[MSFT_Edging]

Seconding this. I learned a little rust and really liked its philosophy. I was then working on a small project that needed to avoid the big data science python libraries because they're miserable in terms of portability to air-gapped networks.

We had re-implemented a few mathematical and curve finding functions and even with the newest python they were uncomfortably slow.

PyO3 made it so easy to implement a runtime switchable rust library exposed to python, it was almost unnerving. By writing those handful of functions in rust we got something like a 30x speedup.

Note this was a small project with limited funding for r&d so the level of effort for performance speedup was really nice.

[dralley]

Why not compile those (python scientific) libraries from source? Why would PyO3 be acceptable while e.g. numpy is not?

[MSFT_Edging]

needing to compile them on the airgapped machine introduces more dependencies. Things also get weird when your airgapped machine is a slight version off from your dev machine. There's a lot of ways around it but we've found for the size of this tool, it was preferable to just write our own handful of math functions over building out a deployment method for extra dependencies.