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]

Bases: kedro.io.core.AbstractVersionedDataSet

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:

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

data_set = TensorFlowModelDataset("saved_model_path")
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

TensorFlowModelDataset.DEFAULT_LOAD_ARGS
TensorFlowModelDataset.DEFAULT_SAVE_ARGS

Methods

TensorFlowModelDataset.__init__(filepath[, …]) Creates a new instance of TensorFlowModelDataset.
TensorFlowModelDataset.exists() Checks whether a data set’s output already exists by calling the provided _exists() method.
TensorFlowModelDataset.from_config(name, config) Create a data set instance using the configuration provided.
TensorFlowModelDataset.load() Loads data by delegation to the provided load method.
TensorFlowModelDataset.release() Release any cached data.
TensorFlowModelDataset.resolve_load_version() Compute the version the dataset should be loaded with.
TensorFlowModelDataset.resolve_save_version() Compute the version the dataset should be saved with.
TensorFlowModelDataset.save(data) Saves data by delegation to the provided save method.
DEFAULT_LOAD_ARGS = {}
DEFAULT_SAVE_ARGS = {'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 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).
Return type:

None

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:Any
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 (Any) – the value to be saved by provided save method.
Raises:DataSetError – when underlying save method raises error.
Return type:None