tf.compat.v1.train.SessionManager

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Training helper that restores from checkpoint and creates session.

tf.compat.v1.train.SessionManager(
    local_init_op=None, ready_op=None, ready_for_local_init_op=None, graph=None,
    recovery_wait_secs=30, local_init_run_options=None, local_init_feed_dict=None
)

This class is a small wrapper that takes care of session creation and checkpoint recovery. It also provides functions that to facilitate coordination among multiple training threads or processes.

• Checkpointing trained variables as the training progresses.
• Initializing variables on startup, restoring them from the most recent checkpoint after a crash, or wait for checkpoints to become available.

Usage:

with tf.Graph().as_default():
   ...add operations to the graph...
  # Create a SessionManager that will checkpoint the model in '/tmp/mydir'.
  sm = SessionManager()
  sess = sm.prepare_session(master, init_op, saver, checkpoint_dir)
  # Use the session to train the graph.
  while True:
    sess.run(<my_train_op>)

prepare_session() initializes or restores a model. It requires init_op and saver as an argument.

A second process could wait for the model to be ready by doing the following:

with tf.Graph().as_default():
   ...add operations to the graph...
  # Create a SessionManager that will wait for the model to become ready.
  sm = SessionManager()
  sess = sm.wait_for_session(master)
  # Use the session to train the graph.
  while True:
    sess.run(<my_train_op>)

wait_for_session() waits for a model to be initialized by other processes.

Methods

prepare_session

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prepare_session(
    master, init_op=None, saver=None, checkpoint_dir=None,
    checkpoint_filename_with_path=None, wait_for_checkpoint=False,
    max_wait_secs=7200, config=None, init_feed_dict=None, init_fn=None
)

Creates a Session. Makes sure the model is ready to be used.

Creates a Session on 'master'. If a saver object is passed in, and checkpoint_dir points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and wait_for_checkpoint is True, then the process would check every recovery_wait_secs, up to max_wait_secs, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the init_op and calling init_fn if they are provided. The local_init_op is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if ready_for_local_init_op passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither init_op nor init_fn will be executed.

It is an error if the model cannot be recovered and no init_op or init_fn or local_init_op are passed.

recover_session

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recover_session(
    master, saver=None, checkpoint_dir=None, checkpoint_filename_with_path=None,
    wait_for_checkpoint=False, max_wait_secs=7200, config=None
)

Creates a Session, recovering if possible.

Creates a new session on 'master'. If the session is not initialized and can be recovered from a checkpoint, recover it.

wait_for_session

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wait_for_session(
    master, config=None, max_wait_secs=float('Inf')
)

Creates a new Session and waits for model to be ready.

Creates a new Session on 'master'. Waits for the model to be initialized or recovered from a checkpoint. It's expected that another thread or process will make the model ready, and that this is intended to be used by threads/processes that participate in a distributed training configuration where a different thread/process is responsible for initializing or recovering the model being trained.