and against major Python and PyTorch versions.
Current build statuses
| System / PyTorch ver. | 1.12 | 1.13 | 2.0 | 2.1 | | :--------------------------------: | :---------------------------------------------------------------------------------------------------------: | ----------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- | | Linux py3.9 [GPUs] | | | | ![Build Status](https://dev.azure.com/Lightning-AI/lightning/_apis/build/status%2Fpytorch-lightning%20%28GPUs%29) | | Linux (multiple Python versions) | ![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg) | ![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg) | ![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg) | ![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg) | | OSX (multiple Python versions) | ![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg) | ![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg) | ![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg) | ![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg) | | Windows (multiple Python versions) | ![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg) | ![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg) | ![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg) | ![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg) |
______________________________________________________________________ ## How To Use ### Step 0: Install Simple installation from PyPI ```bash pip install pytorch-lightning ```
Other installation options #### Install with optional dependencies ```bash pip install pytorch-lightning['extra'] ``` #### Conda ```bash conda install pytorch-lightning -c conda-forge ``` #### Install stable version Install future release from the source ```bash pip install https://github.com/Lightning-AI/lightning/archive/refs/heads/release/stable.zip -U ``` #### Install bleeding-edge Install nightly from the source (no guarantees) ```bash pip install https://github.com/Lightning-AI/lightning/archive/refs/heads/master.zip -U ``` or from testing PyPI ```bash pip install -iU https://test.pypi.org/simple/ pytorch-lightning ```
### Step 1: Add these imports ```python import os import torch from torch import nn import torch.nn.functional as F from torchvision.datasets import MNIST from torch.utils.data import DataLoader, random_split from torchvision import transforms import pytorch_lightning as pl ``` ### Step 2: Define a LightningModule (nn.Module subclass) A LightningModule defines a full *system* (ie: a GAN, autoencoder, BERT or a simple Image Classifier). ```python class LitAutoEncoder(pl.LightningModule): def __init__(self): super().__init__() self.encoder = nn.Sequential(nn.Linear(28 * 28, 128), nn.ReLU(), nn.Linear(128, 3)) self.decoder = nn.Sequential(nn.Linear(3, 128), nn.ReLU(), nn.Linear(128, 28 * 28)) def forward(self, x): # in lightning, forward defines the prediction/inference actions embedding = self.encoder(x) return embedding def training_step(self, batch, batch_idx): # training_step defines the train loop. It is independent of forward x, _ = batch x = x.view(x.size(0), -1) z = self.encoder(x) x_hat = self.decoder(z) loss = F.mse_loss(x_hat, x) self.log("train_loss", loss) return loss def configure_optimizers(self): optimizer = torch.optim.Adam(self.parameters(), lr=1e-3) return optimizer ``` **Note: Training_step defines the training loop. Forward defines how the LightningModule behaves during inference/prediction.** ### Step 3: Train! ```python dataset = MNIST(os.getcwd(), download=True, transform=transforms.ToTensor()) train, val = random_split(dataset, [55000, 5000]) autoencoder = LitAutoEncoder() trainer = pl.Trainer() trainer.fit(autoencoder, DataLoader(train), DataLoader(val)) ``` ## Advanced features Lightning has over [40+ advanced features](https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-flags) designed for professional AI research at scale. Here are some examples:
Highlighted feature code snippets ```python # 8 GPUs # no code changes needed trainer = Trainer(max_epochs=1, accelerator="gpu", devices=8) # 256 GPUs trainer = Trainer(max_epochs=1, accelerator="gpu", devices=8, num_nodes=32) ``` Train on TPUs without code changes ```python # no code changes needed trainer = Trainer(accelerator="tpu", devices=8) ``` 16-bit precision ```python # no code changes needed trainer = Trainer(precision=16) ``` Experiment managers ```python from pytorch_lightning import loggers # tensorboard trainer = Trainer(logger=TensorBoardLogger("logs/")) # weights and biases trainer = Trainer(logger=loggers.WandbLogger()) # comet trainer = Trainer(logger=loggers.CometLogger()) # mlflow trainer = Trainer(logger=loggers.MLFlowLogger()) # neptune trainer = Trainer(logger=loggers.NeptuneLogger()) # ... and dozens more ``` EarlyStopping ```python es = EarlyStopping(monitor="val_loss") trainer = Trainer(callbacks=[es]) ``` Checkpointing ```python checkpointing = ModelCheckpoint(monitor="val_loss") trainer = Trainer(callbacks=[checkpointing]) ``` Export to torchscript (JIT) (production use) ```python # torchscript autoencoder = LitAutoEncoder() torch.jit.save(autoencoder.to_torchscript(), "model.pt") ``` Export to ONNX (production use) ```python autoencoder = LitAutoEncoder() input_sample = torch.randn((1, 64)) with tempfile.NamedTemporaryFile(suffix=".onnx", delete=False) as tmpfile: autoencoder.to_onnx(tmpfile.name, input_sample, export_params=True) ```
### Pro-level control of optimization (advanced users) For complex/professional level work, you have optional full control of the optimizers. ```python class LitAutoEncoder(pl.LightningModule): def __init__(self): super().__init__() self.automatic_optimization = False def training_step(self, batch, batch_idx): # access your optimizers with use_pl_optimizer=False. Default is True opt_a, opt_b = self.optimizers(use_pl_optimizer=True) loss_a = ... self.manual_backward(loss_a, opt_a) opt_a.step() opt_a.zero_grad() loss_b = ... self.manual_backward(loss_b, opt_b, retain_graph=True) self.manual_backward(loss_b, opt_b) opt_b.step() opt_b.zero_grad() ``` ______________________________________________________________________ ## Advantages over unstructured PyTorch - Models become hardware agnostic - Code is clear to read because engineering code is abstracted away - Easier to reproduce - Make fewer mistakes because lightning handles the tricky engineering - Keeps all the flexibility (LightningModules are still PyTorch modules), but removes a ton of boilerplate - Lightning has dozens of integrations with popular machine learning tools. - [Tested rigorously with every new PR](https://github.com/Lightning-AI/lightning/tree/master/tests). We test every combination of PyTorch and Python supported versions, every OS, multi GPUs and even TPUs. - Minimal running speed overhead (about 300 ms per epoch compared with pure PyTorch). ______________________________________________________________________ ## Examples ###### Self-supervised Learning - [CPC transforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#cpc-transforms) - [Moco v2 transforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#moco-v2-transforms) - [SimCLR transforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#simclr-transforms) ###### Convolutional Architectures - [GPT-2](https://lightning-bolts.readthedocs.io/en/stable/models/convolutional.html#gpt-2) - [UNet](https://lightning-bolts.readthedocs.io/en/stable/models/convolutional.html#unet) ###### Reinforcement Learning - [DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#dqn-loss) - [Double DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#double-dqn-loss) - [Per DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#per-dqn-loss) ###### GANs - [Basic GAN](https://lightning-bolts.readthedocs.io/en/stable/models/gans.html#basic-gan) - [DCGAN](https://lightning-bolts.readthedocs.io/en/stable/models/gans.html#dcgan) ###### Classic ML - [Logistic Regression](https://lightning-bolts.readthedocs.io/en/stable/models/classic_ml.html#logistic-regression) - [Linear Regression](https://lightning-bolts.readthedocs.io/en/stable/models/classic_ml.html#linear-regression) ______________________________________________________________________ ## Community The PyTorch Lightning community is maintained by - [10+ core contributors](https://lightning.ai/docs/pytorch/stable/community/governance.html) who are all a mix of professional engineers, Research Scientists, and Ph.D. students from top AI labs. - 680+ active community contributors. Want to help us build Lightning and reduce boilerplate for thousands of researchers? [Learn how to make your first contribution here](https://devblog.pytorchlightning.ai/quick-contribution-guide-86d977171b3a) PyTorch Lightning is also part of the [PyTorch ecosystem](https://pytorch.org/ecosystem/) which requires projects to have solid testing, documentation and support. ### Asking for help If you have any questions please: 1. [Read the docs](https://lightning.ai/docs/pytorch/stable). 1. [Search through existing Discussions](https://github.com/Lightning-AI/lightning/discussions), or [add a new question](https://github.com/Lightning-AI/lightning/discussions/new) 1. [Join our Discord community](https://discord.gg/VptPCZkGNa).