tf.train.ProfilerHook

Class ProfilerHook

Inherits From: SessionRunHook

Defined in tensorflow/python/training/basic_session_run_hooks.py.

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

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

Methods

__init__

__init__(
    save_steps=None,
    save_secs=None,
    output_dir='',
    show_dataflow=True,
    show_memory=False
)

Initializes a hook that takes periodic profiling snapshots.

options.run_metadata argument of tf.Session.Run is used to collect metadata about execution. This hook sets the metadata and dumps it in Chrome Trace format.

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.

after_create_session

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

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

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

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

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.