4 ms·
For state of the art work "a day or two" is pretty fast for a production network, and that's on one or more big GPUs. Not using CUDA is definitely a dealbreaker
by frisco 11y ago
For state of the art work "a day or two" is pretty fast for a production network, and that's on one or more big GPUs. Not using CUDA is definitely a dealbreaker for any kind of real deep learning beyond the mnist tutorials. It's common to leave a Titan X to run over a weekend; that would be weeks on a CPU.
- mindcrime 11y agoWell not using CUDA isn't necessarily synonymous with "use a CPU". There is OpenCL. But still, you have a point even if we might quibble over details. This is why I am very much hoping AMD gets serious about Machine Learning and hoping for OpenCL on AMD chips will eventually reach a level of parity (or near parity) with the CUDA on nVidia stuff.
- sseveran 11y agoIts unlikely that AMD is going to be able to make serious inroads in the near future. nVidia has built quite a lead not just in terms of chips but tooling. I had thought a couple of years ago that AMD should be building a competitor to the Tesla. It should be able to build a more hybrid solution than nVidia can given its in house CPU development talent. But I haven't seen them building anything like that and a competitor to nVidia may have to come from somewhere else. In the absence of a serious competitor OpenCL is not very interesting.