632 lines
23 KiB
Markdown
632 lines
23 KiB
Markdown
<div align="center">
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<img alt="Lightning" src="https://pl-public-data.s3.amazonaws.com/assets_lightning/LightningColor.png" width="800px" style="max-width: 100%;">
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<br/>
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<br/>
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**The deep learning framework to pretrain, finetune and deploy AI models.**
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**NEW- Lightning 2.0 features a clean and stable API!!**
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______________________________________________________________________
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<p align="center">
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<a href="https://lightning.ai/">Lightning.ai</a> •
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<a href="https://lightning.ai/docs/pytorch/stable/">PyTorch Lightning</a> •
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<a href="https://lightning.ai/docs/fabric/stable/">Fabric</a> •
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<a href="https://lightning.ai/docs/app/stable/">Lightning Apps</a> •
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<a href="https://pytorch-lightning.readthedocs.io/en/stable/">Docs</a> •
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<a href="#community">Community</a> •
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<a href="https://lightning.ai/docs/pytorch/stable/generated/CONTRIBUTING.html">Contribute</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/lightning)
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[](https://codecov.io/gh/Lightning-AI/pytorch-lightning)
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[](https://discord.gg/VptPCZkGNa)
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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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## Install Lightning
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Simple installation from PyPI
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```bash
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pip install 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 lightning['extra']
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```
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#### Conda
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```bash
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conda install lightning -c conda-forge
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```
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#### Install stable version
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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/stable.zip -U
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```
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#### Install bleeding-edge
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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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______________________________________________________________________
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## Lightning has 3 core packages
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[PyTorch Lightning: Train and deploy PyTorch at scale](#pytorch-lightning-train-and-deploy-pytorch-at-scale).
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<br/>
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[Lightning Fabric: Expert control](#lightning-fabric-expert-control).
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<br/>
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[Lightning Apps: Build AI products and ML workflows](#lightning-apps-build-ai-products-and-ml-workflows).
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Lightning gives you granular control over how much abstraction you want to add over PyTorch.
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<div align="center">
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<img src="https://pl-public-data.s3.amazonaws.com/assets_lightning/continuum.png" width="80%">
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</div>
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______________________________________________________________________
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# PyTorch Lightning: Train and Deploy PyTorch at Scale
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PyTorch Lightning is just organized PyTorch - Lightning disentangles PyTorch code to decouple the science from the engineering.
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______________________________________________________________________
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### Hello simple model
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```python
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# main.py
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# ! pip install torchvision
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import torch, torch.nn as nn, torch.utils.data as data, torchvision as tv, torch.nn.functional as F
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import lightning as L
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# --------------------------------
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# Step 1: Define a LightningModule
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# --------------------------------
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# A LightningModule (nn.Module subclass) defines a full *system*
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# (ie: an LLM, diffusion model, autoencoder, or simple image classifier).
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class LitAutoEncoder(L.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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# Step 2: Define data
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# -------------------
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dataset = tv.datasets.MNIST(".", download=True, transform=tv.transforms.ToTensor())
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train, val = data.random_split(dataset, [55000, 5000])
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# -------------------
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# Step 3: Train
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# -------------------
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autoencoder = LitAutoEncoder()
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trainer = L.Trainer()
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trainer.fit(autoencoder, data.DataLoader(train), data.DataLoader(val))
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```
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Run the model on your terminal
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```bash
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pip install torchvision
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python main.py
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```
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______________________________________________________________________
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## Advanced features
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Lightning has over [40+ advanced features](https://lightning.ai/docs/pytorch/stable/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>Train on 1000s of GPUs without code changes</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(accelerator="gpu", devices=8)
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# 256 GPUs
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trainer = Trainer(accelerator="gpu", devices=8, num_nodes=32)
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```
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</details>
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<details>
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<summary>Train on other accelerators like 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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</details>
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<details>
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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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</details>
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<details>
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<summary>Experiment managers</summary>
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```python
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from 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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</details>
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<details>
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<summary>Early Stopping</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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</details>
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<details>
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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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</details>
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<details>
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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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</details>
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<details>
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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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______________________________________________________________________
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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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<div align="center">
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<a href="https://lightning.ai/docs/pytorch/stable/">Read the PyTorch Lightning docs</a>
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</div>
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______________________________________________________________________
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# Lightning Fabric: Expert control.
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Run on any device at any scale with expert-level control over PyTorch training loop and scaling strategy. You can even write your own Trainer.
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Fabric is designed for the most complex models like foundation model scaling, LLMs, diffusion, transformers, reinforcement learning, active learning. Of any size.
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<table>
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<tr>
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<th>What to change</th>
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<th>Resulting Fabric Code (copy me!)</th>
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</tr>
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<tr>
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<td>
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<sub>
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```diff
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+ import lightning as L
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import torch; import torchvision as tv
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dataset = tv.datasets.CIFAR10("data", download=True,
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train=True,
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transform=tv.transforms.ToTensor())
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+ fabric = L.Fabric()
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+ fabric.launch()
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model = tv.models.resnet18()
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optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
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- device = "cuda" if torch.cuda.is_available() else "cpu"
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- model.to(device)
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+ model, optimizer = fabric.setup(model, optimizer)
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dataloader = torch.utils.data.DataLoader(dataset, batch_size=8)
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+ dataloader = fabric.setup_dataloaders(dataloader)
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model.train()
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num_epochs = 10
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for epoch in range(num_epochs):
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for batch in dataloader:
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inputs, labels = batch
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- inputs, labels = inputs.to(device), labels.to(device)
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optimizer.zero_grad()
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outputs = model(inputs)
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loss = torch.nn.functional.cross_entropy(outputs, labels)
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- loss.backward()
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+ fabric.backward(loss)
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optimizer.step()
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print(loss.data)
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```
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</sub>
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<td>
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<sub>
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```Python
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import lightning as L
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import torch; import torchvision as tv
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dataset = tv.datasets.CIFAR10("data", download=True,
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train=True,
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transform=tv.transforms.ToTensor())
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fabric = L.Fabric()
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fabric.launch()
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model = tv.models.resnet18()
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optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
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model, optimizer = fabric.setup(model, optimizer)
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dataloader = torch.utils.data.DataLoader(dataset, batch_size=8)
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dataloader = fabric.setup_dataloaders(dataloader)
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model.train()
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num_epochs = 10
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for epoch in range(num_epochs):
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for batch in dataloader:
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inputs, labels = batch
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optimizer.zero_grad()
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outputs = model(inputs)
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loss = torch.nn.functional.cross_entropy(outputs, labels)
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fabric.backward(loss)
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optimizer.step()
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print(loss.data)
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```
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</sub>
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</td>
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</tr>
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</table>
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## Key features
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<details>
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<summary>Easily switch from running on CPU to GPU (Apple Silicon, CUDA, …), TPU, multi-GPU or even multi-node training</summary>
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```python
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# Use your available hardware
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# no code changes needed
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fabric = Fabric()
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# Run on GPUs (CUDA or MPS)
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fabric = Fabric(accelerator="gpu")
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# 8 GPUs
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fabric = Fabric(accelerator="gpu", devices=8)
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# 256 GPUs, multi-node
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fabric = Fabric(accelerator="gpu", devices=8, num_nodes=32)
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# Run on TPUs
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fabric = Fabric(accelerator="tpu")
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```
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</details>
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<details>
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<summary>Use state-of-the-art distributed training strategies (DDP, FSDP, DeepSpeed) and mixed precision out of the box</summary>
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```python
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# Use state-of-the-art distributed training techniques
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fabric = Fabric(strategy="ddp")
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fabric = Fabric(strategy="deepspeed")
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fabric = Fabric(strategy="fsdp")
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# Switch the precision
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fabric = Fabric(precision="16-mixed")
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fabric = Fabric(precision="64")
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```
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</details>
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<details>
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<summary>All the device logic boilerplate is handled for you</summary>
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```diff
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# no more of this!
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- model.to(device)
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- batch.to(device)
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```
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</details>
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<details>
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<summary>Build your own custom Trainer using Fabric primitives for training checkpointing, logging, and more</summary>
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```python
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import lightning as L
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class MyCustomTrainer:
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def __init__(self, accelerator="auto", strategy="auto", devices="auto", precision="32-true"):
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self.fabric = L.Fabric(accelerator=accelerator, strategy=strategy, devices=devices, precision=precision)
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def fit(self, model, optimizer, dataloader, max_epochs):
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self.fabric.launch()
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model, optimizer = self.fabric.setup(model, optimizer)
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dataloader = self.fabric.setup_dataloaders(dataloader)
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model.train()
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for epoch in range(max_epochs):
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for batch in dataloader:
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input, target = batch
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optimizer.zero_grad()
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output = model(input)
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loss = loss_fn(output, target)
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self.fabric.backward(loss)
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optimizer.step()
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```
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You can find a more extensive example in our [examples](examples/fabric/build_your_own_trainer)
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</details>
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______________________________________________________________________
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<div align="center">
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<a href="https://lightning.ai/docs/fabric/stable/">Read the Lightning Fabric docs</a>
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</div>
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______________________________________________________________________
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# Lightning Apps: Build AI products and ML workflows
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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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<div align="center">
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<img src="https://pl-public-data.s3.amazonaws.com/assets_lightning/lightning-apps-teaser.png" width="80%">
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</div>
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## Hello Lightning app world
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```python
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# app.py
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import lightning as L
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class TrainComponent(L.LightningWork):
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def run(self, x):
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print(f"train a model on {x}")
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class AnalyzeComponent(L.LightningWork):
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def run(self, x):
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print(f"analyze model on {x}")
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class WorkflowOrchestrator(L.LightningFlow):
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def __init__(self) -> None:
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super().__init__()
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self.train = TrainComponent(cloud_compute=L.CloudCompute("cpu"))
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self.analyze = AnalyzeComponent(cloud_compute=L.CloudCompute("gpu"))
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def run(self):
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self.train.run("CPU machine 1")
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self.analyze.run("GPU machine 2")
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app = L.LightningApp(WorkflowOrchestrator())
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```
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Run on the cloud or locally
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```bash
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# run on the cloud
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lightning run app app.py --setup --cloud
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# run locally
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lightning run app app.py
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```
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______________________________________________________________________
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<div align="center">
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<a href="https://lightning.ai/docs/app/stable/">Read the Lightning Apps docs</a>
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</div>
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______________________________________________________________________
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## Examples
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###### Self-supervised Learning
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- [CPC transforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#cpc-transforms)
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- [Moco v2 transforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#moco-v2-transforms)
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- [SimCLR transforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#simclr-transforms)
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###### Convolutional Architectures
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- [GPT-2](https://lightning-bolts.readthedocs.io/en/stable/models/convolutional.html#gpt-2)
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- [UNet](https://lightning-bolts.readthedocs.io/en/stable/models/convolutional.html#unet)
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###### Reinforcement Learning
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- [DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#dqn-loss)
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- [Double DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#double-dqn-loss)
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- [Per DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#per-dqn-loss)
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###### GANs
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- [Basic GAN](https://lightning-bolts.readthedocs.io/en/stable/models/gans.html#basic-gan)
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- [DCGAN](https://lightning-bolts.readthedocs.io/en/stable/models/gans.html#dcgan)
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###### Classic ML
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- [Logistic Regression](https://lightning-bolts.readthedocs.io/en/stable/models/classic_ml.html#logistic-regression)
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- [Linear Regression](https://lightning-bolts.readthedocs.io/en/stable/models/classic_ml.html#linear-regression)
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______________________________________________________________________
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## Continuous Integration
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Lightning is rigorously tested across multiple CPUs, GPUs and TPUs and against major Python and PyTorch versions.
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###### \*Codecov is > 90%+ but build delays may show less
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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. | 1.12 | 1.13 | 2.0 | 2.1 |
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| :--------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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| Linux py3.9 \[GPUs\] | | | | [](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=24&branchName=master) |
|
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| Linux py3.9 \[TPUs\] | | | [](https://github.com/Lightning-AI/lightning/actions/workflows/tpu-tests.yml) | |
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| Linux (multiple Python versions) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) |
|
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| OSX (multiple Python versions) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) |
|
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| Windows (multiple Python versions) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) |
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</center>
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</details>
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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://lightning.ai/docs/pytorch/latest/community/governance.html) who are all a mix of professional engineers, Research Scientists, and Ph.D. students from top AI labs.
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- 800+ 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://lightning.ai/docs/pytorch/stable/generated/CONTRIBUTING.html)
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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://lightning.ai/docs).
|
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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 discord](https://discord.com/invite/tfXFetEZxv).
|