it's very common for different projects to have different requirements, especially for fast moving libraries like transformers. if you rarely run python stuff it might not be a big deal, but i'd rather not have to reinstall stuff (especially big stuff like pytorch builds) every time i switch projects.
That's exactly it. Imagine your company has multiple Python repos, and one depends on foo>=1.0,<2.0, and another depends on foo>=2.0. Venvs let you configure completely isolated environments for each so that they can peacefully coexist. I would not for a moment consider using Python without virtualenvs, though I'm not opinionated about which tool manages them. Uv? Great. Poetry? Fine. `python -m venv`? Whatever. They all get the job done.
Honestly, I can't think of a single good reason not to want to use a venv for Python.
Using the same version of everything lets you have a much easier time when a vulnerability is discovered?
How so? That hasn’t been my experience.
Do you monitor CVEs?