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 – Data set name.

  • config – Data set config dictionary.

  • load_version – 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 – 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.

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:

str | None

resolve_save_version()

Compute the version the dataset should be saved with.

Return type:

str | None

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