tf.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)

tf.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)

See the guide: Training > Distributed execution

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

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.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

Args:

• ps_tasks: Number of tasks in the ps job. Ignored if cluster is provided.
• ps_device: String. Device of the ps job. If empty no ps job is used. Defaults to ps.
• worker_device: String. Device of the worker job. If empty no worker job is used.
• merge_devices: Boolean. If True, merges or only sets a device if the device constraint is completely unset. merges device specification rather than overriding them.
• cluster: ClusterDef proto or ClusterSpec.
• ps_ops: List of strings representing Operation types that need to be placed on ps devices. If None, defaults to ["Variable"].
• ps_strategy: A callable invoked for every ps Operation (i.e. matched by ps_ops), that takes the Operation and returns the ps task index to use. If None, defaults to a round-robin strategy across all ps devices.

Returns:

A function to pass to tf.device().

Raises:

TypeError if cluster is not a dictionary or ClusterDef protocol buffer, or if ps_strategy is provided but not a callable.

Defined in tensorflow/python/training/device_setter.py.