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A training helper that checkpoints models and computes summaries.
tf.compat.v1.train.Supervisor(
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
)
This class is deprecated. Please use
tf.compat.v1.train.MonitoredTrainingSession
instead.
The Supervisor is a small wrapper around a Coordinator
, a Saver
,
and a SessionManager
that takes care of common needs of TensorFlow
training programs.
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>)
Within the 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, should_stop()
returns True
. In that case
the training loop should also stop. This is why the training loop has to
check for sv.should_stop()
.
Exceptions that indicate that the training inputs have been exhausted,
tf.errors.OutOfRangeError
, also cause sv.should_stop()
to return True
but are not re-raised from the with
block: they indicate a normal
termination.
Use for multiple replicas
To train with replicas you deploy the same program in a Cluster
.
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, managed_session()
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.
What 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:
Specifying
''
requests an in-process session that does not use RPC.Specifying
'local'
requests a session that uses the RPC-based "Master interface" to run TensorFlow programs. Seetf.train.Server.create_local_server
for details.Specifying
'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 passserver.target
(for sometf.distribute.Server
named `server).
Advanced use
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
controlled by managed_session()
.
Example: Start a thread to print losses. We want this thread to run
every 60 seconds, so we launch it with sv.loop()
.
...
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 summary_op
and 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 summary_op
and saver
parameters.
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.
Args | |
---|---|
graph
|
A 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 tf.compat.v1.report_uninitialized_variables()
If 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 . If 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. |
init_op
|
Operation . Used by chief supervisors to initialize the model
when it can not be recovered. Defaults to an Operation that
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 Tensor objects to feed values.
This feed dictionary will be used when init_op is evaluated.
|
local_init_op
|
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. |
summary_op
|
An Operation that returns a Summary for the event logs. Used
by chief supervisors if a logdir was 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 logdir was
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 None the global
step is not recorded in summaries and checkpoint files. Used by chief
supervisors if a logdir was specified.
|
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. |
session_manager
|
SessionManager , which manages Session creation and
recovery. If it is None , a default SessionManager will be created
with the set of arguments passed in for backwards compatibility.
|
summary_writer
|
SummaryWriter to use or USE_DEFAULT . Can be None to
indicate that no summaries should be written.
|
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.
|
local_init_run_options
|
RunOptions to be passed as the SessionManager local_init_run_options parameter. |
Raises | |
---|---|
RuntimeError
|
If called with eager execution enabled. |
Attributes | |
---|---|
coord
|
Return the Coordinator used by the Supervisor.
The Coordinator can be useful if you want to run multiple threads during your training. |
global_step
|
Return the global_step Tensor used by the supervisor. |
init_feed_dict
|
Return the feed dictionary used when evaluating the init_op .
|
init_op
|
Return the Init Op used by the supervisor. |
is_chief
|
Return True if this is a chief supervisor. |
ready_for_local_init_op
|
|
ready_op
|
Return the Ready Op used by the supervisor. |
save_model_secs
|
Return the delay between checkpoints. |
save_path
|
Return the save path used by the supervisor. |
save_summaries_secs
|
Return the delay between summary computations. |
saver
|
Return the Saver used by the supervisor. |
session_manager
|
Return the SessionManager used by the Supervisor. |
summary_op
|
Return the Summary Tensor used by the chief supervisor. |
summary_writer
|
Return the SummaryWriter used by the chief supervisor. |
Methods
Loop
Loop(
timer_interval_secs, target, args=None, kwargs=None
)
Start a LooperThread that calls a function periodically.
If timer_interval_secs
is None the thread calls target(*args, **kwargs)
repeatedly. Otherwise it calls it every timer_interval_secs
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 stop()
method.
Args | |
---|---|
timer_interval_secs
|
Number. Time boundaries at which to call target .
|
target
|
A callable object. |
args
|
Optional arguments to pass to target when calling it.
|
kwargs
|
Optional keyword arguments to pass to target when calling it.
|
Returns | |
---|---|
The started thread. |
PrepareSession
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
and start_standard_service
is set to True, also call the session
manager to start the standard services.
Args | |
---|---|
master
|
name of the TensorFlow master to use. See the
tf.compat.v1.Session constructor 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. |
Returns | |
---|---|
A Session object that can be used to drive the model. |
RequestStop
RequestStop(
ex=None
)
Request that the coordinator stop the threads.
See Coordinator.request_stop()
.
Args | |
---|---|
ex
|
Optional Exception , or Python exc_info tuple as returned by
sys.exc_info() . If this is the first call to request_stop() the
corresponding exception is recorded and re-raised from join() .
|
ShouldStop
ShouldStop()
Check if the coordinator was told to stop.
See Coordinator.should_stop()
.
Returns | |
---|---|
True if the coordinator was told to stop, False otherwise. |
StartQueueRunners
StartQueueRunners(
sess, queue_runners=None
)
Start threads for QueueRunners
.
Note that the queue runners collected in the graph key QUEUE_RUNNERS
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.
Args | |
---|---|
sess
|
A Session .
|
queue_runners
|
A list of QueueRunners . If not specified, we'll use the
list of queue runners gathered in the graph under the key
GraphKeys.QUEUE_RUNNERS .
|
Returns | |
---|---|
The list of threads started for the QueueRunners .
|
Raises | |
---|---|
RuntimeError
|
If called with eager execution enabled. |
Eager Compatibility
Queues are not compatible with eager execution. To ingest data when eager
execution is enabled, use the tf.data
API.
StartStandardServices
StartStandardServices(
sess
)
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.
Args | |
---|---|
sess
|
A Session. |
Returns | |
---|---|
A list of threads that are running the standard services. You can use
the Supervisor's Coordinator to join these threads with:
sv.coord.Join(
|
Raises | |
---|---|
RuntimeError
|
If called with a non-chief Supervisor. |
ValueError
|
If not logdir was passed to the constructor as the
services need a log directory.
|
Stop
Stop(
threads=None, close_summary_writer=True, ignore_live_threads=False
)
Stop the services and the coordinator.
This does not close the session.
Args | |
---|---|
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
True if the summary writer was created by the supervisor, False
otherwise.
|
ignore_live_threads
|
If True ignores threads that remain running after a
grace period when joining threads via the coordinator, instead of
raising a RuntimeError.
|
StopOnException
StopOnException()
Context handler to stop the supervisor when an exception is raised.
See Coordinator.stop_on_exception()
.
Returns | |
---|---|
A context handler. |
SummaryComputed
SummaryComputed(
sess, summary, global_step=None
)
Indicate that a summary was computed.
Args | |
---|---|
sess
|
A Session object.
|
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.
|
Raises | |
---|---|
TypeError
|
if 'summary' is not a Summary proto or a string. |
RuntimeError
|
if the Supervisor was created without a logdir .
|
WaitForStop
WaitForStop()
Block waiting for the coordinator to stop.
loop
loop(
timer_interval_secs, target, args=None, kwargs=None
)
Start a LooperThread that calls a function periodically.
If timer_interval_secs
is None the thread calls target(*args, **kwargs)
repeatedly. Otherwise it calls it every timer_interval_secs
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 stop()
method.
Args | |
---|---|
timer_interval_secs
|
Number. Time boundaries at which to call target .
|
target
|
A callable object. |
args
|
Optional arguments to pass to target when calling it.
|
kwargs
|
Optional keyword arguments to pass to target when calling it.
|
Returns | |
---|---|
The started thread. |
managed_session
@contextlib.contextmanager
managed_session( master='', config=None, start_standard_services=True, close_summary_writer=True )
Returns a context manager for a managed session.
This context manager creates and automatically recovers a session. It
optionally starts the standard services that handle checkpoints and
summaries. It monitors exceptions raised from the with
block or from the
services and stops the supervisor as needed.
The context manager is typically used as follows:
def train():
sv = tf.compat.v1.train.Supervisor(...)
with sv.managed_session(<master>) as sess:
for step in xrange(..):
if sv.should_stop():
break
sess.run(<my training op>)
...do other things needed at each training step...
An exception raised from the with
block or one of the service threads is
raised again when the block exits. This is done after stopping all threads
and closing the session. For example, an AbortedError
exception, raised
in case of preemption of one of the workers in a distributed model, is
raised again when the block exits.
If you want to retry the training loop in case of preemption you can do it as follows:
def main(...):
while True
try:
train()
except tf.errors.Aborted:
pass
As a special case, exceptions used for control flow, such as
OutOfRangeError
which reports that input queues are exhausted, are not
raised again from the with
block: they indicate a clean termination of
the training loop and are considered normal termination.
Args | |
---|---|
master
|
name of the TensorFlow master to use. See the
tf.compat.v1.Session constructor for how this is interpreted.
|
config
|
Optional ConfigProto proto used to configure the session. Passed
as-is to create the session.
|
start_standard_services
|
Whether to start the standard services, such as checkpoint, summary and step counter. |
close_summary_writer
|
Whether to close the summary writer when closing the session. Defaults to True. |
Returns | |
---|---|
A context manager that yields a Session restored from the latest
checkpoint or initialized from scratch if not checkpoint exists. The
session is closed when the with block exits.
|
prepare_or_wait_for_session
prepare_or_wait_for_session(
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
and start_standard_service
is set to True, also call the session
manager to start the standard services.
Args | |
---|---|
master
|
name of the TensorFlow master to use. See the
tf.compat.v1.Session constructor 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. |
Returns | |
---|---|
A Session object that can be used to drive the model. |
request_stop
request_stop(
ex=None
)
Request that the coordinator stop the threads.
See Coordinator.request_stop()
.
Args | |
---|---|
ex
|
Optional Exception , or Python exc_info tuple as returned by
sys.exc_info() . If this is the first call to request_stop() the
corresponding exception is recorded and re-raised from join() .
|
should_stop
should_stop()
Check if the coordinator was told to stop.
See Coordinator.should_stop()
.
Returns | |
---|---|
True if the coordinator was told to stop, False otherwise. |
start_queue_runners
start_queue_runners(
sess, queue_runners=None
)
Start threads for QueueRunners
.
Note that the queue runners collected in the graph key QUEUE_RUNNERS
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.
Args | |
---|---|
sess
|
A Session .
|
queue_runners
|
A list of QueueRunners . If not specified, we'll use the
list of queue runners gathered in the graph under the key
GraphKeys.QUEUE_RUNNERS .
|
Returns | |
---|---|
The list of threads started for the QueueRunners .
|
Raises | |
---|---|
RuntimeError
|
If called with eager execution enabled. |
Eager Compatibility
Queues are not compatible with eager execution. To ingest data when eager
execution is enabled, use the tf.data
API.
start_standard_services
start_standard_services(
sess
)
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.
Args | |
---|---|
sess
|
A Session. |
Returns | |
---|---|
A list of threads that are running the standard services. You can use
the Supervisor's Coordinator to join these threads with:
sv.coord.Join(
|
Raises | |
---|---|
RuntimeError
|
If called with a non-chief Supervisor. |
ValueError
|
If not logdir was passed to the constructor as the
services need a log directory.
|
stop
stop(
threads=None, close_summary_writer=True, ignore_live_threads=False
)
Stop the services and the coordinator.
This does not close the session.
Args | |
---|---|
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
True if the summary writer was created by the supervisor, False
otherwise.
|
ignore_live_threads
|
If True ignores threads that remain running after a
grace period when joining threads via the coordinator, instead of
raising a RuntimeError.
|
stop_on_exception
stop_on_exception()
Context handler to stop the supervisor when an exception is raised.
See Coordinator.stop_on_exception()
.
Returns | |
---|---|
A context handler. |
summary_computed
summary_computed(
sess, summary, global_step=None
)
Indicate that a summary was computed.
Args | |
---|---|
sess
|
A Session object.
|
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.
|
Raises | |
---|---|
TypeError
|
if 'summary' is not a Summary proto or a string. |
RuntimeError
|
if the Supervisor was created without a logdir .
|
wait_for_stop
wait_for_stop()
Block waiting for the coordinator to stop.