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tf.estimator.ProfilerHook

Captures CPU/GPU profiling information every N steps or seconds.

Inherits From: SessionRunHook

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

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

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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

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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

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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

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