I have installed tensorflow following the https://www.tensorflow.org/install/pip steps. I installed it through Anaconda.
I also tried using a virtual environment without using anaconda as this page provided (please check image). It also gave the same error.
Following images shows the versions I have installed and its given error.
I am using,
Ubuntu 18.04.1 LTS
x86_64
Processor: Intel(R) Core(TM) 2 Duo CPU T5870 @ 2.00 GHz 2.00 GHz
Installed memory (RAM): 2.00 GB
Version:
Error:
I tried to overcome this but, I couldn't find a solution. I am new to tensorflow and trying to install and learn it. Please help me to solve this.
Thanks for the earlier answers. I was facing the exact problem for SSE4.2 instruction. I installed tensorflow 1.5 instead of 1.12 and it is working.
The TensorFlow library was compiled to use SSE4.2 instructions, but these aren't available on your machine.
Aborted (core dumped)
Here is what I did:
Please run these inside virtual environment:
pip uninstall tensorflow
pip install tensorflow==1.5
You can replace 1.5 with any version that suits your machine.
Try importing it again it should run now
Your CPU is too old for running TensorFlow as it lacks the required Instruction set. Starting with TensorFlow 1.6, binaries use AVX instructions which may not run on older CPUs.
- According to Tensorflow.
You have 2 possible solutions:
I have the same error on the laptop HP ProBook 4720s:
python3
Python 3.6.7 (default, Oct 22 2018, 11:32:17)
[GCC 8.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
2019-04-16 17:50:25.829023: F tensorflow/core/platform/cpu_feature_guard.cc:37] The TensorFlow library was compiled to use AVX instructions, but these aren't available on your machine.
Aborted (core dumped)
But I installed TensorFlow via Anaconda conda
installation:
conda install tensorflow # install TensorFlow for CPU
conda activate DL3 # activate virtual environment DL3
python
Python 3.7.3 (default, Mar 27 2019, 22:11:17)
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> print(tf.__version__)
1.13.1
>>> exit()
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