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.

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])>)

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], [])

__iter__

__iter__()