使用图表间复制未初始化Tensorflow变量

我有test.py如下Python代码,它使用分布式Tensorflow的"Between-graph Replication":

import argparse
import logging

import tensorflow as tf

log = logging.getLogger(__name__)

# Job Names
PARAMETER_SERVER = "ps"
WORKER_SERVER = "worker"

# Cluster Details
CLUSTER_SPEC = {
    PARAMETER_SERVER: ["localhost:2222"],
    WORKER_SERVER: ["localhost:1111", "localhost:1112"]}


def parse_command_arguments():
    """ Set up and parse the command line arguments passed for experiment. """
    parser = argparse.ArgumentParser(
        description="Parameters and Arguments for the Test.")
    parser.add_argument(
        "--job_name",
        type=str,
        default="",
        help="One of 'ps', 'worker'"
    )
    # Flags for defining the tf.train.Server
    parser.add_argument(
        "--task_index",
        type=int,
        default=0,
        help="Index of task within the job"
    )

    return parser.parse_args()


def start_server(job_name, task_index):
    """ Create a server based on a cluster spec. """
    cluster = tf.train.ClusterSpec(CLUSTER_SPEC)
    server = tf.train.Server(
        cluster, job_name=job_name, task_index=task_index)

    return server, cluster


def model():
    """ Build up a simple estimator model. """
    # Build a linear model and predict values
    W = tf.Variable([.3], tf.float32)
    b = tf.Variable([-.3], tf.float32)
    x = tf.placeholder(tf.float32)
    linear_model = W * x + b
    y = tf.placeholder(tf.float32)
    global_step = tf.get_variable('global_step', [],
                                  initializer=tf.constant_initializer(0),
                                  trainable=False)

    # Loss sub-graph
    loss = tf.reduce_sum(tf.square(linear_model - y))

    # optimizer
    optimizer = tf.train.GradientDescentOptimizer(0.01)
    train = optimizer.minimize(loss, global_step=global_step)

    init_op = tf.global_variables_initializer()
    log.info("Variables initialized ...")

    return W, b, loss, x, y, train, global_step, init_op


if __name__ == "__main__":
    # Initializing logging with level "INFO".
    logging.basicConfig(level=logging.INFO)

    # Parse arguments from command line.
    arguments = parse_command_arguments()
    job_name = arguments.job_name
    task_index = arguments.task_index

    # Start a server.
    server, cluster = start_server(job_name, task_index)

    if job_name == "ps":
        server.join()
    else:
        with tf.device(tf.train.replica_device_setter(
                worker_device="/job:worker/task:%d" % task_index,
                cluster=cluster)):
            W, b, loss, x, y, train, global_step, init_op = model()
        with tf.train.MonitoredTrainingSession(
                master=server.target,
                is_chief=(arguments.task_index == 0 and (
                            arguments.job_name == 'worker'))) as sess:
            step = 0
            # training data
            x_train = [1, 2, 3, 4]
            y_train = [0, -1, -2, -3]
            while not sess.should_stop() and step <1000:
                _, step = sess.run(
                    [train, global_step], {x: x_train, y: y_train})

            # evaluate training accuracy
            curr_W, curr_b, curr_loss = sess.run(
                [W, b, loss], {x: x_train, y: y_train})
            print("W: %s b: %s loss: %s" % (curr_W, curr_b, curr_loss))

我按照以下顺序在一台机器(只有CPU的MacPro)中运行3个不同进程的代码:

参数服务器: $ python test.py --task_index 0 --job_name ps

工人1: $ python test.py --task_index 0 --job_name worker

工人2: $ python test.py --task_index 1 --job_name worker

我发现"工人2"的过程出现错误:

$ python test.py --task_index 1 --job_name worker
I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:197] Initialize GrpcChannelCache for job ps -> {0 -> localhost:2222}
I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:197] Initialize GrpcChannelCache for job worker -> {0 -> localhost:1111, 1 -> localhost:1112}
I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:211] Started server with target: grpc://localhost:1112
INFO:__main__:Variables initialized ...
I tensorflow/core/distributed_runtime/master_session.cc:993] Start master session 9912c75f2921fe13 with config: 

INFO:tensorflow:Waiting for model to be ready.  Ready_for_local_init_op:  None, ready: Variables not initialized: Variable, Variable_1, global_step
INFO:tensorflow:Waiting for model to be ready.  Ready_for_local_init_op:  None, ready: Variables not initialized: Variable, Variable_1, global_step

那个"工人2"的过程就冻结了.错误显示"Worker 2"的Tensorflow变量未成功初始化,因此我想知道MonitoredTrainingSession在Tensorflow会话或其他地方协调变量初始化方面是否存在错误,或者我在代码中遗漏了一些错误.

NOTE: The code was running with Tensorflow 0.12

我认为这是tf.train.MonitoredTrainingSession协调协议的"预期行为" .在最近的一个回答中,我解释了这个协议如何适应长时间运行的训练工作,因此工作人员在检查变量是否已经初始化之间会睡30秒.

在运行初始化操作的工作人员1和工作人员2检查变量之间存在竞争条件,并且如果工作人员2"赢得"竞赛,则将观察到一些变量未初始化,并且在再次检查之前它将进入30秒睡眠.

但是,程序中的总计算量非常小,因此在这30秒内,Worker 1将能够完成其工作并终止.当Worker 2检查变量是否已初始化时,它将创建一个新的tf.Session尝试连接到其他任务,但Worker 1不再运行,因此您将看到这样的日志消息(每10秒左右重复一次) ):

I tensorflow/core/distributed_runtime/master.cc:193] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0

当训练工作大大超过30秒时,这不会成为问题.

一种解决方法是通过设置"设备过滤器"来消除工作者之间的相互依赖性.由于在典型的图形间配置中,各个工作人员不进行通信,因此您可以告诉TensorFlow在会话创建时忽略另一个工作人员的缺席,使用tf. ConfigProto:

# Each worker only needs to contact the PS task(s) and the local worker task.
cOnfig= tf.ConfigProto(device_filters=[
    '/job:ps', '/job:worker/task:%d' % arguments.task_index])

with tf.train.MonitoredTrainingSession(
    master=server.target,
    cOnfig=config,
    is_chief=(arguments.task_index == 0 and (
              arguments.job_name == 'worker'))) as sess:
  # ...