NVIDIA K2200とTensorflow-GPUが正しく連携していることを確認する方法は？

Question

Windows 10コンピュータにGPU、CUDAソフトウェア、およびCuDNNソフトウェアを2つインストールしました。私はすべてがうまく動作しているかどうかを確認するために行ったthisスタックオーバーフローの答えが、私は警告の束で大きなメッセージを得た。私はそれをどのように解釈するか分からない。メッセージは、何かとTensorFlow/Kerasコードがうまくいかないことを意味しますか？ここで

がmesssageです：

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

あなたは負荷を追加してみてください（例えば、いくつかのモデルを訓練）し、それが働いている間、「NVIDIA-SMI」端末からを確認することができます - それはあなたのGPUの使用率を表示する必要があります。