tf.train.GlobalStepWaiterHook

Delays execution until global step reaches wait_until_step.

Inherits From: SessionRunHook

View aliases

Main aliases

tf.estimator.GlobalStepWaiterHook

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.estimator.GlobalStepWaiterHook, tf.compat.v1.train.GlobalStepWaiterHook, `tf.compat.v2.estimator.GlobalStepWaiterHook`

tf.train.GlobalStepWaiterHook(
    wait_until_step
)

This hook delays execution until global step reaches to wait_until_step. It is used to gradually start workers in distributed settings. One example usage would be setting wait_until_step=int(K*log(task_id+1)) assuming that task_id=0 is the chief.

Args
wait_until_step	an int shows until which global step should we wait.

Methods

after_create_session

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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

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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

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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

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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

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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.