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7a81491366
STT-tensorflow/tensorflow/python/training/checkpoint_management.py
Allen Lavoie 7a81491366 Add tf.contrib.checkpoint.CheckpointManager for deleting old checkpoints 
Removes a bit of boilerplate from training loops (making a prefix from a directory).

Also clarifies the recovery of checkpoint lists (like tf.train.Saver.recover_last_checkpoints, but automatic and more thorough). Adds a couple fields to the CheckpointState proto to support this.

Should live in contrib until I make it work well with tf.keras.Model.save_weights. When used together, save_weights needs to number its checkpoints. (There's a TODO for this.)

PiperOrigin-RevId: 208566198
2018-08-13 16:53:59 -07:00

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# Copyright 2015 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.
# ==============================================================================
# pylint: disable=invalid-name
"""Save and restore variables."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import os.path
import re
import time
from google.protobuf import text_format
from tensorflow.core.protobuf import saver_pb2
from tensorflow.python.eager import context
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.lib.io import file_io
from tensorflow.python.ops import variable_scope
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState
from tensorflow.python.util.tf_export import tf_export
def _GetCheckpointFilename(save_dir, latest_filename):
"""Returns a filename for storing the CheckpointState.
Args:
save_dir: The directory for saving and restoring checkpoints.
latest_filename: Name of the file in 'save_dir' that is used
to store the CheckpointState.
Returns:
The path of the file that contains the CheckpointState proto.
"""
if latest_filename is None:
latest_filename = "checkpoint"
return os.path.join(save_dir, latest_filename)
@tf_export("train.generate_checkpoint_state_proto")
def generate_checkpoint_state_proto(save_dir,
model_checkpoint_path,
all_model_checkpoint_paths=None,
all_model_checkpoint_timestamps=None,
last_preserved_timestamp=None):
"""Generates a checkpoint state proto.
Args:
save_dir: Directory where the model was saved.
model_checkpoint_path: The checkpoint file.
all_model_checkpoint_paths: List of strings. Paths to all not-yet-deleted
checkpoints, sorted from oldest to newest. If this is a non-empty list,
the last element must be equal to model_checkpoint_path. These paths
are also saved in the CheckpointState proto.
all_model_checkpoint_timestamps: A list of floats, indicating the number of
seconds since the Epoch when each checkpoint was generated.
last_preserved_timestamp: A float, indicating the number of seconds since
the Epoch when the last preserved checkpoint was written, e.g. due to a
`keep_checkpoint_every_n_hours` parameter (see
`tf.contrib.checkpoint.CheckpointManager` for an implementation).
Returns:
CheckpointState proto with model_checkpoint_path and
all_model_checkpoint_paths updated to either absolute paths or
relative paths to the current save_dir.
Raises:
ValueError: If `all_model_checkpoint_timestamps` was provided but its length
does not match `all_model_checkpoint_paths`.
"""
if all_model_checkpoint_paths is None:
all_model_checkpoint_paths = []
if (not all_model_checkpoint_paths or
all_model_checkpoint_paths[-1] != model_checkpoint_path):
logging.info("%s is not in all_model_checkpoint_paths. Manually adding it.",
model_checkpoint_path)
all_model_checkpoint_paths.append(model_checkpoint_path)
if (all_model_checkpoint_timestamps
and (len(all_model_checkpoint_timestamps)
!= len(all_model_checkpoint_paths))):
raise ValueError(
("Checkpoint timestamps, if provided, must match checkpoint paths (got "
"paths %s and timestamps %s)")
% (all_model_checkpoint_paths, all_model_checkpoint_timestamps))
# Relative paths need to be rewritten to be relative to the "save_dir"
# if model_checkpoint_path already contains "save_dir".
if not os.path.isabs(save_dir):
if not os.path.isabs(model_checkpoint_path):
model_checkpoint_path = os.path.relpath(model_checkpoint_path, save_dir)
for i in range(len(all_model_checkpoint_paths)):
p = all_model_checkpoint_paths[i]
if not os.path.isabs(p):
all_model_checkpoint_paths[i] = os.path.relpath(p, save_dir)
coord_checkpoint_proto = CheckpointState(
model_checkpoint_path=model_checkpoint_path,
all_model_checkpoint_paths=all_model_checkpoint_paths,
all_model_checkpoint_timestamps=all_model_checkpoint_timestamps,
last_preserved_timestamp=last_preserved_timestamp)
return coord_checkpoint_proto
@tf_export("train.update_checkpoint_state")
def update_checkpoint_state(save_dir,
model_checkpoint_path,
all_model_checkpoint_paths=None,
latest_filename=None,
all_model_checkpoint_timestamps=None,
last_preserved_timestamp=None):
"""Updates the content of the 'checkpoint' file.
This updates the checkpoint file containing a CheckpointState
proto.
Args:
save_dir: Directory where the model was saved.
model_checkpoint_path: The checkpoint file.
all_model_checkpoint_paths: List of strings. Paths to all not-yet-deleted
checkpoints, sorted from oldest to newest. If this is a non-empty list,
the last element must be equal to model_checkpoint_path. These paths
are also saved in the CheckpointState proto.
latest_filename: Optional name of the checkpoint file. Default to
'checkpoint'.
all_model_checkpoint_timestamps: Optional list of timestamps (floats,
seconds since the Epoch) indicating when the checkpoints in
`all_model_checkpoint_paths` were created.
last_preserved_timestamp: A float, indicating the number of seconds since
the Epoch when the last preserved checkpoint was written, e.g. due to a
`keep_checkpoint_every_n_hours` parameter (see
`tf.contrib.checkpoint.CheckpointManager` for an implementation).
Raises:
RuntimeError: If any of the model checkpoint paths conflict with the file
containing CheckpointSate.
"""
update_checkpoint_state_internal(
save_dir=save_dir,
model_checkpoint_path=model_checkpoint_path,
all_model_checkpoint_paths=all_model_checkpoint_paths,
latest_filename=latest_filename,
save_relative_paths=False,
all_model_checkpoint_timestamps=all_model_checkpoint_timestamps,
last_preserved_timestamp=last_preserved_timestamp)
def update_checkpoint_state_internal(save_dir,
model_checkpoint_path,
all_model_checkpoint_paths=None,
latest_filename=None,
save_relative_paths=False,
all_model_checkpoint_timestamps=None,
last_preserved_timestamp=None):
"""Updates the content of the 'checkpoint' file.
This updates the checkpoint file containing a CheckpointState
proto.
Args:
save_dir: Directory where the model was saved.
model_checkpoint_path: The checkpoint file.
all_model_checkpoint_paths: List of strings. Paths to all not-yet-deleted
checkpoints, sorted from oldest to newest. If this is a non-empty list,
the last element must be equal to model_checkpoint_path. These paths
are also saved in the CheckpointState proto.
latest_filename: Optional name of the checkpoint file. Default to
'checkpoint'.
save_relative_paths: If `True`, will write relative paths to the checkpoint
state file.
all_model_checkpoint_timestamps: Optional list of timestamps (floats,
seconds since the Epoch) indicating when the checkpoints in
`all_model_checkpoint_paths` were created.
last_preserved_timestamp: A float, indicating the number of seconds since
the Epoch when the last preserved checkpoint was written, e.g. due to a
`keep_checkpoint_every_n_hours` parameter (see
`tf.contrib.checkpoint.CheckpointManager` for an implementation).
Raises:
RuntimeError: If any of the model checkpoint paths conflict with the file
containing CheckpointSate.
"""
# Writes the "checkpoint" file for the coordinator for later restoration.
coord_checkpoint_filename = _GetCheckpointFilename(save_dir, latest_filename)
if save_relative_paths:
if os.path.isabs(model_checkpoint_path):
rel_model_checkpoint_path = os.path.relpath(
model_checkpoint_path, save_dir)
else:
rel_model_checkpoint_path = model_checkpoint_path
rel_all_model_checkpoint_paths = []
for p in all_model_checkpoint_paths:
if os.path.isabs(p):
rel_all_model_checkpoint_paths.append(os.path.relpath(p, save_dir))
else:
rel_all_model_checkpoint_paths.append(p)
ckpt = generate_checkpoint_state_proto(
save_dir,
rel_model_checkpoint_path,
all_model_checkpoint_paths=rel_all_model_checkpoint_paths,
all_model_checkpoint_timestamps=all_model_checkpoint_timestamps,
last_preserved_timestamp=last_preserved_timestamp)
else:
ckpt = generate_checkpoint_state_proto(
save_dir,
model_checkpoint_path,
all_model_checkpoint_paths=all_model_checkpoint_paths,
all_model_checkpoint_timestamps=all_model_checkpoint_timestamps,
last_preserved_timestamp=last_preserved_timestamp)
if coord_checkpoint_filename == ckpt.model_checkpoint_path:
raise RuntimeError("Save path '%s' conflicts with path used for "
"checkpoint state. Please use a different save path." %
model_checkpoint_path)
# Preventing potential read/write race condition by *atomically* writing to a
# file.
file_io.atomic_write_string_to_file(coord_checkpoint_filename,
text_format.MessageToString(ckpt))
@tf_export("train.get_checkpoint_state")
def get_checkpoint_state(checkpoint_dir, latest_filename=None):
"""Returns CheckpointState proto from the "checkpoint" file.
If the "checkpoint" file contains a valid CheckpointState
proto, returns it.
Args:
checkpoint_dir: The directory of checkpoints.
latest_filename: Optional name of the checkpoint file. Default to
'checkpoint'.
Returns:
A CheckpointState if the state was available, None
otherwise.
Raises:
ValueError: if the checkpoint read doesn't have model_checkpoint_path set.
"""
ckpt = None
coord_checkpoint_filename = _GetCheckpointFilename(checkpoint_dir,
latest_filename)
f = None
try:
# Check that the file exists before opening it to avoid
# many lines of errors from colossus in the logs.
if file_io.file_exists(coord_checkpoint_filename):
file_content = file_io.read_file_to_string(
coord_checkpoint_filename)
ckpt = CheckpointState()
text_format.Merge(file_content, ckpt)
if not ckpt.model_checkpoint_path:
raise ValueError("Invalid checkpoint state loaded from "
+ checkpoint_dir)
# For relative model_checkpoint_path and all_model_checkpoint_paths,
# prepend checkpoint_dir.
if not os.path.isabs(ckpt.model_checkpoint_path):
ckpt.model_checkpoint_path = os.path.join(checkpoint_dir,
ckpt.model_checkpoint_path)
for i in range(len(ckpt.all_model_checkpoint_paths)):
p = ckpt.all_model_checkpoint_paths[i]
if not os.path.isabs(p):
ckpt.all_model_checkpoint_paths[i] = os.path.join(checkpoint_dir, p)
except errors.OpError as e:
# It's ok if the file cannot be read
logging.warning("%s: %s", type(e).__name__, e)
logging.warning("%s: Checkpoint ignored", coord_checkpoint_filename)
return None
except text_format.ParseError as e:
logging.warning("%s: %s", type(e).__name__, e)
logging.warning("%s: Checkpoint ignored", coord_checkpoint_filename)
return None
finally:
if f:
f.close()
return ckpt
def _prefix_to_checkpoint_path(prefix, format_version):
"""Returns the pathname of a checkpoint file, given the checkpoint prefix.
For V1 checkpoint, simply returns the prefix itself (the data file). For V2,
returns the pathname to the index file.
Args:
prefix: a string, the prefix of a checkpoint.
format_version: the checkpoint format version that corresponds to the
prefix.
Returns:
The pathname of a checkpoint file, taking into account the checkpoint
format version.
"""
if format_version == saver_pb2.SaverDef.V2:
return prefix + ".index" # The index file identifies a checkpoint.
return prefix # Just the data file.
@tf_export("train.latest_checkpoint")
def latest_checkpoint(checkpoint_dir, latest_filename=None):
"""Finds the filename of latest saved checkpoint file.
Args:
checkpoint_dir: Directory where the variables were saved.
latest_filename: Optional name for the protocol buffer file that
contains the list of most recent checkpoint filenames.
See the corresponding argument to `Saver.save()`.
Returns:
The full path to the latest checkpoint or `None` if no checkpoint was found.
"""
# Pick the latest checkpoint based on checkpoint state.
ckpt = get_checkpoint_state(checkpoint_dir, latest_filename)
if ckpt and ckpt.model_checkpoint_path:
# Look for either a V2 path or a V1 path, with priority for V2.
v2_path = _prefix_to_checkpoint_path(ckpt.model_checkpoint_path,
saver_pb2.SaverDef.V2)
v1_path = _prefix_to_checkpoint_path(ckpt.model_checkpoint_path,
saver_pb2.SaverDef.V1)
if file_io.get_matching_files(v2_path) or file_io.get_matching_files(
v1_path):
return ckpt.model_checkpoint_path
else:
logging.error("Couldn't match files for checkpoint %s",
ckpt.model_checkpoint_path)
return None
@tf_export("train.checkpoint_exists")
def checkpoint_exists(checkpoint_prefix):
"""Checks whether a V1 or V2 checkpoint exists with the specified prefix.
This is the recommended way to check if a checkpoint exists, since it takes
into account the naming difference between V1 and V2 formats.
Args:
checkpoint_prefix: the prefix of a V1 or V2 checkpoint, with V2 taking
priority. Typically the result of `Saver.save()` or that of
`tf.train.latest_checkpoint()`, regardless of sharded/non-sharded or
V1/V2.
Returns:
A bool, true iff a checkpoint referred to by `checkpoint_prefix` exists.
"""
pathname = _prefix_to_checkpoint_path(checkpoint_prefix,
saver_pb2.SaverDef.V2)
if file_io.get_matching_files(pathname):
return True
elif file_io.get_matching_files(checkpoint_prefix):
return True
else:
return False
@tf_export("train.get_checkpoint_mtimes")
def get_checkpoint_mtimes(checkpoint_prefixes):
"""Returns the mtimes (modification timestamps) of the checkpoints.
Globs for the checkpoints pointed to by `checkpoint_prefixes`. If the files
exist, collect their mtime. Both V2 and V1 checkpoints are considered, in
that priority.
This is the recommended way to get the mtimes, since it takes into account
the naming difference between V1 and V2 formats.
Args:
checkpoint_prefixes: a list of checkpoint paths, typically the results of
`Saver.save()` or those of `tf.train.latest_checkpoint()`, regardless of
sharded/non-sharded or V1/V2.
Returns:
A list of mtimes (in microseconds) of the found checkpoints.
"""
mtimes = []
def match_maybe_append(pathname):
fnames = file_io.get_matching_files(pathname)
if fnames:
mtimes.append(file_io.stat(fnames[0]).mtime_nsec / 1e9)
return True
return False
for checkpoint_prefix in checkpoint_prefixes:
# Tries V2's metadata file first.
pathname = _prefix_to_checkpoint_path(checkpoint_prefix,
saver_pb2.SaverDef.V2)
if match_maybe_append(pathname):
continue
# Otherwise, tries V1, where the prefix is the complete pathname.
match_maybe_append(checkpoint_prefix)
return mtimes
@tf_export("train.remove_checkpoint")
def remove_checkpoint(checkpoint_prefix,
checkpoint_format_version=saver_pb2.SaverDef.V2,
meta_graph_suffix="meta"):
"""Removes a checkpoint given by `checkpoint_prefix`.
Args:
checkpoint_prefix: The prefix of a V1 or V2 checkpoint. Typically the result
of `Saver.save()` or that of `tf.train.latest_checkpoint()`, regardless of
sharded/non-sharded or V1/V2.
checkpoint_format_version: `SaverDef.CheckpointFormatVersion`, defaults to
`SaverDef.V2`.
meta_graph_suffix: Suffix for `MetaGraphDef` file. Defaults to 'meta'.
"""
_delete_file_if_exists(
meta_graph_filename(checkpoint_prefix, meta_graph_suffix))
if checkpoint_format_version == saver_pb2.SaverDef.V2:
# V2 has a metadata file and some data files.
_delete_file_if_exists(checkpoint_prefix + ".index")
_delete_file_if_exists(checkpoint_prefix + ".data-?????-of-?????")
else:
# V1, Legacy. Exact match on the data file.
_delete_file_if_exists(checkpoint_prefix)
def _delete_file_if_exists(filespec):
"""Deletes files matching `filespec`."""
for pathname in file_io.get_matching_files(filespec):
file_io.delete_file(pathname)
def meta_graph_filename(checkpoint_filename, meta_graph_suffix="meta"):
"""Returns the meta graph filename.
Args:
checkpoint_filename: Name of the checkpoint file.
meta_graph_suffix: Suffix for `MetaGraphDef` file. Defaults to 'meta'.
Returns:
MetaGraph file name.
"""
# If the checkpoint_filename is sharded, the checkpoint_filename could
# be of format model.ckpt-step#-?????-of-shard#. For example,
# model.ckpt-123456-?????-of-00005, or model.ckpt-123456-00001-of-00002.
basename = re.sub(r"-[\d\?]+-of-\d+$", "", checkpoint_filename)
suffixed_filename = ".".join([basename, meta_graph_suffix])
return suffixed_filename
# TODO(allenl): Allow tf.keras.Model instances in the constructor directly?
class CheckpointManager(object):
"""Deletes old checkpoints.
Example usage:
```python
import tensorflow as tf
checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model)
manager = tf.contrib.checkpoint.CheckpointManager(
checkpoint, directory="/tmp/model", max_to_keep=5)
status = checkpoint.restore(manager.latest_checkpoint)
while True:
# train
manager.save()
```
`CheckpointManager` preserves its own state across instantiations (see the
`__init__` documentation for details). Only one should be active in a
particular directory at a time.
"""
def __init__(self, checkpoint, directory,
max_to_keep, keep_checkpoint_every_n_hours=None):
"""Configure a `CheckpointManager` for use in `directory`.
If a `CheckpointManager` was previously used in `directory`, its
state will be restored. This includes the list of managed checkpoints and
the timestamp bookkeeping necessary to support
`keep_checkpoint_every_n_hours`. The behavior of the new `CheckpointManager`
will be the same as the previous `CheckpointManager`, including cleaning up
existing checkpoints if appropriate.
Checkpoints are only considered for deletion just after a new checkpoint has
been added. At that point, `max_to_keep` checkpoints will remain in an
"active set". Once a checkpoint is preserved by
`keep_checkpoint_every_n_hours` it will not be deleted by this
`CheckpointManager` or any future `CheckpointManager` instantiated in
`directory` (regardless of the new setting of
`keep_checkpoint_every_n_hours`). The `max_to_keep` checkpoints in the
active set may be deleted by this `CheckpointManager` or a future
`CheckpointManager` instantiated in `directory` (subject to its
`max_to_keep` and `keep_checkpoint_every_n_hours` settings).
Args:
checkpoint: The `tf.train.Checkpoint` instance to save and manage
checkpoints for.
directory: The path to a directory in which to write checkpoints. A
special file named "checkpoint" is also written to this directory (in a
human-readable text format) which contains the state of the
`CheckpointManager`.
max_to_keep: An integer, the number of checkpoints to keep. Unless
preserved by `keep_checkpoint_every_n_hours`, checkpoints will be
deleted from the active set, oldest first, until only `max_to_keep`
checkpoints remain.
keep_checkpoint_every_n_hours: Upon removal from the active set, a
checkpoint will be preserved if it has been at least
`keep_checkpoint_every_n_hours` since the last preserved checkpoint. The
default setting of `None` does not preserve any checkpoints in this way.
Raises:
ValueError: If `max_to_keep` is not a positive integer.
"""
self._checkpoint = checkpoint
self._save_counter_assign = None
if not max_to_keep or max_to_keep < 0:
raise ValueError(
"Expected a positive integer for `max_to_max_to_keep`, got %d."
% (max_to_keep,))
self._max_to_keep = max_to_keep
self._keep_checkpoint_every_n_hours = keep_checkpoint_every_n_hours
self._directory = directory
self._checkpoint_prefix = os.path.join(directory, "ckpt")
recovered_state = get_checkpoint_state(directory)
current_clock = time.time()
self._maybe_delete = collections.OrderedDict()
if recovered_state is None:
self._latest_checkpoint = None
self._last_preserved_timestamp = current_clock
else:
self._latest_checkpoint = recovered_state.model_checkpoint_path
self._last_preserved_timestamp = recovered_state.last_preserved_timestamp
if current_clock < self._last_preserved_timestamp:
# Time seems to have reversed itself. In addition to this warning, we'll
# min() saved checkpoint timestamps with the current time to ensure that
# old checkpoints don't get deleted accidentally.
logging.warning(
("time.time() returned a value %f seconds behind the last "
"preserved checkpoint timestamp.")
% (self._last_preserved_timestamp - current_clock,))
self._last_preserved_timestamp = current_clock
all_timestamps = recovered_state.all_model_checkpoint_timestamps
all_paths = recovered_state.all_model_checkpoint_paths
del recovered_state # Uses modified values from now on
if not all_timestamps:
all_timestamps = [self._last_preserved_timestamp] * len(all_paths)
for filename, timestamp in zip(all_paths, all_timestamps):
timestamp = min(timestamp, current_clock)
if timestamp > self._last_preserved_timestamp:
self._maybe_delete[filename] = timestamp
@property
def latest_checkpoint(self):
"""The prefix of the most recent checkpoint in `directory`.
Equivalent to `tf.train.latest_checkpoint(directory)` where `directory` is
the constructor argument to `CheckpointManager`.
Suitable for passing to `tf.train.Checkpoint.restore` to resume training.
Returns:
The checkpoint prefix. If there are no checkpoints, returns `None`.
"""
return self._latest_checkpoint
@property
def checkpoints(self):
"""A list of managed checkpoints.
Note that checkpoints saved due to `keep_checkpoint_every_n_hours` will not
show up in this list (to avoid ever-growing filename lists).
Returns:
A list of filenames, sorted from oldest to newest.
"""
return list(self._maybe_delete.keys())
def _sweep(self):
"""Deletes or preserves managed checkpoints."""
while len(self._maybe_delete) > self._max_to_keep:
filename, timestamp = self._maybe_delete.popitem(last=False)
# Even if we're keeping this checkpoint due to
# keep_checkpoint_every_n_hours, we won't reference it to avoid
# infinitely-growing CheckpointState protos.
if (self._keep_checkpoint_every_n_hours
and (timestamp - self._keep_checkpoint_every_n_hours * 3600.
>= self._last_preserved_timestamp)):
self._last_preserved_timestamp = timestamp
continue
remove_checkpoint(filename)
def _record_state(self):
"""Saves the `CheckpointManager`'s state in `directory`."""
filenames, timestamps = zip(*self._maybe_delete.items())
update_checkpoint_state_internal(
self._directory,
model_checkpoint_path=self.latest_checkpoint,
all_model_checkpoint_paths=filenames,
all_model_checkpoint_timestamps=timestamps,
last_preserved_timestamp=self._last_preserved_timestamp,
save_relative_paths=True)
@property
def _prefix(self):
"""A common prefix for all checkpoints saved with this manager.
For example, if `directory` (a constructor argument) were `"/tmp/tf-model"`,
`prefix` would be `"/tmp/tf-model/ckpt"` and checkpoints would generally be
numbered `"/tmp/tf-model/ckpt-1"`, `"/tmp/tf-model/ckpt-2"`, and so on. Each
checkpoint has several associated files
(e.g. `"/tmp/tf-model/ckpt-2.index"`).
Returns:
A string prefix.
"""
return self._checkpoint_prefix
def save(self, session=None):
"""Creates a new checkpoint and manages it.
Args:
session: The session to evaluate variables in. Ignored when executing
eagerly. If not provided when graph building, the default session is
used.
Returns:
The path to the new checkpoint. It is also recorded in the `checkpoints`
and `latest_checkpoint` properies.
"""
# Save counter logic duplicated from tf.train.Checkpoint, soon to diverge
# slightly with a custom numbering option.
if context.executing_eagerly():
save_counter = self._checkpoint.save_counter
save_counter.assign_add(1)
checkpoint_number = save_counter.numpy()
else:
if session is None:
session = ops.get_default_session()
def _initializing_creator(next_creator, **kwargs):
"""Initialize the save counter if it has been newly created."""
v = next_creator(**kwargs)
session.run(v.initializer)
return v
with variable_scope.variable_creator_scope(_initializing_creator):
save_counter = self._checkpoint.save_counter
if self._save_counter_assign is None:
self._save_counter_assign = save_counter.assign_add(1, read_value=True)
checkpoint_number = session.run(self._save_counter_assign)
prefix = "%s-%d" % (self._prefix, checkpoint_number)
save_path = self._checkpoint.write(prefix)
timestamp = time.time()
# If this is an overwritten checkpoint we were previously tracking, delete
# and reinsert it to make sure it goes to the end of the queue.
if save_path in self._maybe_delete:
del self._maybe_delete[save_path]
self._maybe_delete[save_path] = timestamp
self._latest_checkpoint = save_path
self._sweep()
self._record_state()
return save_path
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