description: An iterator over tf.distribute.DistributedDataset.

tf.distribute.DistributedIterator

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.

Attributes

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

>>> global_batch_size = 16
>>> strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
>>> dataset = tf.data.Dataset.from_tensors(([1.],[2])).repeat(100).batch(global_batch_size)
>>> distributed_iterator = iter(strategy.experimental_distribute_dataset(dataset))
>>> distributed_iterator.element_spec
(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)))

Methods

get_next

View source

get_next()

Returns the next input from the iterator for all replicas.

Example use:

>>> strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
>>> dataset = tf.data.Dataset.range(100).batch(2)
>>> dist_dataset = strategy.experimental_distribute_dataset(dataset)
>>> dist_dataset_iterator = iter(dist_dataset)
>>> @tf.function
... def one_step(input):
...   return input
>>> step_num = 5
>>> for _ in range(step_num):
...   strategy.run(one_step, args=(dist_dataset_iterator.get_next(),))
>>> strategy.experimental_local_results(dist_dataset_iterator.get_next())
(<tf.Tensor: shape=(1,), dtype=int64, numpy=array([10])>,
 <tf.Tensor: shape=(1,), dtype=int64, numpy=array([11])>)

Returns
A single tf.Tensor or a tf.distribute.DistributedValues which contains the next input for all replicas.

Raises
tf.errors.OutOfRangeError: If the end of the iterator has been reached.

get_next_as_optional

View source

get_next_as_optional()

Returns a tf.experimental.Optional that contains the next value for all replicas.

If the tf.distribute.DistributedIterator has reached the end of the sequence, the returned tf.experimental.Optional will have no value.

Example usage:

>>> strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
>>> global_batch_size = 2
>>> steps_per_loop = 2
>>> dataset = tf.data.Dataset.range(10).batch(global_batch_size)
>>> distributed_iterator = iter(
...     strategy.experimental_distribute_dataset(dataset))
>>> def step_fn(x):
...   # train the model with inputs
...   return x
>>> @tf.function
... def train_fn(distributed_iterator):
...   for _ in tf.range(steps_per_loop):
...     optional_data = distributed_iterator.get_next_as_optional()
...     if not optional_data.has_value():
...       break
...     per_replica_results = strategy.run(step_fn, args=(optional_data.get_value(),))
...     tf.print(strategy.experimental_local_results(per_replica_results))
>>> train_fn(distributed_iterator)
... # ([0 1], [2 3])
... # ([4], [])

Returns
An tf.experimental.Optional object representing the next value from the tf.distribute.DistributedIterator (if it has one) or no value.

__iter__

__iter__()