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The Deep Learning framework to train, deploy, and ship AI products Lightning fast.
The deep learning framework to pretrain, finetune and deploy AI models.
NEW- Lightning 2.0 features a clean and stable API!!
Lightning AI • Examples • PyTorch Lightning • Fabric • Docs • Community • Contribute •
Simple installation from PyPI
pip install lightning
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.
PyTorch Lightning is just organized PyTorch - Lightning disentangles PyTorch code to decouple the science from the engineering.
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) |
# 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
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(accelerator="gpu", devices=8)
# 256 GPUs
trainer = Trainer(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 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")
# 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)
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!) |
---|---|
|
|
# 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 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")
# no more of this!
- model.to(device)
- batch.to(device)
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
Lightning is rigorously tested across multiple CPUs, GPUs and TPUs and against major Python and PyTorch versions.
System / PyTorch ver. | 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) |
The lightning community is maintained by
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.
If you have any questions please:
FAQs
The Deep Learning framework to train, deploy, and ship AI products Lightning fast.
We found that lightning demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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