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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
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.
|