ConfigSys

Extremely simple and powerful configuration system for machine learning systems.
Provides a simple way to use nested dataclasses to specify an arbitrary complex configuration. Configs can be easily serialized to and from yaml files, although we highly recommend specifying configs in python rather than yaml since you get all the benefits of an IDE (e.g. autocomplete, type checking, etc.).

see configsys/example.py

from dataclasses import dataclass

from configsys.config import ConfigMixin

# We recommend using kw_only=True to make sure Configs are specified explicitly
class ImageDataLoader:
    @dataclass(kw_only=True)
    class Config(ConfigMixin):
        _target_ = "configsys.example.ImageDataLoader"
        batch_size: int
        shuffle: bool

    def __init__(self, config: Config):
        self.config = config

    def get_data(self):
        return dict(x=[2, 3], y=[4, 5])


class TextDataLoader:
    @dataclass(kw_only=True)
    class Config(ConfigMixin):
        _target_ = "configsys.example.TextDataLoader"
        batch_size: int
        shuffle: bool

    def __init__(self, config: Config):
        self.config = config

    def get_data(self, x):
        return dict(x=['hello', 'world'])


class MLP:
    @dataclass(kw_only=True)
    class Config(ConfigMixin):
        _target_ = "configsys.example.MLP"
        n_layers: int
        n_hidden: int

    def __init__(self, config: Config):
        self.config = config

    def forward(self, x):
        return x


class Transformer:
    @dataclass(kw_only=True)
    class Config(ConfigMixin):
        _target_ = "configsys.example.Transformer"
        n_heads: int

    def __init__(self, config: Config):
        self.config = config

    def forward(self, x):
        return x


# Configs do not necessarily have to be an attribute of their target class
# This is usually the case when the target is class that you did not write
@dataclass(kw_only=True)
class TrainerConfig(ConfigMixin):
    _target_ = "configsys.example.Trainer"
    dataloader: ImageDataLoader.Config | TextDataLoader.Config
    model: MLP.Config | Transformer.Config
    learning_rate: float
    steps: int


class Trainer:
    def __init__(self, config: TrainerConfig):
        self.config = config

    def train(self):
        print("{self} training...")
        print(self.config.learning_rate)
        print(self.config.steps)


def main(outdir):
    for model_config in [MLP.Config(n_layers=3, n_hidden=128),
                         Transformer.Config(n_heads=6)]:
        config = TrainerConfig(
            dataloader=ImageDataLoader.Config(batch_size=32, shuffle=True),
            model=model_config,
            learning_rate=0.1,
            steps=100,
        )
        model = config.model.instantiate_target()
        dataloader = config.dataloader.instantiate_target()
        trainer = config.instantiate_target()  # equivalent to trainer = Trainer(config=trainer_config)
        trainer.train()

        config.to_yaml_file(f"{outdir}/trainer_config.yaml")

        trainer_config2 = TrainerConfig.from_yaml_file(f"{outdir}/trainer_config.yaml")
        assert config == trainer_config2


if __name__ == '__main__':
    main()