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laprika0 OP t1_iup4bqt wrote

Reply to comment by lqstuart in [D] Machine learning prototyping on Apple silicon? by laprika0

These are interesting points. I think it depends on where in the stack I'll be. At my last place I spent most of my time building and testing abstract ML functionality that I never deployed to production myself (other teams did that) and could be tested on a CPU in a reasonable amount of time. I can imagine the "other team" worked with the restrictions you mention. In my next role, I may well wear both hats.

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lqstuart t1_iuq94uz wrote

The roles where you do a little of both are the most fun! I used to do the algo work, now I work entirely on the infra side of things at one of the larger corps. We support some massive teams that have their own platform team between the AI devs and us, and also some smaller teams where the AI devs do it all themselves and just talk to us directly.

In all cases, where I am now and in my previous infra-only role, the AI teams were kinda stuck on our Linux shit for the reasons I described--specifically, you need to write stuff differently (or use an SDK that's tightly coupled to the underlying compute) for distributed training so there's no real point running it locally.

I personally REALLY miss the ability to develop and test locally with a real IDE, so I hope something changes--however, the trend is heading towards better remote development, not making stuff work on Mac.

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