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From Steffen Rochel <steffenroc...@gmail.com>
Subject Re: Cambricon MLU support for MXNet.
Date Fri, 21 Dec 2018 01:09:51 GMT
Hi Haochong -
welcome to MXNet. I invited you to the Slack channel, which you can use as
another communication path.

Regards,
Steffen

On Tue, Dec 18, 2018 at 5:45 AM Haochong Zhang <zhanghaochong@cambricon.com>
wrote:

> Thank you very much for your valuable feedback!
>
> We will submit the design proposal ASAP. At the same time, we will be
> ready for the appropriate server or cloud.
>
> The cambricon libraries related to MXNet are Cambricon Neuware Machine
> Learning Library (CNML) and Cambricon Neuware Runtime Library (CNRT). The
> libraries' documentation will be available soon.
>
> Look forward to continued participation and contribution in the future.
>
>
> ------------------------------------------------------------------
> 发件人:Skalicky, Sam <sskalic@amazon.com>
> 发送时间:2018年12月18日(星期二) 06:03
> 收件人:dev@mxnet.incubator.apache.org <dev@mxnet.incubator.apache.org>
> 抄 送:张昊翀 <zhanghaochong@cambricon.com>
> 主 题:Re: Cambricon MLU support for MXNet.
>
> Hi Haochong,
>
> I am in the process of putting together a design proposal for an
> accelerator interface for MXNet that would allow hardware vendors to
> integrate their runtime with MXNet. I would like to suggest setting up a
> time to get together so that we can hear more about your needs to
> interface/control your accelerator, and I can share some thought on a
> generic accelerator API that I will be proposing. Id be happy to help you
> prepare a design proposal as well.
>
> I’ll connect with you separately to setup a time to chat.
>
> Sam
>
>
> > On Dec 17, 2018, at 5:49 AM, Pedro Larroy <pedro.larroy.lists@gmail.com>
> wrote:
> >
> > Hi Haochong
> >
> > Welcome to MXNet, It's exciting to have additional hardware platforms
> > added and supported in the project.
> >
> > The CI system for MXNet is donated by AWS to the project. We have a
> > small hardware lab with embedded physical hardware like ARM boards
> > including NVidia Jetson which we are connecting to the CI system.
> > (It's a WIP).
> >
> > However, the bulk of the CI system runs in the AWS Cloud using Jenkins
> > and EC2 GPU and CPU instances. So even though any of the options you
> > mention are possible and could work, I think in the order you
> > mentioned them would be the most preferable. Connecting a remote
> > server or cloud instance to the MXNet Jenkins would be the easiest
> > which wouldn't involve hardware shipping and maintenance.
> >
> > I think once you have the contribution merged and the changes ready to
> > be tested we can make a plan on how to best integrate with CI. For
> > that, the recommendation that Hagay gave (Design proposal in the Wiki)
> > is a good path forward, so other members of the community and the
> > engineers contributing to the CI system can contribute.
> >
> > Pedro.
> >
> > On Mon, Dec 17, 2018 at 3:33 AM 张昊翀 <zhanghaochong@cambricon.com> wrote:
> >>
> >> Dear MXNet community,
> >>
> >> We are from Cambricon, a leading supplier of artificial intelligence
> chips. We have two product lines, including IP products (e.g., Cambricon
> 1A/1H) and chip products (e.g., MLU100 released in May 2018)
> >>
> >> We are now adapting MXNet on Cambricon products. During the follow-up
> session, we plan to open source, and hope to merge these new features into
> the master branch of MXNet and to be a part of MXNet's long-term support.
> We firmly believe that these MLU features will promote the MXNet community
> development.
> >> To this end, we are ready to accept the rigorous inspection of MXNet
> community. In addition, we need advice from the community to achieve high
> quality implementation. On this basis, we very much hope to reach a
> full-scale long-term cooperation with the community.
> >>
> >> In order to achieve the above goals, we hope to keep in touch with the
> community on some issues. Looking forward to your valuable feedback.
> >>
> >> 1. MLU100 mainly focuses on inference, and we plan to first support the
> inference part of MXNet. The training part of MXNet on MLU will be released
> in the future. Is that acceptable for MXNet community?
> >>
> >> 2. Though MLU can support various operators/networks, to guarantee high
> quality, all supported operators submitted to the community should undergo
> rigorous stress test. Thus, at the beginning, we plan to release a small
> number of supported operators and networks, and more of them will be
> continuously added. Is that acceptable or do we have to support all
> networks in the ModelZoo in the first release?
> >>
> >> 3. Currently we plan to support both Python and C++ APIs. More details
> on supported APIs will be provided in a follow-up proposal.
> >>
> >> 4. We need to modify the mShadow in order to support tensor memory
> operations.
> >>
> >> 5. In order to enable the community to run and fully test our code, we
> want to provide the community with a complete test environment. At present,
> we are considering the following three ways.
> >> A) Provides several remote servers for community and integrates with
> the community's Jenkins.
> >> B) Provide a cloud platform to the community.
> >> C) Donate MLU100 to the community's testing platform. However, we don’t
> know the specific ways of donation, and we hope to get help. We are
> wondering about how MXNet's test servers are managed.
> >>
> >> About more technical details, a proposal will be submitted to the
> community before releasing the code.
> >>
> >> In addition to the above points, the remaining questions and
> suggestions are also welcome. Thanks!
> >>
> >> More about Cambricon:
> >> Cambricon is the artificial intelligence computing pioneer that
> engineers and successfully commercializes world’s first dedicated machine
> learning processor. To bring its unique AI processors from edge to cloud,
> enriching and advancing human life, is the firm mission of the company. Dr.
> Tianshi Chen is the founder and CEO of Cambricon, where he brings over 10
> years experience in the fields of micro-processor architecture and
> artificial intelligence.
> >> In 2016, Cambricon released Cambricon 1A processor, the first
> commercial machine learning specific processor in the world. Later, during
> the 3rd World Internet Conference, Cambricon 1A processor was elected as
> one of “World Leading Internet Scientific and Technological Achievements“.
> In May 2018, Cambricon released MLU100, a machine learning chip which is in
> mass production now. By offering revolutionary technology and products,
> Cambricon has established and remains active relationships with various
> companies in the AI industry.
> >>
> >>
> >> Regards,
> >> Haochong Zhang
> >> Cambricon MXNet Development Team
> >>
> >>
>
>
>

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