因此，我在我的笔记本电脑上安装了Tensorflow for Python 3.5，这是一台托管Nvidia Geforce Pascal GPU的Windows计算机。我还安装了CUDA并下载了cuDNN并将其添加到PATH变量中。我的tensorflow代码不会编译，但如果我监视我的GPU，我可以看到，它不计算任何东西，而是我的CPU正在完成整个工作。我也得到控制台输出这证实，被检测的GPU：Tensorflow with GPU，Detected right GPU，但不能用它来计算
2017-06-02 15:22:22.140283: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\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. 2017-06-02 15:22:22.140600: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\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. 2017-06-02 15:22:22.140899: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\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. 2017-06-02 15:22:22.141108: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\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. 2017-06-02 15:22:22.141321: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\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. 2017-06-02 15:22:22.141582: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\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. 2017-06-02 15:22:22.141803: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\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. 2017-06-02 15:22:22.142130: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\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. 2017-06-02 15:22:22.561687: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:887] Found device 0 with properties: name: GeForce GTX 1070 major: 6 minor: 1 memoryClockRate (GHz) 1.645 pciBusID 0000:01:00.0 Total memory: 8.00GiB Free memory: 6.65GiB 2017-06-02 15:22:22.561949: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:908] DMA: 0 2017-06-02 15:22:22.562073: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:918] 0: Y 2017-06-02 15:22:22.562435: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0)
任何人能解释一下我吗？
编辑：好吧，好像，我没有看到使用enaugh准确。 GPU实际上被使用，但只能在小山峰中使用。大部分工作仍由CPU完成。我正在运行带有3个卷积和2个完全连接层的CNN。但那不可能是正确的？ 
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报我加速因素是体系结构相关的没有看到任何暗示TF没有使用你的GPU的东西。 –