**The deep learning framework to pretrain, finetune and deploy AI models.** **NEW- Lightning 2.0 features a clean and stable API!!** ______________________________________________________________________

Lightning.ai • PyTorch Lightning • Fabric • Lightning Apps • Docs • Community • Contribute •

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Install Lightning

Simple installation from PyPI

bash pip install lightning

Lightning has 3 core packages

PyTorch Lightning: Train and deploy PyTorch at scale.
Lightning Fabric: Expert control.
Lightning Apps: Build AI products and ML workflows.

Lightning gives you granular control over how much abstraction you want to add over PyTorch.

PyTorch Lightning: Train and Deploy PyTorch at Scale

PyTorch Lightning is just organized PyTorch - Lightning disentangles PyTorch code to decouple the science from the engineering.

Hello simple model

```python

main.py

! pip install torchvision

import torch, torch.nn as nn, torch.utils.data as data, torchvision as tv, torch.nn.functional as F import lightning as L

--------------------------------

Step 1: Define a LightningModule

--------------------------------

A LightningModule (nn.Module subclass) defines a full system

(ie: an LLM, diffusion model, autoencoder, or simple image classifier).

class LitAutoEncoder(L.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

-------------------

Step 2: Define data

-------------------

dataset = tv.datasets.MNIST(".", download=True, transform=tv.transforms.ToTensor()) train, val = data.random_split(dataset, [55000, 5000])

-------------------

Step 3: Train

-------------------

autoencoder = LitAutoEncoder() trainer = L.Trainer() trainer.fit(autoencoder, data.DataLoader(train), data.DataLoader(val)) ```

Run the model on your terminal

bash pip install torchvision python main.py

Advanced features

Lightning has over 40+ advanced features designed for professional AI research at scale.

Here are some examples:

Lightning Fabric: Expert control.

Run on any device at any scale with expert-level control over PyTorch training loop and scaling strategy. You can even write your own Trainer.

Fabric is designed for the most complex models like foundation model scaling, LLMs, diffusion, transformers, reinforcement learning, active learning. Of any size.

Key features

Lightning Apps: Build AI products and ML workflows

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.

Hello Lightning app world

```python

app.py

import lightning as L

class TrainComponent(L.LightningWork): def run(self, x): print(f"train a model on {x}")

class AnalyzeComponent(L.LightningWork): def run(self, x): print(f"analyze model on {x}")

class WorkflowOrchestrator(L.LightningFlow): def init(self) -> None: super().init() self.train = TrainComponent(cloudcompute=L.CloudCompute("cpu")) self.analyze = AnalyzeComponent(cloudcompute=L.CloudCompute("gpu"))

def run(self):
    self.train.run("CPU machine 1")
    self.analyze.run("GPU machine 2")

app = L.LightningApp(WorkflowOrchestrator()) ```

Run on the cloud or locally

```bash

run on the cloud

lightning run app app.py --setup --cloud

run locally

lightning run app app.py ```

Examples

Self-supervised Learning

• CPC transforms
• Moco v2 transforms
• SimCLR transforms

Convolutional Architectures

• GPT-2
• UNet

Reinforcement Learning

• DQN Loss
• Double DQN Loss
• Per DQN Loss

GANs

• Basic GAN
• DCGAN

Classic ML

• Logistic Regression
• Linear Regression

Continuous Integration

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

*Codecov is > 90%+ but build delays may show less

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.
• 800+ 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 discord.