PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate.
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.
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Lightning disentangles PyTorch code to decouple the science from the engineering.
Lightning structures PyTorch code with these principles:
Lightning forces the following structure to your code which makes it reusable and shareable:
Once you do this, you can train on multiple-GPUs, TPUs, CPUs, IPUs, HPUs and even in 16-bit precision without changing your code!
Get started in just 15 minutes
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 py3.9 [TPUs] | ||||
Linux (multiple Python versions) | ||||
OSX (multiple Python versions) | ||||
Windows (multiple Python versions) |
Simple installation from PyPI
pip install pytorch-lightning
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
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, 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
Note: Training_step defines the training loop. Forward defines how the LightningModule behaves during inference/prediction.
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))
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)
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()
The PyTorch 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
PyTorch Lightning is also part of the PyTorch ecosystem which requires projects to have solid testing, documentation and support.
If you have any questions please: