shared_hashtable.py

import tensorflow as tf

tf.reset_default_graph()

g1 = tf.Graph()
with g1.as_default():
    print g1
    with tf.name_scope('myscope'):
        keys = [1]
        values = ["foo"]
        t1 = tf.contrib.lookup.HashTable(
            tf.contrib.lookup.KeyValueTensorInitializer(keys, values, key_dtype=tf.int64), 
            "no luck", 
            # one line change and hashtable is now shared across sessions
            shared_name="hehe",
            name="my_table")
        init_op = t1.init

        ts = tf.constant([1], dtype=tf.int64)
        lookup_op = t1.lookup(ts)
    
    with tf.Session("grpc://localhost:9000") as sess:
        sess.run(init_op)
        print "t1.name: ", t1.name
        print sess.run(lookup_op)
    
    # complain table not initialized
    with tf.Session("grpc://localhost:9000") as sess:
        # sess.run(init_op)
        print "t1.name: ", t1.name
        print sess.run(lookup_op)

shared_hashtable_different_runtime.py

import tensorflow as tf

tf.reset_default_graph()

g1 = tf.Graph()
with g1.as_default():
    print g1
    with tf.name_scope('myscope'):
        keys = [1]
        values = ["foo"]
        t1 = tf.contrib.lookup.HashTable(
            tf.contrib.lookup.KeyValueTensorInitializer(keys, values, key_dtype=tf.int64), 
            "no luck", 
            shared_name="hehe",
            name="my_table")
        init_op = t1.init

        ts = tf.constant([1], dtype=tf.int64)
        lookup_op = t1.lookup(ts)
    
    with tf.Session("") as sess:
        sess.run(init_op)
        print "t1.name: ", t1.name
        print sess.run(lookup_op)
    
    # tf.Session() creates a new runtime, therefore still complains table not initialized
    # even if hashtable is shared (across different sessions connecting the same runtime)
    with tf.Session("") as sess:
        # sess.run(init_op)
        print "t1.name: ", t1.name
        print sess.run(lookup_op)

tf_variable_vs_hashtable.py

import tensorflow as tf

tf.reset_default_graph()

# variables are shared under the same name, in a distributed runtime across different sessions
g1 = tf.Graph()
with g1.as_default():
    with tf.variable_scope('foo'):
        w = tf.get_variable('w', shape=[2, 4], initializer=tf.constant_initializer([0, 1, 2, 3, 4, 5, 6, 7]))
        init_op = tf.global_variables_initializer()
    
    with tf.Session("grpc://localhost:9000") as sess:
        sess.run(init_op)
        print "w.name: ", w.name
        print sess.run(w)
    
    # variable already initialized
    with tf.Session("grpc://localhost:9000") as sess:
        # sess.run(init_op)
        print "w.name: ", w.name
        print sess.run(w)
        
# hashtables aren't shared under the same name, but under "shared_name", in a distributed runtime, across different sessions
import tensorflow as tf

tf.reset_default_graph()

g1 = tf.Graph()
with g1.as_default():
    print g1
    with tf.name_scope('myscope'):
        keys = [1]
        values = ["foo"]
        t1 = tf.contrib.lookup.HashTable(
            tf.contrib.lookup.KeyValueTensorInitializer(keys, values, key_dtype=tf.int64), 
            "no luck", 
            name="my_table")
        init_op = t1.init

        ts = tf.constant([1], dtype=tf.int64)
        lookup_op = t1.lookup(ts)
    
    with tf.Session("grpc://localhost:9000") as sess:
        sess.run(init_op)
        print "t1.name: ", t1.name
        print sess.run(lookup_op)
    
    # complain table not initialized
    with tf.Session("grpc://localhost:9000") as sess:
        # sess.run(init_op)
        print "t1.name: ", t1.name
        print sess.run(lookup_op)