Issue 1:
#15216

i have a issue, but nobody help me to solve it

Issue 2:
i use tensorflow to distributed trainning models, i use the partition valriables to store an array data, when the data is not so bigger, everything looks ok,but when the array data become larger, when the session initialize, the partition variables can not initialized and the session will wait util time out.

Describe the problem

Describe the problem clearly here. Be sure to convey here why it's a bug in TensorFlow or a feature request. the feat_info can initialize successfully, but the adj_info cannot initialized. the adj_info is larger than feat_info

i use monitored_session or supervisor to do the initialize_variables, when data is not so large, it will initialize successfully, my machine have more than 300GB memory is enough for the larger variable

Have I written custom code

yes

OS Platform and Distribution

linux platform

TensorFlow installed from

N/A

TensorFlow version

r1.4

Bazel version

CUDA/cuDNN version

no gpu

GPU model and memory

no gpu

Exact command to reproduce

N/A

Source code / logs

i use ps_num = 4, worker_num =4 and i also try some other distributed config, like ps_num=1, worker_num=4, the result is the same

source code:

with tf.device(tf.train.replica_device_setter(

worker_device="/job:worker/task:%d" % task_id,

cluster=cluster_spec)):

feat_info = tf.get_variable("feature_info", (len(id_map),FLAGS.features_column), tf.float32, trainable=False, partitioner=tf.fixed_size_partitioner(num_workers))

adj_info = tf.get_variable("adj_info", (len(id_map),FLAGS.max_degree), tf.int64, trainable=False, partitioner=tf.fixed_size_partitioner(num_workers))

with tf.device('/job:worker/task:%d' %task_id):

adj_local = tf.Variable(tf.constant(minibatch.adj, dtype=tf.int64), trainable=False, name="adj_local", collections=[tf.GraphKeys.LOCAL_VARIABLES])

feat_local = tf.Variable(tf.constant(features, dtype=tf.float32), trainable=False, name="feat_local", collections=[tf.GraphKeys.LOCAL_VARIABLES])

length, begin, end = split_node_by_task(len(id_map), task_id, num_workers)

adj = tf.nn.embedding_lookup(adj_info, [x for x in range(begin, end)])

adj = adj_local

feat = tf.nn.embedding_lookup(feat_info, [x for x in range(begin, end)])

feat = feat_local

log:

2017-12-08 23:54:17.377290: I tensorflow/core/distributed_runtime/master_session.cc:998] Start master session c2b3ba9b700261ba with config:

INFO:tensorflow:Waiting for model to be ready. Ready_for_local_init_op: None, ready: Variables not initialized: adj_info/part_0, adj_info/part_1, adj_info/part_2, adj_info/part_3, adj_info/part_4, adj_info/part_5, adj_info/part_6, adj_info/part_7, adj_info/part_8, adj_info/part_9, adj_info/part_10, adj_info/part_11, adj_info/part_12, adj_info/part_13, adj_info/part_14, adj_info/part_15

2017-12-09 00:00:35.637019: I tensorflow/core/distributed_runtime/master_session.cc:998] Start master session f35fcf332e3908ec with config:

INFO:tensorflow:Waiting for model to be ready. Ready_for_local_init_op: None, ready: Variables not initialized: adj_info/part_0, adj_info/part_1, adj_info/part_2, adj_info/part_3, adj_info/part_4, adj_info/part_5, adj_info/part_6, adj_info/part_7, adj_info/part_8, adj_info/part_9, adj_info/part_10, adj_info/part_11, adj_info/part_12, adj_info/part_13, adj_info/part_14, adj_info/part_15

and it will alway waiting adj_info to initialize