tf.compat.v1.train.MonitoredTrainingSession

Creates a MonitoredSession for training.

tf.compat.v1.train.MonitoredTrainingSession(
    master='', is_chief=True, checkpoint_dir=None, scaffold=None, hooks=None,
    chief_only_hooks=None, save_checkpoint_secs=USE_DEFAULT,
    save_summaries_steps=USE_DEFAULT, save_summaries_secs=USE_DEFAULT, config=None,
    stop_grace_period_secs=120, log_step_count_steps=100, max_wait_secs=7200,
    save_checkpoint_steps=USE_DEFAULT, summary_dir=None, save_graph_def=True
)

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.

Args:

master: String the TensorFlow master to use.

is_chief: If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.

checkpoint_dir: A string. Optional path to a directory where to restore variables.

scaffold: A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.

hooks: Optional list of SessionRunHook objects.

chief_only_hooks: list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.

save_checkpoint_secs: The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.

save_summaries_steps: The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.

save_summaries_secs: The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.

config: an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.

stop_grace_period_secs: Number of seconds given to threads to stop after close() has been called.

log_step_count_steps: The frequency, in number of global steps, that the global step/sec is logged.

max_wait_secs: Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.

save_checkpoint_steps: The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.

summary_dir: A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.

save_graph_def: Whether to save the GraphDef and MetaGraphDef to checkpoint_dir. The GraphDef is saved after the session is created as graph.pbtxt. MetaGraphDefs are saved out for every checkpoint as model.ckpt-*.meta.

Returns:

A MonitoredSession object.