633 lines
25 KiB
Markdown
633 lines
25 KiB
Markdown
<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 train, deploy, and ship AI products Lightning fast.**
|
|
|
|
**NEW- Lightning 2.0 is featuring 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/stable/">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/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
|
|
```
|
|
|
|
<!-- following section will be skipped from PyPI description -->
|
|
|
|
<details>
|
|
<summary>Other installation options</summary>
|
|
<!-- following section will be skipped from PyPI description -->
|
|
|
|
#### Install with optional dependencies
|
|
|
|
```bash
|
|
pip install lightning['extra']
|
|
```
|
|
|
|
#### Conda
|
|
|
|
```bash
|
|
conda install lightning -c conda-forge
|
|
```
|
|
|
|
#### Install stable version
|
|
|
|
Install future release from the source
|
|
|
|
```bash
|
|
pip install https://github.com/Lightning-AI/lightning/archive/refs/heads/release/stable.zip -U
|
|
```
|
|
|
|
#### Install bleeding-edge
|
|
|
|
Install nightly from the source (no guarantees)
|
|
|
|
```bash
|
|
pip install https://github.com/Lightning-AI/lightning/archive/refs/heads/master.zip -U
|
|
```
|
|
|
|
or from testing PyPI
|
|
|
|
```bash
|
|
pip install -iU https://test.pypi.org/simple/ pytorch-lightning
|
|
```
|
|
|
|
</details>
|
|
<!-- end skipping PyPI description -->
|
|
|
|
______________________________________________________________________
|
|
|
|
## 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, TPUs, IPUs, and HPUs 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. | 1.12 | 1.13 | 2.0 | 2.1 |
|
|
| :--------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
|
|
| Linux py3.9 \[GPUs\] | [?branchName=master>)](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=24&branchName=master) | [?branchName=master>)](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=24&branchName=master) | [?branchName=master>)](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=24&branchName=master) | [?branchName=master>)](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 py3.8 \[IPUs\] | - | [?branchName=master>)](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=25&branchName=master) | [?branchName=master>)](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=25&branchName=master) | [?branchName=master>)](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=25&branchName=master) |
|
|
| 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) |
|
|
| 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) |
|
|
| 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) |
|
|
|
|
</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).
|