Tensorflow 1.5 built with AVX support

TL;DR – download tensorflow 1.5 with AVX support from the link on the bottom of this post

When running machine learning code on a new hardware using libraries available on PIP we are not using all capabilities provided by our cpu:
2018-01-10 09:35:05.048387: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2

Last night I’ve rebuilt the tensorflow to support AVX CPU instructions. The set up for build takes about an hour. The build itself took 2 hours 20 minutes on my i7-8700k desktop with Windows 10 and hit the computer quite hard.

I’ve used official build manual (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/cmake/README.md), but it doesn’t mention all requirements:
* you need to install numpy in the environment you use for build
* you need to install wheel in the environment you use for build (otherwise it fails after 2 hours of build – sweet)
* if building against cuda9.1 you need to copy math_functions.h from cuda91/include/crt/ to cuda91/include directory (otherwise it fails after 1h of build)

The results?
Sample program without AVX:
start: 2018-01-10 09:35:04.609053
finish:2018-01-10 09:36:00.339329

total: ~55.5s

The same code with AVX:
start: 2018-01-10 09:36:18.167291
finish:2018-01-10 09:36:55.693329

total: ~37.5s

Here is the wheel file with support for AVX tensorflow_gpu-1.5.0rc0-cp36-cp36m-win_amd64.whl if you don’t want to run the build process itself.

And CPU usage during build (I got a new computer yesterday and I’m still excited by new toy :))

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