Mac OS X 10.12.6 16G29
Python 3.6.2 (Anaconda 4.3.23)
Intel Core i7-4770HQ @ 2.20GHz

前言

如果直接通过pip install tensorflow安装Tensorflow的话，在运行时可能会有这种警告

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.

主要是因为你的CPU支持AVX2，SSE等指令集，而pip安装的库没有使用它们。启用这些指令集会提升运算速度，而且每次打印一大坨看着有点烦人，所以这里我们从源码编译安装Tensorflow。

编译&安装

• 先安装一些依赖

$ brew install bazel swig

• 克隆代码仓库

$ git clone https://github.com/tensorflow/tensorflow.git
$ cd tensorflow

• master分支代码一般很新，鬼知道有什么问题，这里用v1.2.1版本

$ git checkout v1.2.1

• 这里先configure，过程中会问一些编译过程中要用到的参数，比如Python的目录，是否开启OpenCL，CUDA支持，我这里直接全否了。

$ ./configure
Please specify the location of python. [Default is /usr/local/anaconda3/bin/python]:
Found possible Python library paths:
  /usr/local/anaconda3/lib/python3.6/site-packages
Please input the desired Python library path to use.  Default is [/usr/local/anaconda3/lib/python3.6/site-packages]

Using python library path: /usr/local/anaconda3/lib/python3.6/site-packages
Do you wish to build TensorFlow with MKL support? [y/N] n
No MKL support will be enabled for TensorFlow
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]: n
Do you wish to build TensorFlow with Google Cloud Platform support? [y/N] n
No Google Cloud Platform support will be enabled for TensorFlow
Do you wish to build TensorFlow with Hadoop File System support? [y/N] n
No Hadoop File System support will be enabled for TensorFlow
Do you wish to build TensorFlow with the XLA just-in-time compiler (experimental)? [y/N] n
No XLA JIT support will be enabled for TensorFlow
Do you wish to build TensorFlow with VERBS support? [y/N] n
No VERBS support will be enabled for TensorFlow
Do you wish to build TensorFlow with OpenCL support? [y/N] n
No OpenCL support will be enabled for TensorFlow
Do you wish to build TensorFlow with CUDA support? [y/N] n
No CUDA support will be enabled for TensorFlow
INFO: Starting clean (this may take a while). Consider using --async if the clean takes more than several minutes.
Configuration finished

• 编译步骤，在--copt=-m后面添加你需要的指令集，之前警告有什么你就在这加什么就可以，我这里添加了avx avx2 fma sse4.1 sse4.2。编译过程时间很长，我的4770hq跑了快25min

$ bazel build -c opt --copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-msse4.1 --copt=-msse4.2 //tensorflow/tools/pip_package:build_pip_package

• 打包成whl

$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

• 安装这个whl，如果之前安装过Tensorflow的话先卸载，再安装

$ pip uninstall -y tensorflow
$ pip install /tmp/tensorflow_pkg/tensorflow-1.2.1-cp36-cp36m-macosx_10_7_x86_64.whl

完事，这样再调用Session就不会有提示了

Hello, TensorFlow!

结尾

最后丢一个我编译好的版本，1.2.1版本，Python 3.6，开启avx avx2 fma sse4.1 sse4.2指令集，度盘，GD。

参考文章

Installing TensorFlow from Sources
Python: Keras/TensorFlow の学習を CPU の拡張命令で高速化する (Mac OS X) - CUBE SUGAR CONTAINER