2017-07-23 16:16:17.281414: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:316] Started server with target: grpc://localhost:2225
Process Process-3:
Traceback (most recent call last):
  File "/home/skay/anaconda2/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap
    self.run()
  File "/home/skay/anaconda2/lib/python2.7/multiprocessing/process.py", line 114, in run
    self._target(*self._args, **self._kwargs)
  File "/home/skay/.PyCharmCE2017.1/config/scratches/scratch_6.py", line 32, in cifar10
    serv = tf.train.Server(cluster, job_name= params.job_name,task_index=params.task_index)
  File "/home/skay/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/server_lib.py", line 145, in __init__
    self._server_def.SerializeToString(), status)
  File "/home/skay/anaconda2/lib/python2.7/contextlib.py", line 24, in __exit__
    self.gen.next()
  File "/home/skay/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
    pywrap_tensorflow.TF_GetCode(status)) UnknownError: Could not start gRPC server

> `2017-07-23 16:27:48.605617: I tensorflow/core/distributed_runtime/master_session.cc:999] Start master session fe9fd6a338e2c9a7 with config:

2017-07-23 16:27:48.607126: I tensorflow/core/distributed_runtime/master_session.cc:999] Start master session 3560417f98b00dea with config:

[  1.   2.   3.   4.   5.   6.   7.   8.   9.  10.]
Process-3
[  1.   2.   3.   4.   5.   6.   7.   8.   9.  10.]
Process-3
[  1.   2.   3.   4.   5.   6.   7.   8.   9.  10.]
Process-3

ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'worker_0/init' type=NoOp>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "/home/skay/.PyCharmCE2017.1/config/scratches/scratch_6.py", line 83, in <module>n    proc.start()', 'File "/home/skay/anaconda2/lib/python2.7/multiprocessing/process.py", line 130, in startn    self._popen = Popen(self)', 'File "/home/skay/anaconda2/lib/python2.7/multiprocessing/forking.py", line 126, in __init__n    code = process_obj._bootstrap()', 'File "/home/skay/anaconda2/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrapn    self.run()', 'File "/home/skay/anaconda2/lib/python2.7/multiprocessing/process.py", line 114, in runn    self._target(*self._args, **self._kwargs)', 'File "/home/skay/.PyCharmCE2017.1/config/scratches/scratch_6.py", line 49, in cifar10n    init_op=tf.initialize_all_variables(),logdir='/tmp/mydir')', 'File "/home/skay/anaconda2/lib/python2.7/site-packages/tensorflow/python/util/tf_should_use.py", line 170, in wrappedn    return _add_should_use_warning(fn(*args, **kwargs))', 'File "/home/skay/anaconda2/lib/python2.7/site-packages/tensorflow/python/util/tf_should_use.py", line 139, in _add_should_use_warningn    wrapped = TFShouldUseWarningWrapper(x)', 'File "/home/skay/anaconda2/lib/python2.7/site-packages/tensorflow/python/util/tf_should_use.py", line 96, in __init__n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
2017-07-23 16:28:28.646871: I tensorflow/core/distributed_runtime/master.cc:209] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2017-07-23 16:28:38.647276: I tensorflow/core/distributed_runtime/master.cc:209] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2017-07-23 16:28:48.647526: I tensorflow/core/distributed_runtime/master.cc:209] CreateSession still waiting for response from worker: /job:worker/replica:

# build a python mutliprocess.py
import multiprocessing
import time
import tensorflow as tf
from tensorflow.contrib.training import HParams
import os
import psutil
import numpy as np
from tensorflow.python.client import device_lib
from resnet import *
import Queue

cluster_spec ={"ps": ["localhost:2226"
                      ],
    "worker": [
        "localhost:2227",
        "localhost:2228"]}

cluster = tf.train.ClusterSpec(cluster_spec)
im_Test = np.linspace(1,10,10)

def model_fun(input):
    print multiprocessing.current_process().name
    return input

def cifar10(device,return_dict,result_t):
    params = HParams(cluster=cluster,
                     job_name = device[0],
                     task_index = device[1])

    serv = tf.train.Server(cluster, job_name= params.job_name,task_index=params.task_index)
    input_img=[]
    true_lab=[]

    if params.job_name == "ps":
        ##try and wait for all the wokers t
        serv.join()
    elif params.job_name == "worker":
        with tf.device(tf.train.replica_device_setter(worker_device="/job:worker/replica:0/task:%d" % params.task_index,
                                                      cluster=cluster)):
            # with tf.Graph().as_default(), tf.device('/cpu:%d' % params.task_index):
            # with tf.container('%s %d' % ('batchname', params.task_index)) as scope:
            input_img = tf.placeholder(dtype=tf.float32, shape=[10,])
            with tf.name_scope('%s_%d' % (params.job_name, params.task_index)) as scope:
                hess_op = model_fun(input_img)
                global_step = tf.contrib.framework.get_or_create_global_step()
                sv = tf.train.Supervisor(is_chief=(params.task_index == 0),
                                         global_step=global_step,
                                         init_op=tf.initialize_all_variables(),logdir='/tmp/mydir')
                with sv.prepare_or_wait_for_session(serv.target) as sess:
                    step = 0
                    while not sv.should_stop() :
                        hess = sess.run(hess_op, feed_dict={input_img:im_Test })
                        print(np.array(hess))
                        print multiprocessing.current_process().name
                        step += 1
                        if(step==3):
                            return_dict[params.job_name] = params.task_index
                            result_t.put(return_dict)
                            break
                    sv.stop()
                    sess.close()


    return

if __name__ == '__main__':

    logger = multiprocessing.log_to_stderr()
    manager = multiprocessing.Manager()
    result = manager.Queue()
    return_dict = manager.dict()
    processes = []
    devices = [['ps', 0],
               ['worker', 0],
               ['worker', 1]
               ]

    for i in (devices):
        start_time = time.time()
        proc = multiprocessing.Process(target=cifar10,args=(i,return_dict,result))
        processes.append(proc)
        proc.start()

    for p in processes:
        p.join()

    # print return_dict.values()
    kill = []
    while True:
        if result.empty() == True:
                break
        kill.append(result.get())
        print kill


    print("time taken = %d" % (start_time - time.time()))