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CUDA is nowhere near the de facto in the ML industry. Beyond some specific application domains like CV where DL makes sense, most ML still runs on commodity har
by mochomocha 6y ago
CUDA is nowhere near the de facto in the ML industry. Beyond some specific application domains like CV where DL makes sense, most ML still runs on commodity hardware, and I happen to believe this will be the case for a long time once the Gartner Cycle of Hype settles on the Deep Learning craze and everyone runs out of VC money.
Despite what cloud providers and NVIDIA want you to believe, no you most likely don't need a lot of compute power for your industry problem. Basic knowledge of statistics would help though.
- Voloskaya 6y agoOP was likely talking about training where NVIDIA has virtually no competition apart from Google's TPUs. In inferencing a lot more models are deployed on GPUs than before and this trend is likely to continue. NVIDIA is alone there.
- mochomocha 6y agoHa I see - I've been training ML models with millions of data points and millions of features on CPUs for a decade now, I didn't know I needed a GPU - I guess I've been doing it all wrong all this time!
- RcouF1uZ4gsC 6y ago>I've been training ML models with millions of data points and millions of features on CPUs for a decade now That is why it is taking a decade to train your models. If you upgraded to a GPU, you could train them much faster :)
- ebalit 6y agoI would be very surprised if a ML pipeline didn't benefit from some GPU acceleration. What kind of algorithms are you using?
- Voloskaya 6y ago> didn't know I needed a GPU - I guess I've been doing it all wrong all this time No, not the whole time, just these last couple of years :)