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 4 core packages
PyTorch Lightning: Train and deploy PyTorch at scale.
Lightning Fabric: Expert control.
Lightning Data: Blazing fast, distributed streaming of training data from cloud storage.
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, _ = 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
```python # 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
```python # no code changes needed trainer = Trainer(accelerator="tpu", devices=8) ```16-bit precision
```python # no code changes needed trainer = Trainer(precision=16) ```Experiment managers
```python 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
```python es = EarlyStopping(monitor="val_loss") trainer = Trainer(callbacks=[es]) ```Checkpointing
```python checkpointing = ModelCheckpoint(monitor="val_loss") trainer = Trainer(callbacks=[checkpointing]) ```Export to torchscript (JIT) (production use)
```python # torchscript autoencoder = LitAutoEncoder() torch.jit.save(autoencoder.to_torchscript(), "model.pt") ```Export to ONNX (production use)
```python # 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) ```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!) |
|---|---|
| ```diff + 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) ``` | ```Python 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
```python # 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
```python # 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
```diff # no more of this! - model.to(device) - batch.to(device) ```Build your own custom Trainer using Fabric primitives for training checkpointing, logging, and more
```python 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](examples/fabric/build_your_own_trainer)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
Examples
Self-supervised Learning
Convolutional Architectures
Reinforcement Learning
GANs
Classic ML
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
Asking for help
If you have any questions please: