443 lines
23 KiB
Markdown
443 lines
23 KiB
Markdown
<div align="center">
|
|
|
|
<img src="docs/source/_static/images/logo.png" width="400px">
|
|
|
|
**The lightweight PyTorch wrapper for high-performance AI research.
|
|
Scale your models, not the boilerplate.**
|
|
|
|
______________________________________________________________________
|
|
|
|
<p align="center">
|
|
<a href="https://www.pytorchlightning.ai/">Website</a> •
|
|
<a href="#key-features">Key Features</a> •
|
|
<a href="#how-to-use">How To Use</a> •
|
|
<a href="https://pytorch-lightning.readthedocs.io/en/stable/">Docs</a> •
|
|
<a href="#examples">Examples</a> •
|
|
<a href="#community">Community</a> •
|
|
<a href="#grid-ai">Grid AI</a> •
|
|
<a href="#license">License</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/pytorch-lightning)
|
|
[](https://hub.docker.com/r/pytorchlightning/pytorch_lightning)
|
|
[](https://codecov.io/gh/PyTorchLightning/pytorch-lightning)
|
|
|
|
[](https://pytorch-lightning.readthedocs.io/en/stable/)
|
|
[](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-pw5v393p-qRaDgEk24~EjiZNBpSQFgQ)
|
|
[](https://github.com/PytorchLightning/pytorch-lightning/blob/master/LICENSE)
|
|
|
|
<!--
|
|
[](https://www.codefactor.io/repository/github/pytorchlightning/pytorch-lightning)
|
|
-->
|
|
|
|
</div>
|
|
|
|
###### \*Codecov is > 90%+ but build delays may show less
|
|
|
|
______________________________________________________________________
|
|
|
|
## PyTorch Lightning is just organized PyTorch
|
|
|
|
Lightning disentangles PyTorch code to decouple the science from the engineering.
|
|
|
|
|
|
______________________________________________________________________
|
|
|
|
## Lightning Design Philosophy
|
|
|
|
Lightning structures PyTorch code with these principles:
|
|
|
|
<div align="center">
|
|
<img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/philosophies.jpg" max-height="250px">
|
|
</div>
|
|
|
|
Lightning forces the following structure to your code which makes it reusable and shareable:
|
|
|
|
- Research code (the LightningModule).
|
|
- Engineering code (you delete, and is handled by the Trainer).
|
|
- Non-essential research code (logging, etc... this goes in Callbacks).
|
|
- Data (use PyTorch DataLoaders or organize them into a LightningDataModule).
|
|
|
|
Once you do this, you can train on multiple-GPUs, TPUs, CPUs and even in 16-bit precision without changing your code!
|
|
|
|
Get started with our [2 step guide](https://pytorch-lightning.readthedocs.io/en/latest/starter/new-project.html)
|
|
|
|
______________________________________________________________________
|
|
|
|
## Continuous Integration
|
|
|
|
Lightning is rigorously tested across multiple GPUs, TPUs CPUs and against major Python and PyTorch versions.
|
|
|
|
<details>
|
|
<summary>Current build statuses</summary>
|
|
|
|
<center>
|
|
|
|
| System / PyTorch ver. | 1.7 (min. req.) | 1.8 (LTS) | 1.9 | 1.10 (latest) |
|
|
| :------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
|
|
| Linux py3.7 \[GPUs\*\*\] | - | [?branchName=master>)](https://dev.azure.com/PytorchLightning/pytorch-lightning/_build/latest?definitionId=6&branchName=master) | - | - |
|
|
| Linux py3.7 \[TPUs\*\*\*\] | - | [](https://circleci.com/gh/PyTorchLightning/pytorch-lightning/tree/master) | - | - |
|
|
| Linux py3.8 (with Conda | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-conda.yml) | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-conda.yml) | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-conda.yml) | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-conda.yml) |
|
|
| Linux py3.{7,9} | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml) | - | - | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml) |
|
|
| OSX py3.{7,9} | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml) | - | - | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml) |
|
|
| Windows py3.{7,9} | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml) | - | - | [](https://github.com/PyTorchLightning/pytorch-lightning/actions/workflows/ci_test-full.yml) |
|
|
|
|
- _\*\* tests run on two NVIDIA P100_
|
|
- _\*\*\* tests run on Google GKE TPUv2/3. TPU py3.7 means we support Colab and Kaggle env._
|
|
|
|
</center>
|
|
</details>
|
|
|
|
______________________________________________________________________
|
|
|
|
## How To Use
|
|
|
|
### Step 0: Install
|
|
|
|
Simple installation from PyPI
|
|
|
|
```bash
|
|
pip install pytorch-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 pytorch-lightning['extra']
|
|
```
|
|
|
|
#### Conda
|
|
|
|
```bash
|
|
conda install pytorch-lightning -c conda-forge
|
|
```
|
|
|
|
#### Install stable 1.5.x
|
|
|
|
the actual status of 1.5 \[stable\] is following:
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Install future release from the source
|
|
|
|
```bash
|
|
pip install git+https://github.com/PytorchLightning/pytorch-lightning.git@release/1.5.x --upgrade
|
|
```
|
|
|
|
#### Install bleeding-edge - future 1.6
|
|
|
|
Install nightly from the source (no guarantees)
|
|
|
|
```bash
|
|
pip install https://github.com/PyTorchLightning/pytorch-lightning/archive/master.zip
|
|
```
|
|
|
|
or from testing PyPI
|
|
|
|
```bash
|
|
pip install -iU https://test.pypi.org/simple/ pytorch-lightning
|
|
```
|
|
|
|
</details>
|
|
<!-- end skipping PyPI description -->
|
|
|
|
### Step 1: Add these imports
|
|
|
|
```python
|
|
import os
|
|
import torch
|
|
from torch import nn
|
|
import torch.nn.functional as F
|
|
from torchvision.datasets import MNIST
|
|
from torch.utils.data import DataLoader, random_split
|
|
from torchvision import transforms
|
|
import pytorch_lightning as pl
|
|
```
|
|
|
|
### Step 2: Define a LightningModule (nn.Module subclass)
|
|
|
|
A LightningModule defines a full *system* (ie: a GAN, autoencoder, BERT or a simple Image Classifier).
|
|
|
|
```python
|
|
class LitAutoEncoder(pl.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
|
|
```
|
|
|
|
**Note: Training_step defines the training loop. Forward defines how the LightningModule behaves during inference/prediction.**
|
|
|
|
### Step 3: Train!
|
|
|
|
```python
|
|
dataset = MNIST(os.getcwd(), download=True, transform=transforms.ToTensor())
|
|
train, val = random_split(dataset, [55000, 5000])
|
|
|
|
autoencoder = LitAutoEncoder()
|
|
trainer = pl.Trainer()
|
|
trainer.fit(autoencoder, DataLoader(train), DataLoader(val))
|
|
```
|
|
|
|
## Advanced features
|
|
|
|
Lightning has over [40+ advanced features](https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html#trainer-flags) designed for professional AI research at scale.
|
|
|
|
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>Highlighted feature code snippets</summary>
|
|
|
|
```python
|
|
# 8 GPUs
|
|
# no code changes needed
|
|
trainer = Trainer(max_epochs=1, gpus=8)
|
|
|
|
# 256 GPUs
|
|
trainer = Trainer(max_epochs=1, gpus=8, num_nodes=32)
|
|
```
|
|
|
|
<summary>Train on TPUs without code changes</summary>
|
|
|
|
```python
|
|
# no code changes needed
|
|
trainer = Trainer(tpu_cores=8)
|
|
```
|
|
|
|
<summary>16-bit precision</summary>
|
|
|
|
```python
|
|
# no code changes needed
|
|
trainer = Trainer(precision=16)
|
|
```
|
|
|
|
<summary>Experiment managers</summary>
|
|
|
|
```python
|
|
from pytorch_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
|
|
```
|
|
|
|
<summary>EarlyStopping</summary>
|
|
|
|
```python
|
|
es = EarlyStopping(monitor="val_loss")
|
|
trainer = Trainer(callbacks=[es])
|
|
```
|
|
|
|
<summary>Checkpointing</summary>
|
|
|
|
```python
|
|
checkpointing = ModelCheckpoint(monitor="val_loss")
|
|
trainer = Trainer(callbacks=[checkpointing])
|
|
```
|
|
|
|
<summary>Export to torchscript (JIT) (production use)</summary>
|
|
|
|
```python
|
|
# torchscript
|
|
autoencoder = LitAutoEncoder()
|
|
torch.jit.save(autoencoder.to_torchscript(), "model.pt")
|
|
```
|
|
|
|
<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>
|
|
|
|
### Pro-level control of training loops (advanced users)
|
|
|
|
For complex/professional level work, you have optional full control of the training loop and optimizers.
|
|
|
|
```python
|
|
class LitAutoEncoder(pl.LightningModule):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.automatic_optimization = False
|
|
|
|
def training_step(self, batch, batch_idx):
|
|
# access your optimizers with use_pl_optimizer=False. Default is True
|
|
opt_a, opt_b = self.optimizers(use_pl_optimizer=True)
|
|
|
|
loss_a = ...
|
|
self.manual_backward(loss_a, opt_a)
|
|
opt_a.step()
|
|
opt_a.zero_grad()
|
|
|
|
loss_b = ...
|
|
self.manual_backward(loss_b, opt_b, retain_graph=True)
|
|
self.manual_backward(loss_b, opt_b)
|
|
opt_b.step()
|
|
opt_b.zero_grad()
|
|
```
|
|
|
|
______________________________________________________________________
|
|
|
|
## 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/PyTorchLightning/pytorch-lightning/tree/master/tests). We test every combination of PyTorch and Python supported versions, every OS, multi GPUs and even TPUs.
|
|
- Minimal running speed overhead (about 300 ms per epoch compared with pure PyTorch).
|
|
|
|
______________________________________________________________________
|
|
|
|
## Lightning Lite
|
|
|
|
<div align="center">
|
|
<img src="docs/source/_static/images/lightning_lite/lite.gif" height="200px" width="600px">
|
|
</div>
|
|
|
|
In the Lighting 1.5 release, LightningLite now enables you to leverage all the capabilities of PyTorch Lightning Accelerators without any refactoring to your training loop. Check out the
|
|
[blogpost](https://devblog.pytorchlightning.ai/scale-your-pytorch-code-with-lightninglite-d5692a303f00) and
|
|
[docs](https://pytorch-lightning.readthedocs.io/en/stable/starter/lightning_lite.html) for more info.
|
|
|
|
______________________________________________________________________
|
|
|
|
## Examples
|
|
|
|
###### Hello world
|
|
|
|
- [MNIST hello world](https://pytorch-lightning.readthedocs.io/en/latest/notebooks/lightning_examples/mnist-hello-world.html)
|
|
|
|
###### Contrastive Learning
|
|
|
|
- [BYOL](https://lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#byol)
|
|
- [CPC v2](https://lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#cpc-v2)
|
|
- [Moco v2](https://lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#moco-v2)
|
|
- [SIMCLR](https://lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#simclr)
|
|
|
|
###### NLP
|
|
|
|
- [GPT-2](https://lightning-bolts.readthedocs.io/en/latest/convolutional.html#gpt-2)
|
|
- [BERT](https://pytorch-lightning.readthedocs.io/en/latest/notebooks/lightning_examples/text-transformers.html)
|
|
|
|
###### Reinforcement Learning
|
|
|
|
- [DQN](https://lightning-bolts.readthedocs.io/en/latest/reinforce_learn.html#dqn-models)
|
|
- [Dueling-DQN](https://lightning-bolts.readthedocs.io/en/latest/reinforce_learn.html#dueling-dqn)
|
|
- [Reinforce](https://lightning-bolts.readthedocs.io/en/latest/reinforce_learn.html#reinforce)
|
|
|
|
###### Vision
|
|
|
|
- [GAN](https://pytorch-lightning.readthedocs.io/en/latest/notebooks/lightning_examples/basic-gan.html)
|
|
|
|
###### Classic ML
|
|
|
|
- [Logistic Regression](https://lightning-bolts.readthedocs.io/en/latest/classic_ml.html#logistic-regression)
|
|
- [Linear Regression](https://lightning-bolts.readthedocs.io/en/latest/classic_ml.html#linear-regression)
|
|
|
|
______________________________________________________________________
|
|
|
|
## Community
|
|
|
|
The lightning community is maintained by
|
|
|
|
- [10+ core contributors](https://pytorch-lightning.readthedocs.io/en/latest/governance.html) who are all a mix of professional engineers, Research Scientists, and Ph.D. students from top AI labs.
|
|
- 590+ active community contributors.
|
|
|
|
Want to help us build Lightning and reduce boilerplate for thousands of researchers? [Learn how to make your first contribution here](https://devblog.pytorchlightning.ai/quick-contribution-guide-86d977171b3a)
|
|
|
|
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://pytorch-lightning.rtfd.io/en/latest).
|
|
1. [Search through existing Discussions](https://github.com/PyTorchLightning/pytorch-lightning/discussions), or [add a new question](https://github.com/PyTorchLightning/pytorch-lightning/discussions/new)
|
|
1. [Join our slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-pw5v393p-qRaDgEk24~EjiZNBpSQFgQ).
|
|
|
|
### Funding
|
|
|
|
[We're venture funded](https://techcrunch.com/2020/10/08/grid-ai-raises-18-6m-series-a-to-help-ai-researchers-and-engineers-bring-their-models-to-production/) to make sure we can provide around the clock support, hire a full-time staff, attend conferences, and move faster through implementing features you request.
|
|
|
|
______________________________________________________________________
|
|
|
|
## Grid AI
|
|
|
|
Grid AI is our platform for training models at scale on the cloud!
|
|
|
|
**Sign up for our FREE community Tier [here](https://www.grid.ai/pricing/)**
|
|
|
|
To use grid, take your regular command:
|
|
|
|
```
|
|
python my_model.py --learning_rate 1e-6 --layers 2 --gpus 4
|
|
```
|
|
|
|
And change it to use the grid train command:
|
|
|
|
```
|
|
grid train --grid_gpus 4 my_model.py --learning_rate 'uniform(1e-6, 1e-1, 20)' --layers '[2, 4, 8, 16]'
|
|
```
|
|
|
|
The above command will launch (20 * 4) experiments each running on 4 GPUs (320 GPUs!) - by making ZERO changes to
|
|
your code.
|