Eh, ML/scientific Python is large and not homogeneous. For code that should work on cluster, I would lean towards a Docker/container solution. For simpler dependancy use cases, pyenv/venv duo is alright. For some specific lib that have a conda package, it might be better to use conda, _might be_.

One illustration is the CUDA toolkit with torch install on conda. If you need a basic setup, it would work (and takes age). But if you need some other specific tools in the suite, or need it to be more lightweight for whatever reason then good luck.

btw, I do not see much interest in uv. pyenv/pip/venv/hatch are simple enough to me. No need for another layer of abstraction between my machine and my env. I will still keep an eye on uv.