> Your precious ML/DL libraries may not even be upgraded, because writing neural network code is not the same as writing thread-safe code. If it comes from a CS

> Your precious ML/DL libraries may not even be upgraded, because writing neural network code is not the same as writing thread-safe code. If it comes from a CS lab, its authors have already left, and there's nothing worthy of a publication in adding thread-safety. PyTorch and sklearn won't stop being maintained though. I don't rely on unmaintained research code in production, I adapt what I need under MIT license. Any other way sounds crazy. Plus, most research code is very high-level and uses the same facilities (from e.g. PyTorch again) that everyone else uses, the actual distributed and multithreaded work happens in the main libraries. You'll still be able to use the same neural network code that worked before. I don't see a huge problem for people who already had their dependency list under control. If you had anything that's both hard to replace and not big enough to be upgraded though, I'd argue that it was always going to bite you at some point.