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A training helper that checkpoints models and computes summaries.
This class is deprecated. Please use
The Supervisor is a small wrapper around a
SessionManager that takes care of common needs of TensorFlow
Use for a single program
with tf.Graph().as_default(): ...add operations to the graph... # Create a Supervisor that will checkpoint the model in '/tmp/mydir'. sv = Supervisor(logdir='/tmp/mydir') # Get a TensorFlow session managed by the supervisor. with sv.managed_session(FLAGS.master) as sess: # Use the session to train the graph. while not sv.should_stop(): sess.run(<my_train_op>)
with sv.managed_session() block all variables in the graph have
been initialized. In addition, a few services have been started to
checkpoint the model and add summaries to the event log.
If the program crashes and is restarted, the managed session automatically reinitialize variables from the most recent checkpoint.
The supervisor is notified of any exception raised by one of the services.
After an exception is raised,
True. In that case
the training loop should also stop. This is why the training loop has to
Exceptions that indicate that the training inputs have been exhausted,
tf.errors.OutOfRangeError, also cause
sv.should_stop() to return
but are not re-raised from the
with block: they indicate a normal
Use for multiple replicas
To train with replicas you deploy the same program in a
One of the tasks must be identified as the chief: the task that handles
initialization, checkpoints, summaries, and recovery. The other tasks
depend on the chief for these services.
The only change you have to do to the single program code is to indicate if the program is running as the chief.
# Choose a task as the chief. This could be based on server_def.task_index, # or job_def.name, or job_def.tasks. It's entirely up to the end user. # But there can be only one *chief*. is_chief = (server_def.task_index == 0) server = tf.distribute.Server(server_def) with tf.Graph().as_default(): ...add operations to the graph... # Create a Supervisor that uses log directory on a shared file system. # Indicate if you are the 'chief' sv = Supervisor(logdir='/shared_directory/...', is_chief=is_chief) # Get a Session in a TensorFlow server on the cluster. with sv.managed_session(server.target) as sess: # Use the session to train the graph. while not sv.should_stop(): sess.run(<my_train_op>)
In the chief task, the
Supervisor works exactly as in the first example
above. In the other tasks
sv.managed_session() waits for the Model to have
been initialized before returning a session to the training code. The
non-chief tasks depend on the chief task for initializing the model.
If one of the tasks crashes and restarts,
checks if the Model is initialized. If yes, it just creates a session and
returns it to the training code that proceeds normally. If the model needs
to be initialized, the chief task takes care of reinitializing it; the other
tasks just wait for the model to have been initialized.
NOTE: This modified program still works fine as a single program. The single program marks itself as the chief.
master string to use
Whether you are running on your machine or in the cluster you can use the following values for the --master flag:
''requests an in-process session that does not use RPC.
'local'requests a session that uses the RPC-based "Master interface" to run TensorFlow programs. See
'grpc://hostname:port'requests a session that uses the RPC interface to a specific host, and also allows the in-process master to access remote tensorflow workers. Often, it is appropriate to pass
Launching additional services
managed_session() launches the Checkpoint and Summary services (threads).
If you need more services to run you can simply launch them in the block
Example: Start a thread to print losses. We want this thread to run
every 60 seconds, so we launch it with
... sv = Supervisor(logdir='/tmp/mydir') with sv.managed_session(FLAGS.master) as sess: sv.loop(60, print_loss, (sess, )) while not sv.should_stop(): sess.run(my_train_op)
Launching fewer services
managed_session() launches the "summary" and "checkpoint" threads which use
either the optionally
saver passed to the constructor, or
default ones created automatically by the supervisor. If you want to run
your own summary and checkpointing logic, disable these services by passing
None to the
Example: Create summaries manually every 100 steps in the chief.
# Create a Supervisor with no automatic summaries. sv = Supervisor(logdir='/tmp/mydir', is_chief=is_chief, summary_op=None) # As summary_op was None, managed_session() does not start the # summary thread. with sv.managed_session(FLAGS.master) as sess: for step in xrange(1000000): if sv.should_stop(): break if is_chief and step % 100 == 0: # Create the summary every 100 chief steps. sv.summary_computed(sess, sess.run(my_summary_op)) else: # Train normally sess.run(my_train_op)
Custom model initialization
managed_session() only supports initializing the model by running an
init_op or restoring from the latest checkpoint. If you have special
initialization needs, see how to specify a
local_init_op when creating the
supervisor. You can also use the
SessionManager directly to create a
session and check if it could be initialized automatically.
__init__( graph=None, ready_op=USE_DEFAULT, ready_for_local_init_op=USE_DEFAULT, is_chief=True, init_op=USE_DEFAULT, init_feed_dict=None, local_init_op=USE_DEFAULT, logdir=None, summary_op=USE_DEFAULT, saver=USE_DEFAULT, global_step=USE_DEFAULT, save_summaries_secs=120, save_model_secs=600, recovery_wait_secs=30, stop_grace_secs=120, checkpoint_basename='model.ckpt', session_manager=None, summary_writer=USE_DEFAULT, init_fn=None, local_init_run_options=None )
Graph. The graph that the model will use. Defaults to the default
Graph. The supervisor may add operations to the graph before creating a session, but the graph should not be modified by the caller after passing it to the supervisor.
ready_op: 1-D string
Tensor. This tensor is evaluated by supervisors in
prepare_or_wait_for_session()to check if the model is ready to use. The model is considered ready if it returns an empty array. Defaults to the tensor returned from
None, the model is not checked for readiness.
ready_for_local_init_op: 1-D string
Tensor. This tensor is evaluated by supervisors in
prepare_or_wait_for_session()to check if the model is ready to run the local_init_op. The model is considered ready if it returns an empty array. Defaults to
None, the model is not checked for readiness before running local_init_op.
is_chief: If True, create a chief supervisor in charge of initializing and restoring the model. If False, create a supervisor that relies on a chief supervisor for inits and restore.
Operation. Used by chief supervisors to initialize the model when it can not be recovered. Defaults to an
Operationthat initializes all global variables. If
None, no initialization is done automatically unless you pass a value for
init_fn, see below.
init_feed_dict: A dictionary that maps
Tensorobjects to feed values. This feed dictionary will be used when
Operation. Used by all supervisors to run initializations that should run for every new supervisor instance. By default these are table initializers and initializers for local variables. If
None, no further per supervisor-instance initialization is done automatically.
logdir: A string. Optional path to a directory where to checkpoint the model and log events for the visualizer. Used by chief supervisors. The directory will be created if it does not exist.
Operationthat returns a Summary for the event logs. Used by chief supervisors if a
logdirwas specified. Defaults to the operation returned from summary.merge_all(). If
None, summaries are not computed automatically.
saver: A Saver object. Used by chief supervisors if a
logdirwas specified. Defaults to the saved returned by Saver(). If
None, the model is not saved automatically.
global_step: An integer Tensor of size 1 that counts steps. The value from 'global_step' is used in summaries and checkpoint filenames. Default to the op named 'global_step' in the graph if it exists, is of rank 1, size 1, and of type tf.int32 or tf.int64. If
Nonethe global step is not recorded in summaries and checkpoint files. Used by chief supervisors if a
save_summaries_secs: Number of seconds between the computation of summaries for the event log. Defaults to 120 seconds. Pass 0 to disable summaries.
save_model_secs: Number of seconds between the creation of model checkpoints. Defaults to 600 seconds. Pass 0 to disable checkpoints.
recovery_wait_secs: Number of seconds between checks that the model is ready. Used by supervisors when waiting for a chief supervisor to initialize or restore the model. Defaults to 30 seconds.
stop_grace_secs: Grace period, in seconds, given to running threads to stop when
stop()is called. Defaults to 120 seconds.
checkpoint_basename: The basename for checkpoint saving.
SessionManager, which manages Session creation and recovery. If it is
None, a default
SessionManagerwill be created with the set of arguments passed in for backwards compatibility.
SummaryWriterto use or
USE_DEFAULT. Can be
Noneto indicate that no summaries should be written.
init_fn: Optional callable used to initialize the model. Called after the optional
init_opis called. The callable must accept one argument, the session being initialized.
local_init_run_options: RunOptions to be passed as the SessionManager local_init_run_options parameter.
RuntimeError: If called with eager execution enabled.
Supervisors are not supported when eager execution is enabled.
Return the Coordinator used by the Supervisor.
The Coordinator can be useful if you want to run multiple threads during your training.
A Coordinator object.
Return the global_step Tensor used by the supervisor.
An integer Tensor for the global_step.
Return the feed dictionary used when evaluating the
A feed dictionary or
Return the Init Op used by the supervisor.
An Op or
Return True if this is a chief supervisor.
Return the Ready Op used by the supervisor.
An Op or
Return the delay between checkpoints.
Return the save path used by the supervisor.
Return the delay between summary computations.
Return the Saver used by the supervisor.
A Saver object.
Return the SessionManager used by the Supervisor.
A SessionManager object.
Return the Summary Tensor used by the chief supervisor.
A string Tensor for the summary or
Return the SummaryWriter used by the chief supervisor.
Loop( timer_interval_secs, target, args=None, kwargs=None )
Start a LooperThread that calls a function periodically.
timer_interval_secs is None the thread calls
repeatedly. Otherwise it calls it every
seconds. The thread terminates when a stop is requested.
The started thread is added to the list of threads managed by the supervisor
so it does not need to be passed to the
timer_interval_secs: Number. Time boundaries at which to call
target: A callable object.
args: Optional arguments to pass to
targetwhen calling it.
kwargs: Optional keyword arguments to pass to
targetwhen calling it.
The started thread.
PrepareSession( master='', config=None, wait_for_checkpoint=False, max_wait_secs=7200, start_standard_services=True )
Make sure the model is ready to be used.
Create a session on 'master', recovering or initializing the model as
needed, or wait for a session to be ready. If running as the chief
start_standard_service is set to True, also call the session
manager to start the standard services.
master: name of the TensorFlow master to use. See the
tf.compat.v1.Sessionconstructor for how this is interpreted.
config: Optional ConfigProto proto used to configure the session, which is passed as-is to create the session.
wait_for_checkpoint: Whether we should wait for the availability of a checkpoint before creating Session. Defaults to False.
max_wait_secs: Maximum time to wait for the session to become available.
start_standard_services: Whether to start the standard services and the queue runners.
A Session object that can be used to drive the model.
Request that the coordinator stop the threads.
Exception, or Python
exc_infotuple as returned by
sys.exc_info(). If this is the first call to
request_stop()the corresponding exception is recorded and re-raised from
Check if the coordinator was told to stop.
True if the coordinator was told to stop, False otherwise.
StartQueueRunners( sess, queue_runners=None )
Start threads for
Note that the queue runners collected in the graph key
are already started automatically when you create a session with the
supervisor, so unless you have non-collected queue runners to start
you do not need to call this explicitly.
queue_runners: A list of
QueueRunners. If not specified, we'll use the list of queue runners gathered in the graph under the key
The list of threads started for the
RuntimeError: If called with eager execution enabled.
Queues are not compatible with eager execution. To ingest data when eager
execution is enabled, use the
Start the standard services for 'sess'.
This starts services in the background. The services started depend on the parameters to the constructor and may include:
- A Summary thread computing summaries every save_summaries_secs.
- A Checkpoint thread saving the model every save_model_secs.
- A StepCounter thread measure step time.
sess: A Session.
A list of threads that are running the standard services. You can use
the Supervisor's Coordinator to join these threads with:
RuntimeError: If called with a non-chief Supervisor.
ValueError: If not
logdirwas passed to the constructor as the services need a log directory.
Stop( threads=None, close_summary_writer=True, ignore_live_threads=False )
Stop the services and the coordinator.
This does not close the session.
threads: Optional list of threads to join with the coordinator. If
None, defaults to the threads running the standard services, the threads started for
QueueRunners, and the threads started by the
loop()method. To wait on additional threads, pass the list in this parameter.
close_summary_writer: Whether to close the
summary_writer. Defaults to
Trueif the summary writer was created by the supervisor,
Trueignores threads that remain running after a grace period when joining threads via the coordinator, instead of raising a RuntimeError.
Context handler to stop the supervisor when an exception is raised.
A context handler.
SummaryComputed( sess, summary, global_step=None )
Indicate that a summary was computed.
summary: A Summary proto, or a string holding a serialized summary proto.
global_step: Int. global step this summary is associated with. If
None, it will try to fetch the current step.
TypeError: if 'summary' is not a Summary proto or a string.
RuntimeError: if the Supervisor was created without a
Block waiting for the coordinator to stop.