W c: f_jenkinshomeworkspace
elease-windevicecpuoswindows ensorflowcoreplatformcpu_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. 2017-04-19 16:35:22.534756: W c: f_jenkinshomeworkspace
elease-windevicecpuoswindows ensorflowcoreplatformcpu_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. 2017-04-19 16:35:22.535027: W c: f_jenkinshomeworkspace
elease-windevicecpuoswindows ensorflowcoreplatformcpu_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. 2017-04-19 16:35:22.535245: W c: f_jenkinshomeworkspace
elease-windevicecpuoswindows ensorflowcoreplatformcpu_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. 2017-04-19 16:35:22.535462: W c: f_jenkinshomeworkspace
elease-windevicecpuoswindows ensorflowcoreplatformcpu_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. 2017-04-19 16:35:22.535680: W c: f_jenkinshomeworkspace
elease-windevicecpuoswindows ensorflowcoreplatformcpu_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. 2017-04-19 16:35:22.536664: W c: f_jenkinshomeworkspace
elease-windevicecpuoswindows ensorflowcoreplatformcpu_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. 2017-04-19 16:35:22.536925: W c: f_jenkinshomeworkspace
elease-windevicecpuoswindows ensorflowcoreplatformcpu_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. b'Hello, TensorFlow!'
解决方法一：
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
解决方法二：
系统属性-环境变量
这种方式一劳永逸，比较好
这两种方法都是治标不治本
解决方法三：
安装bazel，编译tensorflow。
这个问题的原因是CPU支持更快的运算，可是安装的Tensorflow中缺少对应的模块，需要编译安装。
而在使用GPU的情况下，并不需要用到CPU。所以这些warning可以忽略。