Home/ Questions/Solving the error your cpu supports instructions that this tensorflow binary was not compiled to use: avx avx2 fma
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Paul Cornu
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Asked: 2022-05-11T18:33:11+00:00 In:

Solving the error your cpu supports instructions that this tensorflow binary was not compiled to use: avx avx2 fma

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I don’t know what I’m doing wrong, but I’ve already lost a couple of days struggling with this. Here is my command line:

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()
import tensorflow as tf
 hello = tf.constant('Hello, TensorFlow!')
 sess = tf.Session()
 print(sess.run(hello))

This returns:

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

I don’t have any experience with your cpu supports instructions that this tensorflow binary was not compiled to use: avx avx2 fma. In this case, how should I change?

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2 Answers

  1. Best Answer
    Expert

    The cause: You installed tensorflow default distribution without CPU extensions, such as SSE4.1, SSE4.2, AVX, AVX2, FMA,…Another argument is that even with these extensions CPU is much more slower than a GPU, and a medium- and large-scale machine-learning training is expected to be performed on a GPU.

    Solution: If your GPU has AVX support enabled, most of the more expensive operations will be sent to a GPU device (unless you specifically set it not). You can ignore this warning in this instance.

    # 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 set export TF_CPP_MIN_LOG_LEVEL=2 on Unix. These annoying warnings won’t appear if Tensorflow is running fine. You don’t need a GPU but want to make the most of your CPU . Use the source optimized for . Your CPU should have AVX, FMA, and AVX2 enabled. This topic has been covered in this question , and this issue . Tensorflow uses an ad hoc build system called Bazel. Although it’s not easy to create, it is definitely possible. This will make the warning disappear and tensorflow should perform better.

    • 8

  2. This command will update the tensorflow binary on your CPU & OS. 

    pip install --ignore-installed --upgrade "Download URL"

    You can find the download URL for the whl file here


    https://github.com/lakshayg/tensorflow-build

    • 7

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