Thanks, Sandeep, > * If all existing RNN integration tests pass with MKL-DNN build, this should > give > enough confidence?
This should be the baseline of the merge. We still need to confirm the performance gain from new API. > * Also, I remember one of the community member saying "mxnet-mkl" pypi > package is not compiled with MKLDNN. Not sure about this, but, can we please > confirm? After 1.2.0, "*-mkl" is compiled with MKLDNN. But "*-mkl" is not compiled with MKL library (still using OpenBLAS from pypi). We are working on this item and try to make both MKL-DNN and MKL library are compiled under "*-mkl". > -----Original Message----- > From: sandeep krishnamurthy [mailto:sandeep.krishn...@gmail.com] > Sent: Wednesday, July 4, 2018 11:27 AM > To: dev@mxnet.incubator.apache.org > Subject: Re: MKLDNN Integration Stable Release > > * If all existing RNN integration tests pass with MKL-DNN build, this should > give > enough confidence? > * Also, I remember one of the community member saying "mxnet-mkl" pypi > package is not compiled with MKLDNN. Not sure about this, but, can we please > confirm? > > Best, > Sandeep > > On Tue, Jul 3, 2018 at 7:37 PM Zhao, Patric <patric.z...@intel.com> wrote: > > > Hi Alex, > > > > Regarding RNN, the first version of MKL-DNN RNN API is available in > > the MKL-DNN master branch. > > We have integrated it in our local branch and you can try our code > > (still in developments). > > > > > > https://github.com/lihaofd/incubator-mxnet/blob/mkldnn-rnn/src/operato > > r/nn/mkldnn/mkldnn_rnn_impl.h > > > > We plan to PR our integration into MXNET master when both > > functionality and performance are qualified. > > > > Thanks, > > > > --Patric > > > > > -----Original Message----- > > > From: Alex Zai [mailto:aza...@gmail.com] > > > Sent: Wednesday, July 4, 2018 1:17 AM > > > To: dev@mxnet.incubator.apache.org > > > Subject: MKLDNN Integration Stable Release > > > > > > We are preparing a stable release of MKL-DNN integration in 1.3.0 > > > (experimental since 1.2.0), which supports acceleration of > > > operations > > such as > > > Convolution, Deconvolution, FullyConnected, Pooling, Batch > > > Normalization, Activation, LRN, Softmax. Currently the RNN operator > > > is not supported as > > the > > > MKL-DNN API is still experimental; however, they hope to release a > > > more stable version RNN API this or next week in MKL-DNN 0.15. > > > > > > We will have CPP unit test support on these operators and I am > > > planning > > to > > > write python unit tests to compare a RNN network's results from the > > MKLDNN > > > backend with that of the GPU to test accuracy. Is there any > > > additional > > coverage > > > that you think we should cover in the next two weeks? > > > > > > Alex > > > > > -- > Sandeep Krishnamurthy
