An iterator over tf.distribute.DistributedDataset.

tf.distribute.DistributedIterator is the primary mechanism for enumerating elements of a tf.distribute.DistributedDataset. It supports the Python Iterator protocol, which means it can be iterated over using a for-loop or by fetching individual elements explicitly via get_next().

You can create a tf.distribute.DistributedIterator by calling iter on a tf.distribute.DistributedDataset or creating a python loop over a tf.distribute.DistributedDataset.

Visit the tutorial on distributed input for more examples and caveats.

element_spec The type specification of an element of tf.distribute.DistributedIterator.

global_batch_size = 16
strategy = tf.distribute.MirroredStrategy()
dataset =[1.],[2])).repeat(100).batch(global_batch_size)
distributed_iterator = iter(strategy.experimental_distribute_dataset(dataset))
(TensorSpec(shape=(None, 1), dtype=tf.float32, name=None),
TensorSpec(shape=(None, 1), dtype=tf.int32, name=None))

The above example corresponds to the case where you have only one device. If you have two devices, for example,

strategy = tf.distribute.MirroredStrategy(['/gpu:0', '/gpu:1'])

Then the final line will print out:

(PerReplicaSpec(TensorSpec(shape=(None, 1), dtype=tf.float32, name=None),
TensorSpec(shape=(None, 1), dtype=tf.float32, name=None)),
PerReplicaSpec(TensorSpec(shape=(None, 1), dtype=tf.int32, name=None),
TensorSpec(shape=(None, 1), dtype=tf.int32, name=None)))



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