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dataset

CustomDataset

Bases: Dataset

A custom dataset class that extends from datasets.Dataset and is used to generate an Argilla FeedbackDataset instance from the pre-defined configuration within the task provided to Pipeline.generate.

Source code in src/distilabel/dataset.py 
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class CustomDataset(Dataset):
    """A custom dataset class that extends from `datasets.Dataset` and is used to generate
    an Argilla `FeedbackDataset` instance from the pre-defined configuration within the task
    provided to `Pipeline.generate`.
    """

    task: Union["Task", None] = None

    def to_argilla(self) -> "FeedbackDataset":
        """Converts the dataset to an Argilla `FeedbackDataset` instance, based on the
        task defined in the dataset as part of `Pipeline.generate`.

        Raises:
            ImportError: if the argilla library is not installed.
            ValueError: if the task is not set.

        Returns:
            FeedbackDataset: the Argilla `FeedbackDataset` instance.
        """
        if not _ARGILLA_AVAILABLE:
            raise ImportError(
                "To use `to_argilla` method is required to have `argilla` installed. "
                "Please install it with `pip install argilla`."
            )

        if self.task is None:
            raise ValueError(
                "The task is not set. Please set it with `dataset.task = <task>`."
            )

        try:
            rg_dataset = self.task.to_argilla_dataset(dataset_row=self[0])  # type: ignore
        except Exception as e:
            raise ValueError(
                f"Error while converting the dataset to an Argilla `FeedbackDataset` instance: {e}"
            ) from e

        # try:
        #     rg_dataset = infer_model_metadata_properties(
        #         hf_dataset=self, rg_dataset=rg_dataset
        #     )
        # except Exception as e:
        #     warnings.warn(
        #         f"Error while adding the model metadata properties: {e}",
        #         UserWarning,
        #         stacklevel=2,
        #     )

        for dataset_row in self:
            if any(
                dataset_row[input_arg_name] is None  # type: ignore
                for input_arg_name in self.task.input_args_names
            ):
                continue
            try:
                rg_dataset.add_records(
                    self.task._to_argilla_record(dataset_row=dataset_row)  # type: ignore
                )  # type: ignore
            except Exception as e:
                warnings.warn(
                    f"Error while converting a row into an Argilla `FeedbackRecord` instance: {e}",
                    UserWarning,
                    stacklevel=2,
                )
        return rg_dataset

    def save_to_disk(self, dataset_path: PathLike, **kwargs: Any) -> None:
        """Saves the datataset to disk, also saving the task.

        Args:
            dataset_path: Path to the dataset.
            **kwargs: Additional arguments to be passed to `datasets.Dataset.save_to_disk`.
        """
        super().save_to_disk(dataset_path, **kwargs)
        if self.task is not None:
            save_task_to_disk(dataset_path, self.task)

    @classmethod
    def load_from_disk(cls, dataset_path: PathLike, **kwargs: Any):
        """Load a CustomDataset from disk, also reading the task.

        Args:
            dataset_path: Path to the dataset, as you would do with a standard Dataset.

        Returns:
            The loaded dataset.
        """
        ds = super().load_from_disk(dataset_path, *kwargs)
        # Dynamically remaps the `datasets.Dataset` to be a `CustomDataset` instance
        ds.__class__ = cls
        task = load_task_from_disk(dataset_path)
        ds.task = task
        return ds

load_from_disk(dataset_path, **kwargs) classmethod

Load a CustomDataset from disk, also reading the task.

Parameters:

Name Type Description Default
dataset_path PathLike

Path to the dataset, as you would do with a standard Dataset.

 required

Returns:

Type Description

The loaded dataset.
Source code in src/distilabel/dataset.py 
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@classmethod
def load_from_disk(cls, dataset_path: PathLike, **kwargs: Any):
    """Load a CustomDataset from disk, also reading the task.

    Args:
        dataset_path: Path to the dataset, as you would do with a standard Dataset.

    Returns:
        The loaded dataset.
    """
    ds = super().load_from_disk(dataset_path, *kwargs)
    # Dynamically remaps the `datasets.Dataset` to be a `CustomDataset` instance
    ds.__class__ = cls
    task = load_task_from_disk(dataset_path)
    ds.task = task
    return ds

save_to_disk(dataset_path, **kwargs)

Saves the datataset to disk, also saving the task.

Parameters:

Name Type Description Default
dataset_path PathLike

Path to the dataset.

 required
**kwargs Any

Additional arguments to be passed to datasets.Dataset.save_to_disk.

 {}
Source code in src/distilabel/dataset.py 
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def save_to_disk(self, dataset_path: PathLike, **kwargs: Any) -> None:
    """Saves the datataset to disk, also saving the task.

    Args:
        dataset_path: Path to the dataset.
        **kwargs: Additional arguments to be passed to `datasets.Dataset.save_to_disk`.
    """
    super().save_to_disk(dataset_path, **kwargs)
    if self.task is not None:
        save_task_to_disk(dataset_path, self.task)

to_argilla()

Converts the dataset to an Argilla FeedbackDataset instance, based on the task defined in the dataset as part of Pipeline.generate.

Raises:

Type Description
ImportError

if the argilla library is not installed.
ValueError

if the task is not set.

Returns:

Name Type Description
FeedbackDataset FeedbackDataset

the Argilla FeedbackDataset instance.
Source code in src/distilabel/dataset.py 
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def to_argilla(self) -> "FeedbackDataset":
    """Converts the dataset to an Argilla `FeedbackDataset` instance, based on the
    task defined in the dataset as part of `Pipeline.generate`.

    Raises:
        ImportError: if the argilla library is not installed.
        ValueError: if the task is not set.

    Returns:
        FeedbackDataset: the Argilla `FeedbackDataset` instance.
    """
    if not _ARGILLA_AVAILABLE:
        raise ImportError(
            "To use `to_argilla` method is required to have `argilla` installed. "
            "Please install it with `pip install argilla`."
        )

    if self.task is None:
        raise ValueError(
            "The task is not set. Please set it with `dataset.task = <task>`."
        )

    try:
        rg_dataset = self.task.to_argilla_dataset(dataset_row=self[0])  # type: ignore
    except Exception as e:
        raise ValueError(
            f"Error while converting the dataset to an Argilla `FeedbackDataset` instance: {e}"
        ) from e

    # try:
    #     rg_dataset = infer_model_metadata_properties(
    #         hf_dataset=self, rg_dataset=rg_dataset
    #     )
    # except Exception as e:
    #     warnings.warn(
    #         f"Error while adding the model metadata properties: {e}",
    #         UserWarning,
    #         stacklevel=2,
    #     )

    for dataset_row in self:
        if any(
            dataset_row[input_arg_name] is None  # type: ignore
            for input_arg_name in self.task.input_args_names
        ):
            continue
        try:
            rg_dataset.add_records(
                self.task._to_argilla_record(dataset_row=dataset_row)  # type: ignore
            )  # type: ignore
        except Exception as e:
            warnings.warn(
                f"Error while converting a row into an Argilla `FeedbackRecord` instance: {e}",
                UserWarning,
                stacklevel=2,
            )
    return rg_dataset

DatasetCheckpoint dataclass

A checkpoint class that contains the information of a checkpoint.

Parameters:

Name Type Description Default
path Path

The path to the checkpoint.

 cwd() / 'ckpt'
save_frequency int

The frequency at which the checkpoint should be saved By default is set to -1 (no checkpoint is saved to disk, but the dataset is returned upon failure).

 -1
extra_kwargs dict[str, Any]

Additional kwargs to be passed to the save_to_disk method of the Dataset.

 field(default_factory=dict)

Examples:

>>> from distilabel.dataset import DatasetCheckpoint
>>> # Save the dataset every 10% of the records generated.
>>> checkpoint = DatasetCheckpoint(save_frequency=len(dataset) // 10)
>>> # Afterwards, we can access the checkpoint's checkpoint.path.
Source code in src/distilabel/dataset.py 
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@dataclass
class DatasetCheckpoint:
    """A checkpoint class that contains the information of a checkpoint.

    Args:
        path (Path): The path to the checkpoint.
        save_frequency (int): The frequency at which the checkpoint should be saved
            By default is set to -1 (no checkpoint is saved to disk, but the dataset
            is returned upon failure).
        extra_kwargs (dict[str, Any]): Additional kwargs to be passed to the `save_to_disk` method of the Dataset.

    Examples:
        >>> from distilabel.dataset import DatasetCheckpoint
        >>> # Save the dataset every 10% of the records generated.
        >>> checkpoint = DatasetCheckpoint(save_frequency=len(dataset) // 10)
        >>> # Afterwards, we can access the checkpoint's checkpoint.path.
    """

    path: Path = Path.cwd() / "ckpt"
    save_frequency: int = -1
    extra_kwargs: Dict[str, Any] = field(default_factory=dict)

    # Internal fields to keep track of the number of records generated and when to check.
    _total_checks: int = field(repr=False, default=0)

    def do_checkpoint(self, step: int) -> bool:
        """Determines if a checkpoint should be done.

        Args:
            step (int): The number of records generated.

        Returns:
            bool: Whether a checkpoint should be done.
        """
        if self.save_frequency == -1:
            return False

        if (step - self._total_checks * self.save_frequency) // self.save_frequency:
            self._total_checks += 1
            return True
        return False

do_checkpoint(step)

Determines if a checkpoint should be done.

Parameters:

Name Type Description Default
step int

The number of records generated.

 required

Returns:

Name Type Description
bool bool

Whether a checkpoint should be done.
Source code in src/distilabel/dataset.py 
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def do_checkpoint(self, step: int) -> bool:
    """Determines if a checkpoint should be done.

    Args:
        step (int): The number of records generated.

    Returns:
        bool: Whether a checkpoint should be done.
    """
    if self.save_frequency == -1:
        return False

    if (step - self._total_checks * self.save_frequency) // self.save_frequency:
        self._total_checks += 1
        return True
    return False