548 lines
21 KiB
Python
548 lines
21 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Training helper that checkpoints models and creates session."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import time
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import numpy as np
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from tensorflow.python.client import session
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from tensorflow.python.framework import errors
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from tensorflow.python.framework import ops
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from tensorflow.python.platform import tf_logging as logging
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from tensorflow.python.training import saver as saver_mod
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from tensorflow.python.util.tf_export import tf_export
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def _maybe_name(obj):
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"""Returns object name if it has one, or a message otherwise.
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This is useful for names that apper in error messages.
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Args:
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obj: Object to get the name of.
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Returns:
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name, "None", or a "no name" message.
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"""
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if obj is None:
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return "None"
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elif hasattr(obj, "name"):
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return obj.name
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else:
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return "<no name for %s>" % type(obj)
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@tf_export("train.SessionManager")
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class SessionManager(object):
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"""Training helper that restores from checkpoint and creates session.
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This class is a small wrapper that takes care of session creation and
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checkpoint recovery. It also provides functions that to facilitate
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coordination among multiple training threads or processes.
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* Checkpointing trained variables as the training progresses.
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* Initializing variables on startup, restoring them from the most recent
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checkpoint after a crash, or wait for checkpoints to become available.
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### Usage:
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```python
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with tf.Graph().as_default():
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...add operations to the graph...
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# Create a SessionManager that will checkpoint the model in '/tmp/mydir'.
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sm = SessionManager()
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sess = sm.prepare_session(master, init_op, saver, checkpoint_dir)
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# Use the session to train the graph.
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while True:
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sess.run(<my_train_op>)
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```
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`prepare_session()` initializes or restores a model. It requires `init_op`
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and `saver` as an argument.
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A second process could wait for the model to be ready by doing the following:
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```python
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with tf.Graph().as_default():
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...add operations to the graph...
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# Create a SessionManager that will wait for the model to become ready.
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sm = SessionManager()
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sess = sm.wait_for_session(master)
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# Use the session to train the graph.
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while True:
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sess.run(<my_train_op>)
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```
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`wait_for_session()` waits for a model to be initialized by other processes.
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"""
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def __init__(self,
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local_init_op=None,
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ready_op=None,
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ready_for_local_init_op=None,
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graph=None,
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recovery_wait_secs=30,
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local_init_run_options=None):
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"""Creates a SessionManager.
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The `local_init_op` is an `Operation` that is run always after a new session
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was created. If `None`, this step is skipped.
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The `ready_op` is an `Operation` used to check if the model is ready. The
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model is considered ready if that operation returns an empty 1D string
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tensor. If the operation returns a non empty 1D string tensor, the elements
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are concatenated and used to indicate to the user why the model is not
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ready.
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The `ready_for_local_init_op` is an `Operation` used to check if the model
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is ready to run local_init_op. The model is considered ready if that
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operation returns an empty 1D string tensor. If the operation returns a non
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empty 1D string tensor, the elements are concatenated and used to indicate
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to the user why the model is not ready.
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If `ready_op` is `None`, the model is not checked for readiness.
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`recovery_wait_secs` is the number of seconds between checks that
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the model is ready. It is used by processes to wait for a model to
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be initialized or restored. Defaults to 30 seconds.
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Args:
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local_init_op: An `Operation` run immediately after session creation.
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Usually used to initialize tables and local variables.
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ready_op: An `Operation` to check if the model is initialized.
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ready_for_local_init_op: An `Operation` to check if the model is ready
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to run local_init_op.
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graph: The `Graph` that the model will use.
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recovery_wait_secs: Seconds between checks for the model to be ready.
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local_init_run_options: RunOptions to be passed to session.run when
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executing the local_init_op.
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Raises:
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ValueError: If ready_for_local_init_op is not None but local_init_op is
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None
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"""
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# Sets default values of arguments.
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if graph is None:
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graph = ops.get_default_graph()
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self._local_init_op = local_init_op
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self._ready_op = ready_op
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self._ready_for_local_init_op = ready_for_local_init_op
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self._graph = graph
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self._recovery_wait_secs = recovery_wait_secs
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self._target = None
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self._local_init_run_options = local_init_run_options
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if ready_for_local_init_op is not None and local_init_op is None:
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raise ValueError("If you pass a ready_for_local_init_op "
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"you must also pass a local_init_op "
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", ready_for_local_init_op [%s]" %
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ready_for_local_init_op)
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def _restore_checkpoint(self,
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master,
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saver=None,
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checkpoint_dir=None,
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checkpoint_filename_with_path=None,
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wait_for_checkpoint=False,
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max_wait_secs=7200,
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config=None):
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"""Creates a `Session`, and tries to restore a checkpoint.
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Args:
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master: `String` representation of the TensorFlow master to use.
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saver: A `Saver` object used to restore a model.
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checkpoint_dir: Path to the checkpoint files. The latest checkpoint in the
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dir will be used to restore.
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checkpoint_filename_with_path: Full file name path to the checkpoint file.
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wait_for_checkpoint: Whether to wait for checkpoint to become available.
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max_wait_secs: Maximum time to wait for checkpoints to become available.
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config: Optional `ConfigProto` proto used to configure the session.
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Returns:
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A pair (sess, is_restored) where 'is_restored' is `True` if
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the session could be restored, `False` otherwise.
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Raises:
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ValueError: If both checkpoint_dir and checkpoint_filename_with_path are
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set.
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"""
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self._target = master
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sess = session.Session(self._target, graph=self._graph, config=config)
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if checkpoint_dir and checkpoint_filename_with_path:
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raise ValueError("Can not provide both checkpoint_dir and "
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"checkpoint_filename_with_path.")
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# If either saver or checkpoint_* is not specified, cannot restore. Just
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# return.
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if not saver or not (checkpoint_dir or checkpoint_filename_with_path):
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return sess, False
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if checkpoint_filename_with_path:
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saver.restore(sess, checkpoint_filename_with_path)
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return sess, True
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# Waits up until max_wait_secs for checkpoint to become available.
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wait_time = 0
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ckpt = saver_mod.get_checkpoint_state(checkpoint_dir)
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while not ckpt or not ckpt.model_checkpoint_path:
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if wait_for_checkpoint and wait_time < max_wait_secs:
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logging.info("Waiting for checkpoint to be available.")
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time.sleep(self._recovery_wait_secs)
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wait_time += self._recovery_wait_secs
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ckpt = saver_mod.get_checkpoint_state(checkpoint_dir)
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else:
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return sess, False
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# Loads the checkpoint.
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saver.restore(sess, ckpt.model_checkpoint_path)
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saver.recover_last_checkpoints(ckpt.all_model_checkpoint_paths)
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return sess, True
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def prepare_session(self,
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master,
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init_op=None,
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saver=None,
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checkpoint_dir=None,
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checkpoint_filename_with_path=None,
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wait_for_checkpoint=False,
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max_wait_secs=7200,
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config=None,
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init_feed_dict=None,
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init_fn=None):
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"""Creates a `Session`. Makes sure the model is ready to be used.
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Creates a `Session` on 'master'. If a `saver` object is passed in, and
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`checkpoint_dir` points to a directory containing valid checkpoint
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files, then it will try to recover the model from checkpoint. If
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no checkpoint files are available, and `wait_for_checkpoint` is
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`True`, then the process would check every `recovery_wait_secs`,
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up to `max_wait_secs`, for recovery to succeed.
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If the model cannot be recovered successfully then it is initialized by
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running the `init_op` and calling `init_fn` if they are provided.
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The `local_init_op` is also run after init_op and init_fn, regardless of
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whether the model was recovered successfully, but only if
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`ready_for_local_init_op` passes.
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If the model is recovered from a checkpoint it is assumed that all
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global variables have been initialized, in particular neither `init_op`
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nor `init_fn` will be executed.
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It is an error if the model cannot be recovered and no `init_op`
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or `init_fn` or `local_init_op` are passed.
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Args:
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master: `String` representation of the TensorFlow master to use.
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init_op: Optional `Operation` used to initialize the model.
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saver: A `Saver` object used to restore a model.
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checkpoint_dir: Path to the checkpoint files. The latest checkpoint in the
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dir will be used to restore.
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checkpoint_filename_with_path: Full file name path to the checkpoint file.
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wait_for_checkpoint: Whether to wait for checkpoint to become available.
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max_wait_secs: Maximum time to wait for checkpoints to become available.
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config: Optional `ConfigProto` proto used to configure the session.
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init_feed_dict: Optional dictionary that maps `Tensor` objects to feed
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values. This feed dictionary is passed to the session `run()` call when
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running the init op.
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init_fn: Optional callable used to initialize the model. Called after the
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optional `init_op` is called. The callable must accept one argument,
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the session being initialized.
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Returns:
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A `Session` object that can be used to drive the model.
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Raises:
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RuntimeError: If the model cannot be initialized or recovered.
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ValueError: If both checkpoint_dir and checkpoint_filename_with_path are
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set.
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"""
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sess, is_loaded_from_checkpoint = self._restore_checkpoint(
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master,
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saver,
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checkpoint_dir=checkpoint_dir,
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checkpoint_filename_with_path=checkpoint_filename_with_path,
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wait_for_checkpoint=wait_for_checkpoint,
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max_wait_secs=max_wait_secs,
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config=config)
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if not is_loaded_from_checkpoint:
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if init_op is None and not init_fn and self._local_init_op is None:
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raise RuntimeError("Model is not initialized and no init_op or "
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"init_fn or local_init_op was given")
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if init_op is not None:
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sess.run(init_op, feed_dict=init_feed_dict)
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if init_fn:
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init_fn(sess)
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local_init_success, msg = self._try_run_local_init_op(sess)
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if not local_init_success:
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raise RuntimeError(
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"Init operations did not make model ready for local_init. "
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"Init op: %s, init fn: %s, error: %s" % (_maybe_name(init_op),
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init_fn,
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msg))
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is_ready, msg = self._model_ready(sess)
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if not is_ready:
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raise RuntimeError(
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"Init operations did not make model ready. "
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"Init op: %s, init fn: %s, local_init_op: %s, error: %s" %
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(_maybe_name(init_op), init_fn, self._local_init_op, msg))
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return sess
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def recover_session(self,
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master,
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saver=None,
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checkpoint_dir=None,
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checkpoint_filename_with_path=None,
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wait_for_checkpoint=False,
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max_wait_secs=7200,
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config=None):
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"""Creates a `Session`, recovering if possible.
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Creates a new session on 'master'. If the session is not initialized
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and can be recovered from a checkpoint, recover it.
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Args:
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master: `String` representation of the TensorFlow master to use.
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saver: A `Saver` object used to restore a model.
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checkpoint_dir: Path to the checkpoint files. The latest checkpoint in the
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dir will be used to restore.
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checkpoint_filename_with_path: Full file name path to the checkpoint file.
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wait_for_checkpoint: Whether to wait for checkpoint to become available.
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max_wait_secs: Maximum time to wait for checkpoints to become available.
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config: Optional `ConfigProto` proto used to configure the session.
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Returns:
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A pair (sess, initialized) where 'initialized' is `True` if
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the session could be recovered and initialized, `False` otherwise.
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Raises:
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ValueError: If both checkpoint_dir and checkpoint_filename_with_path are
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set.
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"""
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sess, is_loaded_from_checkpoint = self._restore_checkpoint(
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master,
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saver,
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checkpoint_dir=checkpoint_dir,
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checkpoint_filename_with_path=checkpoint_filename_with_path,
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wait_for_checkpoint=wait_for_checkpoint,
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max_wait_secs=max_wait_secs,
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config=config)
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# Always try to run local_init_op
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local_init_success, msg = self._try_run_local_init_op(sess)
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if not is_loaded_from_checkpoint:
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# Do not need to run checks for readiness
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return sess, False
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restoring_file = checkpoint_dir or checkpoint_filename_with_path
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if not local_init_success:
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logging.info(
|
||
|
|
"Restoring model from %s did not make model ready for local init:"
|
||
|
|
" %s", restoring_file, msg)
|
||
|
|
return sess, False
|
||
|
|
|
||
|
|
is_ready, msg = self._model_ready(sess)
|
||
|
|
if not is_ready:
|
||
|
|
logging.info("Restoring model from %s did not make model ready: %s",
|
||
|
|
restoring_file, msg)
|
||
|
|
return sess, False
|
||
|
|
|
||
|
|
logging.info("Restored model from %s", restoring_file)
|
||
|
|
return sess, is_loaded_from_checkpoint
|
||
|
|
|
||
|
|
def wait_for_session(self, master, config=None, max_wait_secs=float("Inf")):
|
||
|
|
"""Creates a new `Session` and waits for model to be ready.
|
||
|
|
|
||
|
|
Creates a new `Session` on 'master'. Waits for the model to be
|
||
|
|
initialized or recovered from a checkpoint. It's expected that
|
||
|
|
another thread or process will make the model ready, and that this
|
||
|
|
is intended to be used by threads/processes that participate in a
|
||
|
|
distributed training configuration where a different thread/process
|
||
|
|
is responsible for initializing or recovering the model being trained.
|
||
|
|
|
||
|
|
NB: The amount of time this method waits for the session is bounded
|
||
|
|
by max_wait_secs. By default, this function will wait indefinitely.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
master: `String` representation of the TensorFlow master to use.
|
||
|
|
config: Optional ConfigProto proto used to configure the session.
|
||
|
|
max_wait_secs: Maximum time to wait for the session to become available.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Session`. May be None if the operation exceeds the timeout
|
||
|
|
specified by config.operation_timeout_in_ms.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
tf.DeadlineExceededError: if the session is not available after
|
||
|
|
max_wait_secs.
|
||
|
|
"""
|
||
|
|
self._target = master
|
||
|
|
|
||
|
|
if max_wait_secs is None:
|
||
|
|
max_wait_secs = float("Inf")
|
||
|
|
timer = _CountDownTimer(max_wait_secs)
|
||
|
|
|
||
|
|
while True:
|
||
|
|
sess = session.Session(self._target, graph=self._graph, config=config)
|
||
|
|
not_ready_msg = None
|
||
|
|
not_ready_local_msg = None
|
||
|
|
local_init_success, not_ready_local_msg = self._try_run_local_init_op(
|
||
|
|
sess)
|
||
|
|
if local_init_success:
|
||
|
|
# Successful if local_init_op is None, or ready_for_local_init_op passes
|
||
|
|
is_ready, not_ready_msg = self._model_ready(sess)
|
||
|
|
if is_ready:
|
||
|
|
return sess
|
||
|
|
|
||
|
|
self._safe_close(sess)
|
||
|
|
|
||
|
|
# Do we have enough time left to try again?
|
||
|
|
remaining_ms_after_wait = (
|
||
|
|
timer.secs_remaining() - self._recovery_wait_secs)
|
||
|
|
if remaining_ms_after_wait < 0:
|
||
|
|
raise errors.DeadlineExceededError(
|
||
|
|
None, None,
|
||
|
|
"Session was not ready after waiting %d secs." % (max_wait_secs,))
|
||
|
|
|
||
|
|
logging.info("Waiting for model to be ready. "
|
||
|
|
"Ready_for_local_init_op: %s, ready: %s",
|
||
|
|
not_ready_local_msg, not_ready_msg)
|
||
|
|
time.sleep(self._recovery_wait_secs)
|
||
|
|
|
||
|
|
def _safe_close(self, sess):
|
||
|
|
"""Closes a session without raising an exception.
|
||
|
|
|
||
|
|
Just like sess.close() but ignores exceptions.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
sess: A `Session`.
|
||
|
|
"""
|
||
|
|
# pylint: disable=broad-except
|
||
|
|
try:
|
||
|
|
sess.close()
|
||
|
|
except Exception:
|
||
|
|
# Intentionally not logging to avoid user complaints that
|
||
|
|
# they get cryptic errors. We really do not care that Close
|
||
|
|
# fails.
|
||
|
|
pass
|
||
|
|
# pylint: enable=broad-except
|
||
|
|
|
||
|
|
def _model_ready(self, sess):
|
||
|
|
"""Checks if the model is ready or not.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
sess: A `Session`.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A tuple (is_ready, msg), where is_ready is True if ready and False
|
||
|
|
otherwise, and msg is `None` if the model is ready, a `String` with the
|
||
|
|
reason why it is not ready otherwise.
|
||
|
|
"""
|
||
|
|
return _ready(self._ready_op, sess, "Model not ready")
|
||
|
|
|
||
|
|
def _model_ready_for_local_init(self, sess):
|
||
|
|
"""Checks if the model is ready to run local_init_op.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
sess: A `Session`.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A tuple (is_ready, msg), where is_ready is True if ready to run
|
||
|
|
local_init_op and False otherwise, and msg is `None` if the model is
|
||
|
|
ready to run local_init_op, a `String` with the reason why it is not ready
|
||
|
|
otherwise.
|
||
|
|
"""
|
||
|
|
return _ready(self._ready_for_local_init_op, sess,
|
||
|
|
"Model not ready for local init")
|
||
|
|
|
||
|
|
def _try_run_local_init_op(self, sess):
|
||
|
|
"""Tries to run _local_init_op, if not None, and is ready for local init.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
sess: A `Session`.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A tuple (is_successful, msg), where is_successful is True if
|
||
|
|
_local_init_op is None, or we ran _local_init_op, and False otherwise;
|
||
|
|
and msg is a `String` with the reason why the model was not ready to run
|
||
|
|
local init.
|
||
|
|
"""
|
||
|
|
if self._local_init_op is not None:
|
||
|
|
is_ready_for_local_init, msg = self._model_ready_for_local_init(sess)
|
||
|
|
if is_ready_for_local_init:
|
||
|
|
logging.info("Running local_init_op.")
|
||
|
|
sess.run(self._local_init_op, options=self._local_init_run_options)
|
||
|
|
logging.info("Done running local_init_op.")
|
||
|
|
return True, None
|
||
|
|
else:
|
||
|
|
return False, msg
|
||
|
|
return True, None
|
||
|
|
|
||
|
|
|
||
|
|
def _ready(op, sess, msg):
|
||
|
|
"""Checks if the model is ready or not, as determined by op.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
op: An op, either _ready_op or _ready_for_local_init_op, which defines the
|
||
|
|
readiness of the model.
|
||
|
|
sess: A `Session`.
|
||
|
|
msg: A message to log to warning if not ready
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A tuple (is_ready, msg), where is_ready is True if ready and False
|
||
|
|
otherwise, and msg is `None` if the model is ready, a `String` with the
|
||
|
|
reason why it is not ready otherwise.
|
||
|
|
"""
|
||
|
|
if op is None:
|
||
|
|
return True, None
|
||
|
|
else:
|
||
|
|
try:
|
||
|
|
ready_value = sess.run(op)
|
||
|
|
# The model is considered ready if ready_op returns an empty 1-D tensor.
|
||
|
|
# Also compare to `None` and dtype being int32 for backward
|
||
|
|
# compatibility.
|
||
|
|
if (ready_value is None or ready_value.dtype == np.int32 or
|
||
|
|
ready_value.size == 0):
|
||
|
|
return True, None
|
||
|
|
else:
|
||
|
|
# TODO(sherrym): If a custom ready_op returns other types of tensor,
|
||
|
|
# or strings other than variable names, this message could be
|
||
|
|
# confusing.
|
||
|
|
non_initialized_varnames = ", ".join(
|
||
|
|
[i.decode("utf-8") for i in ready_value])
|
||
|
|
return False, "Variables not initialized: " + non_initialized_varnames
|
||
|
|
except errors.FailedPreconditionError as e:
|
||
|
|
if "uninitialized" not in str(e):
|
||
|
|
logging.warning("%s : error [%s]", msg, str(e))
|
||
|
|
raise e
|
||
|
|
return False, str(e)
|
||
|
|
|
||
|
|
|
||
|
|
class _CountDownTimer(object):
|
||
|
|
|
||
|
|
def __init__(self, duration_secs):
|
||
|
|
self._start_time_secs = time.time()
|
||
|
|
self._duration_secs = duration_secs
|
||
|
|
|
||
|
|
def secs_remaining(self):
|
||
|
|
diff = self._duration_secs - (time.time() - self._start_time_secs)
|
||
|
|
return max(0, diff)
|