From dev-return-5171-archive-asf-public=cust-asf.ponee.io@mxnet.incubator.apache.org Mon Dec 17 14:50:13 2018 Return-Path: X-Original-To: archive-asf-public@cust-asf.ponee.io Delivered-To: archive-asf-public@cust-asf.ponee.io Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by mx-eu-01.ponee.io (Postfix) with SMTP id A0C9D180652 for ; Mon, 17 Dec 2018 14:50:12 +0100 (CET) Received: (qmail 65410 invoked by uid 500); 17 Dec 2018 13:50:11 -0000 Mailing-List: contact dev-help@mxnet.incubator.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: dev@mxnet.incubator.apache.org Delivered-To: mailing list dev@mxnet.incubator.apache.org Received: (qmail 65394 invoked by uid 99); 17 Dec 2018 13:50:10 -0000 Received: from pnap-us-west-generic-nat.apache.org (HELO spamd4-us-west.apache.org) (209.188.14.142) by apache.org (qpsmtpd/0.29) with ESMTP; Mon, 17 Dec 2018 13:50:10 +0000 Received: from localhost (localhost [127.0.0.1]) by spamd4-us-west.apache.org (ASF Mail Server at spamd4-us-west.apache.org) with ESMTP id 8E477C019A for ; Mon, 17 Dec 2018 13:50:10 +0000 (UTC) X-Virus-Scanned: Debian amavisd-new at spamd4-us-west.apache.org X-Spam-Flag: NO X-Spam-Score: -0.202 X-Spam-Level: X-Spam-Status: No, score=-0.202 tagged_above=-999 required=6.31 tests=[DKIMWL_WL_MED=-0.001, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, DKIM_VALID_EF=-0.1, RCVD_IN_DNSWL_NONE=-0.0001, SPF_PASS=-0.001] autolearn=disabled Authentication-Results: spamd4-us-west.apache.org (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mx1-lw-us.apache.org ([10.40.0.8]) by localhost (spamd4-us-west.apache.org [10.40.0.11]) (amavisd-new, port 10024) with ESMTP id c8koPfGsZPa3 for ; Mon, 17 Dec 2018 13:50:09 +0000 (UTC) Received: from mail-lj1-f169.google.com (mail-lj1-f169.google.com [209.85.208.169]) by mx1-lw-us.apache.org (ASF Mail Server at mx1-lw-us.apache.org) with ESMTPS id E91235FB46 for ; Mon, 17 Dec 2018 13:50:08 +0000 (UTC) Received: by mail-lj1-f169.google.com with SMTP id k19-v6so11004810lji.11 for ; Mon, 17 Dec 2018 05:50:08 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:references:in-reply-to:from:date:message-id:subject:to :content-transfer-encoding; bh=tFZQ6KciPSfn9r9opi8Mg95FmeTlJeA9YDf+TW1EZ1A=; b=rWob5wyJB90FMny9v0BCnC6l7hU8S9kB1rSR++gBvqvNHpIblTxNAFdJSwIIBjnmts rQz7QNYaFkkz2Q3ePj4Bt4Gy+XgMjJXCwRSciwV8WS3W9E1dJL3YVlC0BpZX6QyZRE1F kWcn94in9i1iZCvpcXLFoBBPpF/9ef2wrkCFuxwmrQ1hz9yasHDLocisU9/bQv+DsxR9 d6ifLZfsMUmz4Wl4dg9cHX/8pga9Ti3d7o97ysDm0AKl3jUa9CCIXbQHvhjvKrvX2Rzg G4f+js09lmEUJ4XmBNIzbb0qAlAHv1nZ9HU5r2hMlQ5e1TPaCWBXj9F6Le16YOy3jG9C guyA== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:references:in-reply-to:from:date :message-id:subject:to:content-transfer-encoding; bh=tFZQ6KciPSfn9r9opi8Mg95FmeTlJeA9YDf+TW1EZ1A=; b=enyqbNYjkMLu6wM7eLi8/TB9TbDxrkfWxT9tSYFAhRmzLS//YgfMsImNelY0l++g/d vufQrsmJAJOg40lQq1LxqE0p0Qs0l/EGT7ckZ/tllTLeotODE+G73a06EISQybKNOmzK 0lgwdFl63RE/BYTUHUlPd8ybe/axcqvBPrPkpb1j2YCw5UAn60Me7GfT9ua9SHTGMLZv jas2BBT9cDRdQq0xIfEHIqwPr09r1cWROig81LHe2zpcyfrC+ckjowsFNr9C3tGOKpeL 7Rslu8/z5CYF3ucK3pdxY1y5dqhB9dOT0Y85uchTPa1HF4ROBntWU8SQtFVXcZBx7OiK pRAw== X-Gm-Message-State: AA+aEWY3vKobGFAqiU0+8gbIONOnoykurULf3eB6UATwOZOrvY8u/tsr LGVAfHPt+sx48UXNqPcwNrmLibjNtyhiAlh1BDZ+I7Y9 X-Google-Smtp-Source: AFSGD/XEH/ySYsLs0DU3Z8PAL2iRQ3z4H2cXHRzGs8IsIQzpHbVW2CcXouw97fFFwUfO230OtXwNIGiXrMGOAeKLbPI= X-Received: by 2002:a2e:5dd9:: with SMTP id v86-v6mr8250197lje.86.1545054607110; Mon, 17 Dec 2018 05:50:07 -0800 (PST) MIME-Version: 1.0 References: In-Reply-To: From: Pedro Larroy Date: Mon, 17 Dec 2018 14:49:55 +0100 Message-ID: Subject: Re: Cambricon MLU support for MXNet. To: dev@mxnet.incubator.apache.org, =?UTF-8?B?5byg5piK57+A?= Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable 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 =E5=BC=A0=E6=98=8A=E7=BF=80 wrote: > > Dear MXNet community, > > We are from Cambricon, a leading supplier of artificial intelligence chip= s. 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 ses= sion, 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 f= irmly believe that these MLU features will promote the MXNet community deve= lopment. > To this end, we are ready to accept the rigorous inspection of MXNet comm= unity. In addition, we need advice from the community to achieve high quali= ty implementation. On this basis, we very much hope to reach a full-scale l= ong-term cooperation with the community. > > In order to achieve the above goals, we hope to keep in touch with the co= mmunity on some issues. Looking forward to your valuable feedback. > > 1. MLU100 mainly focuses on inference, and we plan to first support the i= nference 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 q= uality, all supported operators submitted to the community should undergo r= igorous stress test. Thus, at the beginning, we plan to release a small num= ber of supported operators and networks, and more of them will be continuou= sly 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 operat= ions. > > 5. In order to enable the community to run and fully test our code, we wa= nt to provide the community with a complete test environment. At present, w= e 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=E2= =80=99t 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 communi= ty 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=E2=80=99s first dedicated machine le= arning processor. To bring its unique AI processors from edge to cloud, enr= iching and advancing human life, is the firm mission of the company. Dr. Ti= anshi Chen is the founder and CEO of Cambricon, where he brings over 10 yea= rs 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 Wor= ld Internet Conference, Cambricon 1A processor was elected as one of =E2=80= =9CWorld Leading Internet Scientific and Technological Achievements=E2=80= =9C. In May 2018, Cambricon released MLU100, a machine learning chip which = is in mass production now. By offering revolutionary technology and product= s, Cambricon has established and remains active relationships with various = companies in the AI industry. > > > Regards, > Haochong Zhang > Cambricon MXNet Development Team > >