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

Question

I have recently installed tensorflow (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?

Answer 1

Update the tensorflow binary for your CPU & 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

Answer 2

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.

Answer 3

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

Answer 4

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

Answer 5

If you use the 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 the repository and trying to build it, you should build it with CPU AVX support. If you ignore it, you will get a warning every time when you run on the CPU. You can take a look at those too.

Proper way to compile Tensorflow with SSE4.2 and AVX

What is AVX Cpu support in tensorflow

Answer 6

The easiest way that I found to fix this is to uninstall everything then install a specific version of tensorflow-gpu:

1. uninstall tensorflow:

    pip uninstall tensorflow

2. uninstall tensorflow-gpu: (make sure to run this even if you are not sure if you installed it)

    pip uninstall tensorflow-gpu

3. 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

Answer 7

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!

Answer 8

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.

1. Create a new environment in anaconda.
2. conda -c conda-forge tensorflow

Then, it worked.

Answer 9

He provided once the list, deleted by someone but see the answer is Download packages list

Output:

F:\temp\Python>python test_tf_logics_.py
[0, 0, 26, 12, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0]
[ 0  0  0 26 12  0  0  0  2  0  0  0  0  0  0  0  0]
[ 0  0 26 12  0  0  0  2  0  0  0  0  0  0  0  0  0]
2022-03-23 15:47:05.516025: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-03-23 15:47:06.161476: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1
[0 0 2 0 0 0 0 7 0 0 0 0 0 0 0 0 0]
...