Inside virtualenv: How to get tensorflow to support sse 4.2 and avx - tensorflow

Question

Just to say it upfront, I'm aware of all the answers that require bazel and they didn't work for me. I'm using virtualenv as the tensorflow website recommends to.
(tensorflow27)name#computersname:~$ bazel build --linkopt='-lrt' -c opt --copt=-mavx --copt=-msse4.2 --copt=-msse4.1 --copt=-msse3-k //tensorflow/tools/pip_package:build_pip_package
will output
ERROR: The 'build' command is only supported from within a workspace.
Basically I followed all steps from here
But when I run this validation I get
2017-09-02 11:46:52.613368: 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.
2017-09-02 11:46:52.613396: 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.
2017-09-02 11:46:52.613416: 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.
I DO NOT want to surpress the warnings, I actually want to use SSE 4.2 and AVX (my processor supports both) However I wasn't able to find any instructions anywhere how to compile tensorflow inside a virutal environment such that the support for SSE and AVX is enabled from scratch. It's not even listed in their common installation problems section.
Btw. the system I use for this is Ubuntu 14.04 and I don't have an nvidia graphics card (so no cuda for now), but I plan to get one in the future.
I'm a little bit disappointed that tensorflow doesn't detect CPU capabilities before compilation.
Edit: Untrue, later I figured out it actually does
PS: I have setup two virtual environments, one for python 2.7 and the other one for python 3.0. Ideally I would hope that the solution works for both, as I didn't decide yet which python version I will end up using.

Answer 1

OK, so it turned out my problems were pretty much independent of which virtual environment I choose. The bazel build failed simply because I was in the wrong directory. I needed to pull tensorflow from git and then cd into it. Then I could build from there, using the default build option -march=native, which already detects my CPU capabilities.
Setting up two different virtual environments for the two different python versions in advance was useful. I used those environments to compile directly inside of them. This results in automatic python version detection. So building in the 2.7 environment will result in a build for python 2.7 etc.
To be somewhat more detailed:
First I installed bazel. Then I installed the dependencies mentioned on the tensorflow page like this:
sudo apt-get install python3-numpy python3-dev python3-pip python3-wheel
sudo apt-get install python-numpy python-dev python-pip python-wheel
In order to use OpenCL in the build later, I had to download (and complile if I remember correctly) ComputeCpp-CE-0.3.1-Ubuntu.14.04-64bit.tar.gz (For ubuntu 16 it would be ComputeCpp-CE-0.3.1-Ubuntu.16.04-64bit.tar.gz). After compilation I had to move the build result to /usr/local/computecpp:
sudo mkdir /usr/local/computecpp
sudo cp -r ./Downloads/ComputeCpp*/* /usr/local/computecpp
If I remember correctly at this point I then activated my virtual environment with the python version I want to compile against, so the desired python version would be recognized by ./configure.
Then I pulled tensorflow from git and configured it:
git clone https://github.com/tensorflow/tensorflow
cd tensorflow
./configure
During the configuration routine I only answered yes to jemalloc and OpenCL as I don't have a CUDA card right now. When saying yes to OpenCL I was prompted for the ComputeCpp path which was at that location I created under /usr/local/computecpp
Then, while staying in the tensorflow directory I did
bazel build --config=opt --config=mkl //tensorflow/tools/pip_package:build_pip_package
People who activated cuda during ./configure, should also add a "--config=cuda" to this bazel build command. If you have gcc > 5 you will also need to add '--cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0'.
The '--config=mkl' part activates some additional libraries provided by intel specifically to speed up some calculations on their processors, so tensorflow can make use of them. If you don't have an Intel processor, it's probably wise to remove that option.
Btw, at first I also compiled mkl by hand, but that's not necessary it turns out. The bazel build will automatically pull mkl from an online source if it's missing.
Then I created the final package at /tmp/tensorflow_pkg:
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
Depending on the python version in your environment you will see a file name reflecting it:
/tmp/tensorflow_pkg/tensorflow-1.3.0-cp27-cp27mu-linux_x86_64.whl
/tmp/tensorflow_pkg/tensorflow-1.3.0-cp36-cp36m-linux_x86_64.whl
Now I could go to the appropriate environment and install it using pip. If you are using conda, the tensorflow website still recommends to use "pip install" and not "conda install". So if I were in a virtual environment with python 2.7 (doesn't matter if conda or not) I would type:
pip install --ignore-installed --upgrade /tmp/tensorflow_pkg/tensorflow-1.3.0-cp27-cp27mu-linux_x86_64.whl
And if I were under python 3.6 I would type:
pip install --ignore-installed --upgrade /tmp/tensorflow_pkg/tensorflow-1.3.0-cp36-cp36m-linux_x86_64.whl
There was one last hoop I needed to jump over: If you stay in the tensorflow directory (which you pulled from git) and then launch python, you won't be able to import tensorflow. I think it's some kind of a bug. So it's important to escape the tensorflow directory before starting python.
cd ..
Now you can start your python inside your virtual environment and import tensorflow. Hopefully without further errors or any warnings about not using the SSE or AVX capabilities of your processor. At least in my case it worked.