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< div align = "center" >
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< img src = "docs/source/_static/images/logo.png" width = "400px" >
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**The lightweight PyTorch wrapper for high-performance AI research.
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Scale your models, not the boilerplate.**
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______________________________________________________________________
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< p align = "center" >
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< a href = "https://www.pytorchlightning.ai/" > Website< / a > •
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< a href = "#key-features" > Key Features< / a > •
< 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 = "#grid-ai" > Grid AI< / 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/PyTorchLightning/pytorch-lightning)
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[](https://pytorch-lightning.readthedocs.io/en/stable/)
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[](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-12iz3cds1-uyyyBYJLiaL2bqVmMN7n~A)
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[](https://github.com/PytorchLightning/pytorch-lightning/blob/master/LICENSE)
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<!--
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[](https://www.codefactor.io/repository/github/pytorchlightning/pytorch-lightning)
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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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______________________________________________________________________
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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" >
< img src = "https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/philosophies.jpg" max-height = "250px" >
< / div >
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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).
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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 with our [2 step guide ](https://pytorch-lightning.readthedocs.io/en/latest/starter/new-project.html )
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______________________________________________________________________
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## Continuous Integration
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Lightning is rigorously tested across multiple GPUs, TPUs CPUs and against major Python and PyTorch versions.
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< details >
< summary > Current build statuses< / summary >
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< center >
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| System / PyTorch ver. | 1.7 (min. req.) | 1.8 (LTS) | 1.9 | 1.10 (latest) |
| :------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| Linux py3.7 \[GPUs\*\*\] | - | [?branchName=master>)](https://dev.azure.com/PytorchLightning/pytorch-lightning/_build/latest?definitionId=6& branchName=master) | - | - |
| Linux py3.7 \[TPUs\*\*\*\] | - | [](https://circleci.com/gh/PyTorchLightning/pytorch-lightning/tree/master) | - | - |
| Linux py3.8 (with Conda | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-conda.yml) | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-conda.yml) | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-conda.yml) | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-conda.yml) |
| Linux py3.{7,9} | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml) | - | - | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml) |
| OSX py3.{7,9} | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml) | - | - | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml) |
| Windows py3.{7,9} | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml) | - | - | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_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
pip install pytorch-lightning
```
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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
pip install pytorch-lightning['extra']
```
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#### Conda
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```bash
conda install pytorch-lightning -c conda-forge
```
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#### Install stable 1.5.x
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the actual status of 1.5 \[stable\] is following:
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Install future release from the source
```bash
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pip install git+https://github.com/PytorchLightning/pytorch-lightning.git@release/1.5.x --upgrade
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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)
```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
```
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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
import torch
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from torch import nn
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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
```
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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))
self.decoder = nn.Sequential(nn.Linear(3, 128), nn.ReLU(), nn.Linear(128, 28 * 28))
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def forward(self, x):
# in lightning, forward defines the prediction/inference actions
embedding = self.encoder(x)
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)
z = self.encoder(x)
x_hat = self.decoder(z)
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)
return optimizer
```
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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())
train, val = random_split(dataset, [55000, 5000])
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autoencoder = LitAutoEncoder()
trainer = pl.Trainer()
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" >
< img src = "https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/features_2.jpg" max-height = "600px" >
< / div >
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< details >
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< summary > Highlighted feature code snippets< / summary >
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```python
# 8 GPUs
# no code changes needed
trainer = Trainer(max_epochs=1, gpus=8)
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# 256 GPUs
trainer = Trainer(max_epochs=1, gpus=8, num_nodes=32)
```
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< summary > Train on TPUs without code changes< / summary >
```python
# no code changes needed
trainer = Trainer(tpu_cores=8)
```
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< summary > 16-bit precision< / summary >
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```python
# no code changes needed
trainer = Trainer(precision=16)
```
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< summary > Experiment managers< / summary >
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```python
from pytorch_lightning import loggers
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# tensorboard
trainer = Trainer(logger=TensorBoardLogger("logs/"))
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# weights and biases
trainer = Trainer(logger=loggers.WandbLogger())
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# comet
trainer = Trainer(logger=loggers.CometLogger())
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# mlflow
trainer = Trainer(logger=loggers.MLFlowLogger())
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# neptune
trainer = Trainer(logger=loggers.NeptuneLogger())
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# ... and dozens more
```
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< summary > EarlyStopping< / summary >
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```python
es = EarlyStopping(monitor="val_loss")
trainer = Trainer(callbacks=[es])
```
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< summary > Checkpointing< / summary >
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```python
checkpointing = ModelCheckpoint(monitor="val_loss")
trainer = Trainer(callbacks=[checkpointing])
```
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< summary > Export to torchscript (JIT) (production use)< / summary >
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```python
# torchscript
autoencoder = LitAutoEncoder()
torch.jit.save(autoencoder.to_torchscript(), "model.pt")
```
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< summary > Export to ONNX (production use)< / summary >
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```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)
```
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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
class LitAutoEncoder(pl.LightningModule):
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def __init__ (self):
super().__init__()
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 = ...
self.manual_backward(loss_a, opt_a)
opt_a.step()
opt_a.zero_grad()
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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()
```
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______________________________________________________________________
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## Advantages over unstructured PyTorch
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- 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).
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______________________________________________________________________
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## Lightning Lite
< div align = "center" >
< img src = "docs/source/_static/images/lightning_lite/lite.gif" height = "200px" width = "600px" >
< / div >
In the Lighting 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.
______________________________________________________________________
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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 )
- [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 )
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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 )
- [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 )
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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 )
- [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.
### 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/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-pw5v393p-qRaDgEk24~EjiZNBpSQFgQ ).
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### Funding
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[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.
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______________________________________________________________________
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## Grid AI
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Grid AI is our platform for training models at scale on the cloud!
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**Sign up for our FREE community Tier [here ](https://www.grid.ai/pricing/ )**
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To use grid, take your regular command:
```
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python my_model.py --learning_rate 1e-6 --layers 2 --gpus 4
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```
And change it to use the grid train command:
```
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grid train --grid_gpus 4 my_model.py --learning_rate 'uniform(1e-6, 1e-1, 20)' --layers '[2, 4, 8, 16]'
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```
The above command will launch (20 * 4) experiments each running on 4 GPUs (320 GPUs!) - by making ZERO changes to
your code.
