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Gotcha: numpy version conflict if installing in existing environment with tensorflow 2.4.1 #160
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Thank you @CatChenal ! Yes, I've seen a related problem in my Ray tutorials where is TF causing issues with the later versions of NumPy (1.20.x) That's the best workaround that I could see, too. For dependencies, we prefer to pin the versions using ranges. In general I'm reluctant to place an upper bounds, since some people don't use TF and they need the latest NumPy for other integration purposes. Plus, I suspect that TF will catch up, eventually. Pandas and Arrow have some similar issues w.r.t. RAPIDS, although the latter is planning to catch up in the next release. |
Also, I'll added a note in the (upcoming) FAQ |
Thanks @ceteri.
I would hold off for the moment:
Perhaps a warning box in README would suffice, e.g.:
|
Just did a roll back of the NumPy requirement, so this should work fine with >= 1.19.2 now. Many thanks @CatChenal ! |
Problem:
When installing
kglab
using pip in an existing (activated) environment, the latest version of numpy is installed (because requirements.txt includes 'numpy >= 1.19.4'). This may create conflicts with other packages.Specific Case: latest numpy version and tensorflow 2.4.1 version conflict:
My activated env contains tensorflow 2.4.1.
Near the end of the installation process from
pip install kglab
, I got this error message (abbreviated):My fix:
pip uninstall numpy
pip install numpy==1.19.4
My (minimal) tests:
Suggestion/Question:
Perhaps changing the numpy requirement from 'numpy >= 1.19.4' to 'numpy == 1.19.4' would force pip to install this first compatible version instead of the latest?
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