<div align="center">
<img alt="Lightning" src="https://pl-public-data.s3.amazonaws.com/assets_lightning/LightningColor.png" width="800px" style="max-width: 100%;">
<br/>
<br/>
**The deep learning framework to pretrain, finetune and deploy AI models.**
**NEW- Lightning 2.0 features a clean and stable API!!**
______________________________________________________________________
<p align="center">
<a href="https://lightning.ai/">Lightning.ai</a> •
<a href="https://lightning.ai/docs/pytorch/stable/">PyTorch Lightning</a> •
<a href="https://lightning.ai/docs/fabric/stable/">Fabric</a> •
<a href="https://lightning.ai/docs/app/stable/">Lightning Apps</a> •
<a href="https://pytorch-lightning.readthedocs.io/en/2.2.4">Docs</a> •
<a href="#community">Community</a> •
<a href="https://lightning.ai/docs/pytorch/stable/generated/CONTRIBUTING.html">Contribute</a> •
</p>
<!-- DO NOT ADD CONDA DOWNLOADS... README CHANGES MUST BE APPROVED BY EDEN OR WILL -->
[](https://pypi.org/project/pytorch-lightning/)
[](https://badge.fury.io/py/pytorch-lightning)
[](https://pepy.tech/project/pytorch-lightning)
[](https://anaconda.org/conda-forge/lightning)
[](https://codecov.io/gh/Lightning-AI/pytorch-lightning)
[](https://discord.gg/VptPCZkGNa)
[](https://github.com/Lightning-AI/lightning/blob/master/LICENSE)
<!--
[](https://www.codefactor.io/repository/github/Lightning-AI/lightning)
-->
</div>
## Install Lightning
Simple installation from PyPI
```bash
pip install lightning
```
<!-- -->
______________________________________________________________________
## Lightning has 3 core packages
[PyTorch Lightning: Train and deploy PyTorch at scale](#pytorch-lightning-train-and-deploy-pytorch-at-scale).
<br/>
[Lightning Fabric: Expert control](#lightning-fabric-expert-control).
<br/>
[Lightning Apps: Build AI products and ML workflows](#lightning-apps-build-ai-products-and-ml-workflows).
Lightning gives you granular control over how much abstraction you want to add over PyTorch.
<div align="center">
<img src="https://pl-public-data.s3.amazonaws.com/assets_lightning/continuum.png" width="80%">
</div>
______________________________________________________________________
# PyTorch Lightning: Train and Deploy PyTorch at Scale
PyTorch Lightning is just organized PyTorch - Lightning disentangles PyTorch code to decouple the science from the engineering.
______________________________________________________________________
### Hello simple model
```python
# main.py
# ! pip install torchvision
import torch, torch.nn as nn, torch.utils.data as data, torchvision as tv, torch.nn.functional as F
import lightning as L
# --------------------------------
# Step 1: Define a LightningModule
# --------------------------------
# A LightningModule (nn.Module subclass) defines a full *system*
# (ie: an LLM, diffusion model, autoencoder, or simple image classifier).
class LitAutoEncoder(L.LightningModule):
def __init__(self):
super().__init__()
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))
def forward(self, x):
# in lightning, forward defines the prediction/inference actions
embedding = self.encoder(x)
return embedding
def training_step(self, batch, batch_idx):
# training_step defines the train loop. It is independent of forward
x, y = batch
x = x.view(x.size(0), -1)
z = self.encoder(x)
x_hat = self.decoder(z)
loss = F.mse_loss(x_hat, x)
self.log("train_loss", loss)
return loss
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
return optimizer
# -------------------
# Step 2: Define data
# -------------------
dataset = tv.datasets.MNIST(".", download=True, transform=tv.transforms.ToTensor())
train, val = data.random_split(dataset, [55000, 5000])
# -------------------
# Step 3: Train
# -------------------
autoencoder = LitAutoEncoder()
trainer = L.Trainer()
trainer.fit(autoencoder, data.DataLoader(train), data.DataLoader(val))
```
Run the model on your terminal
```bash
pip install torchvision
python main.py
```
______________________________________________________________________
## Advanced features
Lightning has over [40+ advanced features](https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-flags) designed for professional AI research at scale.
Here are some examples:
<div align="center">
<img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/features_2.jpg" max-height="600px">
</div>
<details>
<summary>Train on 1000s of GPUs without code changes</summary>
```python
# 8 GPUs
# no code changes needed
trainer = Trainer(accelerator="gpu", devices=8)
# 256 GPUs
trainer = Trainer(accelerator="gpu", devices=8, num_nodes=32)
```
</details>
<details>
<summary>Train on other accelerators like TPUs without code changes</summary>
```python
# no code changes needed
trainer = Trainer(accelerator="tpu", devices=8)
```
</details>
<details>
<summary>16-bit precision</summary>
```python
# no code changes needed
trainer = Trainer(precision=16)
```
</details>
<details>
<summary>Experiment managers</summary>
```python
from lightning import loggers
# tensorboard
trainer = Trainer(logger=TensorBoardLogger("logs/"))
# weights and biases
trainer = Trainer(logger=loggers.WandbLogger())
# comet
trainer = Trainer(logger=loggers.CometLogger())
# mlflow
trainer = Trainer(logger=loggers.MLFlowLogger())
# neptune
trainer = Trainer(logger=loggers.NeptuneLogger())
# ... and dozens more
```
</details>
<details>
<summary>Early Stopping</summary>
```python
es = EarlyStopping(monitor="val_loss")
trainer = Trainer(callbacks=[es])
```
</details>
<details>
<summary>Checkpointing</summary>
```python
checkpointing = ModelCheckpoint(monitor="val_loss")
trainer = Trainer(callbacks=[checkpointing])
```
</details>
<details>
<summary>Export to torchscript (JIT) (production use)</summary>
```python
# torchscript
autoencoder = LitAutoEncoder()
torch.jit.save(autoencoder.to_torchscript(), "model.pt")
```
</details>
<details>
<summary>Export to ONNX (production use)</summary>
```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)
```
</details>
______________________________________________________________________
## Advantages over unstructured PyTorch
- 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/Lightning-AI/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).
______________________________________________________________________
<div align="center">
<a href="https://lightning.ai/docs/pytorch/stable/">Read the PyTorch Lightning docs</a>
</div>
______________________________________________________________________
# Lightning Fabric: Expert control.
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.
Fabric is designed for the most complex models like foundation model scaling, LLMs, diffusion, transformers, reinforcement learning, active learning. Of any size.
<table>
<tr>
<th>What to change</th>
<th>Resulting Fabric Code (copy me!)</th>
</tr>
<tr>
<td>
<sub>
```diff
+ import lightning as L
import torch; import torchvision as tv
dataset = tv.datasets.CIFAR10("data", download=True,
train=True,
transform=tv.transforms.ToTensor())
+ fabric = L.Fabric()
+ fabric.launch()
model = tv.models.resnet18()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
- device = "cuda" if torch.cuda.is_available() else "cpu"
- model.to(device)
+ model, optimizer = fabric.setup(model, optimizer)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=8)
+ dataloader = fabric.setup_dataloaders(dataloader)
model.train()
num_epochs = 10
for epoch in range(num_epochs):
for batch in dataloader:
inputs, labels = batch
- inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = torch.nn.functional.cross_entropy(outputs, labels)
- loss.backward()
+ fabric.backward(loss)
optimizer.step()
print(loss.data)
```
</sub>
<td>
<sub>
```Python
import lightning as L
import torch; import torchvision as tv
dataset = tv.datasets.CIFAR10("data", download=True,
train=True,
transform=tv.transforms.ToTensor())
fabric = L.Fabric()
fabric.launch()
model = tv.models.resnet18()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
model, optimizer = fabric.setup(model, optimizer)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=8)
dataloader = fabric.setup_dataloaders(dataloader)
model.train()
num_epochs = 10
for epoch in range(num_epochs):
for batch in dataloader:
inputs, labels = batch
optimizer.zero_grad()
outputs = model(inputs)
loss = torch.nn.functional.cross_entropy(outputs, labels)
fabric.backward(loss)
optimizer.step()
print(loss.data)
```
</sub>
</td>
</tr>
</table>
## Key features
<details>
<summary>Easily switch from running on CPU to GPU (Apple Silicon, CUDA, …), TPU, multi-GPU or even multi-node training</summary>
```python
# Use your available hardware
# no code changes needed
fabric = Fabric()
# Run on GPUs (CUDA or MPS)
fabric = Fabric(accelerator="gpu")
# 8 GPUs
fabric = Fabric(accelerator="gpu", devices=8)
# 256 GPUs, multi-node
fabric = Fabric(accelerator="gpu", devices=8, num_nodes=32)
# Run on TPUs
fabric = Fabric(accelerator="tpu")
```
</details>
<details>
<summary>Use state-of-the-art distributed training strategies (DDP, FSDP, DeepSpeed) and mixed precision out of the box</summary>
```python
# Use state-of-the-art distributed training techniques
fabric = Fabric(strategy="ddp")
fabric = Fabric(strategy="deepspeed")
fabric = Fabric(strategy="fsdp")
# Switch the precision
fabric = Fabric(precision="16-mixed")
fabric = Fabric(precision="64")
```
</details>
<details>
<summary>All the device logic boilerplate is handled for you</summary>
```diff
# no more of this!
- model.to(device)
- batch.to(device)
```
</details>
<details>
<summary>Build your own custom Trainer using Fabric primitives for training checkpointing, logging, and more</summary>
```python
import lightning as L
class MyCustomTrainer:
def __init__(self, accelerator="auto", strategy="auto", devices="auto", precision="32-true"):
self.fabric = L.Fabric(accelerator=accelerator, strategy=strategy, devices=devices, precision=precision)
def fit(self, model, optimizer, dataloader, max_epochs):
self.fabric.launch()
model, optimizer = self.fabric.setup(model, optimizer)
dataloader = self.fabric.setup_dataloaders(dataloader)
model.train()
for epoch in range(max_epochs):
for batch in dataloader:
input, target = batch
optimizer.zero_grad()
output = model(input)
loss = loss_fn(output, target)
self.fabric.backward(loss)
optimizer.step()
```
You can find a more extensive example in our [examples](examples/fabric/build_your_own_trainer)
</details>
______________________________________________________________________
<div align="center">
<a href="https://lightning.ai/docs/fabric/stable/">Read the Lightning Fabric docs</a>
</div>
______________________________________________________________________
# Lightning Apps: Build AI products and ML workflows
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.
<div align="center">
<img src="https://pl-public-data.s3.amazonaws.com/assets_lightning/lightning-apps-teaser.png" width="80%">
</div>
## Hello Lightning app world
```python
# app.py
import lightning as L
class TrainComponent(L.LightningWork):
def run(self, x):
print(f"train a model on {x}")
class AnalyzeComponent(L.LightningWork):
def run(self, x):
print(f"analyze model on {x}")
class WorkflowOrchestrator(L.LightningFlow):
def __init__(self) -> None:
super().__init__()
self.train = TrainComponent(cloud_compute=L.CloudCompute("cpu"))
self.analyze = AnalyzeComponent(cloud_compute=L.CloudCompute("gpu"))
def run(self):
self.train.run("CPU machine 1")
self.analyze.run("GPU machine 2")
app = L.LightningApp(WorkflowOrchestrator())
```
Run on the cloud or locally
```bash
# run on the cloud
lightning run app app.py --setup --cloud
# run locally
lightning run app app.py
```
______________________________________________________________________
<div align="center">
<a href="https://lightning.ai/docs/app/stable/">Read the Lightning Apps docs</a>
</div>
______________________________________________________________________
## Examples
###### Self-supervised Learning
- [CPC transforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#cpc-transforms)
- [Moco v2 transforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#moco-v2-transforms)
- [SimCLR transforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#simclr-transforms)
###### Convolutional Architectures
- [GPT-2](https://lightning-bolts.readthedocs.io/en/stable/models/convolutional.html#gpt-2)
- [UNet](https://lightning-bolts.readthedocs.io/en/stable/models/convolutional.html#unet)
###### Reinforcement Learning
- [DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#dqn-loss)
- [Double DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#double-dqn-loss)
- [Per DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#per-dqn-loss)
###### GANs
- [Basic GAN](https://lightning-bolts.readthedocs.io/en/stable/models/gans.html#basic-gan)
- [DCGAN](https://lightning-bolts.readthedocs.io/en/stable/models/gans.html#dcgan)
###### Classic ML
- [Logistic Regression](https://lightning-bolts.readthedocs.io/en/stable/models/classic_ml.html#logistic-regression)
- [Linear Regression](https://lightning-bolts.readthedocs.io/en/stable/models/classic_ml.html#linear-regression)
______________________________________________________________________
## Continuous Integration
Lightning is rigorously tested across multiple CPUs, GPUs and TPUs and against major Python and PyTorch versions.
###### \*Codecov is > 90%+ but build delays may show less
<details>
<summary>Current build statuses</summary>
<center>
| System / PyTorch ver. | 1.13 | 2.0 | 2.1 |
| :--------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| Linux py3.9 \[GPUs\] | | | [](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=24&branchName=master) |
| Linux py3.9 \[TPUs\] | | [](https://github.com/Lightning-AI/lightning/actions/workflows/tpu-tests.yml) | |
| 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) |
| 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) |
| 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) |
</center>
</details>
______________________________________________________________________
## Community
The lightning community is maintained by
- [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.
- 800+ community contributors.
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)
Lightning is also part of the [PyTorch ecosystem](https://pytorch.org/ecosystem/) which requires projects to have solid testing, documentation and support.
### Asking for help
If you have any questions please:
1. [Read the docs](https://lightning.ai/docs).
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)
1. [Join our discord](https://discord.com/invite/tfXFetEZxv).
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"description": "<div align=\"center\">\n\n<img alt=\"Lightning\" src=\"https://pl-public-data.s3.amazonaws.com/assets_lightning/LightningColor.png\" width=\"800px\" style=\"max-width: 100%;\">\n\n<br/>\n<br/>\n\n**The deep learning framework to pretrain, finetune and deploy AI models.**\n\n**NEW- Lightning 2.0 features a clean and stable API!!**\n\n______________________________________________________________________\n\n<p align=\"center\">\n <a href=\"https://lightning.ai/\">Lightning.ai</a> \u2022\n <a href=\"https://lightning.ai/docs/pytorch/stable/\">PyTorch Lightning</a> \u2022\n <a href=\"https://lightning.ai/docs/fabric/stable/\">Fabric</a> \u2022\n <a href=\"https://lightning.ai/docs/app/stable/\">Lightning Apps</a> \u2022\n <a href=\"https://pytorch-lightning.readthedocs.io/en/2.2.4\">Docs</a> \u2022\n <a href=\"#community\">Community</a> \u2022\n <a href=\"https://lightning.ai/docs/pytorch/stable/generated/CONTRIBUTING.html\">Contribute</a> \u2022\n</p>\n\n<!-- DO NOT ADD CONDA DOWNLOADS... README CHANGES MUST BE APPROVED BY EDEN OR WILL -->\n\n[](https://pypi.org/project/pytorch-lightning/)\n[](https://badge.fury.io/py/pytorch-lightning)\n[](https://pepy.tech/project/pytorch-lightning)\n[](https://anaconda.org/conda-forge/lightning)\n[](https://codecov.io/gh/Lightning-AI/pytorch-lightning)\n\n[](https://discord.gg/VptPCZkGNa)\n\n[](https://github.com/Lightning-AI/lightning/blob/master/LICENSE)\n\n<!--\n[](https://www.codefactor.io/repository/github/Lightning-AI/lightning)\n-->\n\n</div>\n\n## Install Lightning\n\nSimple installation from PyPI\n\n```bash\npip install lightning\n```\n\n<!-- -->\n\n______________________________________________________________________\n\n## Lightning has 3 core packages\n\n[PyTorch Lightning: Train and deploy PyTorch at scale](#pytorch-lightning-train-and-deploy-pytorch-at-scale).\n<br/>\n[Lightning Fabric: Expert control](#lightning-fabric-expert-control).\n<br/>\n[Lightning Apps: Build AI products and ML workflows](#lightning-apps-build-ai-products-and-ml-workflows).\n\nLightning gives you granular control over how much abstraction you want to add over PyTorch.\n\n<div align=\"center\">\n <img src=\"https://pl-public-data.s3.amazonaws.com/assets_lightning/continuum.png\" width=\"80%\">\n</div>\n\n______________________________________________________________________\n\n# PyTorch Lightning: Train and Deploy PyTorch at Scale\n\nPyTorch Lightning is just organized PyTorch - Lightning disentangles PyTorch code to decouple the science from the engineering.\n\n\n\n______________________________________________________________________\n\n### Hello simple model\n\n```python\n# main.py\n# ! pip install torchvision\nimport torch, torch.nn as nn, torch.utils.data as data, torchvision as tv, torch.nn.functional as F\nimport lightning as L\n\n# --------------------------------\n# Step 1: Define a LightningModule\n# --------------------------------\n# A LightningModule (nn.Module subclass) defines a full *system*\n# (ie: an LLM, diffusion model, autoencoder, or simple image classifier).\n\n\nclass LitAutoEncoder(L.LightningModule):\n def __init__(self):\n super().__init__()\n self.encoder = nn.Sequential(nn.Linear(28 * 28, 128), nn.ReLU(), nn.Linear(128, 3))\n self.decoder = nn.Sequential(nn.Linear(3, 128), nn.ReLU(), nn.Linear(128, 28 * 28))\n\n def forward(self, x):\n # in lightning, forward defines the prediction/inference actions\n embedding = self.encoder(x)\n return embedding\n\n def training_step(self, batch, batch_idx):\n # training_step defines the train loop. It is independent of forward\n x, y = batch\n x = x.view(x.size(0), -1)\n z = self.encoder(x)\n x_hat = self.decoder(z)\n loss = F.mse_loss(x_hat, x)\n self.log(\"train_loss\", loss)\n return loss\n\n def configure_optimizers(self):\n optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)\n return optimizer\n\n\n# -------------------\n# Step 2: Define data\n# -------------------\ndataset = tv.datasets.MNIST(\".\", download=True, transform=tv.transforms.ToTensor())\ntrain, val = data.random_split(dataset, [55000, 5000])\n\n# -------------------\n# Step 3: Train\n# -------------------\nautoencoder = LitAutoEncoder()\ntrainer = L.Trainer()\ntrainer.fit(autoencoder, data.DataLoader(train), data.DataLoader(val))\n```\n\nRun the model on your terminal\n\n```bash\npip install torchvision\npython main.py\n```\n\n______________________________________________________________________\n\n## Advanced features\n\nLightning has over [40+ advanced features](https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-flags) designed for professional AI research at scale.\n\nHere are some examples:\n\n<div align=\"center\">\n <img src=\"https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/features_2.jpg\" max-height=\"600px\">\n </div>\n\n<details>\n <summary>Train on 1000s of GPUs without code changes</summary>\n\n```python\n# 8 GPUs\n# no code changes needed\ntrainer = Trainer(accelerator=\"gpu\", devices=8)\n\n# 256 GPUs\ntrainer = Trainer(accelerator=\"gpu\", devices=8, num_nodes=32)\n```\n\n</details>\n\n<details>\n <summary>Train on other accelerators like TPUs without code changes</summary>\n\n```python\n# no code changes needed\ntrainer = Trainer(accelerator=\"tpu\", devices=8)\n```\n\n</details>\n\n<details>\n <summary>16-bit precision</summary>\n\n```python\n# no code changes needed\ntrainer = Trainer(precision=16)\n```\n\n</details>\n\n<details>\n <summary>Experiment managers</summary>\n\n```python\nfrom lightning import loggers\n\n# tensorboard\ntrainer = Trainer(logger=TensorBoardLogger(\"logs/\"))\n\n# weights and biases\ntrainer = Trainer(logger=loggers.WandbLogger())\n\n# comet\ntrainer = Trainer(logger=loggers.CometLogger())\n\n# mlflow\ntrainer = Trainer(logger=loggers.MLFlowLogger())\n\n# neptune\ntrainer = Trainer(logger=loggers.NeptuneLogger())\n\n# ... and dozens more\n```\n\n</details>\n\n<details>\n\n<summary>Early Stopping</summary>\n\n```python\nes = EarlyStopping(monitor=\"val_loss\")\ntrainer = Trainer(callbacks=[es])\n```\n\n</details>\n\n<details>\n <summary>Checkpointing</summary>\n\n```python\ncheckpointing = ModelCheckpoint(monitor=\"val_loss\")\ntrainer = Trainer(callbacks=[checkpointing])\n```\n\n</details>\n\n<details>\n <summary>Export to torchscript (JIT) (production use)</summary>\n\n```python\n# torchscript\nautoencoder = LitAutoEncoder()\ntorch.jit.save(autoencoder.to_torchscript(), \"model.pt\")\n```\n\n</details>\n\n<details>\n <summary>Export to ONNX (production use)</summary>\n\n```python\n# onnx\nwith tempfile.NamedTemporaryFile(suffix=\".onnx\", delete=False) as tmpfile:\n autoencoder = LitAutoEncoder()\n input_sample = torch.randn((1, 64))\n autoencoder.to_onnx(tmpfile.name, input_sample, export_params=True)\n os.path.isfile(tmpfile.name)\n```\n\n</details>\n\n______________________________________________________________________\n\n## Advantages over unstructured PyTorch\n\n- Models become hardware agnostic\n- Code is clear to read because engineering code is abstracted away\n- Easier to reproduce\n- Make fewer mistakes because lightning handles the tricky engineering\n- Keeps all the flexibility (LightningModules are still PyTorch modules), but removes a ton of boilerplate\n- Lightning has dozens of integrations with popular machine learning tools.\n- [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.\n- Minimal running speed overhead (about 300 ms per epoch compared with pure PyTorch).\n\n______________________________________________________________________\n\n<div align=\"center\">\n <a href=\"https://lightning.ai/docs/pytorch/stable/\">Read the PyTorch Lightning docs</a>\n</div>\n\n______________________________________________________________________\n\n# Lightning Fabric: Expert control.\n\nRun on any device at any scale with expert-level control over PyTorch training loop and scaling strategy. You can even write your own Trainer.\n\nFabric is designed for the most complex models like foundation model scaling, LLMs, diffusion, transformers, reinforcement learning, active learning. Of any size.\n\n<table>\n<tr>\n<th>What to change</th>\n<th>Resulting Fabric Code (copy me!)</th>\n</tr>\n<tr>\n<td>\n<sub>\n\n```diff\n+ import lightning as L\n import torch; import torchvision as tv\n\n dataset = tv.datasets.CIFAR10(\"data\", download=True,\n train=True,\n transform=tv.transforms.ToTensor())\n\n+ fabric = L.Fabric()\n+ fabric.launch()\n\n model = tv.models.resnet18()\n optimizer = torch.optim.SGD(model.parameters(), lr=0.001)\n- device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n- model.to(device)\n+ model, optimizer = fabric.setup(model, optimizer)\n\n dataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\n+ dataloader = fabric.setup_dataloaders(dataloader)\n\n model.train()\n num_epochs = 10\n for epoch in range(num_epochs):\n for batch in dataloader:\n inputs, labels = batch\n- inputs, labels = inputs.to(device), labels.to(device)\n optimizer.zero_grad()\n outputs = model(inputs)\n loss = torch.nn.functional.cross_entropy(outputs, labels)\n- loss.backward()\n+ fabric.backward(loss)\n optimizer.step()\n print(loss.data)\n```\n\n</sub>\n<td>\n<sub>\n\n```Python\nimport lightning as L\nimport torch; import torchvision as tv\n\ndataset = tv.datasets.CIFAR10(\"data\", download=True,\n train=True,\n transform=tv.transforms.ToTensor())\n\nfabric = L.Fabric()\nfabric.launch()\n\nmodel = tv.models.resnet18()\noptimizer = torch.optim.SGD(model.parameters(), lr=0.001)\nmodel, optimizer = fabric.setup(model, optimizer)\n\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\ndataloader = fabric.setup_dataloaders(dataloader)\n\nmodel.train()\nnum_epochs = 10\nfor epoch in range(num_epochs):\n for batch in dataloader:\n inputs, labels = batch\n optimizer.zero_grad()\n outputs = model(inputs)\n loss = torch.nn.functional.cross_entropy(outputs, labels)\n fabric.backward(loss)\n optimizer.step()\n print(loss.data)\n```\n\n</sub>\n</td>\n</tr>\n</table>\n\n## Key features\n\n<details>\n <summary>Easily switch from running on CPU to GPU (Apple Silicon, CUDA, \u2026), TPU, multi-GPU or even multi-node training</summary>\n\n```python\n# Use your available hardware\n# no code changes needed\nfabric = Fabric()\n\n# Run on GPUs (CUDA or MPS)\nfabric = Fabric(accelerator=\"gpu\")\n\n# 8 GPUs\nfabric = Fabric(accelerator=\"gpu\", devices=8)\n\n# 256 GPUs, multi-node\nfabric = Fabric(accelerator=\"gpu\", devices=8, num_nodes=32)\n\n# Run on TPUs\nfabric = Fabric(accelerator=\"tpu\")\n```\n\n</details>\n\n<details>\n <summary>Use state-of-the-art distributed training strategies (DDP, FSDP, DeepSpeed) and mixed precision out of the box</summary>\n\n```python\n# Use state-of-the-art distributed training techniques\nfabric = Fabric(strategy=\"ddp\")\nfabric = Fabric(strategy=\"deepspeed\")\nfabric = Fabric(strategy=\"fsdp\")\n\n# Switch the precision\nfabric = Fabric(precision=\"16-mixed\")\nfabric = Fabric(precision=\"64\")\n```\n\n</details>\n\n<details>\n <summary>All the device logic boilerplate is handled for you</summary>\n\n```diff\n # no more of this!\n- model.to(device)\n- batch.to(device)\n```\n\n</details>\n\n<details>\n <summary>Build your own custom Trainer using Fabric primitives for training checkpointing, logging, and more</summary>\n\n```python\nimport lightning as L\n\n\nclass MyCustomTrainer:\n def __init__(self, accelerator=\"auto\", strategy=\"auto\", devices=\"auto\", precision=\"32-true\"):\n self.fabric = L.Fabric(accelerator=accelerator, strategy=strategy, devices=devices, precision=precision)\n\n def fit(self, model, optimizer, dataloader, max_epochs):\n self.fabric.launch()\n\n model, optimizer = self.fabric.setup(model, optimizer)\n dataloader = self.fabric.setup_dataloaders(dataloader)\n model.train()\n\n for epoch in range(max_epochs):\n for batch in dataloader:\n input, target = batch\n optimizer.zero_grad()\n output = model(input)\n loss = loss_fn(output, target)\n self.fabric.backward(loss)\n optimizer.step()\n```\n\nYou can find a more extensive example in our [examples](examples/fabric/build_your_own_trainer)\n\n</details>\n\n______________________________________________________________________\n\n<div align=\"center\">\n <a href=\"https://lightning.ai/docs/fabric/stable/\">Read the Lightning Fabric docs</a>\n</div>\n\n______________________________________________________________________\n\n# Lightning Apps: Build AI products and ML workflows\n\nLightning 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.\n\n<div align=\"center\">\n <img src=\"https://pl-public-data.s3.amazonaws.com/assets_lightning/lightning-apps-teaser.png\" width=\"80%\">\n</div>\n\n## Hello Lightning app world\n\n```python\n# app.py\nimport lightning as L\n\n\nclass TrainComponent(L.LightningWork):\n def run(self, x):\n print(f\"train a model on {x}\")\n\n\nclass AnalyzeComponent(L.LightningWork):\n def run(self, x):\n print(f\"analyze model on {x}\")\n\n\nclass WorkflowOrchestrator(L.LightningFlow):\n def __init__(self) -> None:\n super().__init__()\n self.train = TrainComponent(cloud_compute=L.CloudCompute(\"cpu\"))\n self.analyze = AnalyzeComponent(cloud_compute=L.CloudCompute(\"gpu\"))\n\n def run(self):\n self.train.run(\"CPU machine 1\")\n self.analyze.run(\"GPU machine 2\")\n\n\napp = L.LightningApp(WorkflowOrchestrator())\n```\n\nRun on the cloud or locally\n\n```bash\n# run on the cloud\nlightning run app app.py --setup --cloud\n\n# run locally\nlightning run app app.py\n```\n\n______________________________________________________________________\n\n<div align=\"center\">\n <a href=\"https://lightning.ai/docs/app/stable/\">Read the Lightning Apps docs</a>\n</div>\n\n______________________________________________________________________\n\n## Examples\n\n###### Self-supervised Learning\n\n- [CPC transforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#cpc-transforms)\n- [Moco v2 transforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#moco-v2-transforms)\n- [SimCLR transforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#simclr-transforms)\n\n###### Convolutional Architectures\n\n- [GPT-2](https://lightning-bolts.readthedocs.io/en/stable/models/convolutional.html#gpt-2)\n- [UNet](https://lightning-bolts.readthedocs.io/en/stable/models/convolutional.html#unet)\n\n###### Reinforcement Learning\n\n- [DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#dqn-loss)\n- [Double DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#double-dqn-loss)\n- [Per DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#per-dqn-loss)\n\n###### GANs\n\n- [Basic GAN](https://lightning-bolts.readthedocs.io/en/stable/models/gans.html#basic-gan)\n- [DCGAN](https://lightning-bolts.readthedocs.io/en/stable/models/gans.html#dcgan)\n\n###### Classic ML\n\n- [Logistic Regression](https://lightning-bolts.readthedocs.io/en/stable/models/classic_ml.html#logistic-regression)\n- [Linear Regression](https://lightning-bolts.readthedocs.io/en/stable/models/classic_ml.html#linear-regression)\n\n______________________________________________________________________\n\n## Continuous Integration\n\nLightning is rigorously tested across multiple CPUs, GPUs and TPUs and against major Python and PyTorch versions.\n\n###### \\*Codecov is > 90%+ but build delays may show less\n\n<details>\n <summary>Current build statuses</summary>\n\n<center>\n\n| System / PyTorch ver. | 1.13 | 2.0 | 2.1 |\n| :--------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|\n| Linux py3.9 \\[GPUs\\] | | | [](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=24&branchName=master) |\n| Linux py3.9 \\[TPUs\\] | | [](https://github.com/Lightning-AI/lightning/actions/workflows/tpu-tests.yml) | |\n| 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) |\n| 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) |\n| 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) |\n\n</center>\n</details>\n\n______________________________________________________________________\n\n## Community\n\nThe lightning community is maintained by\n\n- [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.\n- 800+ community contributors.\n\nWant 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)\n\nLightning is also part of the [PyTorch ecosystem](https://pytorch.org/ecosystem/) which requires projects to have solid testing, documentation and support.\n\n### Asking for help\n\nIf you have any questions please:\n\n1. [Read the docs](https://lightning.ai/docs).\n1. [Search through existing Discussions](https://github.com/Lightning-AI/lightning/discussions), or [add a new question](https://github.com/Lightning-AI/lightning/discussions/new)\n1. [Join our discord](https://discord.com/invite/tfXFetEZxv).\n\n\n",
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