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

Quick start • Examples • PyTorch Lightning • Fabric • Lightning AI • Community • Docs

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Lightning has 2 core packages

PyTorch Lightning: Train and deploy PyTorch at scale.
Lightning Fabric: Expert control.

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

Quick start

Install Lightning:

bash pip install lightning

PyTorch Lightning example

Define the training workflow. Here's a toy example (explore real examples):

```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, _ = 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

Why PyTorch Lightning?

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

Examples

Explore various types of training possible with PyTorch Lightning. Pretrain and finetune ANY kind of model to perform ANY task like classification, segmentation, summarization and more:

| Task | Description | Run | |-------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------|---| | Hello world | Pretrain - Hello world example | | | Image classification | Finetune - ResNet-34 model to classify images of cars | |
| Image segmentation | Finetune - ResNet-50 model to segment images | |
| Object detection | Finetune - Faster R-CNN model to detect objects | | | Text classification | Finetune - text classifier (BERT model) | |
| Text summarization | Finetune - text summarization (Hugging Face transformer model) | |
| Audio generation | Finetune - audio generator (transformer model) | |
| LLM finetuning | Finetune - LLM (Meta Llama 3.1 8B) | | | Image generation | Pretrain - Image generator (diffusion model) | |

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

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.