429 lines
23 KiB
Markdown
429 lines
23 KiB
Markdown
### \*\* NEWS: PyTorch Lightning has been renamed Lightning! In addition to building models, you can now build research workflows and production pipelines\*\*
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<div align="center">
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<img src="https://pl-flash-data.s3.amazonaws.com/assets_lightning/docs/images/logos/lightning-ai.png" width="400px">
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**Build high-performance (PyTorch) models, research workflows, ML production pipelines.**
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______________________________________________________________________
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<p align="center">
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<a href="https://www.lightning.ai/">Lightning Gallery</a> •
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<a href="#key-features">Key Features</a> •
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<a href="#how-to-use">How To Use</a> •
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<a href="https://pytorch-lightning.readthedocs.io/en/stable/">Docs</a> •
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<a href="#examples">Examples</a> •
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<a href="#community">Community</a> •
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<a href="#license">License</a>
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</p>
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<!-- DO NOT ADD CONDA DOWNLOADS... README CHANGES MUST BE APPROVED BY EDEN OR WILL -->
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[](https://pypi.org/project/pytorch-lightning/)
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[](https://badge.fury.io/py/pytorch-lightning)
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[](https://pepy.tech/project/pytorch-lightning)
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[](https://anaconda.org/conda-forge/pytorch-lightning)
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[](https://hub.docker.com/r/pytorchlightning/pytorch_lightning)
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[](https://codecov.io/gh/Lightning-AI/lightning)
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[](https://pytorch-lightning.readthedocs.io/en/stable/)
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[](https://www.pytorchlightning.ai/community)
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[](https://github.com/Lightning-AI/lightning/blob/master/LICENSE)
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<!--
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[](https://www.codefactor.io/repository/github/Lightning-AI/lightning)
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-->
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</div>
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###### \*Codecov is > 90%+ but build delays may show less
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______________________________________________________________________
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## PyTorch Lightning is just organized PyTorch
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Lightning disentangles PyTorch code to decouple the science from the engineering.
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## Build AI products with Lightning Apps
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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.
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[Browse available Lightning apps here](https://lightning.ai/)
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<div align="center">
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<img src="https://pl-flash-data.s3.amazonaws.com/assets_lightning/docs/images/logos/lightning-apps-teaser.png" width="80%">
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</div>
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### [Learn more about Lightning Apps](src/lightning_app/README.md)
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______________________________________________________________________
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## Lightning Design Philosophy
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Lightning structures PyTorch code with these principles:
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<div align="center">
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<img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/philosophies.jpg" max-height="250px">
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</div>
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Lightning forces the following structure to your code which makes it reusable and shareable:
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- Research code (the LightningModule).
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- Engineering code (you delete, and is handled by the Trainer).
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- Non-essential research code (logging, etc... this goes in Callbacks).
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- Data (use PyTorch DataLoaders or organize them into a LightningDataModule).
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Once you do this, you can train on multiple-GPUs, TPUs, CPUs and even in 16-bit precision without changing your code!
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[Get started in just 15 minutes](https://pytorch-lightning.readthedocs.io/en/latest/starter/introduction.html)
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______________________________________________________________________
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## Continuous Integration
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Lightning is rigorously tested across multiple CPUs, GPUs, TPUs, IPUs, and HPUs and against major Python and PyTorch versions.
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<details>
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<summary>Current build statuses</summary>
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<center>
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| System / PyTorch ver. | 1.9 | 1.10 | 1.12 (latest) |
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| :------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| Linux py3.7 \[GPUs\*\*\] | - | - | - |
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| Linux py3.7 \[TPUs\*\*\*\] | [](https://circleci.com/gh/Lightning-AI/lightning/tree/master) | - | - |
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| Linux py3.8 \[IPUs\] | [?branchName=master>)](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=25&branchName=master) | - | - |
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| Linux py3.8 \[HPUs\] | - | [?branchName=master>)](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=26&branchName=master) | - |
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| Linux py3.8 (with Conda) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-pytorch-test-conda.yml) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-pytorch-test-conda.yml) | - |
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| Linux py3.9 (with Conda) | - | - | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-pytorch-test-conda.yml) |
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| Linux py3.{7,9} | - | - | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-pytorch-test-full.yml) |
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| OSX py3.{7,9} | - | - | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-pytorch-test-full.yml) |
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| Windows py3.{7,9} | - | - | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-pytorch-test-full.yml) |
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- _\*\* tests run on two NVIDIA P100_
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- _\*\*\* tests run on Google GKE TPUv2/3. TPU py3.7 means we support Colab and Kaggle env._
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</center>
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</details>
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______________________________________________________________________
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## How To Use
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### Step 0: Install
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Simple installation from PyPI
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```bash
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pip install pytorch-lightning
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```
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<!-- following section will be skipped from PyPI description -->
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<details>
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<summary>Other installation options</summary>
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<!-- following section will be skipped from PyPI description -->
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#### Install with optional dependencies
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```bash
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pip install pytorch-lightning['extra']
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```
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#### Conda
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```bash
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conda install pytorch-lightning -c conda-forge
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```
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#### Install stable 1.7.x
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The actual status of 1.7 \[stable\] is the following:
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[](https://github.com/Lightning-AI/lightning/actions/workflows/ci-pytorch-test-full.yml?query=branch%3Arelease%2Fpytorch)
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[](https://github.com/Lightning-AI/lightning/actions/workflows/ci-pytorch-test-conda.yml?query=branch%3Arelease%2Fpytorch)
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[](https://dl.circleci.com/status-badge/redirect/gh/Lightning-AI/lightning/tree/release%2Fpytorch)
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[](https://github.com/Lightning-AI/lightning/actions/workflows/docs-checks.yml?query=branch%3Arelease%2Fpytorch)
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Install future release from the source
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```bash
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pip install https://github.com/Lightning-AI/lightning/archive/refs/heads/release/pytorch.zip -U
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```
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#### Install bleeding-edge - future 1.6
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Install nightly from the source (no guarantees)
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```bash
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pip install https://github.com/Lightning-AI/lightning/archive/refs/heads/master.zip -U
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```
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or from testing PyPI
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```bash
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pip install -iU https://test.pypi.org/simple/ pytorch-lightning
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```
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</details>
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<!-- end skipping PyPI description -->
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### Step 1: Add these imports
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```python
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import os
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import torch
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from torch import nn
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import torch.nn.functional as F
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from torchvision.datasets import MNIST
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from torch.utils.data import DataLoader, random_split
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from torchvision import transforms
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import pytorch_lightning as pl
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```
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### Step 2: Define a LightningModule (nn.Module subclass)
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A LightningModule defines a full *system* (ie: a GAN, autoencoder, BERT or a simple Image Classifier).
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```python
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class LitAutoEncoder(pl.LightningModule):
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def __init__(self):
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super().__init__()
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self.encoder = nn.Sequential(nn.Linear(28 * 28, 128), nn.ReLU(), nn.Linear(128, 3))
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self.decoder = nn.Sequential(nn.Linear(3, 128), nn.ReLU(), nn.Linear(128, 28 * 28))
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def forward(self, x):
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# in lightning, forward defines the prediction/inference actions
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embedding = self.encoder(x)
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return embedding
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def training_step(self, batch, batch_idx):
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# training_step defines the train loop. It is independent of forward
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x, y = batch
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x = x.view(x.size(0), -1)
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z = self.encoder(x)
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x_hat = self.decoder(z)
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loss = F.mse_loss(x_hat, x)
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self.log("train_loss", loss)
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return loss
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def configure_optimizers(self):
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optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
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return optimizer
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```
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**Note: Training_step defines the training loop. Forward defines how the LightningModule behaves during inference/prediction.**
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### Step 3: Train!
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```python
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dataset = MNIST(os.getcwd(), download=True, transform=transforms.ToTensor())
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train, val = random_split(dataset, [55000, 5000])
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autoencoder = LitAutoEncoder()
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trainer = pl.Trainer()
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trainer.fit(autoencoder, DataLoader(train), DataLoader(val))
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```
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## Advanced features
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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.
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Here are some examples:
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<div align="center">
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<img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/features_2.jpg" max-height="600px">
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</div>
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<details>
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<summary>Highlighted feature code snippets</summary>
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```python
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# 8 GPUs
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# no code changes needed
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trainer = Trainer(max_epochs=1, accelerator="gpu", devices=8)
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# 256 GPUs
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trainer = Trainer(max_epochs=1, accelerator="gpu", devices=8, num_nodes=32)
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```
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<summary>Train on TPUs without code changes</summary>
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```python
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# no code changes needed
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trainer = Trainer(accelerator="tpu", devices=8)
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```
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<summary>16-bit precision</summary>
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```python
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# no code changes needed
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trainer = Trainer(precision=16)
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```
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<summary>Experiment managers</summary>
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```python
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from pytorch_lightning import loggers
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# tensorboard
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trainer = Trainer(logger=TensorBoardLogger("logs/"))
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# weights and biases
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trainer = Trainer(logger=loggers.WandbLogger())
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# comet
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trainer = Trainer(logger=loggers.CometLogger())
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# mlflow
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trainer = Trainer(logger=loggers.MLFlowLogger())
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# neptune
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trainer = Trainer(logger=loggers.NeptuneLogger())
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# ... and dozens more
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```
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<summary>EarlyStopping</summary>
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```python
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es = EarlyStopping(monitor="val_loss")
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trainer = Trainer(callbacks=[es])
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```
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<summary>Checkpointing</summary>
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```python
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checkpointing = ModelCheckpoint(monitor="val_loss")
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trainer = Trainer(callbacks=[checkpointing])
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```
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<summary>Export to torchscript (JIT) (production use)</summary>
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```python
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# torchscript
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autoencoder = LitAutoEncoder()
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torch.jit.save(autoencoder.to_torchscript(), "model.pt")
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```
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<summary>Export to ONNX (production use)</summary>
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```python
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# onnx
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with tempfile.NamedTemporaryFile(suffix=".onnx", delete=False) as tmpfile:
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autoencoder = LitAutoEncoder()
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input_sample = torch.randn((1, 64))
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autoencoder.to_onnx(tmpfile.name, input_sample, export_params=True)
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os.path.isfile(tmpfile.name)
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```
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</details>
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### Pro-level control of training loops (advanced users)
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For complex/professional level work, you have optional full control of the training loop and optimizers.
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```python
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class LitAutoEncoder(pl.LightningModule):
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def __init__(self):
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super().__init__()
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self.automatic_optimization = False
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def training_step(self, batch, batch_idx):
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# access your optimizers with use_pl_optimizer=False. Default is True
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opt_a, opt_b = self.optimizers(use_pl_optimizer=True)
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loss_a = ...
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self.manual_backward(loss_a, opt_a)
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opt_a.step()
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opt_a.zero_grad()
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loss_b = ...
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self.manual_backward(loss_b, opt_b, retain_graph=True)
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self.manual_backward(loss_b, opt_b)
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opt_b.step()
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opt_b.zero_grad()
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```
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______________________________________________________________________
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## Advantages over unstructured PyTorch
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- Models become hardware agnostic
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- Code is clear to read because engineering code is abstracted away
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- Easier to reproduce
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- Make fewer mistakes because lightning handles the tricky engineering
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- Keeps all the flexibility (LightningModules are still PyTorch modules), but removes a ton of boilerplate
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- Lightning has dozens of integrations with popular machine learning tools.
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- [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.
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- Minimal running speed overhead (about 300 ms per epoch compared with pure PyTorch).
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______________________________________________________________________
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## Lightning Lite
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<div align="center">
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<img src="docs/source-pytorch/_static/images/lightning_lite/lite.gif" height="200px" width="600px">
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</div>
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In the Lightning v1.5 release, LightningLite now enables you to leverage all the capabilities of PyTorch Lightning Accelerators without any refactoring to your training loop. Check out the
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[blogpost](https://devblog.pytorchlightning.ai/scale-your-pytorch-code-with-lightninglite-d5692a303f00) and
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[docs](https://pytorch-lightning.readthedocs.io/en/stable/starter/lightning_lite.html) for more info.
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______________________________________________________________________
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## Examples
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###### Hello world
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- [MNIST hello world](https://pytorch-lightning.readthedocs.io/en/latest/notebooks/lightning_examples/mnist-hello-world.html)
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###### Contrastive Learning
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- [BYOL](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/self_supervised.html#byol)
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- [CPC v2](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/self_supervised.html#cpc-v2)
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- [Moco v2](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/self_supervised.html#moco-v2-api)
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- [SIMCLR](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/self_supervised.html#simclr)
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###### NLP
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- [GPT-2](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/convolutional.html#gpt-2)
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- [BERT](https://pytorch-lightning.readthedocs.io/en/latest/notebooks/lightning_examples/text-transformers.html)
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###### Reinforcement Learning
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- [DQN](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/reinforce_learn.html#dqn-models)
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- [Dueling-DQN](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/reinforce_learn.html#dueling-dqn)
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- [Reinforce](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/reinforce_learn.html#reinforce)
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###### Vision
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- [GAN](https://pytorch-lightning.readthedocs.io/en/latest/notebooks/lightning_examples/basic-gan.html)
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###### Classic ML
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- [Logistic Regression](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/classic_ml.html#logistic-regression)
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- [Linear Regression](https://lightning-bolts.readthedocs.io/en/stable/deprecated/models/classic_ml.html#linear-regression)
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______________________________________________________________________
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## Community
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The lightning community is maintained by
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- [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.
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- 590+ active community contributors.
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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)
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Lightning is also part of the [PyTorch ecosystem](https://pytorch.org/ecosystem/) which requires projects to have solid testing, documentation and support.
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### Asking for help
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If you have any questions please:
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1. [Read the docs](https://pytorch-lightning.rtfd.io/en/latest).
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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)
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1. [Join our slack](https://www.pytorchlightning.ai/community).
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