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<p align="center">
<a href="https://williamfalcon.github.io/pytorch-lightning/">
<img alt="" src="https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/_static/lightning_logo.png" width="50">
</a>
</p>
<h3 align="center">
PyTorch Lightning
</h3>
<p align="center">
The PyTorch Keras for ML researchers. More control. Less boilerplate.
</p>
<p align="center">
<a href="https://badge.fury.io/py/pytorch-lightning"><img src="https://badge.fury.io/py/pytorch-lightning.svg" alt="PyPI version" height="18"></a>
<a href="https://pepy.tech/project/pytorch-lightning"><img src="https://pepy.tech/badge/pytorch-lightning" alt="PyPI version" height="18"></a>
<a href="https://pepy.tech/project/pytorch-lightning"><img src="https://img.shields.io/badge/python-3.6-blue.svg" alt="Supported Python Version" height="18"></a>
<a href="https://github.com/williamFalcon/pytorch-lightning/tree/master/tests"><img src="https://github.com/williamFalcon/pytorch-lightning/blob/master/coverage.svg"></a>
<a href="https://travis-ci.org/williamFalcon/pytorch-lightning"><img src="https://travis-ci.org/williamFalcon/pytorch-lightning.svg?branch=master"></a>
<a href="https://williamfalcon.github.io/pytorch-lightning/"><img src="https://readthedocs.org/projects/pytorch-lightning/badge/?version=latest"></a>
<a href="https://github.com/williamFalcon/pytorch-lightning/blob/master/COPYING"><img src="https://img.shields.io/badge/License-MIT-yellow.svg"></a>
</p>
```bash
pip install pytorch-lightning
```
## Docs
**[View the docs here](https://williamfalcon.github.io/pytorch-lightning/)**
## What is it?
Lightning is a very lightweight wrapper on PyTorch. It defers core training and validation logic to you and automates the rest. It guarantees correct, modern best practices for the core training logic.
## Why do I want to use lightning?
When starting a new project the last thing you want to do is recode a training loop, multi-cluster training, 16-bit precision, early-stopping, model loading/saving, when to validate, etc... You're likely to spend a long time ironing out all the bugs without even getting to the core of your research.
With lightning, you guarantee those parts of your code work so you can focus on what the meat of the research: The data and the training/validation loop logic.
Don't worry about training on multiple gpus or speeding up your code, lightning will do that for you!
## README Table of Contents
- [How do I use it](https://github.com/williamFalcon/pytorch-lightning#how-do-i-do-use-it)
- [What lightning automates](https://github.com/williamFalcon/pytorch-lightning#what-does-lightning-control-for-me)
- [Tensorboard integration](https://github.com/williamFalcon/pytorch-lightning#tensorboard)
- [Lightning features](https://github.com/williamFalcon/pytorch-lightning#lightning-automates-all-of-the-following-each-is-also-configurable)
- [Demos](https://github.com/williamFalcon/pytorch-lightning#demo)
- [Tutorials](https://github.com/williamFalcon/pytorch-lightning#tutorials)
- [Contributing](https://github.com/williamFalcon/pytorch-lightning#contributing)
- [Bleeding edge install](https://github.com/williamFalcon/pytorch-lightning#bleeding-edge)
## How do I do use it?
To use lightning do 2 things:
1. [Define a LightningModel](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/)
```python
import os
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
import torchvision.transforms as transforms
import pytorch_lightning as ptl
class CoolModel(ptl.LightningModule):
def __init__(self):
super(CoolModel, self).__init__()
# not the best model...
self.l1 = torch.nn.Linear(28 * 28, 10)
def forward(self, x):
return torch.relu(self.l1(x.view(x.size(0), -1)))
def my_loss(self, y_hat, y):
return F.cross_entropy(y_hat, y)
def training_step(self, batch, batch_nb):
x, y = batch
y_hat = self.forward(x)
return {'loss': self.my_loss(y_hat, y)}
def validation_step(self, batch, batch_nb):
x, y = batch
y_hat = self.forward(x)
return {'val_loss': self.my_loss(y_hat, y)}
def validation_end(self, outputs):
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
return {'avg_val_loss': avg_loss}
def configure_optimizers(self):
return [torch.optim.Adam(self.parameters(), lr=0.02)]
@ptl.data_loader
def tng_dataloader(self):
return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
@ptl.data_loader
def val_dataloader(self):
return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
@ptl.data_loader
def test_dataloader(self):
return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
```
2. Fit with a [trainer](https://williamfalcon.github.io/pytorch-lightning/Trainer/)
```python
from pytorch_lightning import Trainer
from test_tube import Experiment
model = CoolModel()
exp = Experiment(save_dir=os.getcwd())
# train on cpu using only 10% of the data (for demo purposes)
trainer = Trainer(experiment=exp, max_nb_epochs=1, train_percent_check=0.1)
# train on 4 gpus
# trainer = Trainer(experiment=exp, max_nb_epochs=1, gpus=[0, 1, 2, 3])
# train on 32 gpus across 4 nodes (make sure to submit appropriate SLURM job)
# trainer = Trainer(experiment=exp, max_nb_epochs=1, gpus=[0, 1, 2, 3, 4, 5, 6, 7], nb_gpu_nodes=4)
# train (1 epoch only here for demo)
trainer.fit(model)
# view tensorflow logs
print(f'View tensorboard logs by running\ntensorboard --logdir {os.getcwd()}')
print('and going to http://localhost:6006 on your browser')
```
## What does lightning control for me?
Everything in gray!
You define the blue parts using the LightningModule interface:
<p align="center">
<a href="https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/_static/overview_flat.jpg">
<img alt="" src="https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/_static/overview_flat.jpg" height="700px">
</a>
</p>
```{.python}
# what to do in the training loop
def training_step(self, data_batch, batch_nb):
# what to do in the validation loop
def validation_step(self, data_batch, batch_nb):
# how to aggregate validation_step outputs
def validation_end(self, outputs):
# and your dataloaders
def tng_dataloader():
def val_dataloader():
def test_dataloader():
```
**Could be as complex as seq-2-seq + attention**
```python
# define what happens for training here
def training_step(self, data_batch, batch_nb):
x, y = data_batch
# define your own forward and loss calculation
hidden_states = self.encoder(x)
# even as complex as a seq-2seq + attn model
# (this is just a toy, non-working example to illustrate)
start_token = '<SOS>'
last_hidden = torch.zeros(...)
loss = 0
for step in range(max_seq_len):
attn_context = self.attention_nn(hidden_states, start_token)
pred = self.decoder(start_token, attn_context, last_hidden)
last_hidden = pred
pred = self.predict_nn(pred)
loss += self.loss(last_hidden, y[step])
#toy example as well
loss = loss / max_seq_len
return {'loss': loss}
```
**Or as basic as CNN image classification**
```python
# define what happens for validation here
def validation_step(self, data_batch, batch_nb):
x, y = data_batch
# or as basic as a CNN classification
out = self.forward(x)
loss = my_loss(out, y)
return {'loss': loss}
```
**And you also decide how to collate the output of all validation steps**
```python
def validation_end(self, outputs):
"""
Called at the end of validation to aggregate outputs
:param outputs: list of individual outputs of each validation step
:return:
"""
val_loss_mean = 0
val_acc_mean = 0
for output in outputs:
val_loss_mean += output['val_loss']
val_acc_mean += output['val_acc']
val_loss_mean /= len(outputs)
val_acc_mean /= len(outputs)
tqdm_dic = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
return tqdm_dic
```
## Tensorboard
Lightning is fully integrated with tensorboard.
<p align="center">
<a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#tensorboard-support">
<img alt="" src="https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/_static/tf_loss.png" width="900px">
</a>
</p>
Lightning also adds a text column with all the hyperparameters for this experiment.
<p align="center">
<a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#tensorboard-support">
<img alt="" src="https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/_static/tf_tags.png" width="900px">
</a>
</p>
Simply note the path you set for the Experiment
``` {.python}
from test_tube import Experiment
from pytorch-lightning import Trainer
exp = Experiment(save_dir='/some/path')
trainer = Trainer(experiment=exp)
...
```
And run tensorboard from that dir
```bash
tensorboard --logdir /some/path
```
## Lightning automates all of the following ([each is also configurable](https://williamfalcon.github.io/pytorch-lightning/Trainer/)):
###### Checkpointing
- [Model saving](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving)
- [Model loading](https://williamfalcon.github.io/pytorch-lightning/LightningModule/methods/#load-from-metrics)
###### Computing cluster (SLURM)
- [Running grid search on a cluster](https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#running-grid-search-on-a-cluster)
- [Walltime auto-resubmit](https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#walltime-auto-resubmit)
###### Debugging
- [Fast dev run](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#fast-dev-run)
- [Inspect gradient norms](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#inspect-gradient-norms)
- [Log GPU usage](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#Log-gpu-usage)
- [Make model overfit on subset of data](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#make-model-overfit-on-subset-of-data)
- [Print the parameter count by layer](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-the-parameter-count-by-layer)
- [Print which gradients are nan](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-which-gradients-are-nan)
###### Distributed training
- [16-bit mixed precision](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#16-bit-mixed-precision)
- [Multi-GPU](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-GPU)
- [Multi-node](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-node)
- [Single GPU](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#single-gpu)
- [Self-balancing architecture](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#self-balancing-architecture)
###### Experiment Logging
- [Display metrics in progress bar](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#display-metrics-in-progress-bar)
- [Log metric row every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#log-metric-row-every-k-batches)
- [Process position](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#process-position)
- [Tensorboard support](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#tensorboard-support)
- [Save a snapshot of all hyperparameters](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#save-a-snapshot-of-all-hyperparameters)
- [Snapshot code for a training run](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#snapshot-code-for-a-training-run)
- [Write logs file to csv every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#write-logs-file-to-csv-every-k-batches)
###### Training loop
- [Accumulate gradients](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#accumulated-gradients)
- [Force training for min or max epochs](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-training-for-min-or-max-epochs)
- [Force disable early stop](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-disable-early-stop)
- [Gradient Clipping](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping)
- [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
- [Learning rate scheduling](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
- [Set how much of the training set to check (1-100%)](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check)
###### Validation loop
- [Check validation every n epochs](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#check-validation-every-n-epochs)
- [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
- [Set how much of the validation set to check](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-validation-set-to-check)
- [Set how much of the test set to check](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-test-set-to-check)
- [Set validation check frequency within 1 training epoch](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-validation-check-frequency-within-1-training-epoch)
- [Set the number of validation sanity steps](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-the-number-of-validation-sanity-steps)
## Demo
```bash
# install lightning
pip install pytorch-lightning
# clone lightning for the demo
git clone https://github.com/williamFalcon/pytorch-lightning.git
cd pytorch-lightning
cd examples/new_project_templates/
# all of the following demos use the SAME model to show no modification needs to be made to your code
# train on cpu
python single_cpu_template.py
# train on multiple-gpus
python single_gpu_node_template.py --gpus "0,1"
# train on 32 gpus on a cluster (run on a SLURM managed cluster)
python multi_node_cluster_template.py --nb_gpu_nodes 4 --gpus '0,1,2,3,4,5,6,7'
```
## Tutorials
- [Basic Lightning use](https://towardsdatascience.com/supercharge-your-ai-research-with-pytorch-lightning-337948a99eec)
- [9 key speed features in Pytorch-Lightning](https://towardsdatascience.com/9-tips-for-training-lightning-fast-neural-networks-in-pytorch-8e63a502f565)
- [SLURM, multi-node training with Lightning](https://towardsdatascience.com/trivial-multi-node-training-with-pytorch-lightning-ff75dfb809bd)
## Contributing
Welcome to the PTL community! We're building the most advanced research platform on the planet to implement the latest, best practices that the amazing PyTorch team rolls out!
#### Bug fixes:
1. Submit a github issue.
2. Fix it.
3. Submit a PR!
#### New Features:
1. Submit a github issue.
2. We'll agree on the feature scope.
3. Submit a PR! (with updated docs and tests 🙃).
## Bleeding edge
If you can't wait for the next release, install the most up to date code with:
```bash
pip install git+https://github.com/williamFalcon/pytorch-lightning.git@master --upgrade
```
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