我已经在我的ubuntu 16.04中安装了tensorflow，使用的是ubuntu内置的apt cuda安装。
现在我的问题是，我如何测试tensorflow是否真的使用gpu?我有一个gtx 960m gpu。当我导入tensorflow时，这是输出
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally
这个输出是否足够检查tensorflow是否使用gpu ?
在Jupyter上运行以下命令，
import tensorflow as tf
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
如果你已经正确地设置了环境，你将在运行“jupyter notebook”的终端中得到以下输出，
2017-10-05 14:51:46.335323: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Quadro K620, pci bus id: 0000:02:00.0)
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Quadro K620, pci bus id: 0000:02:00.0
2017-10-05 14:51:46.337418: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\direct_session.cc:265] Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Quadro K620, pci bus id: 0000:02:00.0
你可以看到我在使用Nvidia Quodro K620的TensorFlow。
下面的代码段应该给出tensorflow可用的所有设备。
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
样例输出
(名字:“/ cpu: 0”
device_type:“CPU”
memory_limit: 268435456
位置{
｝
化身:4402277519343584096,
名称:“/ gpu: 0”
device_type:“GPU”
memory_limit: 6772842168
位置{
bus_id: 1
｝
化身:7471795903849088328
physical_device_desc: "设备:0，名称:GeForce GTX 1070, pci总线id: 0000:05:00.0"
］
