如何确认 NVIDIA K2200 和 Tensorflow-GPU 一起正常工作？

Posted 2023-04-15

【中文标题】如何确认 NVIDIA K2200 和 Tensorflow-GPU 一起正常工作？

【英文标题】：How to confirm that NVIDIA K2200 and Tensorflow-GPU are working together correctly?

【发布时间】：2018-01-17 09:40:42

【问题描述】：

我刚刚在我的 Windows 10 计算机上安装了两个 Nvidia K2200 GPU、CUDA 软件和 CuDNN 软件。我按照this Stack Overflow 的回答去检查一切是否正常，但我收到了一条带有大量警告的重要信息。我不知道如何解释它。该消息是否意味着某些东西和我的 TensorFlow/Keras 代码将不起作用？

这是消息：

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"
]

【问题讨论】：

看起来大部分只是警告（“W”）或信息（“I”）。我不认为它们是错误，但我不是专家。

查看显卡负载？

@Paddy 我该怎么做？你能推荐一个程序吗？

【参考方案1】：

您可以尝试添加负载（例如训练某个模型）并在终端运行时检查“nvidia-smi” - 它应该会显示您的 GPU 利用率。

【讨论】：

如果您使用默认安装文件夹，您应该可以在 Windows 上找到它：C:\Program Files\NVIDIA Corporation\NVSMI