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> horizontally-scaled deep learning is overkill for most applications It's difficult to say what "most applications are" but many algos currently seem bottlene
by kajecounterhack 11y ago
> horizontally-scaled deep learning is overkill for most applications
It's difficult to say what "most applications are" but many algos currently seem bottlenecked by how fast you can load data into your GFX card (hint: it's slow). So what you're saying about batching is correct, but it does matter.
- dave_sullivan 11y ago> many algos currently seem bottlenecked by how fast you can load data into your GFX card All of them are, but that's a PCI-e issue and horizontal scaling doesn't fix that (unless using nvlink or similar afaik, but then you face the fact that current horizontal scaling schemes aren't very effective at increasing model accuracy anyway) > It's difficult to say what "most applications are" Nevermind "most applications"--so far, all I've heard is one, that being the absolute bleeding edge of RNN research, assuming you're using a huge softmax instead of an alternative. My point remains: Multi-GPU is way down the list when it comes to features a DL framework should have. Because very very few people need it. kajecounterhack, do you use multi-GPU for your DL work? If so, how often?
- varelse 11y ago>do you use multi-GPU for your DL work? If so, how often? I'm using it right now, to great effect. I can't really say what for, but methinks I'll be using it every hour of every day for the foreseeable future.