**The deep learning framework to pretrain, finetune and deploy AI models.** **NEW- Deploying models? Check out [LitServe](https://github.com/Lightning-AI/litserve), the PyTorch Lightning for model serving** ______________________________________________________________________
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:
pip install lightningAdvanced install options
#### Install with optional dependencies ```bash pip install lightning['extra'] ``` #### Conda ```bash conda install lightning -c conda-forge ``` #### Install stable version Install future release from the source ```bash 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) ```bash pip install https://github.com/Lightning-AI/lightning/archive/refs/heads/master.zip -U ``` or from testing PyPI ```bash pip install -iU https://test.pypi.org/simple/ pytorch-lightning ```PyTorch Lightning example
Define the training workflow. Here's a toy example (explore real examples):
# 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
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) | |
| Recommendation system | Train - recommendation system (factorization and embedding) | |
| Time-series forecasting | Train - Time-series forecasting with LSTM | |
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) ```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).
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)
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
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: