将图节点放置在特定的计算设备上 · 精通 TensorFlow 1.x

# 将图节点放置在特定的计算设备上让我们通过定义配置对象来启用变量放置的记录，将`log_device_placement`属性设置为`true`，然后将此`config`对象传递给会话，如下所示： ```py tf.reset_default_graph() # Define model parameters w = tf.Variable([.3], tf.float32) b = tf.Variable([-.3], tf.float32) # Define model input and output x = tf.placeholder(tf.float32) y = w * x + b config = tf.ConfigProto() config.log_device_placement=True with tf.Session(config=config) as tfs: # initialize and print the variable y tfs.run(global_variables_initializer()) print('output',tfs.run(y,{x:[1,2,3,4]})) ``` 我们在 Jupyter Notebook 控制台中获得以下输出： ```py b: (VariableV2): /job:localhost/replica:0/task:0/device:GPU:0 b/read: (Identity): /job:localhost/replica:0/task:0/device:GPU:0 b/Assign: (Assign): /job:localhost/replica:0/task:0/device:GPU:0 w: (VariableV2): /job:localhost/replica:0/task:0/device:GPU:0 w/read: (Identity): /job:localhost/replica:0/task:0/device:GPU:0 mul: (Mul): /job:localhost/replica:0/task:0/device:GPU:0 add: (Add): /job:localhost/replica:0/task:0/device:GPU:0 w/Assign: (Assign): /job:localhost/replica:0/task:0/device:GPU:0 init: (NoOp): /job:localhost/replica:0/task:0/device:GPU:0 x: (Placeholder): /job:localhost/replica:0/task:0/device:GPU:0 b/initial_value: (Const): /job:localhost/replica:0/task:0/device:GPU:0 Const_1: (Const): /job:localhost/replica:0/task:0/device:GPU:0 w/initial_value: (Const): /job:localhost/replica:0/task:0/device:GPU:0 Const: (Const): /job:localhost/replica:0/task:0/device:GPU:0 ``` 因此，默认情况下，TensorFlow 会在设备上创建变量和操作节点，从而获得最高表现。可以使用 `tf.device()` 函数将变量和操作放在特定设备上。让我们把图放在 CPU 上： ```py tf.reset_default_graph() with tf.device('/device:CPU:0'): # Define model parameters w = tf.get_variable(name='w',initializer=[.3], dtype=tf.float32) b = tf.get_variable(name='b',initializer=[-.3], dtype=tf.float32) # Define model input and output x = tf.placeholder(name='x',dtype=tf.float32) y = w * x + b config = tf.ConfigProto() config.log_device_placement=True with tf.Session(config=config) as tfs: # initialize and print the variable y tfs.run(tf.global_variables_initializer()) print('output',tfs.run(y,{x:[1,2,3,4]})) ``` 在 Jupyter 控制台中，我们看到现在变量已经放在 CPU 上，并且执行也发生在 CPU 上： ```py b: (VariableV2): /job:localhost/replica:0/task:0/device:CPU:0 b/read: (Identity): /job:localhost/replica:0/task:0/device:CPU:0 b/Assign: (Assign): /job:localhost/replica:0/task:0/device:CPU:0 w: (VariableV2): /job:localhost/replica:0/task:0/device:CPU:0 w/read: (Identity): /job:localhost/replica:0/task:0/device:CPU:0 mul: (Mul): /job:localhost/replica:0/task:0/device:CPU:0 add: (Add): /job:localhost/replica:0/task:0/device:CPU:0 w/Assign: (Assign): /job:localhost/replica:0/task:0/device:CPU:0 init: (NoOp): /job:localhost/replica:0/task:0/device:CPU:0 x: (Placeholder): /job:localhost/replica:0/task:0/device:CPU:0 b/initial_value: (Const): /job:localhost/replica:0/task:0/device:CPU:0 Const_1: (Const): /job:localhost/replica:0/task:0/device:CPU:0 w/initial_value: (Const): /job:localhost/replica:0/task:0/device:CPU:0 Const: (Const): /job:localhost/replica:0/task:0/device:CPU:0 ```