Benchmarking State-Of-the-Art Deep Learning Software Tools • Hacker News Board

Re: Benchmarking State-Of-the-Art Deep Learning Software Tools

#11

Post by dgacmu » Sat, Aug 27, 2016, 4:30 PM UTC

dgax wrote: post #9

It's not surprising that TF is the slowest in many cases. It has been widely, sometimes harshly, criticized in the past for that reason. On the other hand, despite its speed TF appears to be the only tool that doesn't have to sit out any of the tests due to incompatibilities or lack of features. Other tools like MXNet deserve a shoutout as well, and it would be interesting to see how a wider group compares. MXNet als…

I concur about missing MXnet, but it's noteworthy that TF is the fastest for the convolutional nets on GPU -- a case many care about, and one covered best by Soumith's benchmarks. (Full disclosure: I help develop TF part time).

Clearly room for optimizing the CPU versions of things. That may be Eigen. Intel now has a preview out of their DNN toolkit -- I wonder if we'll see the same speed convergence as we did with CuDNN.

Re: Benchmarking State-Of-the-Art Deep Learning Software Tools

#12

Post by dave168 » Sat, Aug 27, 2016, 5:34 PM UTC

CNTK is great at scaling out beyond a simple machine. The paper didn't benchmark that but only tested one single box performance.

Re: Benchmarking State-Of-the-Art Deep Learning Software Tools

#13

Post by Eridrus » Sat, Aug 27, 2016, 5:36 PM UTC

dave168 wrote: post #12

CNTK is great at scaling out beyond a simple machine. The paper didn't benchmark that but only tested one single box performance.

Realistically, most people barely get to multiple GPUs, let alone multiple machines. You're more likely to do hyperparameter tuning across machines before you do distributed training.