I just installed Tensorflow 1.0.0 using pip. When running, I get warnings like the one shown below.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
I get 5 more similar warning for SSE4.1, SSE4.2, AVX, AVX2, FMA.
Despite these warnings the program seems to run fine.
So to knock out these warnings in a single blow, do import warnings then warnings. filterwarnings('ignore') , then run your tensorflow imports and and code that relies on the broken alpha-tensorflow code, then turn warnings back on via warnings. resetwarnings() .
TensorFlow is tested and supported on the following 64-bit systems: Python 3.7–3.10. Ubuntu 16.04 or later.
export TF_CPP_MIN_LOG_LEVEL=2
solved the problem for me on Ubuntu.
https://github.com/tensorflow/tensorflow/issues/7778
My proposed way to solve the problem:
#!/usr/bin/env python3
import os
import tensorflow as tf
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
Should work at least on any Debian or Ubuntu systems.
I don't know much about C, but I found this
bazel build --linkopt='-lrt' -c opt --copt=-mavx --copt=-msse4.2 --copt=-msse4.1 --copt=-msse3-k //tensorflow/tools/pip_package:build_pip_package
How you build you program?
It seems that even if you don't have a compatible (i.e. Nvidia) GPU, you can actually still install the precompiled package for tensorflow-gpu via pip install tensorflow-gpu
. It looks like in addition to the GPU support it also supports (or at least doesn't complain about) the CPU instruction set extensions like SSE3, AVX, etc. The only downside I've observed is that the Python wheel is a fair bit larger: 90MB for tensorflow-gpu instead of 42MB for plain tensorflow.
On my machine without an Nvidia GPU I've confirmed that tensorflow-gpu 1.0 runs fine without displaying the cpu_feature_guard warnings.
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