View source on GitHub |
Delays execution until global step reaches wait_until_step
.
Inherits From: SessionRunHook
tf.estimator.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
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. |