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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______________________________________________________________________ ## Train and deploy with PyTorch Lightning 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)

How to use PyTorch Lightning

### Step 1: Add these imports ```python import lightning as L 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 ``` ### Step 2: Define a LightningModule (nn.Module subclass) A LightningModule defines a full *system* (ie: a GAN, autoencoder, BERT or a simple Image Classifier). ```python 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 ``` **Note: Training_step defines the training loop. Forward defines how the LightningModule behaves during inference/prediction.** ### Step 3: Train! ```python dataset = MNIST(os.getcwd(), download=True, transform=transforms.ToTensor()) train, val = random_split(dataset, [55000, 5000]) autoencoder = LitAutoEncoder() trainer = L.Trainer() trainer.fit(autoencoder, DataLoader(train), DataLoader(val)) ``` ## 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:

Highlighted feature code snippets

```python # 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

```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 ```

EarlyStopping

```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) ```

### Pro-level control of optimization (advanced users) For complex/professional level work, you have optional full control of the optimizers. ```python class LitAutoEncoder(L.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() ``` ______________________________________________________________________ ## 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). ______________________________________________________________________ ## 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) ______________________________________________________________________ ### [Read more about PyTorch Lightning](src/pytorch_lightning/README.md)

______________________________________________________________________ ## Scale PyTorch With Lightning Fabric Fabric allows you to scale any PyTorch model to distributed machines while maintianing full control over your training loop. Just add a few lines of code and run on any device! Use this library for complex tasks like reinforcement learning, active learning, and transformers without losing control over your training code.

Learn more about Fabric

With just a few code changes, run any PyTorch model on any distributed hardware, no boilerplate! - Easily switch from running on CPU to GPU (Apple Silicon, CUDA, …), TPU, multi-GPU or even multi-node training - Use state-of-the-art distributed training strategies (DDP, FSDP, DeepSpeed) and mixed precision out of the box - All the device logic boilerplate is handled for you - Designed with multi-billion parameter models in mind - Build your own custom Trainer using Fabric primitives for training checkpointing, logging, and more ```diff + import lightning as L import torch import torch.nn as nn from torch.utils.data import DataLoader, Dataset class PyTorchModel(nn.Module): ... class PyTorchDataset(Dataset): ... + fabric = L.Fabric(accelerator="cuda", devices=8, strategy="ddp") + fabric.launch() - device = "cuda" if torch.cuda.is_available() else "cpu model = PyTorchModel(...) optimizer = torch.optim.SGD(model.parameters()) + model, optimizer = fabric.setup(model, optimizer) dataloader = DataLoader(PyTorchDataset(...), ...) + dataloader = fabric.setup_dataloaders(dataloader) model.train() for epoch in range(num_epochs): for batch in dataloader: input, target = batch - input, target = input.to(device), target.to(device) optimizer.zero_grad() output = model(input) loss = loss_fn(output, target) - loss.backward() + fabric.backward(loss) optimizer.step() lr_scheduler.step() ``` ### [Read more about Fabric](src/lightning_fabric/README.md)

______________________________________________________________________ ## Build AI products with Lightning Apps 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](https://lightning.ai/)

Learn more about apps

Build machine learning components that can plug into existing ML workflows. A Lightning component organizes arbitrary code to run on the cloud, manage its own infrastructure, cloud costs, networking, and more. Focus on component logic and not engineering. Use components on their own, or compose them into full-stack AI apps with our next-generation Lightning orchestrator. to package your code into Lightning components which can plug into your existing ML workflows. ## Run your first Lightning App 1. Install a simple training and deployment app by typing: ```bash # install lightning pip install lightning lightning install app lightning/quick-start ``` 1. If everything was successful, move into the new directory: ```bash cd lightning-quick-start ``` 1. Run the app locally ```bash lightning run app app.py ``` 1. Alternatively, run it on the public Lightning Cloud to share your app! ```bash lightning run app app.py --cloud ``` Apps run the same on the cloud and locally on your choice of hardware. ## run the app on the --cloud lightning run app app.py --setup --cloud ### [Learn more about Lightning Apps](src/lightning_app/README.md)

______________________________________________________________________ ## 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 py3.8 \[HPUs\] | - | - | [![Build Status]()](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=26&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 |

______________________________________________________________________ ## Install 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 ```

______________________________________________________________________ ## Community The lightning community is maintained by - [10+ core contributors](https://lightning.ai/docs/pytorch/latest/governance.html) 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](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).