**NEWS: PyTorch Lightning has been renamed Lightning!**

**The Deep Learning framework to train, deploy, and ship AI products Lightning fast.** ______________________________________________________________________

Lightning Gallery • Key Features • How To Use • Docs • Examples • Community • Contribute • License

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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](docs/source-pytorch/_static/images/general/pl_quick_start_full_compressed.gif) ## Build AI products with Lightning Apps Once you're done building models, publish a paper demo or build a full production end-to-end ML system with Lightning Apps. Lightning Apps remove the cloud infrastructure boilerplate so you can focus on solving the research or business problems. Lightning Apps can run on the Lightning Cloud, your own cluster or a private cloud. [Browse available Lightning apps here](https://lightning.ai/)

### [Learn more about Lightning Apps](src/lightning_app/README.md) ______________________________________________________________________ ## 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 and even in 16-bit precision without changing your code! [Get started in just 15 minutes](https://pytorch-lightning.readthedocs.io/en/latest/starter/introduction.html) ______________________________________________________________________ ## Continuous Integration Lightning is rigorously tested across multiple CPUs, GPUs, TPUs, IPUs, and HPUs and against major Python and PyTorch versions.

Current build statuses

| System / PyTorch ver. | 1.11 | 1.12 | 1.13 | 2.0 | | :--------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---- | | Linux py3.9 \[GPUs\] | - | [![Build Status]()](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=24&branchName=master) | [![Build Status]()](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=24&branchName=master) | Soon | | Linux py3.9 \[TPUs\] | - | [![Test PyTorch - TPU](https://github.com/Lightning-AI/lightning/actions/workflows/tpu-tests.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/tpu-tests.yml) | | Soon | | Linux py3.8 \[IPUs\] | - | - | [![Build Status]()](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=25&branchName=master) | Soon | | Linux py3.8 \[HPUs\] | - | - | [![Build Status]()](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=26&branchName=master) | Soon | | Linux (multiple Python versions) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | Soon | | OSX (multiple Python versions) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | Soon | | Windows (multiple Python versions) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | Soon |

______________________________________________________________________ ## 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, y = 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://pytorch-lightning.readthedocs.io/en/latest/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 tranforms](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 lightning community is maintained by - [10+ core contributors](https://pytorch-lightning.readthedocs.io/en/latest/governance.html) who are all a mix of professional engineers, Research Scientists, and Ph.D. students from top AI labs. - 590+ 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://pytorch-lightning.readthedocs.io/en/stable/generated/CONTRIBUTING.html) 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://pytorch-lightning.rtfd.io/en/latest). 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 slack](https://www.pytorchlightning.ai/community).