tfdbg.TensorBoardDebugHook

Class TensorBoardDebugHook

Inherits From: GrpcDebugHook

Defined in tensorflow/python/debug/wrappers/hooks.py.

A tfdbg hook that can be used with TensorBoard Debugger Plugin.

This hook is the same as GrpcDebugHook, except that it uses a predefined watch_fn that 1) uses DebugIdentity debug ops with the gated_grpc attribute set to True, to allow the interactive enabling and disabling of tensor breakpoints. 2) watches all tensors in the graph. This saves the need for the user to define a watch_fn.

Methods

__init__

__init__(
    grpc_debug_server_addresses,
    thread_name_filter=None,
    send_traceback_and_source_code=True,
    log_usage=True
)

Constructor of TensorBoardDebugHook.

Args:

• grpc_debug_server_addresses: gRPC address(es) of debug server(s), as a str or a list of strs. E.g., "localhost:2333", "grpc://localhost:2333", ["192.168.0.7:2333", "192.168.0.8:2333"].
• thread_name_filter: Optional filter for thread names.
• send_traceback_and_source_code: Whether traceback of graph elements and the source code are to be sent to the debug server(s).
• log_usage: Whether the usage of this class is to be logged (if applicable).

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 right before a session is run.

Args:

• run_context: A session_run_hook.SessionRunContext. Encapsulates information on the run.

Returns:

A session_run_hook.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.