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Issues installing in M1 conda environment #219
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Thanks for the extensive issue! We have had quite some issues with making everything M1 chip compatible. In fact partly because of that we transitioned form tensorflow to pytorch (since 2.0). However, I was not aware that there were also installation issues with pytorch on M1. Until recent it was impossible to automatically test installing on macos + M1 chip using github actions. However, I think they have released that earlier this year, so I will try to implement that. If I get that up and running I will add this to the readme and change recommended python version to 3.11. |
Trying to implement in #221 based on actions/runner-images#9254 |
Weirdly enough the python 3.11 seems to fail for me now... |
Yeah, while all dependencies installed, I have been running into some finicky behavior downstream. I am trying to run:
But I am having some issues with the msp file formatting. I wasn't sure if that was just my input files though since some of them are custom generated. I'm currently trying to resolve and will let you know if I figure out a solution. |
I ran the tests and they seemed to work fine in my Python 3.11 environment (from what i can tell, just warnings but no errors):
|
I had to make some modifications to my input msp files and convert the generators to lists, but it seems to be working in my local environment. |
Hi all,
I don't know if this is just occurring on my machine, but just wanted to make you aware of an issue I came across trying to install m2deepscore on my M1 mac.
What doesn't work
README environment install method # 1
README environment install method # 3
README environment install method # 1 with no Python version specified
A slightly different error:
Along with several other combinations.
What does work
README environment install method # 1 with Python == 3.11
python3.11_environment.yml
No set Python version (v3.12 works here) and a custom
setup.py
And then I specifically need to download the repository (@v2.0.0) and remove any forced versioning for dependencies:
And then install via:
pip install .
(from within the local copy of the repo). I've attached the exported conda environment from my working setup:python3.12_environment.yml
Although this method leads to issues later with numpy compilation as
matchms
is also forcing certain versions of dependencies.Conclusion
It seems like the environment is very sensitive to different forced version requirements on certain dependencies.
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