tf.distribute.experimental.coordinator.RemoteValue

strategy = tf.distribute.experimental.ParameterServerStrategy(
    cluster_resolver=...)
coordinator = (
    tf.distribute.experimental.coordinator.ClusterCoordinator(strategy))

with strategy.scope():
  v1 = tf.Variable(initial_value=0.0)
  v2 = tf.Variable(initial_value=1.0)

@tf.function
def worker_fn():
  v1.assign_add(0.1)
  v2.assign_sub(0.2)
  return v1.read_value() / v2.read_value()

result = coordinator.schedule(worker_fn)
# Note that `fetch()` gives the actual result instead of a `tf.Tensor`.
assert result.fetch() == 0.125

for _ in range(10):
  # `worker_fn` will be run on arbitrary workers that are available. The
  # `result` value will be available later.
  result = coordinator.schedule(worker_fn)

fetch()

get()