Type BatchConfig
Namespace tensorflow_estimator.python.estimator.tpu.tpu_estimator
Parent PythonObjectContainer
Interfaces IBatchConfig
Methods
Properties
Public static methods
BatchConfig NewDyn(object num_batch_threads, object max_batch_size, object batch_timeout_micros, object allowed_batch_sizes, ImplicitContainer<T> max_enqueued_batches)
Configure a `CheckpointManager` for use in `directory`. If a `CheckpointManager` was previously used in `directory`, its
state will be restored. This includes the list of managed checkpoints and
the timestamp bookkeeping necessary to support
`keep_checkpoint_every_n_hours`. The behavior of the new `CheckpointManager`
will be the same as the previous `CheckpointManager`, including cleaning up
existing checkpoints if appropriate. Checkpoints are only considered for deletion just after a new checkpoint has
been added. At that point, `max_to_keep` checkpoints will remain in an
"active set". Once a checkpoint is preserved by
`keep_checkpoint_every_n_hours` it will not be deleted by this
`CheckpointManager` or any future `CheckpointManager` instantiated in
`directory` (regardless of the new setting of
`keep_checkpoint_every_n_hours`). The `max_to_keep` checkpoints in the
active set may be deleted by this `CheckpointManager` or a future
`CheckpointManager` instantiated in `directory` (subject to its
`max_to_keep` and `keep_checkpoint_every_n_hours` settings).