![Logo](docs/source/_images/logos/lightning_logo.svg) # PyTorch Lightning **The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.**

Masterclass • Key Features • How To Use • Docs • Resources • Community • FAQ • Licence

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###### *Codecov is > 90%+ but build delays may show less ## PyTorch Lightning is just organized PyTorch ![PT to PL](/docs/source/_images/general/pl_quick_start_full_compressed.gif) Lightning disentangles PyTorch code to decouple the science from the engineering by organizing it into 4 categories: 1. Research code (the LightningModule). 2. Engineering code (you delete, and is handled by the Trainer). 3. Non-essential research code (logging, etc... this goes in Callbacks). 4. 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 with our [3 steps guide](https://pytorch-lightning.readthedocs.io/en/stable/new-project.html) --- ## Trending contributors [![](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/images/0)](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/links/0) [![](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/images/1)](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/links/1) [![](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/images/2)](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/links/2) [![](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/images/3)](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/links/3) [![](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/images/4)](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/links/4) [![](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/images/5)](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/links/5) [![](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/images/6)](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/links/6) [![](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/images/7)](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/links/7) --- ## Continuous Integration
| System / PyTorch ver. | 1.3 (min. req.)* | 1.4 | 1.5 | 1.6 (latest) | | :---: | :---: | :---: | :---: | :---: | | Conda py3.7 [linux] | [![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.svg)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22PyTorch+%26+Conda%22+branch%3Amaster) | [![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.svg)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22PyTorch+%26+Conda%22+branch%3Amaster) | [![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.svg)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22PyTorch+%26+Conda%22+branch%3Amaster) | [![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.svg)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22PyTorch+%26+Conda%22+branch%3Amaster) | | Linux py3.7 [GPUs**] | - | - | - | [![Build Status](http://35.192.60.23/api/badges/PyTorchLightning/pytorch-lightning/status.svg)](http://35.192.60.23/PyTorchLightning/pytorch-lightning) | | Linux py3.7 [TPUs***] | - | - | - | [![TPU tests](https://github.com/PyTorchLightning/pytorch-lightning/workflows/TPU%20tests/badge.svg)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22TPU+tests%22+branch%3Amaster) | | Linux py3.6 / py3.7 / py3.8 | [![CI testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20testing/badge.svg?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | - | - | [![CI testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20testing/badge.svg?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | | OSX py3.6 / py3.7 | - | [![CI testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20testing/badge.svg?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | - | [![CI testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20testing/badge.svg?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | | Windows py3.6 / py3.7 / py3.8 | [![CI testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20testing/badge.svg?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | - | - | [![CI testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20testing/badge.svg?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) - _\* `torch>=1.4` is the minimal pytorch version for Python 3.8_ - _\** tests run on two NVIDIA K80_ - _\*** tests run on Google GKE TPUv2/3_
--- ## [PyTorch Lightning Masterclass](https://www.youtube.com/watch?v=DbESHcCoWbM&list=PLaMu-SDt_RB5NUm67hU2pdE75j6KaIOv2) ### [New lessons weekly!](https://www.youtube.com/watch?v=DbESHcCoWbM&list=PLaMu-SDt_RB5NUm67hU2pdE75j6KaIOv2)

From PyTorch to PyTorch Lightning

From PyTorch to PyTorch Lightning

Converting a VAE to PyTorch Lightning

From PyTorch to PyTorch Lightning
--- ## Key Features * Scale your models to run on any hardware (CPU, GPUs, TPUs) without changing your model * Making code more readable by decoupling the research code from the engineering * Easier to reproduce * Less error prone by automtaing most of the training loop and tricky engineering * Keeps all the flexibility (LightningModules are still PyTorch modules), but removes a ton of boilerplate * Lightning has out-of-the-box integration with the popular logging/visualizing frameworks ([Tensorboard](https://pytorch.org/docs/stable/tensorboard.html), [MLFlow](https://mlflow.org/), [Neptune.ai](https://neptune.ai/), [Comet.ml](https://www.comet.ml/site/), [Wandb](https://www.wandb.com/)). * [Tested rigorously with every new PR](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/tests). We test every combination og 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 automates 40+ parts of DL/ML research - GPU training - Distributed GPU (cluster) training - TPU training - EarlyStopping - Logging/Visualizing - Checkpointing - Experiment management - [Full list here](https://pytorch-lightning.readthedocs.io/en/latest/#common-use-cases) --- ## How To Use ##### Install Simple installation from PyPI ```bash pip install pytorch-lightning ``` From Conda ```bash conda install pytorch-lightning -c conda-forge ``` ##### Here's a minimal example without a test loop. ```python import os import torch 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 ``` ```python # this is just a plain nn.Module with some structure class LitClassifier(pl.LightningModule): def __init__(self): super().__init__() self.l1 = torch.nn.Linear(28 * 28, 10) def forward(self, x): return torch.relu(self.l1(x.view(x.size(0), -1))) def training_step(self, batch, batch_idx): x, y = batch y_hat = self(x) loss = F.cross_entropy(y_hat, y) result = pl.TrainResult(loss) result.log('train_loss', loss, on_epoch=True) return result def validation_step(self, batch, batch_idx): x, y = batch y_hat = self(x) loss = F.cross_entropy(y_hat, y) result = pl.EvalResult(checkpoint_on=loss) result.log('val_loss', loss) return result def configure_optimizers(self): return torch.optim.Adam(self.parameters(), lr=0.02) # train! dataset = MNIST(os.getcwd(), download=True, transform=transforms.ToTensor()) train, val = random_split(dataset, [55000, 5000]) model = LitClassifier() trainer = pl.Trainer() trainer.fit(model, DataLoader(train), DataLoader(val)) ``` #### And without changing a single line of code, you could run on GPUs ```python # 8 GPUs trainer = Trainer(max_epochs=1, gpus=8) # 256 GPUs trainer = Trainer(max_epochs=1, gpus=8, num_nodes=32) ``` Or TPUs ```python # Distributes TPU core training trainer = Trainer(tpu_cores=8) # Single TPU core training trainer = Trainer(tpu_cores=[1]) ``` --- ### Docs - [master](https://pytorch-lightning.readthedocs.io/en/latest) - [stable](https://pytorch-lightning.readthedocs.io/en/stable) - [0.9.0](https://pytorch-lightning.readthedocs.io/en/0.9.0/) - [0.8.5](https://pytorch-lightning.readthedocs.io/en/0.8.5/) - [0.8.4](https://pytorch-lightning.readthedocs.io/en/0.8.4/) - [0.8.3](https://pytorch-lightning.readthedocs.io/en/0.8.3/) - [0.8.1](https://pytorch-lightning.readthedocs.io/en/0.8.1/) --- ## Resources ### Examples ###### Hello world [MNIST hello world](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=gEulmrbxwaYL) [MNIST on TPUs](https://colab.research.google.com/drive/1-_LKx4HwAxl5M6xPJmqAAu444LTDQoa3) ###### Contrastive Learning [BYOL](https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#byol) [CPC v2](https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#cpc-v2) [Moco v2](https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#moco-v2) [SIMCLR](https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#simclr) ###### NLP [BERT](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=7uQVI-xv9Ddj) [GPT-2](https://pytorch-lightning-bolts.readthedocs.io/en/latest/convolutional.html#gpt-2) ###### Reinforcement Learning [DQN](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=NWvMLBDySQI5) [Dueling-DQN](https://pytorch-lightning-bolts.readthedocs.io/en/latest/reinforce_learn.html#dueling-dqn) [Reinforce](https://pytorch-lightning-bolts.readthedocs.io/en/latest/reinforce_learn.html#reinforce) ###### Vision [GAN](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=P0bSmCw57aV5) ###### Classic ML [Logistic Regression](https://pytorch-lightning-bolts.readthedocs.io/en/latest/classic_ml.html#logistic-regression) [Linear Regression](https://pytorch-lightning-bolts.readthedocs.io/en/latest/classic_ml.html#linear-regression) ### Tutorials Check out our [introduction guide](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html) to get started. Or jump straight into [our tutorials](https://pytorch-lightning.readthedocs.io/en/latest/#tutorials). --- ## Community The lightning cimmunity is maintained by - [15 core contributors](https://pytorch-lightning.readthedocs.io/en/latest/governance.html) who are all a mix of professional engineers, Research Scientists, Ph.D. students from top AI labs. - 200+ community contributors. 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/). 2. [Search through the issues](https://github.com/PytorchLightning/pytorch-lightning/issues?utf8=%E2%9C%93&q=my++question). 3. [Join our slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-f6bl2l0l-JYMK3tbAgAmGRrlNr00f1A). 4. [Ask on stackoverflow](https://stackoverflow.com/questions/ask?guided=false) with the tag pytorch-lightning. ### Funding Building open-source software with only a few part-time people is hard! We've secured funding to make sure we can hire a full-time staff, attend conferences, and move faster through implementing features you request. Our goal is to build an incredible research platform and a big supportive community. Many open-source projects have gone on to fund operations through things like support and special help for big corporations! If you are one of these corporations, please feel free to reach out to will@pytorchlightning.ai! --- ## FAQ **Starting a new project?** [Use our seed-project aimed at reproducibility!](https://github.com/PytorchLightning/pytorch-lightning-conference-seed) **Why lightning?** Although your research/production project might start simple, once you add things like GPU AND TPU training, 16-bit precision, etc, you end up spending more time engineering than researching. Lightning automates AND rigorously tests those parts for you. Lightning has 3 goals in mind: 1. Maximal flexibility while abstracting out the common boilerplate across research projects. 2. Reproducibility. If all projects use the LightningModule template, it will be much much easier to understand what's going on and where to look! It will also mean every implementation follows a standard format. 3. Democratizing PyTorch power-user features. Distributed training? 16-bit? know you need them but don't want to take the time to implement? All good... these come built into Lightning. **Who is Lightning for?** - Professional researchers - Ph.D. students - Corporate production teams If you're just getting into deep learning, we recommend you learn PyTorch first! Once you've implemented a few models, come back and use all the advanced features of Lightning :) **What does lightning control for me?** Everything in Blue! This is how lightning separates the science (red) from engineering (blue). ![Overview](docs/source/_images/general/pl_overview.gif) **How much effort is it to convert?** If your code is not a huge mess you should be able to organize it into a LightningModule in less than 1 hour. If your code IS a mess, then you needed to clean up anyhow ;) [Check out this step-by-step guide](https://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09). [Or watch this video](https://www.youtube.com/watch?v=QHww1JH7IDU). **How flexible is it?** As you see, you're just organizing your PyTorch code - there's no abstraction. And for the stuff that the Trainer abstracts out, you can [override any part](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html#extensibility) you want to do things like implement your own distributed training, 16-bit precision, or even a custom backward pass. For example, here you could do your own backward pass without worrying about GPUs, TPUs or 16-bit since we already handle it. ```python class LitModel(LightningModule): def optimizer_zero_grad(self, current_epoch, batch_idx, optimizer, opt_idx): optimizer.zero_grad() ``` For anything else you might need, we have an extensive [callback system](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html#callbacks) you can use to add arbitrary functionality not implemented by our team in the Trainer. **What types of research works?** Anything! Remember, that this is just organized PyTorch code. The Training step defines the core complexity found in the training loop. ##### Could be as complex as a seq2seq ```python # define what happens for training here def training_step(self, batch, batch_idx): x, y = batch # define your own forward and loss calculation hidden_states = self.encoder(x) # even as complex as a seq-2-seq + attn model # (this is just a toy, non-working example to illustrate) start_token = '' last_hidden = torch.zeros(...) loss = 0 for step in range(max_seq_len): attn_context = self.attention_nn(hidden_states, start_token) pred = self.decoder(start_token, attn_context, last_hidden) last_hidden = pred pred = self.predict_nn(pred) loss += self.loss(last_hidden, y[step]) #toy example as well loss = loss / max_seq_len return {'loss': loss} ``` ##### Or as basic as CNN image classification ```python # define what happens for validation here def validation_step(self, batch, batch_idx): x, y = batch # or as basic as a CNN classification out = self(x) loss = my_loss(out, y) return {'loss': loss} ``` **Does Lightning Slow my PyTorch?** No! Lightning is meant for research/production cases that require high-performance. We have tests to ensure we get the EXACT same results in under 600 ms difference per epoch. In reality, lightning adds about a 300 ms overhead per epoch. [Check out the parity tests here](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/benchmarks). Overall, Lightning guarantees rigorously tested, correct, modern best practices for the automated parts. **How does Lightning compare with Ignite and fast.ai?** [Here's a thorough comparison](https://medium.com/@_willfalcon/pytorch-lightning-vs-pytorch-ignite-vs-fast-ai-61dc7480ad8a). **Is this another library I have to learn?** Nope! We use pure Pytorch everywhere and don't add unnecessary abstractions! **Are there plans to support Python 2?** Nope. **Are there plans to support virtualenv?** Nope. Please use anaconda or miniconda. ```bash conda activate my_env pip install pytorch-lightning ``` --- ## Licence Please observe the Apache 2.0 license that is listed in this repository. In addition the Lightning framework is Patent Pending. ## BibTeX If you want to cite the framework feel free to use this (but only if you loved it 😊): ```bibtex @article{falcon2019pytorch, title={PyTorch Lightning}, author={Falcon, WA}, journal={GitHub. Note: https://github.com/PyTorchLightning/pytorch-lightning Cited by}, volume={3}, year={2019} } ```