如何確認NVIDIA K2200和Tensorflow-GPU正常工作？

Question

我剛在我的Windows 10電腦上安裝了兩個Nvidia K2200 GPU，CUDA軟件和CuDNN軟件。我通過遵循this堆棧溢出的答案去檢查一切是否正常，但是我收到了一條帶有一堆警告的大消息。我不知道如何解釋它。消息是否意味着某些東西和我的TensorFlow/Keras代碼不起作用？

這裏是訊息話題：

2017-08-09 09:03:52.984209: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations. 
2017-08-09 09:03:52.984358: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations. 
2017-08-09 09:03:52.985302: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations. 
2017-08-09 09:03:52.986429: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. 
2017-08-09 09:03:52.987150: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. 
2017-08-09 09:03:52.990185: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. 
2017-08-09 09:03:52.990775: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. 
2017-08-09 09:03:52.991261: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. 
2017-08-09 09:03:53.310243: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:940] Found device 0 with properties: 
name: Quadro K2200 
major: 5 minor: 0 memoryClockRate (GHz) 1.124 
pciBusID 0000:04:00.0 
Total memory: 4.00GiB 
Free memory: 3.35GiB 
2017-08-09 09:03:53.405531: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\stream_executor\cuda\cuda_driver.cc:523] A non-primary context 000001B8981C7F00 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that. 
2017-08-09 09:03:53.406260: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:940] Found device 1 with properties: 
name: Quadro K2200 
major: 5 minor: 0 memoryClockRate (GHz) 1.124 
pciBusID 0000:01:00.0 
Total memory: 4.00GiB 
Free memory: 3.35GiB 
2017-08-09 09:03:53.409719: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:832] Peer access not supported between device ordinals 0 and 1 
2017-08-09 09:03:53.411660: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:832] Peer access not supported between device ordinals 1 and 0 
2017-08-09 09:03:53.412396: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:961] DMA: 0 1 
2017-08-09 09:03:53.413047: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:971] 0: Y N 
2017-08-09 09:03:53.413445: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:971] 1: N Y 
2017-08-09 09:03:53.414996: 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 K2200, pci bus id: 0000:04:00.0) 
2017-08-09 09:03:53.415559: 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:1) -> (device: 1, name: Quadro K2200, pci bus id: 0000:01:00.0) 
[name: "/cpu:0" 
device_type: "CPU" 
memory_limit: 268435456 
locality { 
} 
incarnation: 15789200439240454107 
, name: "/gpu:0" 
device_type: "GPU" 
memory_limit: 3280486400 
locality { 
    bus_id: 1 
} 
incarnation: 685299155373543396 
physical_device_desc: "device: 0, name: Quadro K2200, pci bus id: 0000:04:00.0" 
, name: "/gpu:1" 
device_type: "GPU" 
memory_limit: 3280486400 
locality { 
    bus_id: 1 
} 
incarnation: 16323028758437337139 
physical_device_desc: "device: 1, name: Quadro K2200, pci bus id: 0000:01:00.0" 
] 

Answer 1

你可以嘗試添加負載（例如訓練一些模型），並檢查「NVIDIA-SMI」從終端，同時它的工作 - 它應該表現出你的GPU利用率。