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From Dominic Divakaruni <dominic.divakar...@gmail.com>
Subject Re: What's everyone working on?
Date Tue, 03 Oct 2017 04:15:34 GMT
on a separate but equally exciting note, how about we start to talk about a
1.0 release in the future and what everyone would want that to look like?
I'll start a separate thread. :)

On Mon, Oct 2, 2017 at 9:07 PM, Dominic Divakaruni <
dominic.divakaruni@gmail.com> wrote:

> Seb is talking about support for Cuda 9 and cuDNN 7. Pull requests below.
> @ptrendx and Dick Carter are working through some performance issues but
> should be done in a week (hopefully).
>
> Jun, Bhavin,
> Tensor RT runtime is a different subject. Nvidia is helping build a
> converter for MXNet models. Not sure on the ETA. Tensor RT helps accelerate
> vision models on the V100, TX2, P4/40 etc...
>
>
>    - Enabling persistent batch norm with cuDNN 7:
>    https://github.com/apache/incubator-mxnet/pull/7876
>    <https://github.com/apache/incubator-mxnet/pull/7876>
>    - Making mixed precision work with all optimizers:https://github.com/
>    apache/incubator-mxnet/pull/7654
>    <https://github.com/apache/incubator-mxnet/pull/7654>
>    - Faster IO pipeline needed for Volta:https://github.com/
>    apache/incubator-mxnet/pull/7152
>    <https://github.com/apache/incubator-mxnet/pull/7152>​;
>    - Expose Tell in RecordIO reader:https://github.com/
>    dmlc/dmlc-core/pull/301
>
>
> On Mon, Oct 2, 2017 at 8:44 PM, Bhavin Thaker <bhavinthaker@gmail.com>
> wrote:
>
>> Hi Seb: please use a different email thread for new topics of discussion.
>>
>> Hi Jun: I think Seb may be referring to Volta V100 support in MXNet and
>> NOT
>> P4/P40 inference accelerators.
>>
>> Corrections/clarifications welcome.
>>
>> Bhavin Thaker.
>>
>> On Mon, Oct 2, 2017 at 8:22 PM Jun Wu <wujun.nju@gmail.com> wrote:
>>
>> > Thanks for your attention, Seb. We are inclined to be cautious on what
>> can
>> > claim for this project. TensorRT has already supported converting
>> > TensorFlow and Caffe models to its compatible format for fast inference,
>> > but not MXNet. In this sense, it may not be fair to claim MXNet as the
>> > first one supporting Nvidia Volta.
>> >
>> > What we are working on is more experimental and research oriented. We
>> want
>> > to get the first-hand materials in our own hands by building a INT-8
>> > inference prototype and have a thorough understanding on its strength
>> and
>> > limitation, rather than handing it off completely to TensorRT, which is
>> > transparent to us. Considering that the project is experimental, it's
>> still
>> > too early to make a conclusion here as there are plenty of known/unknown
>> > issues and unfinished work.
>> >
>> > On the other hand, we are glad to hear that Nvidia is working on
>> supporting
>> > model conversion from MXNet to TensorRT (Dom please correct me if I'm
>> > mistaken). It would be super beneficial to MXNet on INT-8 if they could
>> > open-source their work as we would be able to maintain and add new
>> features
>> > on our side.
>> >
>> >
>> > On Mon, Oct 2, 2017 at 8:04 PM, Dominic Divakaruni <
>> > dominic.divakaruni@gmail.com> wrote:
>> >
>> > > 👏
>> > >
>> > > On Mon, Oct 2, 2017 at 8:02 PM Seb Kiureghian <sebouhdk@gmail.com>
>> > wrote:
>> > >
>> > > > It would be awesome if MXNet were the first DL framework to support
>> > > Nvidia
>> > > > Volta. What do you all think about cutting a v0.12 release once that
>> > > > integration is ready?
>> > > >
>> > > > On Wed, Sep 27, 2017 at 10:38 PM, Jun Wu <wujun.nju@gmail.com>
>> wrote:
>> > > >
>> > > > > I had been working on the sparse tensor project with Haibin.
>> After it
>> > > was
>> > > > > wrapped up for the first stage, I started my work on the
>> quantization
>> > > > > project (INT-8 inference). The benefits of using quantized models
>> for
>> > > > > inference include much higher inference throughput than FP32
model
>> > with
>> > > > > acceptable accuracy loss and compact models saved on small
>> devices.
>> > The
>> > > > > work currently aims at quantizing ConvNets, and we will consider
>> > > > expanding
>> > > > > it to RNN networks after getting good results for images.
>> Meanwhile,
>> > > it's
>> > > > > expected to support quantization on CPU, GPU, and mobile devices.
>> > > > >
>> > > >
>> > > --
>> > >
>> > >
>> > > Dominic Divakaruni
>> > > 206.475.9200 Cell
>> > >
>> >
>>
>
>
>
> --
>
>
> Dominic Divakaruni
> 206.475.9200 <(206)%20475-9200> Cell
>



-- 


Dominic Divakaruni
206.475.9200 Cell

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