4 ms·

I don't think anyone is claiming TF won't get much, much faster (probably very soon). But claiming that right now - today - TF kills existing toolkits like Torc

I don't think anyone is claiming TF won't get much, much faster (probably very soon). But claiming that right now - today - TF kills existing toolkits like Torch, Caffe, and Theano (which I have seen here and elsewhere - though not claiming you are in this camp) is a bit premature. Even with v4 support which is coming soon, the general setup that users have is one to two GPUs in one machine. This means adding the right inplace operations can have a huge impact on performance, and is probably a usecase that Google has not focused on given their internal infrastructure. I am sure they will normalize to "at or slightly above" the performance of other toolkits, but the question is when? This benchmark doesn't have anything to do with multi-GPU as far as I am aware - these are single machine, single GPU results. I would wager 90% of the deep learning hobbyist and research communities run in this setting, so benchmarking this is really important. For people with huge amounts of networked and distributed resources - distributed support will be very amazing!

varelse 11y ago
Inplace is a nice late phase optimization, but the root problem here appears to be that Google's convolution kernels are crap compared to those in cuDNN3 and Neon (I asked Scott Gray about this directly and I trust his wisdom). No surprises there whatsoever. The tensorflow engine is the most gold-plated POS I've seen in a long time. if it were running on 1000+ servers, I'd get the level of overengineering they've applied here. But single server pthreads? WTF? Also parameter server is dumb unless you sweat the implementation of the gather/reduction ops, but I digress.
- blazespin 11y ago
  Do we know if this is a just a reference implementation or what Google uses in production? My guess the numbers will come down pretty quick. From what I can see from mxnet versus theano versus torch is that GPUs are really the final determinant of speed and not the framework.

varelse 11y ago

Inplace is a nice late phase optimization, but the root problem here appears to be that Google's convolution kernels are crap compared to those in cuDNN3 and Neon (I asked Scott Gray about this directly and I trust his wisdom). No surprises there whatsoever. The tensorflow engine is the most gold-plated POS I've seen in a long time. if it were running on 1000+ servers, I'd get the level of overengineering they've applied here. But single server pthreads? WTF? Also parameter server is dumb unless you sweat the implementation of the gather/reduction ops, but I digress.

blazespin 11y ago
Do we know if this is a just a reference implementation or what Google uses in production? My guess the numbers will come down pretty quick. From what I can see from mxnet versus theano versus torch is that GPUs are really the final determinant of speed and not the framework.