tf.compat.v1.train.SessionManager 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.
 


 Args
  local_init_op   An Operation run immediately after session creation. Usually used to initialize tables and local variables.  
  ready_op   An Operation to check if the model is initialized.  
  ready_for_local_init_op   An Operation to check if the model is ready to run local_init_op.  
  graph   The Graph that the model will use.  
  recovery_wait_secs   Seconds between checks for the model to be ready.  
  local_init_run_options   RunOptions to be passed to session.run when executing the local_init_op.  
  local_init_feed_dict   Optional session feed dictionary to use when running the local_init_op.   
 


 Raises
  ValueError   If ready_for_local_init_op is not None but local_init_op is None    Methods prepare_session View source 
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.
 


 Args
  master   String representation of the TensorFlow master to use.  
  init_op   Optional Operation used to initialize the model.  
  saver   A Saver object used to restore a model.  
  checkpoint_dir   Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.  
  checkpoint_filename_with_path   Full file name path to the checkpoint file.  
  wait_for_checkpoint   Whether to wait for checkpoint to become available.  
  max_wait_secs   Maximum time to wait for checkpoints to become available.  
  config   Optional ConfigProto proto used to configure the session.  
  init_feed_dict   Optional dictionary that maps Tensor objects to feed values. This feed dictionary is passed to the session run() call when running the init op.  
  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.   
 


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

 


 Raises
  RuntimeError   If the model cannot be initialized or recovered.  
  ValueError   If both checkpoint_dir and checkpoint_filename_with_path are set.    recover_session View source 
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.
 


 Args
  master   String representation of the TensorFlow master to use.  
  saver   A Saver object used to restore a model.  
  checkpoint_dir   Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.  
  checkpoint_filename_with_path   Full file name path to the checkpoint file.  
  wait_for_checkpoint   Whether to wait for checkpoint to become available.  
  max_wait_secs   Maximum time to wait for checkpoints to become available.  
  config   Optional ConfigProto proto used to configure the session.   
 


 Returns   A pair (sess, initialized) where 'initialized' is True if the session could be recovered and initialized, False otherwise.  

 


 Raises
  ValueError   If both checkpoint_dir and checkpoint_filename_with_path are set.    wait_for_session View source 
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. NB: The amount of time this method waits for the session is bounded by max_wait_secs. By default, this function will wait indefinitely.
 


 Args
  master   String representation of the TensorFlow master to use.  
  config   Optional ConfigProto proto used to configure the session.  
  max_wait_secs   Maximum time to wait for the session to become available.   
 


 Returns   A Session. May be None if the operation exceeds the timeout specified by config.operation_timeout_in_ms.  

 


 Raises
  tf.DeadlineExceededError   if the session is not available after max_wait_secs.