** NEWS: PyTorch Lightning has been renamed Lightning! In addition to building models, you can now build lightning apps that glue together everything around the models, without the pain of infrastructure, cost management, scaling and everything else.**
Build and train PyTorch models and connect them to the ML lifecycle using Lightning App templates, without handling DIY infrastructure, cost management, scaling, and other headaches.
Lightning Gallery • Key Features • How To Use • Docs • Examples • Community • License
Lightning disentangles PyTorch code to decouple the science from the engineering.
Once you're done building models, publish a paper demo or build a full production end-to-end ML system with Lightning Apps. 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.
Browse available Lightning apps here
Lightning structures PyTorch code with these principles:
Lightning forces the following structure to your code which makes it reusable and shareable:
- Research code (the LightningModule).
- Engineering code (you delete, and is handled by the Trainer).
- Non-essential research code (logging, etc... this goes in Callbacks).
- Data (use PyTorch DataLoaders or organize them into a LightningDataModule).
Once you do this, you can train on multiple-GPUs, TPUs, CPUs and even in 16-bit precision without changing your code!
Get started in just 15 minutes
Lightning is rigorously tested across multiple CPUs, GPUs, TPUs, IPUs, and HPUs and against major Python and PyTorch versions.
Current build statuses
- ** tests run on two NVIDIA P100
- *** tests run on Google GKE TPUv2/3. TPU py3.7 means we support Colab and Kaggle env.
Simple installation from PyPI
pip install pytorch-lightning
Other installation options
pip install pytorch-lightning['extra']
conda install pytorch-lightning -c conda-forge
The actual status of 1.7 [stable] is the following:
Install future release from the source
pip install https://github.com/Lightning-AI/lightning/archive/refs/heads/release/pytorch.zip -U
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
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:
Highlighted feature code snippets
# 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)
Train on 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 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
EarlyStopping
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)
For complex/professional level work, you have optional full control of the training loop and 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()
- 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).
In the Lightning v1.5 release, LightningLite now enables you to leverage all the capabilities of PyTorch Lightning Accelerators without any refactoring to your training loop. Check out the blogpost and docs for more info.
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.
- 590+ active 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.
If you have any questions please: