[claytonjy]

I have successfully transitioned an ML/AI team of seasoned researchers away from conda and to poetry. Some also use pyenv, I suspect a lot don't bother but may get bitten eventually.

It's definitely a learning curve, but it turns out every conda user has been bit by the irreproducible tendencies of conda quite often. Nobody uses the conda env file, they just start an env and pip install things into it. They don't realize the base env has stuff, too, and conda envs are hierarchical rather than isolated. I know it's possible to use conda in an isolated and reproducible way, but have yet to meet someone that does so.

So it hasn't been hard to pitch poetry to these folks, and while many complain about the learning curve they appreciate the outcomes.

We're a pytorch shop, and torch mostly just works with pip or poetry these days, as long as you skip the versions the torch maintainers mispackaged. We rarely need anything higher-level that only conda could install.

We really like having more than two dependency groups as this allows us to keep research and production in the same repository. main, dev, research. Then researchers contribute to the core library of a project and keep research and production using the same code for running and evaluating models.