將圖節點放置在特定的計算設備上 · 精通 TensorFlow 1.x

# 將圖節點放置在特定的計算設備上讓我們通過定義配置對象來啟用變量放置的記錄，將`log_device_placement`屬性設置為`true`，然后將此`config`對象傳遞給會話，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]})) ``` 我們在 Jupyter Notebook 控制臺中獲得以下輸出： ```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，默認情況下，TensorFlow 會在設備上創建變量和操作節點，從而獲得最高表現。可以使用 `tf.device()` 函數將變量和操作放在特定設備上。讓我們把圖放在 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 控制臺中，我們看到現在變量已經放在 CPU 上，并且執行也發生在 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 ```