Your CPU supports instructions that this TensorFlow binary was not compiled to use AVX AVX2

Question

I am new to TensorFlow  I have recently installed it  Windows CPU version  and received the following message      Successfully installed tensorflow-1 4 0 tensorflow-tensorboard-0 4 0rc2   Then when I tried to run  import tensorflow as tf hello   tf constant  Hello  TensorFlow    sess   tf Session   sess run hello   Hello  TensorFlow   a   tf constant 10  b   tf constant 32  sess run a   b  42 sess close      which I found through https   github com tensorflow tensorflow   I received the following message      2017-11-02 01 56 21 698935  I C  tf jenkins home workspace rel-win M windows 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   But when I ran   import tensorflow as tf hello   tf constant  Hello  TensorFlow    sess   tf Session   print sess run hello     it ran as it should and output Hello  TensorFlow   which indicates that the installation was successful indeed but there is something else that is wrong   Do you know what the problem is and how to fix it

User · Answer

What worked for me tho is this library https   pypi org project silence-tensorflow    Install this library and do as instructed on the page  it works like a charm

User · Answer

For Windows  Thanks to the owner f040225   go to here  https   github com fo40225 tensorflow-windows-wheel to fetch the url for your environment based on the combination of  tf   python   cpu instruction extension   Then use this cmd to install   pip install --ignore-installed --upgrade  URL    If you encounter the  File is not a zip file  error  download the  whl to your local computer  and use this cmd to install   pip install --ignore-installed --upgrade  path target whl

User · Answer

What is this warning about  Modern CPUs provide a lot of low-level instructions  besides the usual arithmetic and logic  known as extensions  e g  SSE2  SSE4  AVX  etc  From the Wikipedia   Advanced Vector Extensions  AVX  are extensions to the x86 instruction set architecture for microprocessors from Intel and AMD proposed by Intel in March 2008 and first supported by Intel with the Sandy Bridge processor shipping in Q1 2011 and later on by AMD with the Bulldozer processor shipping in Q3 2011  AVX provides new features  new instructions and a new coding scheme   In particular  AVX introduces fused multiply-accumulate  FMA  operations  which speed up linear algebra computation  namely dot-product  matrix multiply  convolution  etc  Almost every machine-learning training involves a great deal of these operations  hence will be faster on a CPU that supports AVX and FMA  up to 300    The warning states that your CPU does support AVX  hooray    I d like to stress here  it s all about CPU only  Why isn t it used then  Because tensorflow default distribution is built without CPU extensions  such as SSE4 1  SSE4 2  AVX  AVX2  FMA  etc  The default builds  ones from pip install tensorflow  are intended to be compatible with as many CPUs as possible  Another argument is that even with these extensions CPU is a lot slower than a GPU  and it s expected for medium- and large-scale machine-learning training to be performed on a GPU  What should you do  If you have a GPU  you shouldn t care about AVX support  because most expensive ops will be dispatched on a GPU device  unless explicitly set not to   In this case  you can simply ignore this warning by   Just disables the warning  doesn t take advantage of AVX FMA to run faster import os os environ  TF CPP MIN LOG LEVEL      2       or by setting export TF CPP MIN LOG LEVEL 2 if you re on Unix  Tensorflow is working fine anyway  but you won t see these annoying warnings   If you don t have a GPU and want to utilize CPU as much as possible  you should build tensorflow from the source optimized for your CPU with AVX  AVX2  and FMA enabled if your CPU supports them  It s been discussed in this question and also this GitHub issue  Tensorflow uses an ad-hoc build system called bazel and building it is not that trivial  but is certainly doable  After this  not only will the warning disappear  tensorflow performance should also improve

User · Answer

CPU optimization with GPU  There are performance gains you can get by installing TensorFlow from the source even if you have a GPU and use it for training and inference   The reason is that some TF operations only have CPU implementation and cannot run on your GPU   Also  there are some performance enhancement tips that makes good use of your CPU   TensorFlow s performance guide recommends the following      Placing input pipeline operations on the CPU can significantly improve performance  Utilizing the CPU for the input pipeline frees the GPU to focus on training    For best performance  you should write your code to utilize your CPU and GPU to work in tandem  and not dump it all on your GPU if you have one    Having your TensorFlow binaries optimized for your CPU could pay off hours of saved running time and you have to do it once

User · Answer

Try using anaconda  I had the same error  One lone option was to build tensorflow from source which took long time  I tried using conda and it worked   Create a new environment in anaconda  conda -c conda-forge tensorflow  Then  it worked

User · Answer

For Windows  you can check the official Intel MKL optimization for TensorFlow wheels that are compiled with AVX2  This solution speeds up my inference  x3   conda install tensorflow-mkl

User · Answer

If you use pip version of tensorflow  it means it s already compiled and you are just installing it  Basically you install tensorflow-gpu  but when you download it from repository and trying to build  you should build it with CPU AVX support  If you ignore it  you will get the warning every time when you run on cpu

User · Answer

The easiest way that I found to fix this is to uninstall everything then install a specific version of tensorflow-gpu    uninstall tensorflow         pip uninstall tensorflow    uninstall tensorflow-gpu   make sure to run this even if you are not sure if you installed it        pip uninstall tensorflow-gpu    Install specific tensorflow-gpu version        pip install tensorflow-gpu  2 0 0     pip install tensorflow hub     pip install tensorflow datasets   You can check if this worked by adding the following code into a python file   from   future   import absolute import  division  print function  unicode literals  import numpy as np  import tensorflow as tf import tensorflow hub as hub import tensorflow datasets as tfds  print  Version     tf   version    print  Eager mode     tf executing eagerly    print  Hub Version     hub   version    print  GPU is    available  if tf config experimental list physical devices  GPU   else  NOT AVAILABLE     Run the file and then the output should be something like this   Version   2 0 0 Eager mode   True Hub Version   0 7 0 GPU is available   Hope this helps

User · Answer

Update the tensorflow binary for your CPU  amp  OS using this command  pip install --ignore-installed --upgrade  Download URL    The download url of the whl file can be found here  https   github com lakshayg tensorflow-build

[tensorflow] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2

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