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tensorflowdistributed
nakosung
nakosung

Reputation: 63

Distributed tensorflow with multiple gpu

It seems that tf.train.replica_device_setter doesn't allow specify gpu which work with.

What I want to do is like below:

 with tf.device(
   tf.train.replica_device_setter(
   worker_device='/job:worker:task:%d/gpu:%d' % (deviceindex, gpuindex)):
     <build-some-tf-graph>

Upvotes: 0

Views: 2281

Answers (2)

Qu Kevin
Qu Kevin

Reputation: 1

I didn't check previous versions, but in Tensorflow 1.4/1.5, you can specify devices in replica_device_setter(worker_device='job:worker/task:%d/gpu:%d' % (FLAGS.task_index, i), cluster=self.cluster).

See tensorflow/python/training/device_setter.py line 199-202:

if ps_ops is None: # TODO(sherrym): Variables in the LOCAL_VARIABLES collection should not be # placed in the parameter server. ps_ops = ["Variable", "VariableV2", "VarHandleOp"]

Thanks to the code provided by @Yaroslav Bulatov, but his protocol is different from replica_device_setter, and may fail in some cases.

Upvotes: 0

Yaroslav Bulatov
Yaroslav Bulatov

Reputation: 57883

If your parameters are not sharded, you could do it with a simplified version of replica_device_setter like below:

def assign_to_device(worker=0, gpu=0, ps_device="/job:ps/task:0/cpu:0"):
    def _assign(op):
        node_def = op if isinstance(op, tf.NodeDef) else op.node_def
        if node_def.op == "Variable":
            return ps_device
        else:
            return "/job:worker/task:%d/gpu:%d" % (worker, gpu)
    return _assign

with tf.device(assign_to_device(1, 2)):
  # this op goes on worker 1 gpu 2
  my_op = tf.ones(())

Upvotes: 7

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