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https://github.com/pwwang/plkit
A wrapper of pytorch-lightning that makes you write even less code.
https://github.com/pwwang/plkit
boilerplate deep-learning pytorch pytorch-lightning
Last synced: 14 days ago
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A wrapper of pytorch-lightning that makes you write even less code.
- Host: GitHub
- URL: https://github.com/pwwang/plkit
- Owner: pwwang
- License: mit
- Created: 2020-07-20T17:35:12.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-01-08T21:41:20.000Z (almost 4 years ago)
- Last Synced: 2024-12-13T03:51:31.479Z (21 days ago)
- Topics: boilerplate, deep-learning, pytorch, pytorch-lightning
- Language: Python
- Homepage: https://pwwang.github.com/plkit
- Size: 701 KB
- Stars: 5
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# plkit
A wrapper of [pytorch-lightning][1] that makes you write even less code.
[![pypi version][3]][4] [![github tag][5]][6] [![codacy quality][7]][8] [![coverage][9]][8] ![docs][10] ![building][11]
## Installation
```
pip install -U plkit
```
## Principles
- Being compatible with pytorch-lightning
- Using configurations instead of coding if possible
## Features
- Compatible with `pytorch-lightning`
- Exposed terminal logger
- Better formatted warnings and errors from `pytorch-lightning`
- Trainer from a dictionary (not only from an `ArgumentParser` or a `Namespace`)
- Data module with automatic split for train, val and test datasets
- Auto loss function and optimizer
- Optuna integration
## Usage
### From pytorch-lightning's minimal example
```python
"""A minimal example for plkit"""
from pathlib import Path
import torch
from torchvision import transforms
from torchvision.datasets import MNIST
from plkit import Module, DataModule, run
class Data(DataModule):
def data_reader(self):
return MNIST(Path(__file__).parent / 'data', train=True,
download=True, transform=transforms.ToTensor())
class LitClassifier(Module):
def __init__(self, config):
super().__init__(config)
self.l1 = torch.nn.Linear(28 * 28, 10)
def forward(self, x):
return torch.relu(self.l1(x.view(x.size(0), -1).float()))
def training_step(self, batch, _):
x, y = batch
loss = self.loss_function(self(x), y)
return {'loss': loss}
if __name__ == '__main__':
configuration = {
'gpus': 1,
'data_tvt': .05, # use a small proportion for training
'batch_size': 32,
'max_epochs': 11
}
run(configuration, Data, LitClassifier)
```
### Using exposed logger
```python
from plkit import logger
# Your logic
if __name__ == "__main__":
# Now you are able to log something outside Modules
logger.info('Fantastic starts ...')
# ...
logger.info('Pipeline done.')
```
### Best practice (Boilerplate)
You can use each objects (trainer, module, datamodule) as the way you do with `pytorch-lightning`, but we suggest you use them in the following ways:
#### Running locally
```python
from plkit import Module, DataModule, run
class MyData(DataModule):
...
class MyModel(Module):
...
if __name__ == '__main__':
config = {
...
}
run(config, MyData, MyModel)
```
#### Running via SGE
```python
from plkit import Module, DataModule, SGERunner, run
# MyData and MyModel definitions
if __name__ == '__main__':
config = {
...
}
sge = SGERunner(...)
run(config, MyData, MyModel, runner=sge)
# or
sge.run(config, MyData, MyModel)
```
#### With optuna
```python
from plkit import (
Module,
DataModule,
Optuna,
OptunaSuggest,
LocalRunner,
run
)
# MyData and MyModel definitions
if __name__ == '__main__':
config = {
...
hparam1=OptunaSuggest(, , *args, **kwargs),
hparam2=OptunaSuggest(, , *args, **kwargs),
}
runner = LocalRunner()
# requires `val_acc` to be logged in `validation_epoch_end`
optuna = Optuna(on='val_acc', n_trials=10, direction='maximize')
run(config, MyData, MyModel, runner=runner, optuna=optuna)
# or
runner.run(config, MyData, MyModel, optuna)
```
## Resources
See more examples in `./examples` and [full documentation][2]
[1]: https://github.com/PyTorchLightning/pytorch-lightning
[2]: https://pwwang.github.com/plkit
[3]: https://img.shields.io/pypi/v/plkit?style=flat-square
[4]: https://pypi.org/project/plkit/
[5]: https://img.shields.io/github/v/tag/pwwang/plkit?style=flat-square
[6]: https://github.com/pwwang/plkit
[7]: https://img.shields.io/codacy/grade/60b6b06fa06c4539b4a7b48ba30fd2bb?style=flat-square
[8]: https://app.codacy.com/gh/pwwang/plkit
[9]: https://img.shields.io/codacy/coverage/60b6b06fa06c4539b4a7b48ba30fd2bb?style=flat-square
[10]: https://img.shields.io/github/workflow/status/pwwang/plkit/Build%20Docs?label=docs&style=flat-square
[11]: https://img.shields.io/github/workflow/status/pwwang/plkit/Build%20and%20Deploy?style=flat-square