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0 pointsoefrha6y ago0 comments

I've worked on tons of small to medium-small Python projects over the years where I didn't fix dependency versions at all, not even major versions, just a requirements.txt with a list of package names (usually it's a list of maybe at most ten well-known libraries, resulting in at most twenty actual packages pulled from PyPI). Come back three years later, pull the latest versions of everything, code still works fine.

Now try that with JavaScript or Rust. If you don't fix versions, come back three months later and compatibility is usually fucked up beyond all recognition.

Some languages embraced better dependency locking because they absolutely couldn't not solve the problem.

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DougBTX6y ago

I’ve only recently started working with Python, and I’ve already been bitten by TensorFlow v1 and v2 packages having different APIs, so the viability of that approach will depend heavily on which packages you use.

However in SemVer a major version number change is how breaking changes are documented, so seeing a v1 to v2 change coupled with having to do some work to fix breakage is just expected, something that may well be necessary for a project to make progress.

oefrhaOP6y ago

ML, pydata etc. are really worlds apart from more traditional Python ecosystems; guido himself a couple years back admitted he had no idea about those silos and sat down with some leaders of those communities to hear their needs. Those communities tend to have their own recommendations and best practices.

My very brief exposure to TF seems to suggest that dev environments surrounding TF are way harder to set up than my “list of bare packages in a requirements.txt file” scenario which is sufficient for a lot of more traditional endeavors.

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