ertk.train.ExperimentConfig

class ertk.train.ExperimentConfig(name: str = 'default', data: ~ertk.dataset.dataset.DataLoadConfig = '???', model: ~ertk.train.ModelConfig = '???', eval: ~ertk.train.EvalConfig | None = None, evals: ~typing.Dict[str, ~ertk.train.EvalConfig] = <factory>, training: ~ertk.train.TrainConfig = <factory>, results: str = '')

Bases: ERTKConfig

Class to hold experiment configuration.

__init__(name: str = 'default', data: ~ertk.dataset.dataset.DataLoadConfig = '???', model: ~ertk.train.ModelConfig = '???', eval: ~ertk.train.EvalConfig | None = None, evals: ~typing.Dict[str, ~ertk.train.EvalConfig] = <factory>, training: ~ertk.train.TrainConfig = <factory>, results: str = '') None

Methods

__init__([name, data, model, eval, evals, ...])

from_file(path[, override])

Create config from YAML file and optionlly override some values.

Inherited Methods

from_config(config)

Create config object from any compatible config.

merge_with_args([args])

Merge config with command-line arguments.

to_dictconfig()

Convert config to DictConfig.

to_file(path)

Write config to YAML file.

to_string()

Generate YAML string representation of config.

Attributes

data

Data loading configuration.

eval

Evaluation configuration.

model

Model configuration.

name

Experiment name.

results

Path to output results.

evals

Dictionary of evaluation configurations.

training

Training configuration.

data: DataLoadConfig = '???'

Data loading configuration.

eval: EvalConfig | None = None

Evaluation configuration.

evals: Dict[str, EvalConfig]

Dictionary of evaluation configurations.

classmethod from_file(path: PathLike | str, override: List[str] | None = None) T

Create config from YAML file and optionlly override some values.

Parameters:
path: os.Pathlike or str

The path to YAML file containing config.

override: list of str, optional

Argument overrides in the form of key=value pairs.

Returns:
ERKConfig

The resulting config.

model: ModelConfig = '???'

Model configuration.

name: str = 'default'

Experiment name.

results: str = ''

Path to output results.

training: TrainConfig

Training configuration.