378 lines
17 KiB
Markdown
378 lines
17 KiB
Markdown
<div align="center">
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# PyTorch Lightning
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**The PyTorch Keras for ML researchers. More control. Less boilerplate.**
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[](https://badge.fury.io/py/pytorch-lightning)
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[](https://pepy.tech/project/pytorch-lightning)
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[](https://travis-ci.org/williamFalcon/pytorch-lightning)
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<!--
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removed until windows install issues resolved.
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[](https://ci.appveyor.com/project/Borda/pytorch-lightning) -->
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<!--
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removed until codecov badge isn't empy. likely a config error showing nothing on master.
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[](https://codecov.io/gh/Borda/pytorch-lightning)
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-->
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[](https://github.com/williamFalcon/pytorch-lightning/tree/master/tests#running-coverage)
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[](https://www.codefactor.io/repository/github/borda/pytorch-lightning)
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[](https://pytorch-lightning.readthedocs.io/en/latest)
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[](https://github.com/williamFalcon/pytorch-lightning/blob/master/LICENSE)
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</div>
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Simple installation from PyPI
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```bash
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pip install pytorch-lightning
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```
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## Docs
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**[View the docs here](https://williamfalcon.github.io/pytorch-lightning/)**
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## What is it?
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Lightning is a very lightweight wrapper on PyTorch. This means you don't have to learn a new library. It defers core training and validation logic to you and automates the rest. It guarantees tested, correct, modern best practices for the automated parts.
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## Why do I want to use lightning?
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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.
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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.
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Don't worry about training on multiple gpus or speeding up your code, lightning will do that for you!
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---
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## README Table of Contents
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- [How do I use it](https://github.com/williamFalcon/pytorch-lightning#how-do-i-do-use-it)
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- [What lightning automates](https://github.com/williamFalcon/pytorch-lightning#what-does-lightning-control-for-me)
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- [Tensorboard integration](https://github.com/williamFalcon/pytorch-lightning#tensorboard)
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- [Lightning features](https://github.com/williamFalcon/pytorch-lightning#lightning-automates-all-of-the-following-each-is-also-configurable)
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- [Demos](https://github.com/williamFalcon/pytorch-lightning#demo)
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- [Tutorials](https://github.com/williamFalcon/pytorch-lightning#tutorials)
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- [Contributing](https://github.com/williamFalcon/pytorch-lightning/blob/master/CONTRIBUTING.md)
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- [Bleeding edge install](https://github.com/williamFalcon/pytorch-lightning#bleeding-edge)
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- [Lightning Design Principles](https://github.com/williamFalcon/pytorch-lightning#lightning-design-principles)
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- [FAQ](https://github.com/williamFalcon/pytorch-lightning#faq)
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---
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## How do I do use it?
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To use lightning do 2 things:
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1. [Define a LightningModel](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/)
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```python
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import os
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import torch
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from torch.nn import functional as F
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from torch.utils.data import DataLoader
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from torchvision.datasets import MNIST
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import torchvision.transforms as transforms
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import pytorch_lightning as pl
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class CoolModel(pl.LightningModule):
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def __init__(self):
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super(CoolModel, self).__init__()
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# not the best model...
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self.l1 = torch.nn.Linear(28 * 28, 10)
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def forward(self, x):
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return torch.relu(self.l1(x.view(x.size(0), -1)))
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def my_loss(self, y_hat, y):
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return F.cross_entropy(y_hat, y)
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def training_step(self, batch, batch_nb):
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x, y = batch
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y_hat = self.forward(x)
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return {'loss': self.my_loss(y_hat, y)}
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def validation_step(self, batch, batch_nb):
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x, y = batch
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y_hat = self.forward(x)
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return {'val_loss': self.my_loss(y_hat, y)}
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def validation_end(self, outputs):
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avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
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return {'avg_val_loss': avg_loss}
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def configure_optimizers(self):
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return [torch.optim.Adam(self.parameters(), lr=0.02)]
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@pl.data_loader
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def tng_dataloader(self):
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return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
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@pl.data_loader
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def val_dataloader(self):
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return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
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@pl.data_loader
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def test_dataloader(self):
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return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
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```
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2. Fit with a [trainer](https://williamfalcon.github.io/pytorch-lightning/Trainer/)
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```python
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from pytorch_lightning import Trainer
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model = CoolModel()
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# most basic trainer, uses good defaults
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trainer = Trainer()
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trainer.fit(model)
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```
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Or with tensorboard logger and some options turned on such as multi-gpu, etc...
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```python
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from test_tube import Experiment
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# PyTorch summarywriter with a few bells and whistles
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exp = Experiment(save_dir=os.getcwd())
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# train on cpu using only 10% of the data (for demo purposes)
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# pass in experi
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trainer = Trainer(experiment=exp, max_nb_epochs=1, train_percent_check=0.1)
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# train on 4 gpus
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# trainer = Trainer(experiment=exp, max_nb_epochs=1, gpus=[0, 1, 2, 3])
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# train on 32 gpus across 4 nodes (make sure to submit appropriate SLURM job)
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# trainer = Trainer(experiment=exp, max_nb_epochs=1, gpus=[0, 1, 2, 3, 4, 5, 6, 7], nb_gpu_nodes=4)
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# train (1 epoch only here for demo)
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trainer.fit(model)
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# view tensorflow logs
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print('View tensorboard logs by running\ntensorboard --logdir %s' % os.getcwd())
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print('and going to http://localhost:6006 on your browser')
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```
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## What does lightning control for me?
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Everything in gray!
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You define the blue parts using the LightningModule interface:
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```{.python}
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# what to do in the training loop
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def training_step(self, data_batch, batch_nb):
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# what to do in the validation loop
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def validation_step(self, data_batch, batch_nb):
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# how to aggregate validation_step outputs
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def validation_end(self, outputs):
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# and your dataloaders
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def tng_dataloader():
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def val_dataloader():
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def test_dataloader():
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```
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**Could be as complex as seq-2-seq + attention**
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```python
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# define what happens for training here
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def training_step(self, data_batch, batch_nb):
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x, y = data_batch
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# define your own forward and loss calculation
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hidden_states = self.encoder(x)
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# even as complex as a seq-2-seq + attn model
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# (this is just a toy, non-working example to illustrate)
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start_token = '<SOS>'
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last_hidden = torch.zeros(...)
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loss = 0
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for step in range(max_seq_len):
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attn_context = self.attention_nn(hidden_states, start_token)
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pred = self.decoder(start_token, attn_context, last_hidden)
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last_hidden = pred
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pred = self.predict_nn(pred)
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loss += self.loss(last_hidden, y[step])
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#toy example as well
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loss = loss / max_seq_len
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return {'loss': loss}
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```
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**Or as basic as CNN image classification**
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```python
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# define what happens for validation here
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def validation_step(self, data_batch, batch_nb):
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x, y = data_batch
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# or as basic as a CNN classification
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out = self.forward(x)
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loss = my_loss(out, y)
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return {'loss': loss}
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```
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**And you also decide how to collate the output of all validation steps**
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```python
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def validation_end(self, outputs):
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"""
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Called at the end of validation to aggregate outputs
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:param outputs: list of individual outputs of each validation step
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:return:
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"""
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val_loss_mean = 0
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val_acc_mean = 0
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for output in outputs:
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val_loss_mean += output['val_loss']
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val_acc_mean += output['val_acc']
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val_loss_mean /= len(outputs)
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val_acc_mean /= len(outputs)
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tqdm_dic = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
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return tqdm_dic
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```
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## Tensorboard
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Lightning is fully integrated with tensorboard.
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Lightning also adds a text column with all the hyperparameters for this experiment.
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Simply note the path you set for the Experiment
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``` {.python}
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from test_tube import Experiment
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from pytorch-lightning import Trainer
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exp = Experiment(save_dir='/some/path')
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trainer = Trainer(experiment=exp)
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...
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```
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And run tensorboard from that dir
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```bash
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tensorboard --logdir /some/path
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```
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## Lightning automates all of the following ([each is also configurable](https://williamfalcon.github.io/pytorch-lightning/Trainer/)):
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###### Checkpointing
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- [Model saving](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving)
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- [Model loading](https://williamfalcon.github.io/pytorch-lightning/LightningModule/methods/#load-from-metrics)
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- [Restoring training session](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#restoring-training-session)
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###### Computing cluster (SLURM)
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- [Running grid search on a cluster](https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#running-grid-search-on-a-cluster)
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- [Walltime auto-resubmit](https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#walltime-auto-resubmit)
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###### Debugging
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- [Fast dev run](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#fast-dev-run)
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- [Inspect gradient norms](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#inspect-gradient-norms)
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- [Log GPU usage](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#Log-gpu-usage)
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- [Make model overfit on subset of data](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#make-model-overfit-on-subset-of-data)
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- [Print the parameter count by layer](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-the-parameter-count-by-layer)
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- [Print which gradients are nan](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-which-gradients-are-nan)
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- [Print input and output size of every module in system](https://williamfalcon.github.io/pytorch-lightning/LightningModule/properties/#example_input_array)
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###### Distributed training
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- [16-bit mixed precision](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#16-bit-mixed-precision)
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- [Multi-GPU](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-GPU)
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- [Multi-node](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-node)
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- [Single GPU](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#single-gpu)
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- [Self-balancing architecture](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#self-balancing-architecture)
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###### Experiment Logging
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- [Display metrics in progress bar](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#display-metrics-in-progress-bar)
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- [Log metric row every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#log-metric-row-every-k-batches)
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- [Process position](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#process-position)
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- [Tensorboard support](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#tensorboard-support)
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- [Save a snapshot of all hyperparameters](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#save-a-snapshot-of-all-hyperparameters)
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- [Snapshot code for a training run](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#snapshot-code-for-a-training-run)
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- [Write logs file to csv every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#write-logs-file-to-csv-every-k-batches)
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###### Training loop
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- [Accumulate gradients](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#accumulated-gradients)
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- [Force training for min or max epochs](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-training-for-min-or-max-epochs)
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- [Force disable early stop](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-disable-early-stop)
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- [Gradient Clipping](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping)
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- [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
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- [Learning rate scheduling](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
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- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
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- [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)
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###### Validation loop
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- [Check validation every n epochs](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#check-validation-every-n-epochs)
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- [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
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- [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)
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- [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)
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- [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)
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- [Set the number of validation sanity steps](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-the-number-of-validation-sanity-steps)
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## Demo
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```bash
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# install lightning
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pip install pytorch-lightning
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# clone lightning for the demo
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git clone https://github.com/williamFalcon/pytorch-lightning.git
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cd pytorch-lightning
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cd examples/new_project_templates/
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# all of the following demos use the SAME model to show no modification needs to be made to your code
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# train on cpu
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python single_cpu_template.py
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# train on multiple-gpus
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python single_gpu_node_template.py --gpus "0,1"
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# train on 32 gpus on a cluster (run on a SLURM managed cluster)
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python multi_node_cluster_template.py --nb_gpu_nodes 4 --gpus '0,1,2,3,4,5,6,7'
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```
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## Tutorials
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- [Basic Lightning use](https://towardsdatascience.com/supercharge-your-ai-research-with-pytorch-lightning-337948a99eec)
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- [9 key speed features in Pytorch-Lightning](https://towardsdatascience.com/9-tips-for-training-lightning-fast-neural-networks-in-pytorch-8e63a502f565)
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- [SLURM, multi-node training with Lightning](https://towardsdatascience.com/trivial-multi-node-training-with-pytorch-lightning-ff75dfb809bd)
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---
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## FAQ
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**Why was Lightning created?**
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Lightning has 3 goals in mind:
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1. Maximal flexibility while abstracting out the common boilerplate across research projects.
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2. Reproducibility. If all projects use the LightningModule template, it will be much much easier to understand what's going on and where to look! It will also mean every implementation follows a standard format.
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3. Democratizing PyTorch power user features. Distributed training? 16-bit? know you need them but don't want to take the time to implement? All good... these come built into Lightning.
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**How does Lightning compare with Ignite and fast.ai?**
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[Here's a thorough comparison](https://medium.com/@_willfalcon/pytorch-lightning-vs-pytorch-ignite-vs-fast-ai-61dc7480ad8a).
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**Is this another library I have to learn?**
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Nope! We use pure Pytorch everywhere and don't add unecessary abstractions!
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**Are there plans to support Python 2?**
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Nope.
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**Are there plans to support virtualenv?**
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Nope. Please use anaconda or miniconda.
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## Bleeding edge
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If you can't wait for the next release, install the most up to date code with:
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```bash
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pip install git+https://github.com/williamFalcon/pytorch-lightning.git@master --upgrade
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```
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