Comment by mathisfun123 | Hacker News Reader

Comment by mathisfun123 | Hacker News Reader

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> wants to learn more about the landscape and find the way to enter it eventually

let's swap roles and let's pretend i'm an ML engineer asking you how to enter CRUD. what would you tell me? my strong suspicion (if i caught you in an honest, frank, moment) is you would say to me "why the fuck would you want to do that - it sucks". i have this suspicion because i did actually used to do CRUD and it does suck! but here's your moment of zen: so does ML/DL/AI. it really really does suck. it's basically just as bad as webdev in terms of tedium/boredom/incidental complexity/etc. it's not fun, interesting, exciting, whatever else you're projecting based on an outside-looking-in-perspective.

now i'll acknowledge that there's one big difference: the pay is way better at the far end of the distribution - meaning if you can get to a FAANG ML team then you'll get more money than you're probably getting now (and a ton more stress too) and it's even more than the CRUD devs in FAANG. fine. but ask yourself if it's really worth learning a whole heap of new bullshit just for a chance at more money (no guarantee).

okay now a useful/practical answer: i went back to school for a PhD but i should've just dropped out with the MS. do that. even better do Georgia Tech's online MS.

I'm from the ML platforms and systems domain.

I strongly recommend it if one's able. It's a bit more stable than a quickly evolving ML/DL/AI ecosystem or frontend ecosystem. The skills are more durable. It repays deep investment and knowledge.

It allows you to straddle both the distributed systems and services domain and the ML domain.

ML systems problems are extremely interesting since they require extremes of compute, storage, network, and latency, in very different parts of the model lifecycle. Its unique problem is the scarcity and cost of hardware accelerators.

I've worked eleven years in the space and rarely have had the desire to leave.

> rarely have had the desire to leave.

I'm currently a GPU compiler engineer in FAANG specializing in compute (not graphics). So clearly ML systems. Prior I have worked at every level of stack above and during my PhD I worked below (RTL). I hate it and think about leaving every day (I stay because of the money and like wtf else am I gonna do lol).

Are you willing to take a pay cut?

No? Why would I? Like to what end?

> I hate it and think about leaving every day

most people hate their jobs; some people are just lucky enough that their jobs compensate them well enough for the emotional labor as well as the physical labor. also i have worked other SWE jobs (as already mentioned) and i've hated them all.

You can also not hate your job and be paid fairly well for it

Didn't I just tell you that I've hated every swe job I've had? Are you proposing I become a baker or what?

Would being a baker make you happy? Because this conversation makes me feel like you are one of those guys who is like "if I was a baker I will join the permanent underclass which means I cannot be happy doing that either"

I don't understand why you're asking these questions at all - my happiness is not the subject here? Throughout I'm merely maintaining that swe sucks no matter where you go despite people thinking the grass is somehow greener in ML.

I mean, it sucks for you. Definitely does not for everyone else

Would you (or someone else passionate about this topic) consider answering the question directly? I am curious about this too.

Pytorch is widely accepted as the de facto ML framework in both research and industry. TensorFlow comes second in industry. Jax is hardly used at all, but uses the same backend as TensorFlow.

Triton is a python-like language to define ML math operations that run efficiently on hardware accelerators like GPUs or TPUs. OpenAI open sourced it. If there's a particular math operation you have a unique need for in your model, and it hasn't already been implemented by some other library, and it's important for efficiency, you'd probably write it in triton these days. It'll be compiled to an intermediate representation, then to an efficient runtime.

The course linked deals with "MLSys", or "ml systems". That means using GPUs and other hardware accelerators efficiently to run ML math operations on one or more computers.

95% of working ML engineers will never need to write Triton, and will be more than satisfied with PyTorch. Many more ML engineers will, nevertheless, write Triton code, because it is interesting, fun, easy, and people are impressed when you tell them you did.

Hosting pytorch models efficiently is currently awkward, because there's no clear winner in the ecosystem. ONNX is a way of representing model graphs in a framework-agnostic way. Other systems can interpret ONNX graphs to do inference. So sometimes, when someone wants to host a pytorch model, they turn it into an ONNX model and run it with an efficient runtime on CPUs or GPUs.

> Triton is a python-like language to define ML math operations that run efficiently on hardware accelerators like GPUs or TPUs. OpenAI open sourced it.

This is incorrect. Triton has literally no path to TPU and it has always been open source because it was Philippe Tillet's PhD project (OAI simply hired Philippe).

> 95% of working ML engineers will never need to write Triton, and will be more than satisfied with PyTorch.

Maybe 95% of hobbyist ML engineers but professional ML engineers are absolutely writing Triton day-to-day (eg FB has an army of such people). Even if you're not writing Triton you're still using Triton through inductor.

> because it is interesting, fun, easy, and people are impressed when you tell them you did

Professionals write Triton not for any of the reasons you mentioned but for the same reason they wrote CUDA kernels prior: it's a path to peak performance for their specific workloads (where stock PyTorch kernels have mediocre performance).

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