I just installed two Nvidia K2200 GPU's, CUDA software, and CuDNN software on my Windows 10 computer. I went to check if everything is working well by following this Stack Overflow answer but I got a big message with a bunch of warnings. I am not sure how to interpret it. Does the message mean that something and my TensorFlow/Keras code won't work?
Here is the messsage:
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" ] 
