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I know it's going to become an even bigger bottleneck moving forward, but it really raises the barrier for us hobbyists. I own a 1080ti (12GB RAM) - and I cons

I know it's going to become an even bigger bottleneck moving forward, but it really raises the barrier for us hobbyists. I own a 1080ti (12GB RAM) - and I consider this "high-end" for many people who aren't actively employed for machine learning (College kids and younger especially). I know you can "use the cloud" but I would really prefer not to...

darkmighty 7y ago
Yea some state of the art results are just inaccessible without large budgets, simply because models can scale (and because some orgs have a lot of money to train those scaled models). You can always just use smaller models and/or lower resolutions though; of course the results won't be on par but it may reach a qualitative result (for research and experimentation purposes) or good enough result (for personal application purposes). E.g. hobbyists don't need AlphaGo-level go playing AI (which I'm sure had aggregate costs in 5 figures or more to train), reduced versions play all far above our levels -- although in this case there's the interesting effort of pooling hobbyist resources to indeed reach SOTA, see LeelaZero[1] and LCZero. Some kinds of research will be effective only at large orgs, that's always been true. There was indeed a brief period when people realized GPUs could unleash deep learning/CNNs that you could do anything with a good GPU, but that was very much an exception. To borrow from another field, you cannot do a level of car engine research without all infrastructure to fabricate and test engine prototypes (though you can do some/other kinds of theoretical analysis). [1] http://zero.sjeng.org/home http://zero.sjeng.org/home
xxs 7y ago
1080ti is 11GB, not 12. Titan is 12
rrss 7y ago
There's some work on enabling larger stuff to run (slowly) via oversubscription. Maybe pytorch will get this capability eventually. https://developer.download.nvidia.com/video/gputechconf/gtc/2019/presentation/s9726-unified-memory-for-data-analytics-and-deep-learning.pdf https://developer.download.nvidia.com/video/gputechconf/gtc/...

darkmighty 7y ago

Yea some state of the art results are just inaccessible without large budgets, simply because models can scale (and because some orgs have a lot of money to train those scaled models). You can always just use smaller models and/or lower resolutions though; of course the results won't be on par but it may reach a qualitative result (for research and experimentation purposes) or good enough result (for personal application purposes). E.g. hobbyists don't need AlphaGo-level go playing AI (which I'm sure had aggregate costs in 5 figures or more to train), reduced versions play all far above our levels -- although in this case there's the interesting effort of pooling hobbyist resources to indeed reach SOTA, see LeelaZero[1] and LCZero. Some kinds of research will be effective only at large orgs, that's always been true. There was indeed a brief period when people realized GPUs could unleash deep learning/CNNs that you could do anything with a good GPU, but that was very much an exception. To borrow from another field, you cannot do a level of car engine research without all infrastructure to fabricate and test engine prototypes (though you can do some/other kinds of theoretical analysis). [1] http://zero.sjeng.org/home http://zero.sjeng.org/home

xxs 7y ago

1080ti is 11GB, not 12. Titan is 12

rrss 7y ago

There's some work on enabling larger stuff to run (slowly) via oversubscription. Maybe pytorch will get this capability eventually. https://developer.download.nvidia.com/video/gputechconf/gtc/2019/presentation/s9726-unified-memory-for-data-analytics-and-deep-learning.pdf https://developer.download.nvidia.com/video/gputechconf/gtc/...