> We can see that the scheme that uses sequential batching actually performs worse than the CPU alone, whereas the new approach using DuHL achieves a 10× speed-up over the CPU.
I had to get down to the graph to realize they're talking about SVM, not deep learning.
This could be pretty cool. Training a SVM has usually been "load ALL the data and go", and sequential implementations are almost non-existent. Even if this was 1x or 0.5x speed and didn't require the entire dataset at once it's a big win.
Indeed, for relatively "simple" models, SVM can get very, very close to deep learning accuracy for classification, with only a fraction of the computing time needed.
Yes, i felt cheated when i read it was about training 1/10th of ImageNet on a SVM. I guess IBM are desparate not to be left behind in the race for distributed deep learning platforms.
I'll second that. For all the attention DL/ANNs get... there's still a lot of legwork going on out there using linear models, basic trees, etc. IIRC this years kaggle survey ranked Logistic Regression as the #1 most used model by a long shot.
you say that as if it's empty hype. but it's not: deep learning works, and works much better by any reasonable metric than SVMs in most problems that require high to very high model capacity.
Yeah they get you really hyped up first and drop the bomb at the end. Still quite impressive speedup, would be better though to show it on a benchmark where SVM's are used in practice
Looking at the normal stuff coming out of IBM they're not associated with good software in my mind. So the more outrageous their claim, the less I believe them. They need to earn a reputation first.
That's like judging all of Google based on the quality of one product. With 10x as many employees as Google and a very loose organization, expecting any kind of reputation is folly.
A product from an IBM consultant is about as related to a product from IBM Watson as is a product from Microsoft being related to a product from Apple.
IBM has 400k employees and god knows how many subsidiaries and divisions, do you really think you can paint them all with one brush because of some negative experience you had with one of their products ?
I had to get down to the graph to realize they're talking about SVM, not deep learning.
This could be pretty cool. Training a SVM has usually been "load ALL the data and go", and sequential implementations are almost non-existent. Even if this was 1x or 0.5x speed and didn't require the entire dataset at once it's a big win.