Source code for dioptra_builtins.backend_configs.tensorflow

# This Software (Dioptra) is being made available as a public service by the
# National Institute of Standards and Technology (NIST), an Agency of the United
# States Department of Commerce. This software was developed in part by employees of
# NIST and in part by NIST contractors. Copyright in portions of this software that
# were developed by NIST contractors has been licensed or assigned to NIST. Pursuant
# to Title 17 United States Code Section 105, works of NIST employees are not
# subject to copyright protection in the United States. However, NIST may hold
# international copyright in software created by its employees and domestic
# copyright (or licensing rights) in portions of software that were assigned or
# licensed to NIST. To the extent that NIST holds copyright in this software, it is
# being made available under the Creative Commons Attribution 4.0 International
# license (CC BY 4.0). The disclaimers of the CC BY 4.0 license apply to all parts
# of the software developed or licensed by NIST.
#
# ACCESS THE FULL CC BY 4.0 LICENSE HERE:
# https://creativecommons.org/licenses/by/4.0/legalcode
"""A task plugin module for initializing and configuring Tensorflow."""

from __future__ import annotations

import structlog
from structlog.stdlib import BoundLogger

from dioptra import pyplugs
from dioptra.sdk.exceptions import TensorflowDependencyError
from dioptra.sdk.utilities.decorators import require_package

LOGGER: BoundLogger = structlog.stdlib.get_logger()


try:
    import tensorflow as tf

except ImportError:  # pragma: nocover
    LOGGER.warn(
        "Unable to import one or more optional packages, functionality may be reduced",
        package="tensorflow",
    )


@pyplugs.register
@require_package("tensorflow", exc_type=TensorflowDependencyError)
def init_tensorflow(seed: int) -> None:
    """Initializes Tensorflow to ensure reproducibility.

    This task plugin **must** be run before any other features from Tensorflow are used
    to ensure reproducibility.

    Args:
        seed: The seed to use for Tensorflow's random number generator.
    """
    tf.random.set_seed(seed)