TensorFlow是Google設計的開源Python庫,用於開發機器學習模型和深度學習神經網絡。
device()用於顯式指定應在其中執行操作的設備。
用法:tesorflow.device( device_name )
參數:
- device_name:它指定在此上下文中使用的設備名稱。
返回值:它返回一個上下文管理器,該上下文管理器指定用於新創建的操作的默認設備。
範例1:
Python3
# Importing the library
import tensorflow as tf
# Initializing Device Specification
device_spec = tf.DeviceSpec(job ="localhost", replica = 0, device_type = "CPU")
# Printing the DeviceSpec
print('Device Spec:', device_spec.to_string())
# Enabling device logging
tf.debugging.set_log_device_placement(True)
# Specifying the device
with tf.device(device_spec):
a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
c = tf.matmul(a, b)
輸出:
Device Spec: /job:localhost/replica:0/device:CPU:* Executing op MatMul in device /job:localhost/replica:0/task:0/device:CPU:0
範例2:在此示例設備規範中指定了要使用的GPU,但係統找不到GPU,因此它將在CPU上運行操作。
Python3
# Importing the library
import tensorflow as tf
# Initializing Device Specification
device_spec = tf.DeviceSpec(job ="localhost", replica = 0, device_type = "GPU")
# Printing the DeviceSpec
print('Device Spec:', device_spec.to_string())
# Enabling device logging
tf.debugging.set_log_device_placement(True)
# Specifying the device
with tf.device(device_spec):
a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
c = tf.matmul(a, b)
輸出:
Device Spec: /job:localhost/replica:0/device:GPU:* Executing op MatMul in device /job:localhost/replica:0/task:0/device:CPU:0
相關用法
注:本文由純淨天空篩選整理自aman neekhara大神的英文原創作品 Python – tensorflow.device()。非經特殊聲明,原始代碼版權歸原作者所有,本譯文未經允許或授權,請勿轉載或複製。