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 •

Install Lightning

Simple installation from PyPI

pip install lightning

Other installation options

Install with optional dependencies

pip install lightning['extra']

Conda

conda install 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

---

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

# 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

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:

Train on 1000s of GPUs without code changes

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

# 256 GPUs
trainer = Trainer(accelerator="gpu", devices=8, num_nodes=32)

Train on other accelerators like TPUs without code changes

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

16-bit precision

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

Experiment managers

from 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

Early Stopping

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

Checkpointing

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

Export to torchscript (JIT) (production use)

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

Export to ONNX (production use)

# onnx
with tempfile.NamedTemporaryFile(suffix=".onnx", delete=False) as tmpfile:
    autoencoder = LitAutoEncoder()
    input_sample = torch.randn((1, 64))
    autoencoder.to_onnx(tmpfile.name, input_sample, export_params=True)
    os.path.isfile(tmpfile.name)

---

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).

---

Read the PyTorch Lightning docs

---

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.

What to change

Resulting Fabric Code (copy me!)

+ import lightning as L import torch; import torchvision as tv dataset = tv.datasets.CIFAR10("data", download=True, train=True, transform=tv.transforms.ToTensor()) + fabric = L.Fabric() + fabric.launch() model = tv.models.resnet18() optimizer = torch.optim.SGD(model.parameters(), lr=0.001) - device = "cuda" if torch.cuda.is_available() else "cpu" - model.to(device) + model, optimizer = fabric.setup(model, optimizer) dataloader = torch.utils.data.DataLoader(dataset, batch_size=8) + dataloader = fabric.setup_dataloaders(dataloader) model.train() num_epochs = 10 for epoch in range(num_epochs): for batch in dataloader: inputs, labels = batch - inputs, labels = inputs.to(device), labels.to(device) optimizer.zero_grad() outputs = model(inputs) loss = torch.nn.functional.cross_entropy(outputs, labels) - loss.backward() + fabric.backward(loss) optimizer.step() print(loss.data)

import lightning as L import torch; import torchvision as tv dataset = tv.datasets.CIFAR10("data", download=True, train=True, transform=tv.transforms.ToTensor()) fabric = L.Fabric() fabric.launch() model = tv.models.resnet18() optimizer = torch.optim.SGD(model.parameters(), lr=0.001) model, optimizer = fabric.setup(model, optimizer) dataloader = torch.utils.data.DataLoader(dataset, batch_size=8) dataloader = fabric.setup_dataloaders(dataloader) model.train() num_epochs = 10 for epoch in range(num_epochs): for batch in dataloader: inputs, labels = batch optimizer.zero_grad() outputs = model(inputs) loss = torch.nn.functional.cross_entropy(outputs, labels) fabric.backward(loss) optimizer.step() print(loss.data)

Key features

Easily switch from running on CPU to GPU (Apple Silicon, CUDA, …), TPU, multi-GPU or even multi-node training

# Use your available hardware
# no code changes needed
fabric = Fabric()

# Run on GPUs (CUDA or MPS)
fabric = Fabric(accelerator="gpu")

# 8 GPUs
fabric = Fabric(accelerator="gpu", devices=8)

# 256 GPUs, multi-node
fabric = Fabric(accelerator="gpu", devices=8, num_nodes=32)

# Run on TPUs
fabric = Fabric(accelerator="tpu")

Use state-of-the-art distributed training strategies (DDP, FSDP, DeepSpeed) and mixed precision out of the box

# Use state-of-the-art distributed training techniques
fabric = Fabric(strategy="ddp")
fabric = Fabric(strategy="deepspeed")
fabric = Fabric(strategy="fsdp")

# Switch the precision
fabric = Fabric(precision="16-mixed")
fabric = Fabric(precision="64")

All the device logic boilerplate is handled for you

  # no more of this!
- model.to(device)
- batch.to(device)

Build your own custom Trainer using Fabric primitives for training checkpointing, logging, and more

import lightning as L


class MyCustomTrainer:
    def __init__(self, accelerator="auto", strategy="auto", devices="auto", precision="32-true"):
        self.fabric = L.Fabric(accelerator=accelerator, strategy=strategy, devices=devices, precision=precision)

    def fit(self, model, optimizer, dataloader, max_epochs):
        self.fabric.launch()

        model, optimizer = self.fabric.setup(model, optimizer)
        dataloader = self.fabric.setup_dataloaders(dataloader)
        model.train()

        for epoch in range(max_epochs):
            for batch in dataloader:
                input, target = batch
                optimizer.zero_grad()
                output = model(input)
                loss = loss_fn(output, target)
                self.fabric.backward(loss)
                optimizer.step()

You can find a more extensive example in our examples

---

Read the Lightning Fabric docs

---

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

# 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(cloud_compute=L.CloudCompute("cpu"))
        self.analyze = AnalyzeComponent(cloud_compute=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

# run on the cloud
lightning run app app.py --setup --cloud

# run locally
lightning run app app.py

---

Read the Lightning Apps docs

---

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

Current build statuses

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

---

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.