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 <zhanghaoch...@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 <sska...@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 <zhanghaoch...@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.li...@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 <zhanghaoch...@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 > >> > >> > > >
