查看keras认得到的GPU
from keras import backend as K K.tensorflow_backend._get_available_gpus()
Out[28]:
['/job:localhost/replica:0/task:0/device:GPU:0']
查看更详细device信息
from tensorflow.python.client import device_lib import tensorflow as tf print(device_lib.list_local_devices()) print(tf.test.is_built_with_cuda())
output:
[name: "/device:CPU:0"
device_type: "CPU" memory_limit: 268435456 locality { } incarnation: 9443078288448539683 , name: "/device:XLA_CPU:0" device_type: "XLA_CPU" memory_limit: 17179869184 locality { } incarnation: 14589028880023685106 physical_device_desc: "device: XLA_CPU device" , name: "/device:XLA_GPU:0" device_type: "XLA_GPU" memory_limit: 17179869184 locality { } incarnation: 12944586764183584921 physical_device_desc: "device: XLA_GPU device" , name: "/device:GPU:0" device_type: "GPU" memory_limit: 8365150044 locality { bus_id: 1 links { } } incarnation: 8725535454902618392 physical_device_desc: "device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0" ] True
查看正在使用的GPU
import tensorflow as tf print (tf.__version__) if tf.test.gpu_device_name(): print('Default GPU Device: {}'.format(tf.test.gpu_device_name())) else: print("Please install GPU version of TF")
output:
1.13.1 Default GPU Device: /device:GPU:0
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