[kvnhn]

IMHO, Poetry is the best we have in the Python dep mgmt space, and it's still endlessly frustrating. It's especially hard to recommend it for newbies looking to get up and running with even a simple ML stack. Check out this thread[1] on the Kafkaesque nightmare that is trying to install PyTorch with Poetry.

[1]: https://github.com/python-poetry/poetry/issues/6409

[appplication]

Poetry won’t ever be the answer because the Poetry maintainers haven’t shown the maturity to be real leaders in the space. It’s good, as many narrowly opinionated projects are, but ultimately the core maintainers are not interested in use cases they see as outside their vision for the tool. Which means it will never be the one tool to rule them all.

[zwaps]

For ML projects, conda is still better since it usually manages to resolve a working environment including pytorch and cuda.

Sure, it doesn’t lead to the same exact environment on every machine, but that stuff never ever works anyway at least with portry.

[cxr]

The whole "Python is how science gets done" meme is one of the dumbest things we've allowed to be foisted on otherwise unaware/unsuspecting users (such as the kinds of academics who end up being the victims of the Python ecosystem shitshow). Who knows how many setbacks in science we've suffered, not to mention billions of dollars of productivity lost, sticking to such an unworthy programming system/language/environment. All because, like, colons and significant whitespace make programming so much easier to pick up when you compare it to making someone look at curly braces—which, as we all know is the hardest part of programming.

[physicsguy]

Once you start to look at scientific packages in other languages, they have the same issues as Python does, because they start to use scientific libraries written in C and Fortran, as rewriting 50+ years of code is actually really hard.