plkit

A wrapper of pytorch-lightning that makes you write even less code.

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

"""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

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

from plkit import Module, DataModule, run

class MyData(DataModule):
    ...

class MyModel(Module):
    ...

if __name__ == '__main__':
    config = {
        ...
    }
    run(config, MyData, MyModel)

Running via SGE

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

from plkit import (
    Module,
    DataModule,
    Optuna,
    OptunaSuggest,
    LocalRunner,
    run
)

# MyData and MyModel definitions

if __name__ == '__main__':
    config = {
        ...
        hparam1=OptunaSuggest(<default>, <type>, *args, **kwargs),
        hparam2=OptunaSuggest(<default>, <type>, *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)