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From kellen sunderland <kellen.sunderl...@gmail.com>
Subject Re: Include MKLDNN into default mxnet pip package
Date Wed, 17 Oct 2018 22:12:12 GMT
First of all thanks to Intel for these improvements, really a great effort.

What would the compatibility story look like for users that don't have
these AVX instructions?  Would there be any negative affect for AMD users?

Regarding TensorRT: It's a possibility but not planned in the short term. A
few considerations would be the limits on PyPi package sizes and the bloat
incurred with TRT, the requirements of TRT to be installed on the user
side, and the TRT engine build times which are non-trivial.  We can work
towards fixing or working around these issues in the future if default TRT
is something the user community would like to see for Cuda packages.  While
the feature is experimental we'll likely continue to use
'mxnet-tensorrt-cu92' and 'mxnet-tensorrt-cu90'.

On Wed, Oct 17, 2018 at 2:12 PM Alfredo Luque
<alfredo.luque@airbnb.com.invalid> wrote:

> This is huge. Thanks for working on this. Is there a similar plan with eg;
> tensor-rt support being ported into the main cuda-9.x packages?
>
> On October 17, 2018 at 2:10:20 PM, Alex Zai (azai91@gmail.com) wrote:
>
> Hey all,
> We have been working hard these past few months to integrate and stabilize
> Intel’s MKLDNN deep learning CPU accelerator into Mxnet and have made
> incredible progress. On CPUs with AVX512 instructions (such as c5.18x) we
> have seen performance increase up to 12x and on other platforms (Macs,
> AVX2) we seen a speedup of 1.5+. Full list of benchmarks can be found here
> (
> https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=95650764
> and https://github.com/apache/incubator-mxnet/pull/12591).
>
> Currently, using this accelerator requires the developer to either pip
> install the mxnet-mkl version of mxnet or to build it themselves from
> source. Given that we should try to provide the best performance "out of
> the box” with mxnet we should include this in the default build. The mkldnn
> library is included with in the pip package build so it does not require an
> external dependency.
>
> There were concerns that MKLDNN could cause regressions on certain
> platforms (as it did with the tensorflow version a while back); but we
> added a env flag (MXNET_MKLDNN_ENABLED) that allows users to turn of this
> feature during runtime. Please bring up any other concerns you may have and
> your thoughts on including this accelerator in the default build.
>
> Best,
> Alex
>
> —
> Alfredo Luque
> Software Engineer
> Machine Learning Infrastructure
> Airbnb
> San Francisco, CA
>

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