data_juicer.core.executor

class data_juicer.core.executor.ExecutorBase(cfg: Namespace | None = None)

Bases: ABC

abstract __init__(cfg: Namespace | None = None)

abstract run(load_data_np: Annotated[int, Gt(gt=0)] | None = None, skip_return=False)

class data_juicer.core.executor.ExecutorFactory

Bases: object

static create_executor(executor_type: str) → DefaultExecutor | RayExecutor

class data_juicer.core.executor.DefaultExecutor(cfg: Namespace | None = None)

Bases: ExecutorBase

This Executor class is used to process a specific dataset.

It will load the dataset and unify the format, then apply all the ops in the config file in order and generate a processed dataset.

__init__(cfg: Namespace | None = None)

Initialization method.

Parameters:

cfg – optional jsonargparse Namespace.

run(dataset: Dataset | NestedDataset | None = None, load_data_np: Annotated[int, Gt(gt=0)] | None = None, skip_return=False)

Running the dataset process pipeline.

Parameters:

• dataset – a Dataset object to be executed.

• load_data_np – number of workers when loading the dataset.

• skip_return – skip return for API called.

Returns:

processed dataset.

sample_data(dataset_to_sample: Dataset | None = None, load_data_np=None, sample_ratio: float = 1.0, sample_algo: str = 'uniform', **kwargs)

Sample a subset from the given dataset. TODO add support other than LocalExecutor

Parameters:

• dataset_to_sample – Dataset to sample from. If None, will use the formatter linked by the executor. Default is None.

• load_data_np – number of workers when loading the dataset.

• sample_ratio – The ratio of the sample size to the original dataset size. Default is 1.0 (no sampling).

• sample_algo – Sampling algorithm to use. Options are “uniform”, “frequency_specified_field_selector”, or “topk_specified_field_selector”. Default is “uniform”.

Returns:

A sampled Dataset.