Parallel programming in Tensorflow code with Session.run

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I am trying to implement distributed execution in my Tensorflow code. I created a simple example. When I run it, the program does not yield any result. My guess is the host locations are not set properly for my Linux system.

import tensorflow as tf


cluster = tf.train.ClusterSpec({"local": ["localhost:2222", "localhost:2223"]})

x = tf.constant(2)


with tf.device("/job:local/task:1"):
    y2 = x - 66

with tf.device("/job:local/task:0"):
    y1 = x + 300
    y = y1 + y2


with tf.Session("grpc://localhost:2222") as sess:
    result = sess.run(y)
    print(result)
grpc tensorflow parallel-processing cluster-computing python
1 Answers

Before running the session above, it is required to start 2 workers with another script (python tfserver.py 0 & python tfserver.py 1). Additionally I had to replace localhost with the actual server name due to some restrictions in the cluster.

# Get task number from command line
import sys
task_number = int(sys.argv[1])

import tensorflow as tf

cluster = tf.train.ClusterSpec({"local": ["localhost:2222", "localhost:2223"]})
server = tf.train.Server(cluster, job_name="local", task_index=task_number)

print("Starting server #{}".format(task_number))

server.start()
server.join()

Source: https://databricks.com/tensorflow/distributed-computing-with-tensorflow

More advanced usage here: https://github.com/tensorflow/examples/blob/master/community/en/docs/deploy/distributed.md

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