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Continuously yield new checkpoint files as they appear.
tf.train.checkpoints_iterator(
checkpoint_dir, min_interval_secs=0, timeout=None, timeout_fn=None
)
The iterator only checks for new checkpoints when control flow has been
reverted to it. This means it can miss checkpoints if your code takes longer
to run between iterations than min_interval_secs
or the interval at which
new checkpoints are written.
The timeout
argument is the maximum number of seconds to block waiting for
a new checkpoint. It is used in combination with the timeout_fn
as
follows:
- If the timeout expires and no
timeout_fn
was specified, the iterator stops yielding. - If a
timeout_fn
was specified, that function is called and if it returns a true boolean value the iterator stops yielding. - If the function returns a false boolean value then the iterator resumes the wait for new checkpoints. At this point the timeout logic applies again.
This behavior gives control to callers on what to do if checkpoints do not
come fast enough or stop being generated. For example, if callers have a way
to detect that the training has stopped and know that no new checkpoints
will be generated, they can provide a timeout_fn
that returns True
when
the training has stopped. If they know that the training is still going on
they return False
instead.
Yields | |
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String paths to latest checkpoint files as they arrive. |