Source code for kedro.extras.datasets.pickle.pickle_dataset

"""``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.
"""
import importlib
from copy import deepcopy
from pathlib import PurePosixPath
from typing import Any, Dict

import fsspec

from kedro.io.core import (
    AbstractVersionedDataSet,
    DataSetError,
    Version,
    get_filepath_str,
    get_protocol_and_path,
)


[docs]class PickleDataSet(AbstractVersionedDataSet): """``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. Example adding a catalog entry with `YAML API <https://kedro.readthedocs.io/en/stable/05_data/\ 01_data_catalog.html#using-the-data-catalog-with-the-yaml-api>`_: .. code-block:: yaml >>> 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: >>> compression: lz4 >>> load_args: >>> compression: lz4 Example using Python API: :: >>> from kedro.extras.datasets.pickle import PickleDataSet >>> import pandas as pd >>> >>> data = pd.DataFrame({'col1': [1, 2], 'col2': [4, 5], >>> 'col3': [5, 6]}) >>> >>> # data_set = PickleDataSet(filepath="gcs://bucket/test.pkl") >>> data_set = PickleDataSet(filepath="test.pkl", backend="pickle") >>> data_set.save(data) >>> reloaded = data_set.load() >>> assert data.equals(reloaded) >>> >>> # Add "compress_pickle[lz4]" to requirements.txt >>> data_set = PickleDataSet(filepath="test.pickle.lz4", >>> backend="compress_pickle", >>> load_args={"compression":"lz4"}, >>> save_args={"compression":"lz4"}) >>> data_set.save(data) >>> reloaded = data_set.load() >>> assert data.equals(reloaded) """ DEFAULT_LOAD_ARGS = {} # type: Dict[str, Any] DEFAULT_SAVE_ARGS = {} # type: Dict[str, Any] # pylint: disable=too-many-arguments,too-many-locals
[docs] def __init__( self, filepath: str, backend: str = "pickle", load_args: Dict[str, Any] = None, save_args: Dict[str, Any] = None, version: Version = None, credentials: Dict[str, Any] = None, fs_args: Dict[str, Any] = None, ) -> None: """Creates a new instance of ``PickleDataSet`` pointing to a concrete Pickle file on a specific filesystem. ``PickleDataSet`` supports custom backends to serialize/deserialize 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/dill.html#dill._dill.load compress_pickle.load: https://lucianopaz.github.io/compress_pickle/html/api/compress_pickle.html#compress_pickle.compress_pickle.load 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/dill.html#dill._dill.dump compress_pickle.dump: https://lucianopaz.github.io/compress_pickle/html/api/compress_pickle.html#compress_pickle.compress_pickle.dump 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. Raises: ValueError: If ``backend`` does not satisfy the `pickle` interface. ImportError: If the ``backend`` module could not be imported. """ 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) super().__init__( filepath=PurePosixPath(path), version=version, exists_function=self._fs.exists, glob_function=self._fs.glob, ) self._backend = imported_backend # Handle default load and save arguments self._load_args = deepcopy(self.DEFAULT_LOAD_ARGS) if load_args is not None: self._load_args.update(load_args) self._save_args = deepcopy(self.DEFAULT_SAVE_ARGS) if save_args is not None: self._save_args.update(save_args) _fs_open_args_save.setdefault("mode", "wb") self._fs_open_args_load = _fs_open_args_load self._fs_open_args_save = _fs_open_args_save
def _describe(self) -> Dict[str, Any]: return dict( filepath=self._filepath, backend=self._backend, protocol=self._protocol, load_args=self._load_args, save_args=self._save_args, version=self._version, ) 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: return self._backend.load(fs_file, **self._load_args) # type: ignore 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: self._backend.dump(data, fs_file, **self._save_args) # type: ignore except Exception as exc: raise DataSetError( f"{data.__class__} was not serialized due to: {exc}" ) from exc self._invalidate_cache() 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) def _release(self) -> None: super()._release() self._invalidate_cache() def _invalidate_cache(self) -> None: """Invalidate underlying filesystem caches.""" filepath = get_filepath_str(self._filepath, self._protocol) self._fs.invalidate_cache(filepath)