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

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PyTorch Lightning is just organized PyTorch

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

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

Continuous Integration

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

System / PyTorch ver.	1.12	1.13	2.0	2.1
Linux py3.9 [GPUs]
Linux (multiple Python versions)
OSX (multiple Python versions)
Windows (multiple Python versions)

How To Use

Step 0: Install

Simple installation from PyPI

pip install pytorch-lightning

Install with optional dependencies

pip install pytorch-lightning['extra']

Conda

conda install pytorch-lightning -c conda-forge

Install stable version

Install future release from the source

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)

pip install https://github.com/Lightning-AI/lightning/archive/refs/heads/master.zip -U

or from testing PyPI

pip install -iU https://test.pypi.org/simple/ pytorch-lightning

Step 1: Add these imports

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).

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!

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:

# 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)

# no code changes needed
trainer = Trainer(accelerator="tpu", devices=8)

# no code changes needed
trainer = Trainer(precision=16)

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

es = EarlyStopping(monitor="val_loss")
trainer = Trainer(callbacks=[es])

checkpointing = ModelCheckpoint(monitor="val_loss")
trainer = Trainer(callbacks=[checkpointing])

# torchscript
autoencoder = LitAutoEncoder()
torch.jit.save(autoencoder.to_torchscript(), "model.pt")

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.

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).

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

Community

The PyTorch 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.
• 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

PyTorch 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 community.