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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Chapter 12 – Distributed TensorFlow
This notebook contains all the sample code and solutions to the exercises in chapter 12.
Setup
First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures
Step1:  Local server
Step2:  Cluster
Step3:  Pinning operations across devices and servers
Step4:  Readers
Step5:  Queue runners and coordinators
Step6:  Setting a timeout
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Python Code:
# To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
def reset_graph(seed=42):
    tf.reset_default_graph()
    tf.set_random_seed(seed)
    np.random.seed(seed)
# To plot pretty figures
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
# Where to save the figures
PROJECT_ROOT_DIR = "."
CHAPTER_ID = "distributed"
def save_fig(fig_id, tight_layout=True):
    path = os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID, fig_id + ".png")
    print("Saving figure", fig_id)
    if tight_layout:
        plt.tight_layout()
    plt.savefig(path, format='png', dpi=300)
Explanation: Chapter 12 – Distributed TensorFlow
This notebook contains all the sample code and solutions to the exercises in chapter 12.
Setup
First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:
End of explanation
import tensorflow as tf
c = tf.constant("Hello distributed TensorFlow!")
server = tf.train.Server.create_local_server()
with tf.Session(server.target) as sess:
    print(sess.run(c))
Explanation: Local server
End of explanation
cluster_spec = tf.train.ClusterSpec({
    "ps": [
        "127.0.0.1:2221",  # /job:ps/task:0
        "127.0.0.1:2222",  # /job:ps/task:1
    ],
    "worker": [
        "127.0.0.1:2223",  # /job:worker/task:0
        "127.0.0.1:2224",  # /job:worker/task:1
        "127.0.0.1:2225",  # /job:worker/task:2
    ]})
task_ps0 = tf.train.Server(cluster_spec, job_name="ps", task_index=0)
task_ps1 = tf.train.Server(cluster_spec, job_name="ps", task_index=1)
task_worker0 = tf.train.Server(cluster_spec, job_name="worker", task_index=0)
task_worker1 = tf.train.Server(cluster_spec, job_name="worker", task_index=1)
task_worker2 = tf.train.Server(cluster_spec, job_name="worker", task_index=2)
Explanation: Cluster
End of explanation
reset_graph()
with tf.device("/job:ps"):
    a = tf.Variable(1.0, name="a")
with tf.device("/job:worker"):
    b = a + 2
with tf.device("/job:worker/task:1"):
    c = a + b
with tf.Session("grpc://127.0.0.1:2221") as sess:
    sess.run(a.initializer)
    print(c.eval())
reset_graph()
with tf.device(tf.train.replica_device_setter(
        ps_tasks=2,
        ps_device="/job:ps",
        worker_device="/job:worker")):
    v1 = tf.Variable(1.0, name="v1")  # pinned to /job:ps/task:0 (defaults to /cpu:0)
    v2 = tf.Variable(2.0, name="v2")  # pinned to /job:ps/task:1 (defaults to /cpu:0)
    v3 = tf.Variable(3.0, name="v3")  # pinned to /job:ps/task:0 (defaults to /cpu:0)
    s = v1 + v2            # pinned to /job:worker (defaults to task:0/cpu:0)
    with tf.device("/task:1"):
        p1 = 2 * s         # pinned to /job:worker/task:1 (defaults to /cpu:0)
        with tf.device("/cpu:0"):
            p2 = 3 * s     # pinned to /job:worker/task:1/cpu:0
config = tf.ConfigProto()
config.log_device_placement = True
with tf.Session("grpc://127.0.0.1:2221", config=config) as sess:
    v1.initializer.run()
Explanation: Pinning operations across devices and servers
End of explanation
reset_graph()
test_csv = open("my_test.csv", "w")
test_csv.write("x1, x2 , target\n")
test_csv.write("1.,    , 0\n")
test_csv.write("4., 5. , 1\n")
test_csv.write("7., 8. , 0\n")
test_csv.close()
filename_queue = tf.FIFOQueue(capacity=10, dtypes=[tf.string], shapes=[()])
filename = tf.placeholder(tf.string)
enqueue_filename = filename_queue.enqueue([filename])
close_filename_queue = filename_queue.close()
reader = tf.TextLineReader(skip_header_lines=1)
key, value = reader.read(filename_queue)
x1, x2, target = tf.decode_csv(value, record_defaults=[[-1.], [-1.], [-1]])
features = tf.stack([x1, x2])
instance_queue = tf.RandomShuffleQueue(
    capacity=10, min_after_dequeue=2,
    dtypes=[tf.float32, tf.int32], shapes=[[2],[]],
    name="instance_q", shared_name="shared_instance_q")
enqueue_instance = instance_queue.enqueue([features, target])
close_instance_queue = instance_queue.close()
minibatch_instances, minibatch_targets = instance_queue.dequeue_up_to(2)
with tf.Session() as sess:
    sess.run(enqueue_filename, feed_dict={filename: "my_test.csv"})
    sess.run(close_filename_queue)
    try:
        while True:
            sess.run(enqueue_instance)
    except tf.errors.OutOfRangeError as ex:
        print("No more files to read")
    sess.run(close_instance_queue)
    try:
        while True:
            print(sess.run([minibatch_instances, minibatch_targets]))
    except tf.errors.OutOfRangeError as ex:
        print("No more training instances")
#coord = tf.train.Coordinator()
#threads = tf.train.start_queue_runners(coord=coord)
#filename_queue = tf.train.string_input_producer(["test.csv"])
#coord.request_stop()
#coord.join(threads)
Explanation: Readers
End of explanation
reset_graph()
filename_queue = tf.FIFOQueue(capacity=10, dtypes=[tf.string], shapes=[()])
filename = tf.placeholder(tf.string)
enqueue_filename = filename_queue.enqueue([filename])
close_filename_queue = filename_queue.close()
reader = tf.TextLineReader(skip_header_lines=1)
key, value = reader.read(filename_queue)
x1, x2, target = tf.decode_csv(value, record_defaults=[[-1.], [-1.], [-1]])
features = tf.stack([x1, x2])
instance_queue = tf.RandomShuffleQueue(
    capacity=10, min_after_dequeue=2,
    dtypes=[tf.float32, tf.int32], shapes=[[2],[]],
    name="instance_q", shared_name="shared_instance_q")
enqueue_instance = instance_queue.enqueue([features, target])
close_instance_queue = instance_queue.close()
minibatch_instances, minibatch_targets = instance_queue.dequeue_up_to(2)
n_threads = 5
queue_runner = tf.train.QueueRunner(instance_queue, [enqueue_instance] * n_threads)
coord = tf.train.Coordinator()
with tf.Session() as sess:
    sess.run(enqueue_filename, feed_dict={filename: "my_test.csv"})
    sess.run(close_filename_queue)
    enqueue_threads = queue_runner.create_threads(sess, coord=coord, start=True)
    try:
        while True:
            print(sess.run([minibatch_instances, minibatch_targets]))
    except tf.errors.OutOfRangeError as ex:
        print("No more training instances")
reset_graph()
def read_and_push_instance(filename_queue, instance_queue):
    reader = tf.TextLineReader(skip_header_lines=1)
    key, value = reader.read(filename_queue)
    x1, x2, target = tf.decode_csv(value, record_defaults=[[-1.], [-1.], [-1]])
    features = tf.stack([x1, x2])
    enqueue_instance = instance_queue.enqueue([features, target])
    return enqueue_instance
filename_queue = tf.FIFOQueue(capacity=10, dtypes=[tf.string], shapes=[()])
filename = tf.placeholder(tf.string)
enqueue_filename = filename_queue.enqueue([filename])
close_filename_queue = filename_queue.close()
instance_queue = tf.RandomShuffleQueue(
    capacity=10, min_after_dequeue=2,
    dtypes=[tf.float32, tf.int32], shapes=[[2],[]],
    name="instance_q", shared_name="shared_instance_q")
minibatch_instances, minibatch_targets = instance_queue.dequeue_up_to(2)
read_and_enqueue_ops = [read_and_push_instance(filename_queue, instance_queue) for i in range(5)]
queue_runner = tf.train.QueueRunner(instance_queue, read_and_enqueue_ops)
with tf.Session() as sess:
    sess.run(enqueue_filename, feed_dict={filename: "my_test.csv"})
    sess.run(close_filename_queue)
    coord = tf.train.Coordinator()
    enqueue_threads = queue_runner.create_threads(sess, coord=coord, start=True)
    try:
        while True:
            print(sess.run([minibatch_instances, minibatch_targets]))
    except tf.errors.OutOfRangeError as ex:
        print("No more training instances")
Explanation: Queue runners and coordinators
End of explanation
reset_graph()
q = tf.FIFOQueue(capacity=10, dtypes=[tf.float32], shapes=[()])
v = tf.placeholder(tf.float32)
enqueue = q.enqueue([v])
dequeue = q.dequeue()
output = dequeue + 1
config = tf.ConfigProto()
config.operation_timeout_in_ms = 1000
with tf.Session(config=config) as sess:
    sess.run(enqueue, feed_dict={v: 1.0})
    sess.run(enqueue, feed_dict={v: 2.0})
    sess.run(enqueue, feed_dict={v: 3.0})
    print(sess.run(output))
    print(sess.run(output, feed_dict={dequeue: 5}))
    print(sess.run(output))
    print(sess.run(output))
    try:
        print(sess.run(output))
    except tf.errors.DeadlineExceededError as ex:
        print("Timed out while dequeuing")
Explanation: Setting a timeout
End of explanation
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