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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Monitor is responsible for training, checkpointing and recovery."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.eager import context
from tensorflow.python.framework import errors
from tensorflow.python.ops import variables
class Monitor(object):
"""Executes training steps, recovers and checkpoints.
Note that this class is particularly preliminary, experimental, and
expected to change.
"""
# TODO(isaprykin): Support step functions that need multiple session calls.
# TODO(isaprykin): Support extra arguments to the step function.
# TODO(isaprykin): Support recovery, checkpointing and summaries.
def __init__(self, step_callable, session=None):
"""Initialize the Monitor with components for executing training steps.
Args:
step_callable: a training `Step` that's capable of signaling when done.
session: a `Session` instance that's needed for graph mode.
Raises:
ValueError: if `session` was provided for eager mode or not provided for
graph mode.
"""
if context.executing_eagerly():
if session is not None:
raise ValueError("Should not provide a `session` in Eager mode.")
self._run_step = step_callable
else:
if session is None:
raise ValueError("Should provide a `session` in Graph mode.")
self._run_step = session.make_callable(step_callable())
session.run(variables.global_variables_initializer())
def run_steps(self, num_steps=None):
step = 0
while num_steps is None or step < num_steps:
try:
self._run_step()
step += 1
except errors.OutOfRangeError:
break
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