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我是深度学习的新手。我只是想测试一些想法，所以我在 Azure VM NC6 上成功玩了https://github.com/anishathalye/neural-style（NC6对我来说就像一个 Instamatic ^_^）。但我得到了一些奇怪的日志。
在日志之前，我应该展示一下 NC6 的特性： 

NC series：NVIDIA k80 GPU. Double GPU，4992 CUDA，24GB，double:2.91TFLOPS，flout:8.73TFLOPS.  
NC6：6 cores + 56GiB memory + 340GiB disk + 1X K80. $0.9/hour.

I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally
W 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.
W 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.
W 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.
W 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.
W 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.
W 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.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:
name: Tesla K80
major: 3 minor: 7 memoryClockRate (GHz) 0.8235
pciBusID 9909:00:00.0
Total memory: 11.17GiB
Free memory: 11.11GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K80, pci bus id: 9909:00:00.0)

我有两个问题：

  1. 日志显示总内存只有 11GiB。但是对于 NC6，内存是 56GiB，GPU 是 24GiB。它们都不像 11GiB。我使用了 top 命令，它显示可用内存约为 55GiB。那么如何更有效地使用NC6 VM呢？有什么配置吗？或者只是在神经风格中添加一些 python 代码（使用 config = tf.ConfigProto() 来改变 GPU 内存的分配方式？）？

  2. 日志显示了关于 SSE3、SSE4.1、SSE4.2 的六个警告。AVX、AVX2 和 FMA 都是关于 CPU 计算的。我应该忽略 GPU 计算模式下的警告吗？

非常感谢！

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