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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,
        dataset_columns: List[str] = None,
        vector_strategy: Union[bool, "SentenceTransformersExtractor"] = True,
        metric_strategy: Union[bool, "TextDescriptivesExtractor"] = True,
    ) -> "FeedbackDataset":
        """Converts the dataset to an Argilla `FeedbackDataset` instance, based on the
        task defined in the dataset as part of `Pipeline.generate`.

        Args:
            dataset_columns (List[str]): the dataset columns or fields to be used for the Argilla `FeedbackDataset` instance.
                By default, the first 5 columns or fields will be used.
            vector_strategy (Union[bool, SentenceTransformersExtractor]): the strategy to be used for
                adding vectors to the dataset. If `True`, the default `SentenceTransformersExtractor`
                will be used with the `TaylorAI/bge-micro-2` model. If `False`, no vectors will be added to the dataset.
            metric_strategy (Union[bool, TextDescriptivesExtractor]): the strategy to be used for
                adding metrics to the dataset. If `True`, the default `TextDescriptivesExtractor`
                will be used. If `False`, no metrics will be added to the dataset.

        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

        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,
                )

        selected_fields = infer_field_from_dataset_columns(
            dataset_columns=dataset_columns, dataset=rg_dataset, task=self.task
        )
        rg_dataset = self.add_vectors_to_argilla_dataset(
            dataset=rg_dataset, vector_strategy=vector_strategy, fields=selected_fields
        )
        rg_dataset = self.add_metrics_to_argilla_dataset(
            dataset=rg_dataset, metric_strategy=metric_strategy, fields=selected_fields
        )
        return rg_dataset

    def add_vectors_to_argilla_dataset(
        self,
        dataset: Union["FeedbackRecord", List["FeedbackRecord"], "FeedbackDataset"],
        vector_strategy: Union[bool, "SentenceTransformersExtractor"],
        fields: List[str] = None,
    ) -> Union["FeedbackRecord", List["FeedbackRecord"], "FeedbackDataset"]:
        if _ARGILLA_AVAILABLE and vector_strategy:
            if isinstance(vector_strategy, SentenceTransformersExtractor):
                ste: SentenceTransformersExtractor = vector_strategy
            elif vector_strategy:
                ste = SentenceTransformersExtractor()
            dataset = ste.update_dataset(dataset=dataset, fields=fields)

        elif not _ARGILLA_AVAILABLE and vector_strategy:
            warnings.warn(
                "An error occurred while adding vectors to the dataset: "
                "The `argilla`/`sentence-transformers` packages are not installed or the installed version is not compatible with the"
                " required version. If you want to add vectors to your dataset, please run `pip install 'distilabel[argilla]'`.",
                stacklevel=2,
            )
        return dataset

    def add_metrics_to_argilla_dataset(
        self,
        dataset: Union["FeedbackRecord", List["FeedbackRecord"], "FeedbackDataset"],
        metric_strategy: Union[bool, "TextDescriptivesExtractor"],
        fields: List[str] = None,
    ) -> Union["FeedbackRecord", List["FeedbackRecord"], "FeedbackDataset"]:
        if _ARGILLA_AVAILABLE and metric_strategy:
            if isinstance(metric_strategy, TextDescriptivesExtractor):
                tde: TextDescriptivesExtractor = metric_strategy
            elif metric_strategy:
                tde = TextDescriptivesExtractor()

            dataset = tde.update_dataset(dataset=dataset, fields=fields)
        elif not _ARGILLA_AVAILABLE and metric_strategy:
            warnings.warn(
                "An error occurred while adding metrics to the dataset: "
                "The `argilla`/`text-descriptives` packages are not installed or the installed version is not compatible with the"
                " required version. If you want to add metrics to your dataset, please run `pip install 'distilabel[argilla]'`.",
                stacklevel=2,
            )
        return dataset

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

        Args:
            dataset_path (os.PathLike): Path to the dataset.
            kwargs (Any): Additional arguments to be passed to `datasets.Dataset.save_to_disk`.

        Examples:
            >>> from distilabel.dataset import CustomDataset
            >>> dataset = CustomDataset(...)
            >>> dataset.save_to_disk("path/to/dataset")
        """
        super().save_to_disk(dataset_path, **kwargs)
        if self.task is not None:
            self.task.save(Path(dataset_path))

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

        Args:
            dataset_path (os.PathLike): Path to the dataset.
            kwargs (Any): Keyword arguments passed to Dataset.load_from_disk.

        Returns:
            dataset: The loaded dataset.

        Examples:
            >>> from distilabel.dataset import CustomDataset
            >>> dataset: CustomDataset = CustomDataset.load_from_disk("path/to/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

    def push_to_hub(
        self,
        repo_id: str,
        *args: Optional[Any],
        push_task: bool = True,
        **kwargs: Optional[Any],
    ) -> None:
        """Same method as `datasets.Dataset.push_to_hub`, but also pushes the task to simplify
        creating a CustomDataset from HuggingFace hub.

        Args:
            repo_id (str):
                The ID of the repository to push to in the following format: `<user>/<dataset_name>` or
                `<org>/<dataset_name>`. Also accepts `<dataset_name>`, which will default to the namespace
                of the logged-in user.
            args (Any): Additional arguments to be passed to `datasets.Dataset.push_to_hub`.
            push_task (bool, optional):
                Whether to push the `Task` contained in the `CustomDataset`. Useful if you want to resuse
                the functionality of the `CustomDataset` out of the box. It will upload a json
                file (distilabel-task.json) containing the task to the hub.
                Defaults to True.
            kwargs (Any): Additional arguments to be passed to `datasets.Dataset.push_to_hub`.

        Examples:
            >>> from distilabel.dataset import CustomDataset
            >>> dataset = CustomDataset(...)
            >>> dataset.push_to_hub("org/dataset-name")
        """
        super().push_to_hub(repo_id, *args, **kwargs)
        if self.task is not None and push_task:
            try:
                logger.info("Pushing task to the hub...")
                with tempfile.NamedTemporaryFile(mode="w", delete=False) as f:
                    f.write(json.dumps(self.task.dump(), indent=2))
                    f.flush()

                    HfApi().upload_file(
                        path_or_fileobj=f.name,
                        path_in_repo=TASK_FILE_NAME,
                        repo_id=repo_id,
                        repo_type="dataset",
                        token=kwargs.get("token"),
                    )
            except Exception as e:
                logger.info(f"Error while pushing the task to the hub: {e}.")

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.

 required
kwargs Any

Keyword arguments passed to Dataset.load_from_disk.

 {}

Returns:

Name Type Description
dataset CustomDataset

The loaded dataset.

Examples:

>>> from distilabel.dataset import CustomDataset
>>> dataset: CustomDataset = CustomDataset.load_from_disk("path/to/dataset")
Source code in src/distilabel/dataset.py 
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@classmethod
def load_from_disk(
    cls, dataset_path: os.PathLike, **kwargs: Any
) -> "CustomDataset":
    """Load a CustomDataset from disk, also reading the task.

    Args:
        dataset_path (os.PathLike): Path to the dataset.
        kwargs (Any): Keyword arguments passed to Dataset.load_from_disk.

    Returns:
        dataset: The loaded dataset.

    Examples:
        >>> from distilabel.dataset import CustomDataset
        >>> dataset: CustomDataset = CustomDataset.load_from_disk("path/to/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

push_to_hub(repo_id, *args, push_task=True, **kwargs)

Same method as datasets.Dataset.push_to_hub, but also pushes the task to simplify creating a CustomDataset from HuggingFace hub.

Parameters:

Name Type Description Default
repo_id str

The ID of the repository to push to in the following format: <user>/<dataset_name> or <org>/<dataset_name>. Also accepts <dataset_name>, which will default to the namespace of the logged-in user.

 required
args Any

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

 ()
push_task bool

Whether to push the Task contained in the CustomDataset. Useful if you want to resuse the functionality of the CustomDataset out of the box. It will upload a json file (distilabel-task.json) containing the task to the hub. Defaults to True.

 True
kwargs Any

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

 {}

Examples:

>>> from distilabel.dataset import CustomDataset
>>> dataset = CustomDataset(...)
>>> dataset.push_to_hub("org/dataset-name")
Source code in src/distilabel/dataset.py 
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def push_to_hub(
    self,
    repo_id: str,
    *args: Optional[Any],
    push_task: bool = True,
    **kwargs: Optional[Any],
) -> None:
    """Same method as `datasets.Dataset.push_to_hub`, but also pushes the task to simplify
    creating a CustomDataset from HuggingFace hub.

    Args:
        repo_id (str):
            The ID of the repository to push to in the following format: `<user>/<dataset_name>` or
            `<org>/<dataset_name>`. Also accepts `<dataset_name>`, which will default to the namespace
            of the logged-in user.
        args (Any): Additional arguments to be passed to `datasets.Dataset.push_to_hub`.
        push_task (bool, optional):
            Whether to push the `Task` contained in the `CustomDataset`. Useful if you want to resuse
            the functionality of the `CustomDataset` out of the box. It will upload a json
            file (distilabel-task.json) containing the task to the hub.
            Defaults to True.
        kwargs (Any): Additional arguments to be passed to `datasets.Dataset.push_to_hub`.

    Examples:
        >>> from distilabel.dataset import CustomDataset
        >>> dataset = CustomDataset(...)
        >>> dataset.push_to_hub("org/dataset-name")
    """
    super().push_to_hub(repo_id, *args, **kwargs)
    if self.task is not None and push_task:
        try:
            logger.info("Pushing task to the hub...")
            with tempfile.NamedTemporaryFile(mode="w", delete=False) as f:
                f.write(json.dumps(self.task.dump(), indent=2))
                f.flush()

                HfApi().upload_file(
                    path_or_fileobj=f.name,
                    path_in_repo=TASK_FILE_NAME,
                    repo_id=repo_id,
                    repo_type="dataset",
                    token=kwargs.get("token"),
                )
        except Exception as e:
            logger.info(f"Error while pushing the task to the hub: {e}.")

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.

 {}

Examples:

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

    Args:
        dataset_path (os.PathLike): Path to the dataset.
        kwargs (Any): Additional arguments to be passed to `datasets.Dataset.save_to_disk`.

    Examples:
        >>> from distilabel.dataset import CustomDataset
        >>> dataset = CustomDataset(...)
        >>> dataset.save_to_disk("path/to/dataset")
    """
    super().save_to_disk(dataset_path, **kwargs)
    if self.task is not None:
        self.task.save(Path(dataset_path))

to_argilla(dataset_columns=None, vector_strategy=True, metric_strategy=True)

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

Parameters:

Name Type Description Default
dataset_columns List[str]

the dataset columns or fields to be used for the Argilla FeedbackDataset instance. By default, the first 5 columns or fields will be used.

 None
vector_strategy Union[bool, SentenceTransformersExtractor]

the strategy to be used for adding vectors to the dataset. If True, the default SentenceTransformersExtractor will be used with the TaylorAI/bge-micro-2 model. If False, no vectors will be added to the dataset.

 True
metric_strategy Union[bool, TextDescriptivesExtractor]

the strategy to be used for adding metrics to the dataset. If True, the default TextDescriptivesExtractor will be used. If False, no metrics will be added to the dataset.

 True

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,
    dataset_columns: List[str] = None,
    vector_strategy: Union[bool, "SentenceTransformersExtractor"] = True,
    metric_strategy: Union[bool, "TextDescriptivesExtractor"] = True,
) -> "FeedbackDataset":
    """Converts the dataset to an Argilla `FeedbackDataset` instance, based on the
    task defined in the dataset as part of `Pipeline.generate`.

    Args:
        dataset_columns (List[str]): the dataset columns or fields to be used for the Argilla `FeedbackDataset` instance.
            By default, the first 5 columns or fields will be used.
        vector_strategy (Union[bool, SentenceTransformersExtractor]): the strategy to be used for
            adding vectors to the dataset. If `True`, the default `SentenceTransformersExtractor`
            will be used with the `TaylorAI/bge-micro-2` model. If `False`, no vectors will be added to the dataset.
        metric_strategy (Union[bool, TextDescriptivesExtractor]): the strategy to be used for
            adding metrics to the dataset. If `True`, the default `TextDescriptivesExtractor`
            will be used. If `False`, no metrics will be added to the dataset.

    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

    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,
            )

    selected_fields = infer_field_from_dataset_columns(
        dataset_columns=dataset_columns, dataset=rg_dataset, task=self.task
    )
    rg_dataset = self.add_vectors_to_argilla_dataset(
        dataset=rg_dataset, vector_strategy=vector_strategy, fields=selected_fields
    )
    rg_dataset = self.add_metrics_to_argilla_dataset(
        dataset=rg_dataset, metric_strategy=metric_strategy, fields=selected_fields
    )
    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
strategy CheckpointStrategies

The strategy to be used to save the checkpoint. Available options are: "disk" (to save the dataset to disk) and "hf-hub" (to push the dataset to the HuggingFace hub). By default is set to "disk".

 'disk'
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.
>>> # You can also push the dataset to the hub by setting the strategy to `hf-hub`.
>>> checkpoint = DatasetCheckpoint(
...     strategy="hf-hub",
...     extra_kwargs={"repo_id": "username/dataset-name"}
... )
>>> # Keep in mind, if the environmnet variable "HF_API_TOKEN" is not set, you are required
>>> # to pass the token as an argument to the `extra_kwargs`.
>>> checkpoint = DatasetCheckpoint(
...     strategy="hf-hub",
...     extra_kwargs={"repo_id": "username/dataset-name", "token": "hf_..."}
... )
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).
        strategy (CheckpointStrategies): The strategy to be used to save the checkpoint.
            Available options are: "disk" (to save the dataset to disk) and "hf-hub"
            (to push the dataset to the HuggingFace hub). By default is set to "disk".
        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.
        >>> # You can also push the dataset to the hub by setting the strategy to `hf-hub`.
        >>> checkpoint = DatasetCheckpoint(
        ...     strategy="hf-hub",
        ...     extra_kwargs={"repo_id": "username/dataset-name"}
        ... )
        >>> # Keep in mind, if the environmnet variable "HF_API_TOKEN" is not set, you are required
        >>> # to pass the token as an argument to the `extra_kwargs`.
        >>> checkpoint = DatasetCheckpoint(
        ...     strategy="hf-hub",
        ...     extra_kwargs={"repo_id": "username/dataset-name", "token": "hf_..."}
        ... )
    """

    path: Path = Path.cwd() / "ckpt"
    save_frequency: int = -1
    strategy: CheckpointStrategies = "disk"
    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 __post_init__(self) -> None:
        """Checks validity of arguments.

        Raises:
            ValueError:
                - If the strategy is not valid.
                - If the `repo_id` is not passed when using the `hf-hub` strategy.
        """
        if self.strategy not in get_args(CheckpointStrategies):
            raise ValueError(
                f"Invalid strategy, valid ones are: {get_args(CheckpointStrategies)}"
            )

        if self.strategy == "hf-hub":
            if not self.extra_kwargs.get("repo_id"):
                raise ValueError(
                    "The `repo_id` is required when using the `hf-hub` strategy, please pass it as on 'extra_kwargs' argument."
                )
            if token := os.getenv("HF_API_TOKEN") or (
                token := self.extra_kwargs.get("token")
            ):
                hf_hub_login(token=token)
            else:
                raise ValueError(
                    "To use the `hf-hub` strategy, a token is required, you can either set it "
                    "as an environment variable `HF_API_TOKEN` or pass it as an argument `token` "
                    "via the `extra_kwargs`."
                )

    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

    def save(self, dataset: CustomDataset) -> None:
        """Save the dataset given the strategy selected.

        Args:
            dataset (CustomDataset): Dataset to be saved.

        Raises:
            ValueError: If the strategy is not valid.
        """
        if self.strategy == "disk":
            self._save_to_disk(dataset, **self.extra_kwargs)

        elif self.strategy == "hf-hub":
            self._push_to_hub(dataset, **self.extra_kwargs)

        else:
            # We shouldn't reach this point after __post_init__, but just in case.
            raise ValueError(
                f"Invalid strategy, valid ones are: {get_args(CheckpointStrategies)}"
            )

    def _save_to_disk(
        self, dataset: Union[Dataset, CustomDataset], **kwargs: Any
    ) -> None:
        """Saves the dataset to disk.

        Args:
            dataset (Union[Dataset, CustomDataset]): The dataset to be saved.
        """
        dataset.save_to_disk(self.path, **kwargs)
        logger.info(f"Checkpoint saved to disk: {self.path}.")

    def _push_to_hub(self, dataset: CustomDataset, **kwargs: Any) -> None:
        """Pushes the dataset to the hub.

        Args:
            dataset (Union[Dataset, CustomDataset]): The dataset to be pushed.
        """
        repo_id = kwargs.pop("repo_id", None)
        if not repo_id:
            raise ValueError(
                "The `repo_id` is required when pushing to the hub, please pass it as on 'extra_kwargs' argument."
            )

        try:
            dataset.push_to_hub(repo_id, **kwargs)
            logger.info(f"Checkpoint saved to hub: {repo_id}.")
        except Exception as e:
            # TODO(plaguss): Check possible errors to provide a more informative message.
            # And maybe save to disk as a fallback.
            logger.info(f"Error while pushing the dataset to the hub: {e}.")

__post_init__()

Checks validity of arguments.

Raises:

Type Description
ValueError
  • If the strategy is not valid.
  • If the repo_id is not passed when using the hf-hub strategy.
Source code in src/distilabel/dataset.py 
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def __post_init__(self) -> None:
    """Checks validity of arguments.

    Raises:
        ValueError:
            - If the strategy is not valid.
            - If the `repo_id` is not passed when using the `hf-hub` strategy.
    """
    if self.strategy not in get_args(CheckpointStrategies):
        raise ValueError(
            f"Invalid strategy, valid ones are: {get_args(CheckpointStrategies)}"
        )

    if self.strategy == "hf-hub":
        if not self.extra_kwargs.get("repo_id"):
            raise ValueError(
                "The `repo_id` is required when using the `hf-hub` strategy, please pass it as on 'extra_kwargs' argument."
            )
        if token := os.getenv("HF_API_TOKEN") or (
            token := self.extra_kwargs.get("token")
        ):
            hf_hub_login(token=token)
        else:
            raise ValueError(
                "To use the `hf-hub` strategy, a token is required, you can either set it "
                "as an environment variable `HF_API_TOKEN` or pass it as an argument `token` "
                "via the `extra_kwargs`."
            )

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

save(dataset)

Save the dataset given the strategy selected.

Parameters:

Name Type Description Default
dataset CustomDataset

Dataset to be saved.

 required

Raises:

Type Description
ValueError

If the strategy is not valid.
Source code in src/distilabel/dataset.py 
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def save(self, dataset: CustomDataset) -> None:
    """Save the dataset given the strategy selected.

    Args:
        dataset (CustomDataset): Dataset to be saved.

    Raises:
        ValueError: If the strategy is not valid.
    """
    if self.strategy == "disk":
        self._save_to_disk(dataset, **self.extra_kwargs)

    elif self.strategy == "hf-hub":
        self._push_to_hub(dataset, **self.extra_kwargs)

    else:
        # We shouldn't reach this point after __post_init__, but just in case.
        raise ValueError(
            f"Invalid strategy, valid ones are: {get_args(CheckpointStrategies)}"
        )

load_dataset(path, *args, **kwargs)

Load a dataset from HuggingFace hub.

Overloads the datasets.load_dataset method to return a CustomDataset instance, downloading the Task from the hub (if any).

Parameters:

Name Type Description Default
path str

Path to the dataset in the hub.

 required
args, kwargs

and any other arguments used by datasets.load_dataset

 required

Returns:

Name Type Description
dataset Union[Dataset, CustomDataset]

CustomDataset instance, with the Task stored if available.

Examples:

>>> from distilabel.dataset import load_dataset
>>> dataset: CustomDataset = load_dataset("argilla/distilabel-sample-evol-instruct", split="train")
Source code in src/distilabel/dataset.py 
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def load_dataset(path: str, *args: Any, **kwargs: Any) -> Union[Dataset, CustomDataset]:
    """Load a dataset from HuggingFace hub.

    Overloads the `datasets.load_dataset` method to return a `CustomDataset` instance,
    downloading the `Task` from the hub (if any).

    Args:
        path (str): Path to the dataset in the hub.
        args, kwargs: and any other arguments used by `datasets.load_dataset`

    Returns:
        dataset: CustomDataset instance, with the `Task` stored if available.

    Examples:
        >>> from distilabel.dataset import load_dataset
        >>> dataset: CustomDataset = load_dataset("argilla/distilabel-sample-evol-instruct", split="train")
    """
    from datasets import load_dataset as _load_dataset

    ds = _load_dataset(path, *args, **kwargs)
    cds = CustomDataset(ds.data.table)
    # download the task
    try:
        task_path = hf_hub_download(
            repo_id=path, filename=TASK_FILE_NAME, repo_type="dataset"
        )
        task = load_from_dict(read_json(task_path))
        cds.task = task
    except Exception as e:
        logger.info(f"Error while downloading the task from the hub: {e}.")
    return cds