**The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.** ______________________________________________________________________

Website • Key Features • How To Use • Docs • Examples • Community • Grid AI • License

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###### \*Codecov is > 90%+ but build delays may show less ______________________________________________________________________ ## PyTorch Lightning is just organized PyTorch Lightning disentangles PyTorch code to decouple the science from the engineering. ![PT to PL](docs/source/_static/images/general/pl_quick_start_full_compressed.gif) ______________________________________________________________________ ## Lightning Design Philosophy 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, IPUs, HPUs and even in 16-bit precision without changing your code! Get started with our [2 step guide](https://pytorch-lightning.readthedocs.io/en/latest/starter/new-project.html) ______________________________________________________________________ ## Continuous Integration Lightning is rigorously tested across multiple CPUs, GPUs, TPUs, IPUs, and HPUs and against major Python and PyTorch versions.
Current build statuses
| System / PyTorch ver. | 1.8 (LTS, min. req.) | 1.9 | 1.10 | 1.11 (latest) | | :------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | Linux py3.7 \[GPUs\*\*\] | [![Build Status]()](https://dev.azure.com/PytorchLightning/pytorch-lightning/_build/latest?definitionId=6&branchName=master) | - | - | - | | Linux py3.7 \[TPUs\*\*\*\] | - | [![CircleCI](https://circleci.com/gh/PyTorchLightning/pytorch-lightning/tree/master.svg?style=svg)](https://circleci.com/gh/PyTorchLightning/pytorch-lightning/tree/master) | - | - | | Linux py3.8 \[IPUs\] | - | [![Build Status]()](https://dev.azure.com/PytorchLightning/pytorch-lightning/_build/latest?definitionId=6&branchName=master) | - | - | | Linux py3.8 \[HPUs\] | - | - | [![Build Status]()](https://dev.azure.com/PytorchLightning/pytorch-lightning/_build/latest?definitionId=6&branchName=master) | - | | Linux py3.8 (with Conda) | [![Test](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-conda.yml/badge.svg?branch=master&event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-conda.yml) | [![Test](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-conda.yml/badge.svg?branch=master&event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-conda.yml) | [![Test](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-conda.yml/badge.svg?branch=master&event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-conda.yml) | - | | Linux py3.9 (with Conda) | - | - | - | [![Test](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-conda.yml/badge.svg?branch=master&event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-conda.yml) | | Linux py3.{7,9} | [![Test](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml/badge.svg?branch=master&event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml) | - | - | [![Test](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml/badge.svg?branch=master&event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml) | | OSX py3.{7,9} | [![Test](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml/badge.svg?branch=master&event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml) | - | - | [![Test](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml/badge.svg?branch=master&event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml) | | Windows py3.{7,9} | [![Test](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml/badge.svg?branch=master&event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml) | - | - | [![Test](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml/badge.svg?branch=master&event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml) | - _\*\* tests run on two NVIDIA P100_ - _\*\*\* tests run on Google GKE TPUv2/3. TPU py3.7 means we support Colab and Kaggle env._
______________________________________________________________________ ## How To Use ### Step 0: Install Simple installation from PyPI ```bash pip install pytorch-lightning ```
Other installation options #### Install with optional dependencies ```bash pip install pytorch-lightning['extra'] ``` #### Conda ```bash conda install pytorch-lightning -c conda-forge ``` #### Install stable 1.5.x the actual status of 1.5 \[stable\] is following: ![CI basic testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20basic%20testing/badge.svg?branch=release%2F1.5.x&event=push) ![CI complete testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20complete%20testing/badge.svg?branch=release%2F1.5.x&event=push) ![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.svg?branch=release%2F1.5.x&event=push) ![TPU tests](https://github.com/PyTorchLightning/pytorch-lightning/workflows/TPU%20tests/badge.svg?branch=release%2F1.5.x&event=push) ![Docs check](https://github.com/PyTorchLightning/pytorch-lightning/workflows/Docs%20check/badge.svg?branch=release%2F1.5.x&event=push) Install future release from the source ```bash pip install git+https://github.com/PytorchLightning/pytorch-lightning.git@release/1.5.x --upgrade ``` #### Install bleeding-edge - future 1.6 Install nightly from the source (no guarantees) ```bash pip install https://github.com/PyTorchLightning/pytorch-lightning/archive/master.zip ``` or from testing PyPI ```bash pip install -iU https://test.pypi.org/simple/ pytorch-lightning ```
### Step 1: Add these imports ```python 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 ``` ### 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(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.** ### Step 3: Train! ```python 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)) ``` ## Advanced features Lightning has over [40+ advanced features](https://pytorch-lightning.readthedocs.io/en/latest/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 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 ```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 training loops (advanced users) For complex/professional level work, you have optional full control of the training loop and optimizers. ```python 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() ``` ______________________________________________________________________ ## 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/PyTorchLightning/pytorch-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). ______________________________________________________________________ ## Lightning Lite
In the Lightning 1.5 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](https://devblog.pytorchlightning.ai/scale-your-pytorch-code-with-lightninglite-d5692a303f00) and [docs](https://pytorch-lightning.readthedocs.io/en/stable/starter/lightning_lite.html) for more info. ______________________________________________________________________ ## Examples ###### Hello world - [MNIST hello world](https://pytorch-lightning.readthedocs.io/en/latest/notebooks/lightning_examples/mnist-hello-world.html) ###### Contrastive Learning - [BYOL](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/self_supervised.html#byol) - [CPC v2](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/self_supervised.html#cpc-v2) - [Moco v2](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/self_supervised.html#moco-v2-api) - [SIMCLR](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/self_supervised.html#simclr) ###### NLP - [GPT-2](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/convolutional.html#gpt-2) - [BERT](https://pytorch-lightning.readthedocs.io/en/latest/notebooks/lightning_examples/text-transformers.html) ###### Reinforcement Learning - [DQN](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/reinforce_learn.html#dqn-models) - [Dueling-DQN](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/reinforce_learn.html#dueling-dqn) - [Reinforce](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/reinforce_learn.html#reinforce) ###### Vision - [GAN](https://pytorch-lightning.readthedocs.io/en/latest/notebooks/lightning_examples/basic-gan.html) ###### Classic ML - [Logistic Regression](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/classic_ml.html#logistic-regression) - [Linear Regression](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/classic_ml.html#linear-regression) ______________________________________________________________________ ## Community The lightning community is maintained by - [10+ core contributors](https://pytorch-lightning.readthedocs.io/en/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://devblog.pytorchlightning.ai/quick-contribution-guide-86d977171b3a) 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://pytorch-lightning.rtfd.io/en/latest). 1. [Search through existing Discussions](https://github.com/PyTorchLightning/pytorch-lightning/discussions), or [add a new question](https://github.com/PyTorchLightning/pytorch-lightning/discussions/new) 1. [Join our slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-12iz3cds1-uyyyBYJLiaL2bqVmMN7n~A). ### Funding [We're venture funded](https://techcrunch.com/2020/10/08/grid-ai-raises-18-6m-series-a-to-help-ai-researchers-and-engineers-bring-their-models-to-production/) to make sure we can provide around the clock support, hire a full-time staff, attend conferences, and move faster through implementing features you request. ______________________________________________________________________ ## Grid AI Grid AI is our platform for training models at scale on the cloud! **Sign up for our FREE community Tier [here](https://www.grid.ai/pricing/)** To use grid, take your regular command: ``` python my_model.py --learning_rate 1e-6 --layers 2 --accelerator 'gpu' --devices 4 ``` And change it to use the grid train command: ``` grid train --grid_gpus 4 my_model.py --learning_rate 'uniform(1e-6, 1e-1, 20)' --layers '[2, 4, 8, 16]' ``` The above command will launch (20 * 4) experiments each running on 4 GPUs (320 GPUs!) - by making ZERO changes to your code.