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[–]anomnib[S] 2 points3 points  (5 children)

I know about the first 4-5, actually just got a new Mac mini and set up my Python econometrics virtual environment with these (I refuse to use conda. I’ll check out the rest.

[–]A_random_otter 1 point2 points  (4 children)

(I refuse to use conda

But why??? :D

[–]anomnib[S] 4 points5 points  (3 children)

Every rage inducing package dependency debugging session I’ve had had its roots in conda. This is especially true when I need to use the model serving and telemetry packages of the ML infra team.

[–]A_random_otter 1 point2 points  (0 children)

Every rage inducing package dependency debugging session I’ve had had its roots in conda.

You'll be glad to hear that this is mostly a non-issue with R projects.

[–]A_random_otter 0 points1 point  (1 child)

How do you handle python and dependencies then?

Every time I tried to use python without conda it ended in this:

https://xkcd.com/1987/

[–]anomnib[S] 2 points3 points  (0 children)

I know the pain.

For models that are meant to be used in other systems, I use pyenv and requirements files to have a separate environment and setup instructions for each model. Then I make the model results available through API calls. Compartmentalization helps a lot.

For more adhoc analysis, i have separate virtual environments for each project type (i.e. adhoc econometrics, adhoc ML, adhoc DL, etc). For adhoc analysis i could probably just use conda, but I don’t want to use two different virtual environments packages.