Lightning

**The Deep Learning framework to train, deploy, and ship AI products Lightning fast.** **NEW- Lightning 2.0 is featuring a clean and stable API!!** ______________________________________________________________________

Lightning.ai • PyTorch Lightning • Fabric • Lightning Apps • Docs • Community • Contribute •

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## Install Lightning Simple installation from PyPI ```bash pip install lightning ```
Other installation 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 ```
______________________________________________________________________ ## Lightning has 3 core packages [PyTorch Lightning: Train and deploy PyTorch at scale](#pytorch-lightning-train-and-deploy-pytorch-at-scale).
[Lightning Fabric: Expert control](#lightning-fabric-expert-control).
[Lightning Apps: Build AI products and ML workflows](#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. ![PT to PL](docs/source-pytorch/_static/images/general/pl_quick_start_full_compressed.gif) ______________________________________________________________________ ### Hello simple model ```python # 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, 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 # ------------------- # 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 ```bash pip install torchvision python main.py ``` ______________________________________________________________________ ## Advanced features Lightning has over [40+ advanced features](https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-flags) 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](https://github.com/Lightning-AI/lightning/tree/master/tests). 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). ______________________________________________________________________
Read the PyTorch Lightning docs
______________________________________________________________________ # 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)
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Read the Lightning Fabric docs
______________________________________________________________________ # 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 ```python # 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 ```bash # run on the cloud lightning run app app.py --setup --cloud # run locally lightning run app app.py ``` ______________________________________________________________________
Read the Lightning Apps docs
______________________________________________________________________ ## Examples ###### Self-supervised Learning - [CPC transforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#cpc-transforms) - [Moco v2 tranforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#moco-v2-transforms) - [SimCLR transforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#simclr-transforms) ###### Convolutional Architectures - [GPT-2](https://lightning-bolts.readthedocs.io/en/stable/models/convolutional.html#gpt-2) - [UNet](https://lightning-bolts.readthedocs.io/en/stable/models/convolutional.html#unet) ###### Reinforcement Learning - [DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#dqn-loss) - [Double DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#double-dqn-loss) - [Per DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#per-dqn-loss) ###### GANs - [Basic GAN](https://lightning-bolts.readthedocs.io/en/stable/models/gans.html#basic-gan) - [DCGAN](https://lightning-bolts.readthedocs.io/en/stable/models/gans.html#dcgan) ###### Classic ML - [Logistic Regression](https://lightning-bolts.readthedocs.io/en/stable/models/classic_ml.html#logistic-regression) - [Linear Regression](https://lightning-bolts.readthedocs.io/en/stable/models/classic_ml.html#linear-regression) ______________________________________________________________________ ## Continuous Integration Lightning is rigorously tested across multiple CPUs, GPUs, TPUs, IPUs, and HPUs and against major Python and PyTorch versions. ###### \*Codecov is > 90%+ but build delays may show less
Current build statuses
| System / PyTorch ver. | 1.11 | 1.12 | 1.13 | 2.0 | | :--------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---- | | Linux py3.9 \[GPUs\] | - | [![Build Status]()](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=24&branchName=master) | [![Build Status]()](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=24&branchName=master) | Soon | | Linux py3.9 \[TPUs\] | - | [![Test PyTorch - TPU](https://github.com/Lightning-AI/lightning/actions/workflows/tpu-tests.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/tpu-tests.yml) | | Soon | | Linux py3.8 \[IPUs\] | - | - | [![Build Status]()](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=25&branchName=master) | Soon | | Linux (multiple Python versions) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | Soon | | OSX (multiple Python versions) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | Soon | | Windows (multiple Python versions) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [![Test PyTorch](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml/badge.svg)](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | Soon |
______________________________________________________________________ ## Community The lightning community is maintained by - [10+ core contributors](https://lightning.ai/docs/pytorch/latest/community/governance.html) 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](https://lightning.ai/docs/pytorch/stable/generated/CONTRIBUTING.html) Lightning is also part of the [PyTorch ecosystem](https://pytorch.org/ecosystem/) which requires projects to have solid testing, documentation and support. ### Asking for help If you have any questions please: 1. [Read the docs](https://lightning.ai/docs). 1. [Search through existing Discussions](https://github.com/Lightning-AI/lightning/discussions), or [add a new question](https://github.com/Lightning-AI/lightning/discussions/new) 1. [Join our discord](https://discord.com/invite/tfXFetEZxv).