There’s a weird bug afoot that you might encounter when setting up a ‘lily white’ (brand new) development environment to play around with Tensorflow. As it seems to have vexed quite a few people, I thought I’ll put my solution here to help future tensorflowers find their way. The problem presents after you have set up your new virtualenv . You install Jupyter and Tensorflow, and when importing, you get this:
In [1]: import tensorflow as tf --------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) in () ----> 1 import tensorflow as tf ModuleNotFoundError: No module named 'tensorflow'
Oh.
Added perplexion
Say you are a dogged pursuer of bugs, and wish to check if you might have installed Tensorflow and Jupyter into different virtualenvs. One way to do that is to simply activate your virtualenv (using activate or source activate, depending on whether you use virtualenvwrapper), and starting a Python shell. Perplexingly, importing Tensorflow here will work just fine.
The solution
In general, it is advisable to start fixing these issues by destroying your virtualenv and starting anew, although that’s not strictly necessary. Create a virtualenv, and note the base Python executable’s version (it has to be a version for which there is a Tensorflow wheel for your platform, i.e. 2.7 or 3.3-3.6).
Go to the PyPI website to find the Tensorflow installation appropriate to your system and your Python version (e.g. cp36 for Python 3.6). Copy the path of the correct version, then open up a terminal window and declare it as the environment variable TF_BINARY_URL. Use pip to install from the URL you set as the environment variable, then install Jupyter.
[email protected] ~ $ export TF_BINARY_URL=https://pypi.python.org/packages/b1/74/873a5fc04f1aa8d275ef1349b25c75dd87cbd7fb84fe41fc8c0a1d9afbe9/tensorflow-1.1.0rc2-cp36-cp36m-macosx_10_11_x86_64.whl#md5=c9b6f7741d955d1d3b4991a7942f48b9 [email protected] ~ $ pip install --upgrade $TF_BINARY_URL jupyter Collecting tensorflow==1.1.0rc2 from https://pypi.python.org/packages/b1/74/873a5fc04f1aa8d275ef1349b25c75dd87cbd7fb84fe41fc8c0a1d9afbe9/tensorflow-1.1.0rc2-cp36-cp36m-macosx_10_11_x86_64.whl#md5=c9b6f7741d955d1d3b4991a7942f48b9 Using cached tensorflow-1.1.0rc2-cp36-cp36m-macosx_10_11_x86_64.whl Collecting jupyter Using cached jupyter-1.0.0-py2.py3-none-any.whl (... lots more installation steps to follow ...) Successfully installed ipykernel-4.6.1 ipython-6.0.0 jedi-0.10.2 jinja2-2.9.6 jupyter-1.0.0 jupyter-client-5.0.1 jupyter-console-5.1.0 notebook-5.0.0 prompt-toolkit-1.0.14 protobuf-3.2.0 qtconsole-4.3.0 setuptools-35.0.1 tensorflow-1.1.0rc2 tornado-4.5.1 webencodings-0.5.1 werkzeug-0.12.1
Enter which jupyter to find out where the Jupyter link is pointing. If it is pointing to a path within your virtualenvs folder, you’re good to go. Otherwise, open a new terminal window and activate your virtualenv. Check where the jupyter command is pointing now – it should point to the virtualenv.
As always, let me know if it works for you in the comments, or if you’ve found some alternative ways to fix this issue. Hopefully, this helps you on your way to delve into Tensorflow and explore this fantastic deep learning framework!
Header image: courtesy of Jeff Dean, Large Scale Deep Learning for Intelligent Computer Systems, adapted from Untangling invariant object recognition by DiCarlo and Cox (2007).