kedro_datasets.pickle.PickleDataset

class kedro_datasets.pickle.PickleDataset(*, filepath, backend='pickle', load_args=None, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)[source]

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 usage for 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

Example usage for the Python API:

 from kedro_datasets.pickle import PickleDataset
 import pandas as pd

 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)

Attributes

DEFAULT_FS_ARGS

DEFAULT_LOAD_ARGS

DEFAULT_SAVE_ARGS

Methods

exists()

Checks whether a dataset's output already exists by calling the provided _exists() method.

from_config(name, config[, load_version, ...])

Create a dataset instance using the configuration provided.

load()

Loads data by delegation to the provided load method.

release()

Release any cached data.

resolve_load_version()

Compute the version the dataset should be loaded with.

resolve_save_version()

Compute the version the dataset should be saved with.

save(data)

Saves data by delegation to the provided save method.

to_config()

Converts the dataset instance into a dictionary-based configuration for serialization.
DEFAULT_FS_ARGS: dict[str, Any] = {'open_args_save': {'mode': 'wb'}}
DEFAULT_LOAD_ARGS: dict[str, Any] = {}
DEFAULT_SAVE_ARGS: dict[str, Any] = {}
__init__(*, filepath, backend='pickle', load_args=None, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)[source]

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

Parameters:
Raises:

  • ValueError – If backend does not satisfy the pickle interface.

  • ImportError – If the backend module could not be imported.

exists()[source]

Checks whether a dataset’s output already exists by calling the provided _exists() method.

Return type:

bool

Returns:

Flag indicating whether the output already exists.

Raises:

DatasetError – when underlying exists method raises error.

classmethod from_config(name, config, load_version=None, save_version=None)[source]

Create a dataset instance using the configuration provided.

Parameters:

  • name (str) – Data set name.

  • config (dict[str, Any]) – Data set config dictionary.

  • load_version (Optional[str]) – Version string to be used for load operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

  • save_version (Optional[str]) – Version string to be used for save operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

Return type:

AbstractDataset

Returns:

An instance of an AbstractDataset subclass.

Raises:

DatasetError – When the function fails to create the dataset from its config.

load()[source]

Loads data by delegation to the provided load method.

Return type:

Any

Returns:

Data returned by the provided load method.

Raises:

DatasetError – When underlying load method raises error.

release()[source]

Release any cached data.

Raises:

DatasetError – when underlying release method raises error.

Return type:

None

resolve_load_version()[source]

Compute the version the dataset should be loaded with.

Return type:

Optional[str]

resolve_save_version()[source]

Compute the version the dataset should be saved with.

Return type:

Optional[str]

save(data)[source]

Saves data by delegation to the provided save method.

Parameters:

data (Any) – the value to be saved by provided save method.

Raises:

  • DatasetError – when underlying save method raises error.

  • FileNotFoundError – when save method got file instead of dir, on Windows.

  • NotADirectoryError – when save method got file instead of dir, on Unix.

Return type:

None

to_config()[source]

Converts the dataset instance into a dictionary-based configuration for serialization. Ensures that any subclass-specific details are handled, with additional logic for versioning and caching implemented for CachedDataset.

Adds a key for the dataset’s type using its module and class name and includes the initialization arguments.

For CachedDataset it extracts the underlying dataset’s configuration, handles the versioned flag and removes unnecessary metadata. It also ensures the embedded dataset’s configuration is appropriately flattened or transformed.

If the dataset has a version key, it sets the versioned flag in the configuration.

Removes the metadata key from the configuration if present.

Return type:

dict[str, Any]

Returns:

A dictionary containing the dataset’s type and initialization arguments.