Python – tensorflow.device（）
TensorFlow 是谷歌设计的开源 Python 库，用于开发机器学习模型和深度学习神经网络。
device() 用于明确指定应该在其中执行操作的设备。
语法:tensorflow . device(device _ name)
参数:
- 设备名称:指定在此上下文中使用的设备名称。
返回:它返回一个上下文管理器，指定用于新创建的 ops 的默认设备。
例 1:
Python 3
# 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)
输出:
Device Spec: /job:localhost/replica:0/device:CPU:*
Executing op MatMul in device /job:localhost/replica:0/task:0/device:CPU:0
示例 2: 在此示例中，设备规范指定了要使用的 GPU，但系统找不到 GPU，因此它将在 CPU 上运行操作。
Python 3
# 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)
输出:
Device Spec: /job:localhost/replica:0/device:GPU:*
Executing op MatMul in device /job:localhost/replica:0/task:0/device:CPU:0
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