# tf.device给你了很多可伸缩性在TensorFlow的计算图中选择放置你的单独的操作

# 在许多的情况下，有很多启发可以工作的很好

# 例如tf.train.replica_device_setter API可以和tf.device一起使用，在进行数据分布式并行性话训练的时候

# 列如下面的代码块显示了tf.train.replica_device_setter  如何在tf.Variable 对象和其他的操作之间

# 应用不同的参数放置策略

with tf.device(tf.train.replica_device_setter(ps_tasks=3)):

    # tf.Variable objects are , by default , placed on tasks in "/job:ps" in a

    # round-robin fashion

    w_0 = tf.Variable(...)  # placed on "/job:ps/task:0"

    b_0 = tf.Variable(...)  # placed on "/job:ps/task:1"

    w_1 = tf.Variable(...)  # placed on "/job:ps/task:2"

    b_1 = tf.Variable(...)  # placed on "/job:ps/task:0"

    input_data = tf.placeholder(tf.float32)  # placed on "/job:worker"

    layer_0 = tf.matmul(input_data, w_0) + b_0  # placed on "/job:worker"

    layer_1 = tf.matmul(layer_0, w_1) + b_1  # placed on "/job:worker"