Just to kick the discussion off, I'm building web stuff, and ML-related back ends with some CLI tooling. Docker's in the mix, too. I liked Pipenv's clear expres

Just to kick the discussion off, I'm building web stuff, and ML-related back ends with some CLI tooling. Docker's in the mix, too. I liked Pipenv's clear expression of dependencies and its separate lockfile so that we could be clear about intent versus specific dependency version used. However, lately we've had real struggles with pytorch, which admittedly is an odd participant in the Python ecosystem, in that it has a variety of installables depending on the CUDA version you want. And lately I've lost many hours trying to figure out how internal ML library A and dependent CLI tools package B can express their dependencies in ways that they can be installed as regular pip packages from where they live in our private GitHub repos. The most recent serious comparison I've seen since March is this one: https://frostming.com/2021/03-26/pm-review-2021/ https://frostming.com/2021/03-26/pm-review-2021/ That includes pipenv, poetry, and PDM. One of our data science folks has suggested using conda. That gives me the willies, as it seems too heavy and too differently targeted, but that's not a real reason to say no.