"""``TensorFlowModelDataset`` is a dataset implementation which can save and load
TensorFlow models.
"""
import copy
import tempfile
import warnings
from pathlib import PurePath, PurePosixPath
from typing import Any, Dict
import fsspec
import tensorflow as tf
from kedro.io.core import Version, get_filepath_str, get_protocol_and_path
from kedro_datasets import KedroDeprecationWarning
from kedro_datasets._io import AbstractVersionedDataset, DatasetError
TEMPORARY_H5_FILE = "tmp_tensorflow_model.h5"
[docs]class TensorFlowModelDataset(AbstractVersionedDataset[tf.keras.Model, tf.keras.Model]):
"""``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 <https://kedro.readthedocs.io/en/stable/data/\
data_catalog_yaml_examples.html>`_:
.. code-block:: yaml
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 <https://kedro.readthedocs.io/en/stable/data/\
advanced_data_catalog_usage.html>`_:
::
>>> from kedro_datasets.tensorflow import TensorFlowModelDataset
>>> import tensorflow as tf
>>> import numpy as np
>>>
>>> dataset = TensorFlowModelDataset("data/06_models/tensorflow_model.h5")
>>> model = tf.keras.Model()
>>> predictions = model.predict([...])
>>>
>>> dataset.save(model)
>>> loaded_model = dataset.load()
>>> new_predictions = loaded_model.predict([...])
>>> np.testing.assert_allclose(predictions, new_predictions, rtol=1e-6, atol=1e-6)
"""
DEFAULT_LOAD_ARGS: Dict[str, Any] = {}
DEFAULT_SAVE_ARGS: Dict[str, Any] = {"save_format": "tf"}
[docs] def __init__( # noqa: PLR0913
self,
filepath: str,
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,
metadata: Dict[str, Any] = None,
) -> None:
"""Creates a new instance of ``TensorFlowModelDataset``.
Args:
filepath: 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: 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: 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: 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``).
metadata: Any arbitrary metadata.
This is ignored by Kedro, but may be consumed by users or external plugins.
"""
_fs_args = copy.deepcopy(fs_args) or {}
_credentials = copy.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)
self.metadata = metadata
super().__init__(
filepath=PurePosixPath(path),
version=version,
exists_function=self._fs.exists,
glob_function=self._fs.glob,
)
self._tmp_prefix = "kedro_tensorflow_tmp" # temp prefix pattern
# Handle default load and save arguments
self._load_args = copy.deepcopy(self.DEFAULT_LOAD_ARGS)
if load_args is not None:
self._load_args.update(load_args)
self._save_args = copy.deepcopy(self.DEFAULT_SAVE_ARGS)
if save_args is not None:
self._save_args.update(save_args)
self._is_h5 = self._save_args.get("save_format") == "h5"
def _load(self) -> tf.keras.Model:
load_path = get_filepath_str(self._get_load_path(), self._protocol)
with tempfile.TemporaryDirectory(prefix=self._tmp_prefix) as path:
if self._is_h5:
path = str(PurePath(path) / TEMPORARY_H5_FILE) # noqa: PLW2901
self._fs.copy(load_path, path)
else:
self._fs.get(load_path, path, recursive=True)
# Pass the local temporary directory/file path to keras.load_model
device_name = self._load_args.pop("tf_device", None)
if device_name:
with tf.device(device_name):
model = tf.keras.models.load_model(path, **self._load_args)
else:
model = tf.keras.models.load_model(path, **self._load_args)
return model
def _save(self, data: tf.keras.Model) -> None:
save_path = get_filepath_str(self._get_save_path(), self._protocol)
with tempfile.TemporaryDirectory(prefix=self._tmp_prefix) as path:
if self._is_h5:
path = str(PurePath(path) / TEMPORARY_H5_FILE) # noqa: PLW2901
tf.keras.models.save_model(data, path, **self._save_args)
# Use fsspec to take from local tempfile directory/file and
# put in ArbitraryFileSystem
if self._is_h5:
self._fs.copy(path, save_path)
else:
if self._fs.exists(save_path):
self._fs.rm(save_path, recursive=True)
self._fs.put(path, save_path, recursive=True)
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 _describe(self) -> Dict[str, Any]:
return {
"filepath": self._filepath,
"protocol": self._protocol,
"load_args": self._load_args,
"save_args": self._save_args,
"version": self._version,
}
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)
_DEPRECATED_CLASSES = {
"TensorFlowModelDataSet": TensorFlowModelDataset,
}
def __getattr__(name):
if name in _DEPRECATED_CLASSES:
alias = _DEPRECATED_CLASSES[name]
warnings.warn(
f"{repr(name)} has been renamed to {repr(alias.__name__)}, "
f"and the alias will be removed in Kedro-Datasets 2.0.0",
KedroDeprecationWarning,
stacklevel=2,
)
return alias
raise AttributeError( # pragma: no cover
f"module {repr(__name__)} has no attribute {repr(name)}"
)