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一、获得当前主机上特定运算设备的列表
gpus = tf.config.experimental.list_physical_devices(device_type='GPU') cpus =
tf.config.experimental.list_physical_devices(device_type='CPU') print(gpus,
cpus)
二、设置当前程序可见的设备范围
默认情况下 TensorFlow 会使用其所能够使用的所有 GPU。
tf.config.experimental.set_visible_devices(devices=gpus[2:4],
device_type='GPU')
设置之后，当前程序只会使用自己可见的设备，不可见的设备不会被当前程序使用。
另一种方式是使用环境变量 CUDA_VISIBLE_DEVICES 也可以控制程序所使用的 GPU。
在终端输入
export CUDA_VISIBLE_DEVICES=2,3
或者在代码里加入
import os os.environ['CUDA_VISIBLE_DEVICES'] = "2,3"
都可以达到同样的效果。
三、显存的使用
默认情况下，TensorFlow 将使用几乎所有可用的显存，以避免内存碎片化所带来的性能损失。
但是TensorFlow 提供两种显存使用策略，让我们能够更灵活地控制程序的显存使用方式：
1. 仅在需要时申请显存空间（程序初始运行时消耗很少的显存，随着程序的运行而动态申请显存）；
2. 限制消耗固定大小的显存（程序不会超出限定的显存大小，若超出的报错）。
设置仅在需要时申请显存空间。
for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True)
下面的方式是设置Tensorflow固定消耗GPU:0的2GB显存。
tf.config.experimental.set_virtual_device_configuration( gpus[0],
[tf.config.experimental.VirtualDeviceConfiguration(memory_limit=2048)] )
四、单GPU模拟多GPU环境
上面的方式不仅可以设置显存的使用，还可以在只有单GPU的环境模拟多GPU进行调试。
tf.config.experimental.set_virtual_device_configuration( gpus[0],
[tf.config.experimental.VirtualDeviceConfiguration(memory_limit=2048),
tf.config.experimental.VirtualDeviceConfiguration(memory_limit=2048)])
上面的代码就在GPU:0上建立了两个显存均为 2GB 的虚拟 GPU。
