PickleDataset

PickleDataset loads and saves data using Python's pickle module.

kedro_datasets.pickle.PickleDataset

PickleDataset(
    *,
    filepath,
    backend="pickle",
    load_args=None,
    save_args=None,
    version=None,
    credentials=None,
    fs_args=None,
    metadata=None
)

Bases: AbstractVersionedDataset[Any, Any]

PickleDataset loads/saves data from/to a Pickle file using an underlying filesystem (e.g.: local, S3, GCS). The underlying functionality is supported by the specified backend library passed in (defaults to the pickle library), so it supports all allowed options for loading and saving pickle files.

Examples:

Using the YAML API:

test_model: # simple example without compression
  type: pickle.PickleDataset
  filepath: data/07_model_output/test_model.pkl
  backend: pickle

final_model: # example with load and save args
  type: pickle.PickleDataset
  filepath: s3://your_bucket/final_model.pkl.lz4
  backend: joblib
  credentials: s3_credentials
  save_args:
    compress: lz4

Using the Python API:

>>> import pandas as pd
>>> from kedro_datasets.pickle import PickleDataset
>>>
>>> data = pd.DataFrame({"col1": [1, 2], "col2": [4, 5], "col3": [5, 6]})
>>>
>>> dataset = PickleDataset(filepath="test.pkl", backend="pickle")
>>> dataset.save(data)
>>> reloaded = dataset.load()
>>> assert data.equals(reloaded)
>>>
>>> dataset = PickleDataset(
...     filepath=tmp_path / "test.pickle.lz4",
...     backend="compress_pickle",
...     load_args={"compression": "lz4"},
...     save_args={"compression": "lz4"},
... )
>>> dataset.save(data)
>>> reloaded = dataset.load()
>>> assert data.equals(reloaded)

serialise/deserialise objects.

Example backends that are compatible - non-exhaustive

• pickle
• joblib
• dill
• compress_pickle

Example backends that are incompatible

• torch

Parameters:

• filepath (str | PathLike) –

  Filepath in POSIX format to a Pickle file prefixed with a protocol like s3://. If prefix is not provided, file protocol (local filesystem) will be used. The prefix should be any protocol supported by fsspec. Note: http(s) doesn't support versioning.

• backend (str, default: 'pickle' ) –

  Backend to use, must be an import path to a module which satisfies the pickle interface. That is, contains a load and dump function. Defaults to 'pickle'.

• load_args (dict[str, Any] | None, default: None ) –

  Pickle options for loading pickle files. You can pass in arguments that the backend load function specified accepts, e.g: pickle.load: https://docs.python.org/3/library/pickle.html#pickle.load joblib.load: https://joblib.readthedocs.io/en/latest/generated/joblib.load.html dill.load: https://dill.readthedocs.io/en/latest/index.html#dill.load compress_pickle.load: https://lucianopaz.github.io/compress_pickle/html/api/compress_pickle.html#compress_pickle.compress_pickle.load cloudpickle.load: https://github.com/cloudpipe/cloudpickle/blob/master/tests/cloudpickle_test.py All defaults are preserved.

• save_args (dict[str, Any] | None, default: None ) –

  Pickle options for saving pickle files. You can pass in arguments that the backend dump function specified accepts, e.g: pickle.dump: https://docs.python.org/3/library/pickle.html#pickle.dump joblib.dump: https://joblib.readthedocs.io/en/latest/generated/joblib.dump.html dill.dump: https://dill.readthedocs.io/en/latest/index.html#dill.dump compress_pickle.dump: https://lucianopaz.github.io/compress_pickle/html/api/compress_pickle.html#compress_pickle.compress_pickle.dump cloudpickle.dump: https://github.com/cloudpipe/cloudpickle/blob/master/tests/cloudpickle_test.py All defaults are preserved.

• version (Version | None, default: None ) –

  If specified, should be an instance of kedro.io.core.Version. If its load attribute is None, the latest version will be loaded. If its save attribute is None, save version will be autogenerated.

• credentials (dict[str, Any] | None, default: None ) –

  Credentials required to get access to the underlying filesystem. E.g. for GCSFileSystem it should look like {"token": None}.

• fs_args (dict[str, Any] | None, default: None ) –

  Extra arguments to pass into underlying filesystem class constructor (e.g. {"project": "my-project"} for GCSFileSystem), as well as to pass to the filesystem's open method through nested keys open_args_load and open_args_save. Here you can find all available arguments for open: https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.open All defaults are preserved, except mode, which is set to wb when saving.

• metadata (dict[str, Any] | None, default: None ) –

  Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

Raises:

• ValueError –

  If backend does not satisfy the pickle interface.

• ImportError –

  If the backend module could not be imported.

Source code in kedro_datasets/pickle/pickle_dataset.py

def __init__(  # noqa: PLR0913
    self,
    *,
    filepath: str | os.PathLike,
    backend: str = "pickle",
    load_args: dict[str, Any] | None = None,
    save_args: dict[str, Any] | None = None,
    version: Version | None = None,
    credentials: dict[str, Any] | None = None,
    fs_args: dict[str, Any] | None = None,
    metadata: dict[str, Any] | None = None,
) -> None:
    """Creates a new instance of ``PickleDataset`` pointing to a concrete Pickle
    file on a specific filesystem. ``PickleDataset`` supports custom backends to
    serialise/deserialise objects.

    Example backends that are compatible - non-exhaustive:
        * `pickle`
        * `joblib`
        * `dill`
        * `compress_pickle`

    Example backends that are incompatible:
        * `torch`

    Args:
        filepath: Filepath in POSIX format to a Pickle file prefixed with a protocol like
            `s3://`. If prefix is not provided, `file` protocol (local filesystem) will be used.
            The prefix should be any protocol supported by ``fsspec``.
            Note: `http(s)` doesn't support versioning.
        backend: Backend to use, must be an import path to a module which satisfies the
            ``pickle`` interface. That is, contains a `load` and `dump` function.
            Defaults to 'pickle'.
        load_args: Pickle options for loading pickle files.
            You can pass in arguments that the backend load function specified accepts, e.g:
            pickle.load: https://docs.python.org/3/library/pickle.html#pickle.load
            joblib.load: https://joblib.readthedocs.io/en/latest/generated/joblib.load.html
            dill.load: https://dill.readthedocs.io/en/latest/index.html#dill.load
            compress_pickle.load:
            https://lucianopaz.github.io/compress_pickle/html/api/compress_pickle.html#compress_pickle.compress_pickle.load
            cloudpickle.load:
            https://github.com/cloudpipe/cloudpickle/blob/master/tests/cloudpickle_test.py
            All defaults are preserved.
        save_args: Pickle options for saving pickle files.
            You can pass in arguments that the backend dump function specified accepts, e.g:
            pickle.dump: https://docs.python.org/3/library/pickle.html#pickle.dump
            joblib.dump: https://joblib.readthedocs.io/en/latest/generated/joblib.dump.html
            dill.dump: https://dill.readthedocs.io/en/latest/index.html#dill.dump
            compress_pickle.dump:
            https://lucianopaz.github.io/compress_pickle/html/api/compress_pickle.html#compress_pickle.compress_pickle.dump
            cloudpickle.dump:
            https://github.com/cloudpipe/cloudpickle/blob/master/tests/cloudpickle_test.py
            All defaults are preserved.
        version: If specified, should be an instance of
            ``kedro.io.core.Version``. If its ``load`` attribute is
            None, the latest version will be loaded. If its ``save``
            attribute is None, save version will be autogenerated.
        credentials: Credentials required to get access to the underlying filesystem.
            E.g. for ``GCSFileSystem`` it should look like `{"token": None}`.
        fs_args: Extra arguments to pass into underlying filesystem class constructor
            (e.g. `{"project": "my-project"}` for ``GCSFileSystem``), as well as
            to pass to the filesystem's `open` method through nested keys
            `open_args_load` and `open_args_save`.
            Here you can find all available arguments for `open`:
            https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.open
            All defaults are preserved, except `mode`, which is set to `wb` when saving.
        metadata: Any arbitrary metadata.
            This is ignored by Kedro, but may be consumed by users or external plugins.

    Raises:
        ValueError: If ``backend`` does not satisfy the `pickle` interface.
        ImportError: If the ``backend`` module could not be imported.
    """
    # We do not store `imported_backend` as an attribute to be used in `load`/`save`
    # as this would mean the dataset cannot be deepcopied (module objects cannot be
    # pickled). The import here is purely to raise any errors as early as possible.
    # Repeated imports in the `load` and `save` methods should not be a significant
    # performance hit as Python caches imports.
    try:
        imported_backend = importlib.import_module(backend)
    except ImportError as exc:
        raise ImportError(
            f"Selected backend '{backend}' could not be imported. "
            "Make sure it is installed and importable."
        ) from exc

    if not (
        hasattr(imported_backend, "load") and hasattr(imported_backend, "dump")
    ):
        raise ValueError(
            f"Selected backend '{backend}' should satisfy the pickle interface. "
            "Missing one of 'load' and 'dump' on the backend."
        )

    _fs_args = deepcopy(fs_args) or {}
    _fs_open_args_load = _fs_args.pop("open_args_load", {})
    _fs_open_args_save = _fs_args.pop("open_args_save", {})
    _credentials = deepcopy(credentials) or {}

    protocol, path = get_protocol_and_path(filepath, version)
    if protocol == "file":
        _fs_args.setdefault("auto_mkdir", True)

    self._protocol = protocol
    self._fs = fsspec.filesystem(self._protocol, **_credentials, **_fs_args)

    self.metadata = metadata

    super().__init__(
        filepath=PurePosixPath(path),
        version=version,
        exists_function=self._fs.exists,
        glob_function=self._fs.glob,
    )

    self._backend = backend

    # Handle default load and save arguments
    self._load_args = {**self.DEFAULT_LOAD_ARGS, **(load_args or {})}
    self._save_args = {**self.DEFAULT_SAVE_ARGS, **(save_args or {})}
    self._fs_open_args_load = {
        **self.DEFAULT_FS_ARGS.get("open_args_load", {}),
        **(_fs_open_args_load or {}),
    }
    self._fs_open_args_save = {
        **self.DEFAULT_FS_ARGS.get("open_args_save", {}),
        **(_fs_open_args_save or {}),
    }

DEFAULT_FS_ARGS class-attribute instance-attribute

DEFAULT_FS_ARGS = {'open_args_save': {'mode': 'wb'}}

DEFAULT_LOAD_ARGS class-attribute instance-attribute

DEFAULT_LOAD_ARGS = {}

DEFAULT_SAVE_ARGS class-attribute instance-attribute

DEFAULT_SAVE_ARGS = {}

_backend instance-attribute

_backend = backend

_fs instance-attribute

_fs = filesystem(_protocol, **_credentials, **_fs_args)

_fs_open_args_load instance-attribute

_fs_open_args_load = {
    None: get("open_args_load", {}),
    None: _fs_open_args_load or {},
}

_fs_open_args_save instance-attribute

_fs_open_args_save = {
    None: get("open_args_save", {}),
    None: _fs_open_args_save or {},
}

_load_args instance-attribute

_load_args = {
    None: DEFAULT_LOAD_ARGS,
    None: load_args or {},
}

_protocol instance-attribute

_protocol = protocol

_save_args instance-attribute

_save_args = {
    None: DEFAULT_SAVE_ARGS,
    None: save_args or {},
}

metadata instance-attribute

metadata = metadata

_describe

_describe()

Source code in kedro_datasets/pickle/pickle_dataset.py

def _describe(self) -> dict[str, Any]:
    return {
        "filepath": self._filepath,
        "backend": self._backend,
        "protocol": self._protocol,
        "load_args": self._load_args,
        "save_args": self._save_args,
        "version": self._version,
    }

_exists

_exists()

Source code in kedro_datasets/pickle/pickle_dataset.py

def _exists(self) -> bool:
    try:
        load_path = get_filepath_str(self._get_load_path(), self._protocol)
    except DatasetError:
        return False

    return self._fs.exists(load_path)

_invalidate_cache

_invalidate_cache()

Invalidate underlying filesystem caches.

Source code in kedro_datasets/pickle/pickle_dataset.py

def _invalidate_cache(self) -> None:
    """Invalidate underlying filesystem caches."""
    filepath = get_filepath_str(self._filepath, self._protocol)
    self._fs.invalidate_cache(filepath)

_release

_release()

Source code in kedro_datasets/pickle/pickle_dataset.py

def _release(self) -> None:
    super()._release()
    self._invalidate_cache()

load

load()

Source code in kedro_datasets/pickle/pickle_dataset.py

def load(self) -> Any:
    load_path = get_filepath_str(self._get_load_path(), self._protocol)

    with self._fs.open(load_path, **self._fs_open_args_load) as fs_file:
        imported_backend = importlib.import_module(self._backend)
        return imported_backend.load(fs_file, **self._load_args)  # type: ignore

save

save(data)

Source code in kedro_datasets/pickle/pickle_dataset.py

def save(self, data: Any) -> None:
    save_path = get_filepath_str(self._get_save_path(), self._protocol)

    with self._fs.open(save_path, **self._fs_open_args_save) as fs_file:
        try:
            imported_backend = importlib.import_module(self._backend)
            imported_backend.dump(data, fs_file, **self._save_args)  # type: ignore
        except Exception as exc:
            raise DatasetError(
                f"{data.__class__} was not serialised due to: {exc}"
            ) from exc

    self._invalidate_cache()