**Build high-performance PyTorch models and deploy them with Lightning Apps (scalable end-to-end ML systems).** ______________________________________________________________________

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PyTorch Lightning is just organized PyTorch

Lightning disentangles PyTorch code to decouple the science from the engineering.

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

Learn more about Lightning Apps

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 with our 2 step guide

Continuous Integration

Lightning is rigorously tested across multiple GPUs, TPUs CPUs and against major Python and PyTorch versions.

How To Use

Step 0: Install

Simple installation from PyPI

bash pip install 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 designed for professional AI research at scale.

Here are some examples:

Pro-level control of training loops (advanced users)

For complex/professional level work, you have optional full control of the training loop and 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. 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).

Lightning Lite

In the Lightning v1.5 release, LightningLite now enables you to leverage all the capabilities of PyTorch Lightning Accelerators without any refactoring to your training loop. Check out the blogpost and docs for more info.

Examples

Hello world

• MNIST hello world

Contrastive Learning

• BYOL
• CPC v2
• Moco v2
• SIMCLR

NLP

• GPT-2
• BERT

Reinforcement Learning

• DQN
• Dueling-DQN
• Reinforce

Vision

• GAN

Classic ML

• Logistic Regression
• Linear Regression

Community

The lightning community is maintained by

• 10+ core contributors 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

Lightning is also part of the PyTorch ecosystem which requires projects to have solid testing, documentation and support.

Asking for help

If you have any questions please:

1. Read the docs.
2. Search through existing Discussions, or add a new question
3. Join our slack.