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Sorry but even tho the big companies produce a lot of interesting research, I challenge you to not find any interesting model trained on a single GPU from recen

Sorry but even tho the big companies produce a lot of interesting research, I challenge you to not find any interesting model trained on a single GPU from recent publications (the majority coming from academia). Actually it's very rare to find a paper where largely distributed training is necessary (i.e: the training would fail or would be unreasonably too long). Yes having more money help you to scale your experiments, it's nothing new and it's not something specific to AI.

bitL 8y ago
A trivial example is BERT_large; won't fit into 12/16GB and takes ~year to train from the scratch on a single 15TFlops machine. It's now a base model for transfer learning for NLP.
- caenorst 8y ago
  I'm not saying that there's no very big model, just saying that it's a minority of publications, for any trivia example of big model I can show you 10x trivia examples of relevant non-big models. Also you are talking about a model which is specifically designed for TPU (the dimensionality of the networks is especially fine-tuned). And even tho, BERT_large still fit in the memory of a single GPU (for very small batch), there is an implementation on Pytorch. I don't understand, are people complaining that Deep Learning is actually (reasonably) scaling ? Isn't it a good news ?
  - bitL 8y ago
    You need to study state-of-art a bit more. BERT_large can't reproduce results its authors achieved with TPUs on a Titan V/Tesla P100 as for getting there you need to use substantially larger batch sizes that won't fit into 12/16GB. If you get a V100/Titan RTX, it would fit, but you'd wait ~1 year for a single training session (40 epochs) to finish. MS already published another model based on BERT that is even better. It's unlikely memory x #GPUs would go down in foreseeable future; it's more like that everybody will start as large models as their infrastructure allows if they find something that improves target metrics.
fnbr 8y ago
Yes, absolutely. I'm an AI researcher, and most of my colleagues just use their desktop computers with a standard GPU to do their work. Of course, long-running jobs get put on the cloud, as do large distributed jobs, but those are surprisingly rare. We just submitted a paper, for instance, that is entirely CPU based, and required running 4 CPUs for a few days to reproduce (and even then, you can reproduce 90% of the paper within minutes on a single machine).

bitL 8y ago

A trivial example is BERT_large; won't fit into 12/16GB and takes ~year to train from the scratch on a single 15TFlops machine. It's now a base model for transfer learning for NLP.

caenorst 8y ago
I'm not saying that there's no very big model, just saying that it's a minority of publications, for any trivia example of big model I can show you 10x trivia examples of relevant non-big models. Also you are talking about a model which is specifically designed for TPU (the dimensionality of the networks is especially fine-tuned). And even tho, BERT_large still fit in the memory of a single GPU (for very small batch), there is an implementation on Pytorch. I don't understand, are people complaining that Deep Learning is actually (reasonably) scaling ? Isn't it a good news ?
- bitL 8y ago
  You need to study state-of-art a bit more. BERT_large can't reproduce results its authors achieved with TPUs on a Titan V/Tesla P100 as for getting there you need to use substantially larger batch sizes that won't fit into 12/16GB. If you get a V100/Titan RTX, it would fit, but you'd wait ~1 year for a single training session (40 epochs) to finish. MS already published another model based on BERT that is even better. It's unlikely memory x #GPUs would go down in foreseeable future; it's more like that everybody will start as large models as their infrastructure allows if they find something that improves target metrics.

fnbr 8y ago

Yes, absolutely. I'm an AI researcher, and most of my colleagues just use their desktop computers with a standard GPU to do their work. Of course, long-running jobs get put on the cloud, as do large distributed jobs, but those are surprisingly rare. We just submitted a paper, for instance, that is entirely CPU based, and required running 4 CPUs for a few days to reproduce (and even then, you can reproduce 90% of the paper within minutes on a single machine).