如何确认 NVIDIA K2200 和 Tensorflow-GPU 是否正确协同工作？

Question

我刚刚安装了两个 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 Stack Overflow 答案，但我收到了一条包含大量警告的重要消息。我不确定如何解释它。该消息是否意味着我的 TensorFlow/Keras 代码无法正常工作？

消息如下：

2017-08-09 09:03:52.984209: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\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\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\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\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\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\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\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\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\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\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\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\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\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\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\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\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\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\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

您可以尝试添加负载（例如训练某些模型）并在终端运行时从终端检查 "nvidia-smi" - 它应该会显示您的 GPU 利用率。

如何确认 NVIDIA K2200 和 Tensorflow-GPU 是否正确协同工作？

How to confirm that NVIDIA K2200 and Tensorflow-GPU are working together correctly?

tensorflow

gpu

keras

nvidia

tensorflow-gpu