tf.compat.v1.train.replica_device_setter

Return a device function to use when building a Graph for replicas.

tf.compat.v1.train.replica_device_setter(
    ps_tasks=0, ps_device='/job:ps', worker_device='/job:worker',
    merge_devices=True, cluster=None, ps_ops=None, ps_strategy=None
)

Device Functions are used in with tf.device(device_function): statement to automatically assign devices to Operation objects as they are constructed, Device constraints are added from the inner-most context first, working outwards. The merging behavior adds constraints to fields that are yet unset by a more inner context. Currently the fields are (job, task, cpu/gpu).

If cluster is None, and ps_tasks is 0, the returned function is a no-op. Otherwise, the value of ps_tasks is derived from cluster.

By default, only Variable ops are placed on ps tasks, and the placement strategy is round-robin over all ps tasks. A custom ps_strategy may be used to do more intelligent placement, such as tf.contrib.training.GreedyLoadBalancingStrategy.

For example,

# To build a cluster with two ps jobs on hosts ps0 and ps1, and 3 worker
# jobs on hosts worker0, worker1 and worker2.
cluster_spec = {
    "ps": ["ps0:2222", "ps1:2222"],
    "worker": ["worker0:2222", "worker1:2222", "worker2:2222"]}
with
tf.device(tf.compat.v1.train.replica_device_setter(cluster=cluster_spec)):
  # Build your graph
  v1 = tf.Variable(...)  # assigned to /job:ps/task:0
  v2 = tf.Variable(...)  # assigned to /job:ps/task:1
  v3 = tf.Variable(...)  # assigned to /job:ps/task:0
# Run compute