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I guess probably some AI developers are training using a GPU physically in their machine. TBH I don't know if that's the right way to do it though. If you're de
by opportune 8y ago
I guess probably some AI developers are training using a GPU physically in their machine. TBH I don't know if that's the right way to do it though. If you're developing locally in some kind of notebook environment and then deploying to the cloud/some local servers/etc. (which I think you should do) then I don't think it would matter much.
- robbrit 8y agoThere's an in-between: develop locally in a non-notebook environment, and deploy that software in a container. Notebooks are great for fiddling around with new ideas, but when building production AI systems they tend to fall over. What we'll often do is use a small dataset locally on our desktop to confirm that something works, and then ship it to a multi-GPU setup in our cluster to train things for real-world uses. It's much easier to do this when you're developing on the same OS that is running in the cluster, hence Ubuntu desktop.
- opportune 8y agoThat also works, I guess I just mean that I don't think people people should be relying on training on local GPUs. Since yeah, a lot of use cases require multi-GPU training running on the order of days, seems very amateurish to really need a beefy local GPU to get your work done. I guess I rarely run into ubuntu vs macos compatibility issues while I do notice the lack of QOL features on ubuntu. I would rather stay on macos