tf.estimator.ProfilerHook Captures CPU/GPU profiling information every N steps or seconds. Inherits From: SessionRunHook  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.estimator.ProfilerHook, tf.compat.v1.train.ProfilerHook  
tf.estimator.ProfilerHook(
    save_steps=None, save_secs=None, output_dir='', show_dataflow=True,
    show_memory=False
)
 This produces files called "timeline-.json", which are in Chrome Trace format. For more information see: https://github.com/catapult-project/catapult/blob/master/tracing/README.md
 


 Args
  save_steps   int, save profile traces every N steps. Exactly one of save_secs and save_steps should be set.  
  save_secs   int or float, save profile traces every N seconds.  
  output_dir   string, the directory to save the profile traces to. Defaults to the current directory.  
  show_dataflow   bool, if True, add flow events to the trace connecting producers and consumers of tensors.  
  show_memory   bool, if True, add object snapshot events to the trace showing the sizes and lifetimes of tensors.    Methods after_create_session View source 
after_create_session(
    session, coord
)
 Called when new TensorFlow session is created. This is called to signal the hooks that a new session has been created. This has two essential differences with the situation in which begin is called:  When this is called, the graph is finalized and ops can no longer be added to the graph. This method will also be called as a result of recovering a wrapped session, not only at the beginning of the overall session. 
 


 Args
  session   A TensorFlow Session that has been created.  
  coord   A Coordinator object which keeps track of all threads.    after_run View source 
after_run(
    run_context, run_values
)
 Called after each call to run(). The run_values argument contains results of requested ops/tensors by before_run(). The run_context argument is the same one send to before_run call. run_context.request_stop() can be called to stop the iteration. If session.run() raises any exceptions then after_run() is not called.
 


 Args
  run_context   A SessionRunContext object.  
  run_values   A SessionRunValues object.    before_run View source 
before_run(
    run_context
)
 Called before each call to run(). You can return from this call a SessionRunArgs object indicating ops or tensors to add to the upcoming run() call. These ops/tensors will be run together with the ops/tensors originally passed to the original run() call. The run args you return can also contain feeds to be added to the run() call. The run_context argument is a SessionRunContext that provides information about the upcoming run() call: the originally requested op/tensors, the TensorFlow Session. At this point graph is finalized and you can not add ops.
 


 Args
  run_context   A SessionRunContext object.   
 


 Returns   None or a SessionRunArgs object.  
 begin View source 
begin()
 Called once before using the session. When called, the default graph is the one that will be launched in the session. The hook can modify the graph by adding new operations to it. After the begin() call the graph will be finalized and the other callbacks can not modify the graph anymore. Second call of begin() on the same graph, should not change the graph. end View source 
end(
    session
)
 Called at the end of session. The session argument can be used in case the hook wants to run final ops, such as saving a last checkpoint. If session.run() raises exception other than OutOfRangeError or StopIteration then end() is not called. Note the difference between end() and after_run() behavior when session.run() raises OutOfRangeError or StopIteration. In that case end() is called but after_run() is not called.
 


 Args
  session   A TensorFlow Session that will be soon closed.