TensorFlow指定GPU/CPU进行训练和输出devices信息

快来打我* 2022-01-08 00:15 429阅读 0赞

## TensorFlow指定GPU/CPU进行训练和输出devices信息 ##

### 1.在tensorflow代码中指定GPU/CPU进行训练 ###

with tf.device('/gpu:0'): .... with tf.device('/gpu:1'): ... with tf.device('/cpu:0'): ...

### 2.输出devices的信息 ###

在指定devices的时候往往不知道具体的设备信息，这时可用下面的代码查看对应的信息

进入Python环境

from tensorflow.python.client import device_lib print(device_lib.list_local_devices())

输出以下信息：

2019-05-23 20:12:47.415412: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
    2019-05-23 20:12:47.509275: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
    2019-05-23 20:12:47.509632: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x14b6e60 executing computations on platform CUDA. Devices:
    2019-05-23 20:12:47.509660: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): GeForce MX150, Compute Capability 6.1
    2019-05-23 20:12:47.529891: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 1992000000 Hz
    2019-05-23 20:12:47.530293: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x1b7b140 executing computations on platform Host. Devices:
    2019-05-23 20:12:47.530318: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
    2019-05-23 20:12:47.530451: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: 
    name: GeForce MX150 major: 6 minor: 1 memoryClockRate(GHz): 1.341
    pciBusID: 0000:01:00.0
    totalMemory: 1.96GiB freeMemory: 1.92GiB
    2019-05-23 20:12:47.530468: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
    2019-05-23 20:12:47.531469: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
    2019-05-23 20:12:47.531487: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 
    2019-05-23 20:12:47.531494: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N 
    2019-05-23 20:12:47.531563: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/device:GPU:0 with 1738 MB memory) -> physical GPU (device: 0, name: GeForce MX150, pci bus id: 0000:01:00.0, compute capability: 6.1)
    [name: "/device:CPU:0"
    device_type: "CPU"
    memory_limit: 268435456
    locality {
    }
    incarnation: 1736381910647465363
    , name: "/device:XLA_GPU:0"
    device_type: "XLA_GPU"
    memory_limit: 17179869184
    locality {
    }
    incarnation: 10300285037066135290
    physical_device_desc: "device: XLA_GPU device"
    , name: "/device:XLA_CPU:0"
    device_type: "XLA_CPU"
    memory_limit: 17179869184
    locality {
    }
    incarnation: 6680013036417599682
    physical_device_desc: "device: XLA_CPU device"
    , name: "/device:GPU:0"
    device_type: "GPU"
    memory_limit: 1823080448
    locality {
      bus_id: 1
      links {
      }
    }
    incarnation: 7894169161128462449
    physical_device_desc: "device: 0, name: GeForce MX150, pci bus id: 0000:01:00.0, compute capability: 6.1"
    ]

找到对应devices的name，复制双引号下的名字，替换第1的代码中的单引号的内容，就可以指定对应的设备进行训练了。

转载于:https://www.cnblogs.com/youpeng/p/10914237.html