Tensorflow installation error: not a supported whe

Question

when I try to install tensorflow by cloning from git, I run into the error "no module named copyreg," so I tried installing using a virtualenv. However, I then run into this error:

pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl

tensorflow-0.5.0-py2-none-any.whl is not a supported wheel on this platform.

I don't see this under the common problems section, so any help would be appreciated! Thank you.

Answer 1

I was trying to install CPU TF on Ubuntu 18.04, and the best way (for me...) I found for it was using it on top of Conda, for that:

1. To create Conda ‘tensorflow’ env. Follow https://linuxize.com/post/how-to-install-anaconda-on-ubuntu-18-04/
2. After all installed see https://conda.io/projects/conda/en/latest/user-guide/getting-started.html And use it according to https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#managing-environments
3. conda create --name tensorflow
4. source activate tensorflow
5. pip install --upgrade pip
6. pip install tensorflow
7. pip install --ignore-installed --upgrade tensorflow
8. Test TF E.g. on 'Where' with:

python

import tensorflow as tf

tf.where([[True, False], [False, True]])

expected result:

<tf.Tensor: shape=(2, 2), dtype=int64, numpy=
array([[0, 0],
       [1, 1]])>

• After Conda upgrade I got: DeprecationWarning: 'source deactivate' is deprecated. Use 'conda deactivate'.

So you should use:

‘conda activate tensorflow’ / ‘conda deactivate’

Answer 2

After activating the virtualenv, be sure to upgrade pip to the latest version.

(your_virtual_env)$  pip install --upgrade pip

And now you'll be able to install tensor-flow correctly (for linux):

(your_virtual_env)$  pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.7.0-py2-none-linux_x86_64.whl

Answer 3

On Windows 10, with Python 3.6.X version I was facing same then after checking deliberately , I noticed I had Python-32 bit installation on my 64 bit machine. Remember TensorFlow is only compatible with 64bit installation of python. Not 32 bit of Python

If we download Python from python.org , the default installation would be 32 bit. So we have to download 64 bit installer manually to install Python 64 bit. And then add

1. C:\Users\\AppData\Local\Programs\Python\Python36
2. C:\Users\\AppData\Local\Programs\Python\Python36\Scripts

Then run gpupdate /Force on command prompt. If python command doesnt work for 64 bit restart your machine.

Then run python on command prompt. It should show 64 bit

C:\Users\YOURNAME>python
Python 3.6.3 (v3.6.3:2c5fed8, Oct  3 2017, 18:11:49) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.

Then run below command to install tensorflow CPU version(recommended)

pip3 install --upgrade tensorflow

Answer 4

Seems that tensorflow only work on python 3.5 at the moment, try to run this command before running the pip install

conda create --name tensorflow python=3.5

After this running the following lines :

For cpu :

pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.1.0-cp35-cp35m-win_amd64.whl

For gpu :

pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.1.0-cp35-cp35m-win_amd64.whl

Should work like a charm,

Cheers

Answer 5

I was trying to do the windows-based install and kept getting this error.

Turns out you have to have python 3.5.2. Not 2.7, not 3.6.x-- nothing other than 3.5.2.

After installing python 3.5.2 the pip install worked.

Answer 6

This may mean that you are installing the wrong pre-build binary

since my CPU on Ubuntu 18.04 my download url was: https://github.com/lakshayg/tensorflow-build/releases/download/tf1.12.0-ubuntu18.04-py2-py3/tensorflow-1.12.0-cp36-cp36m-linux_x86_64.whl

as it can be found on this github page: https://github.com/lakshayg/tensorflow-build

pip install --ignore-installed --upgrade <LOCAL PATH / BINARY-URL>

resolved the issue for me.