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Training helper that restores from checkpoint and creates session.
tf.compat.v1.train.SessionManager(
local_init_op=None, ready_op=None, ready_for_local_init_op=None, graph=None,
recovery_wait_secs=30, local_init_run_options=None, local_init_feed_dict=None
)
This class is a small wrapper that takes care of session creation and checkpoint recovery. It also provides functions that to facilitate coordination among multiple training threads or processes.
with tf.Graph().as_default():
...add operations to the graph...
# Create a SessionManager that will checkpoint the model in '/tmp/mydir'.
sm = SessionManager()
sess = sm.prepare_session(master, init_op, saver, checkpoint_dir)
# Use the session to train the graph.
while True:
sess.run(<my_train_op>)
prepare_session()
initializes or restores a model. It requires init_op
and saver
as an argument.
A second process could wait for the model to be ready by doing the following:
with tf.Graph().as_default():
...add operations to the graph...
# Create a SessionManager that will wait for the model to become ready.
sm = SessionManager()
sess = sm.wait_for_session(master)
# Use the session to train the graph.
while True:
sess.run(<my_train_op>)
wait_for_session()
waits for a model to be initialized by other processes.
local_init_op
: An Operation
run immediately after session creation.
Usually used to initialize tables and local variables.ready_op
: An Operation
to check if the model is initialized.ready_for_local_init_op
: An Operation
to check if the model is ready
to run local_init_op.graph
: The Graph
that the model will use.recovery_wait_secs
: Seconds between checks for the model to be ready.local_init_run_options
: RunOptions to be passed to session.run when
executing the local_init_op.local_init_feed_dict
: Optional session feed dictionary to use when running
the local_init_op.ValueError
: If ready_for_local_init_op is not None but local_init_op is
Noneprepare_session
prepare_session(
master, init_op=None, saver=None, checkpoint_dir=None,
checkpoint_filename_with_path=None, wait_for_checkpoint=False,
max_wait_secs=7200, config=None, init_feed_dict=None, init_fn=None
)
Creates a Session
. Makes sure the model is ready to be used.
Creates a Session
on 'master'. If a saver
object is passed in, and
checkpoint_dir
points to a directory containing valid checkpoint
files, then it will try to recover the model from checkpoint. If
no checkpoint files are available, and wait_for_checkpoint
is
True
, then the process would check every recovery_wait_secs
,
up to max_wait_secs
, for recovery to succeed.
If the model cannot be recovered successfully then it is initialized by
running the init_op
and calling init_fn
if they are provided.
The local_init_op
is also run after init_op and init_fn, regardless of
whether the model was recovered successfully, but only if
ready_for_local_init_op
passes.
If the model is recovered from a checkpoint it is assumed that all
global variables have been initialized, in particular neither init_op
nor init_fn
will be executed.
It is an error if the model cannot be recovered and no init_op
or init_fn
or local_init_op
are passed.
master
: String
representation of the TensorFlow master to use.init_op
: Optional Operation
used to initialize the model.saver
: A Saver
object used to restore a model.checkpoint_dir
: Path to the checkpoint files. The latest checkpoint in the
dir will be used to restore.checkpoint_filename_with_path
: Full file name path to the checkpoint file.wait_for_checkpoint
: Whether to wait for checkpoint to become available.max_wait_secs
: Maximum time to wait for checkpoints to become available.config
: Optional ConfigProto
proto used to configure the session.init_feed_dict
: Optional dictionary that maps Tensor
objects to feed
values. This feed dictionary is passed to the session run()
call when
running the init op.init_fn
: Optional callable used to initialize the model. Called after the
optional init_op
is called. The callable must accept one argument,
the session being initialized.A Session
object that can be used to drive the model.
RuntimeError
: If the model cannot be initialized or recovered.ValueError
: If both checkpoint_dir and checkpoint_filename_with_path are
set.recover_session
recover_session(
master, saver=None, checkpoint_dir=None, checkpoint_filename_with_path=None,
wait_for_checkpoint=False, max_wait_secs=7200, config=None
)
Creates a Session
, recovering if possible.
Creates a new session on 'master'. If the session is not initialized and can be recovered from a checkpoint, recover it.
master
: String
representation of the TensorFlow master to use.saver
: A Saver
object used to restore a model.checkpoint_dir
: Path to the checkpoint files. The latest checkpoint in the
dir will be used to restore.checkpoint_filename_with_path
: Full file name path to the checkpoint file.wait_for_checkpoint
: Whether to wait for checkpoint to become available.max_wait_secs
: Maximum time to wait for checkpoints to become available.config
: Optional ConfigProto
proto used to configure the session.A pair (sess, initialized) where 'initialized' is True
if
the session could be recovered and initialized, False
otherwise.
ValueError
: If both checkpoint_dir and checkpoint_filename_with_path are
set.wait_for_session
wait_for_session(
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.
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.A Session
. May be None if the operation exceeds the timeout
specified by config.operation_timeout_in_ms.
tf.DeadlineExceededError
: if the session is not available after
max_wait_secs.