It’s especially frustrating that dependency hell seems to be embedded in the Python culture. The amount of “oh no this lib will only work with Python 3.10+ and a slew of other libs at random versions we won’t bother to tell you” while some other lib that it depends on will only work on “3.8.56z but not if you look at it funny and only if you say pretty please” is maddening. Semver is apparently not standard practice either.

I am probably biased against Python, so take this opinion with a grain of salt, but it feels to me like a whole ecosystem of amateur software devs (but professional ML-engineers, data scientists etc) cobbling together something that barely works.

I’m old enough at this point that I remember the whole old guard of software engineers falling over themselves to hate on JS and Node, call the ecosystem immature, and be quick to point out how that is not “real” software. But in the past 10-15 years it appears JS and Node got their shit together, while Python is still completely and utterly stuck in managing dependencies and environments like it is 2012. And if you ask professional Pythonistas about this, you always get an answer like “oh it’s actually really easy, you must not have taken the time to really look at it, because Python is easy, it’s just pseudocode look how amazing it all is”

I really wish ML hadn’t standarized on Python. As a user of ML tools ans frameworks but not a fulltime ML engineer it just constant pain.