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.

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. See tf.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 pass server.target (for some tf.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 range(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.

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.

RuntimeError If called with eager execution enabled.

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

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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.

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.

PrepareSession

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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.

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.

RequestStop

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RequestStop(
    ex=None
)

Request that the coordinator stop the threads.

See Coordinator.request_stop().

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

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ShouldStop()

Check if the coordinator was told to stop.

See Coordinator.should_stop().

StartQueueRunners

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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.

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.

RuntimeError If called with eager execution enabled.

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