tf.distribute.InputOptions

Run options for experimental_distribute_dataset(s_from_function).

tf.distribute.InputOptions(
    experimental_prefetch_to_device=True,
    experimental_replication_mode=tf.distribute.InputReplicationMode.PER_WORKER,
    experimental_place_dataset_on_device=False
)

This can be used to hold some strategy specific configs.

# Setup TPUStrategy
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.TPUStrategy(resolver)

dataset = tf.data.Dataset.range(16)
distributed_dataset_on_host = (
    strategy.experimental_distribute_dataset(
        dataset,
        tf.distribute.InputOptions(
            experimental_replication_mode=
            experimental_replication_mode.PER_WORKER,
            experimental_place_dataset_on_device=False)))

Attributes
experimental_prefetch_to_device	Boolean. Defaults to True. If True, dataset elements will be prefetched to accelerator device memory. When False, dataset elements are prefetched to host device memory. Must be False when using TPUEmbedding API. experimental_prefetch_to_device can only be used with experimental_replication_mode=PER_WORKER
experimental_replication_mode	Replication mode for the input function. Currently, the InputReplicationMode.PER_REPLICA is only supported with tf.distribute.MirroredStrategy. experimental_distribute_datasets_from_function. The default value is InputReplicationMode.PER_WORKER.
experimental_place_dataset_on_device	Boolean. Default to False. When True, dataset will be placed on the device, otherwise it will remain on the host. experimental_place_dataset_on_device=True can only be used with experimental_replication_mode=PER_REPLICA