Hey, #Python friends, I'm still learning... A question...
If you're using virtual environments - what do you do?
I keep finding documents that suggest either is fine (of course), but I wonder if there is some logic behind the choice.
I sometimes forget which one I used and then have to check first, which seems inefficient.
- python -m venv ./venv (50%, 2 votes)
- python -m venv ./.venv (50%, 2 votes)
- Something else? (0%, 0 votes)
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- software used (easiest part)
- training set (hard to find, even for so-called "open source" LLM
- training parameters (weight...)
- hardware (GPU type and core number)
- durability (this LLM won't be available on huggingface forever, is there an alternative and more durable deposit somewhere?
Bah “software used" (easiest part)“ is already a thing. 
For instance: « What’s in a package ». Considering the well-know PyTorch AI framework.
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in reply to popey • • •Speaking as someone reasonably well versed in the arts of Python packaging and virtual environment management: it really doesn't matter. I mean, it doesn't make a whit of difference for the "machinery" of virtual environments; it's your choice whether you want your venvs hidden or not. (Be wary of anyone trying to tell you that there is a standard practice on this.)
It can be convenient to let a higher-level tool manage venvs for you: pipenv, pdm, hatch, poetry, uv, etc. I would definitely endorse the idea of trying out these tools to understand how they work, and then if you want to use them, go ahead; if not, manually creating a venv works too and is sometimes easier. Personally I use a mix.
Though, the one kind of special case I'd call out is that, if you're installing a Python application, pipx (or some equivalent) is *so convenient* compared to manually managing the venv that I would highly recommend it.
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