tf.compat.v1.distribute.experimental.CentralStorageStrategy

A one-machine strategy that puts all variables on a single device.

Inherits From: Strategy

Variables are assigned to local CPU or the only GPU. If there is more than one GPU, compute operations (other than variable update operations) will be replicated across all GPUs.

For Example:

strategy = tf.distribute.experimental.CentralStorageStrategy()
# Create a dataset
ds = tf.data.Dataset.range(5).batch(2)
# Distribute that dataset
dist_dataset = strategy.experimental_distribute_dataset(ds)

with strategy.scope():
  @tf.function
  def train_step(val):
    return val + 1

  # Iterate over the distributed dataset
  for x in dist_dataset:
    # process dataset elements
    strategy.run(train_step, args=(x,))

cluster_resolver Returns the cluster resolver associated with this strategy.

In general, when using a multi-worker tf.distribute strategy such as