**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 •
Licence
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pytorch-lightning)](https://pypi.org/project/pytorch-lightning/)
[![PyPI Status](https://badge.fury.io/py/pytorch-lightning.svg)](https://badge.fury.io/py/pytorch-lightning)
[![PyPI Status](https://pepy.tech/badge/pytorch-lightning)](https://pepy.tech/project/pytorch-lightning)
[![Conda](https://img.shields.io/conda/v/conda-forge/pytorch-lightning?label=conda&color=success)](https://anaconda.org/conda-forge/pytorch-lightning)
[![DockerHub](https://img.shields.io/docker/pulls/pytorchlightning/pytorch_lightning.svg)](https://hub.docker.com/r/pytorchlightning/pytorch_lightning)
[![codecov](https://codecov.io/gh/PyTorchLightning/pytorch-lightning/branch/master/graph/badge.svg)](https://codecov.io/gh/PyTorchLightning/pytorch-lightning)
[![ReadTheDocs](https://readthedocs.org/projects/pytorch-lightning/badge/?version=stable)](https://pytorch-lightning.readthedocs.io/en/stable/)
[![Slack](https://img.shields.io/badge/slack-chat-green.svg?logo=slack)](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-f6bl2l0l-JYMK3tbAgAmGRrlNr00f1A)
[![Discourse status](https://img.shields.io/discourse/status?server=https%3A%2F%2Fforums.pytorchlightning.ai)](https://forums.pytorchlightning.ai/)
[![license](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/PytorchLightning/pytorch-lightning/blob/master/LICENSE)
[![Next Release](https://img.shields.io/badge/Next%20Release-Nov%2021-
.svg)](https://shields.io/)
###### *Codecov is > 90%+ but build delays may show less
---
## NEWS
[Dec 2020 - Read about how Facebook uses Lightning to standardize deep learning across research and production teams](https://ai.facebook.com/blog/reengineering-facebook-ais-deep-learning-platforms-for-interoperability)
---
## 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 Philosophy
Lightning is designed with these principles in mind:
Principle 1: Enable maximal flexibility.
Principle 2: Abstract away unnecessary boilerplate, but make it accessible when needed.
Principle 3: Systems should be self-contained (ie: optimizers, computation code, etc).
Principle 4: Deep learning code should be organized into 4 distinct categories.
- 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 with our [2 step guide](https://pytorch-lightning.readthedocs.io/en/stable/new-project.html)
---
## Inference
Lightning is also designed for the fast inference AI researchers and production teams need to scale up things like BERT and self-supervised learning.
Lightning can automatically export to ONNX or TorchScript for those cases.
---
## Continuous Integration