tf.compat.v1.train.Supervisor

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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.

NOTE: This modified program still works fine as a single program. The single program marks itself as the chief.

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:

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:

Attributes:

Raises:

Methods

Loop

View source

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:

Returns:

The started thread.

PrepareSession

View source

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:

Returns:

A Session object that can be used to drive the model.

RequestStop

View source

RequestStop(
    ex=None
)

Request that the coordinator stop the threads.

See Coordinator.request_stop().

Args:

ShouldStop

View source

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

View source

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:

Returns:

The list of threads started for the QueueRunners.

Raises:

Eager Compatibility

Queues are not compatible with eager execution. To ingest data when eager execution is enabled, use the tf.data API.

StartStandardServices

View source

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:

Args:

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:

Stop

View source

Stop(
    threads=None, close_summary_writer=True, ignore_live_threads=False
)

Stop the services and the coordinator.

This does not close the session.

Args:

StopOnException

View source

StopOnException()

Context handler to stop the supervisor when an exception is raised.

See Coordinator.stop_on_exception().

Returns:

A context handler.

SummaryComputed

View source

SummaryComputed(
    sess, summary, global_step=None
)

Indicate that a summary was computed.

Args:

Raises:

WaitForStop

View source

WaitForStop()

Block waiting for the coordinator to stop.

loop

View source

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:

Returns:

The started thread.

managed_session

@contextlib.contextmanager
managed_session(
    *args, **kwds
)

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:

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

View source

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:

Returns:

A Session object that can be used to drive the model.

request_stop

View source

request_stop(
    ex=None
)

Request that the coordinator stop the threads.

See Coordinator.request_stop().

Args:

should_stop

View source

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

View source

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:

Returns:

The list of threads started for the QueueRunners.

Raises:

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

View source

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:

Args:

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:

stop

View source

stop(
    threads=None, close_summary_writer=True, ignore_live_threads=False
)

Stop the services and the coordinator.

This does not close the session.

Args:

stop_on_exception

View source

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

View source

summary_computed(
    sess, summary, global_step=None
)

Indicate that a summary was computed.

Args:

Raises:

wait_for_stop

View source

wait_for_stop()

Block waiting for the coordinator to stop.

Class Variables