kedro.extras.datasets.tensorflow.TensorFlowModelDataset

class kedro.extras.datasets.tensorflow.TensorFlowModelDataset(filepath, load_args=None, save_args=None, version=None, credentials=None, fs_args=None)[source]

TensorflowModelDataset loads and saves TensorFlow models. The underlying functionality is supported by, and passes input arguments through to, TensorFlow 2.X load_model and save_model methods.

Example usage for the YAML API:

tensorflow_model:
  type: tensorflow.TensorFlowModelDataset
  filepath: data/06_models/tensorflow_model.h5
  load_args:
    compile: False
  save_args:
    overwrite: True
    include_optimizer: False
  credentials: tf_creds

Example usage for the Python API:

from kedro.extras.datasets.tensorflow import TensorFlowModelDataset
import tensorflow as tf
import numpy as np

data_set = TensorFlowModelDataset("data/06_models/tensorflow_model.h5")
model = tf.keras.Model()
predictions = model.predict([...])

data_set.save(model)
loaded_model = data_set.load()
new_predictions = loaded_model.predict([...])
np.testing.assert_allclose(predictions, new_predictions, rtol=1e-6, atol=1e-6)

Attributes

DEFAULT_LOAD_ARGS

DEFAULT_SAVE_ARGS

Methods

exists()

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

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

Create a data set 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.
DEFAULT_LOAD_ARGS: Dict[str, Any] = {}
DEFAULT_SAVE_ARGS: Dict[str, Any] = {'save_format': 'tf'}
__init__(filepath, load_args=None, save_args=None, version=None, credentials=None, fs_args=None)[source]

Creates a new instance of TensorFlowModelDataset.

Parameters

  • filepath (str) – Filepath in POSIX format to a TensorFlow model directory 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.

  • load_args (Optional[Dict[str, Any]]) – TensorFlow options for loading models. Here you can find all available arguments: https://www.tensorflow.org/api_docs/python/tf/keras/models/load_model All defaults are preserved.

  • save_args (Optional[Dict[str, Any]]) – TensorFlow options for saving models. Here you can find all available arguments: https://www.tensorflow.org/api_docs/python/tf/keras/models/save_model All defaults are preserved, except for “save_format”, which is set to “tf”.

  • version (Optional[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 (Optional[Dict[str, Any]]) – Credentials required to get access to the underlying filesystem. E.g. for GCSFileSystem it should look like {‘token’: None}.

  • fs_args (Optional[Dict[str, Any]]) – Extra arguments to pass into underlying filesystem class constructor (e.g. {“project”: “my-project”} for GCSFileSystem).

exists()

Checks whether a data set’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)

Create a data set 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 data set is versioned. Has no effect on the data set if versioning was not enabled.

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

Return type

AbstractDataSet

Returns

An instance of an AbstractDataSet subclass.

Raises

DataSetError – When the function fails to create the data set from its config.

load()

Loads data by delegation to the provided load method.

Return type

TypeVar(_DO)

Returns

Data returned by the provided load method.

Raises

DataSetError – When underlying load method raises error.

release()

Release any cached data.

Raises

DataSetError – when underlying release method raises error.

Return type

None

resolve_load_version()

Compute the version the dataset should be loaded with.

Return type

Optional[str]

resolve_save_version()

Compute the version the dataset should be saved with.

Return type

Optional[str]

save(data)

Saves data by delegation to the provided save method.

Parameters

data (TypeVar(_DI)) – 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