Confirmed, and please update my affiliation to 'Qihoo 360'. Thanks. 2017-01-14 16:08 GMT+08:00 梁德澎 <liangdep...@gmail.com>: > Hi, > > I’ve been working on the MXNet-ScalaPkg for a while with Yizhi Liu > (@javelinjs). > Please sign me up as a committer of MxNet. > > GitHub ID: Ldpe2G > Email: liangdep...@gmail.com > Affiliations: Sun Yat-sen University > > 2017-01-14 13:49 GMT+08:00 Henri Yandell <bay...@apache.org>: > >> Thanks for all the feedback and interested parties :) >> >> My aim is to propose a vote on Monday, unless someone raises an issue >> before then. >> >> Hen >> >> On Thu, Jan 5, 2017 at 9:12 PM, Henri Yandell <bay...@apache.org> wrote: >> >> > Hello Incubator, >> > >> > I'd like to propose a new incubator Apache MXNet podling. >> > >> > The existing MXNet project (http://mxnet.io - 1.5 years old, 15 >> > committers, 200 contributors) is very interested in joining Apache. MXNet >> > is an open-source deep learning framework that allows you to define, >> train, >> > and deploy deep neural networks on a wide array of devices, from cloud >> > infrastructure to mobile devices. >> > >> > The wiki proposal page is located here: >> > >> > https://wiki.apache.org/incubator/MXNetProposal >> > >> > I've included the text below in case anyone wants to focus on parts of it >> > in a reply. >> > >> > Looking forward to your thoughts, and for lots of interested Apache >> > members to volunteer to mentor the project in addition to Sebastian and >> > myself. >> > >> > Currently the list of committers is based on the current active coders, >> so >> > we're also very interested in hearing from anyone else who is interested >> in >> > working on the project, be they current or future contributor! >> > >> > Thanks, >> > >> > Hen >> > On behalf of the MXNet project >> > >> > --------- >> > >> > = MXNet: Apache Incubator Proposal = >> > >> > == Abstract == >> > >> > MXNet is a Flexible and Efficient Library for Deep Learning >> > >> > == Proposal == >> > >> > MXNet is an open-source deep learning framework that allows you to >> define, >> > train, and deploy deep neural networks on a wide array of devices, from >> > cloud infrastructure to mobile devices. It is highly scalable, allowing >> for >> > fast model training, and supports a flexible programming model and >> multiple >> > languages. MXNet allows you to mix symbolic and imperative programming >> > flavors to maximize both efficiency and productivity. MXNet is built on a >> > dynamic dependency scheduler that automatically parallelizes both >> symbolic >> > and imperative operations on the fly. A graph optimization layer on top >> of >> > that makes symbolic execution fast and memory efficient. The MXNet >> library >> > is portable and lightweight, and it scales to multiple GPUs and multiple >> > machines. >> > >> > == Background == >> > >> > Deep learning is a subset of Machine learning and refers to a class of >> > algorithms that use a hierarchical approach with non-linearities to >> > discover and learn representations within data. Deep Learning has >> recently >> > become very popular due to its applicability and advancement of domains >> > such as Computer Vision, Speech Recognition, Natural Language >> Understanding >> > and Recommender Systems. With pervasive and cost effective cloud >> computing, >> > large labeled datasets and continued algorithmic innovation, Deep >> Learning >> > has become the one of the most popular classes of algorithms for machine >> > learning practitioners in recent years. >> > >> > == Rational == >> > >> > The adoption of deep learning is quickly expanding from initial deep >> > domain experts rooted in academia to data scientists and developers >> working >> > to deploy intelligent services and products. Deep learning however has >> many >> > challenges. These include model training time (which can take days to >> > weeks), programmability (not everyone writes Python or C++ and like >> > symbolic programming) and balancing production readiness (support for >> > things like failover) with development flexibility (ability to program >> > different ways, support for new operators and model types) and speed of >> > execution (fast and scalable model training). Other frameworks excel on >> > some but not all of these aspects. >> > >> > >> > == Initial Goals == >> > >> > MXNet is a fairly established project on GitHub with its first code >> > contribution in April 2015 and roughly 200 contributors. It is used by >> > several large companies and some of the top research institutions on the >> > planet. Initial goals would be the following: >> > >> > 1. Move the existing codebase(s) to Apache >> > 1. Integrate with the Apache development process/sign CLAs >> > 1. Ensure all dependencies are compliant with Apache License version 2.0 >> > 1. Incremental development and releases per Apache guidelines >> > 1. Establish engineering discipline and a predictable release cadence of >> > high quality releases >> > 1. Expand the community beyond the current base of expert level users >> > 1. Improve usability and the overall developer/user experience >> > 1. Add additional functionality to address newer problem types and >> > algorithms >> > >> > >> > == Current Status == >> > >> > === Meritocracy === >> > >> > The MXNet project already operates on meritocratic principles. Today, >> > MXNet has developers worldwide and has accepted multiple major patches >> from >> > a diverse set of contributors within both industry and academia. We would >> > like to follow ASF meritocratic principles to encourage more developers >> to >> > contribute in this project. We know that only active and committed >> > developers from a diverse set of backgrounds can make MXNet a successful >> > project. We are also improving the documentation and code to help new >> > developers get started quickly. >> > >> > === Community === >> > >> > Acceptance into the Apache foundation would bolster the growing user and >> > developer community around MXNet. That community includes around 200 >> > contributors from academia and industry. The core developers of our >> project >> > are listed in our contributors below and are also represented by logos on >> > the mxnet.io site including Amazon, Baidu, Carnegie Mellon University, >> > Turi, Intel, NYU, Nvidia, MIT, Microsoft, TuSimple, University of >> Alberta, >> > University of Washington and Wolfram. >> > >> > === Core Developers === >> > >> > (with GitHub logins) >> > >> > * Tianqi Chen (@tqchen) >> > * Mu Li (@mli) >> > * Junyuan Xie (@piiswrong) >> > * Bing Xu (@antinucleon) >> > * Chiyuan Zhang (@pluskid) >> > * Minjie Wang (@jermainewang) >> > * Naiyan Wang (@winstywang) >> > * Yizhi Liu (@javelinjs) >> > * Tong He (@hetong007) >> > * Qiang Kou (@thirdwing) >> > * Xingjian Shi (@sxjscience) >> > >> > === Alignment === >> > >> > ASF is already the home of many distributed platforms, e.g., Hadoop, >> Spark >> > and Mahout, each of which targets a different application domain. MXNet, >> > being a distributed platform for large-scale deep learning, focuses on >> > another important domain for which there still lacks a scalable, >> > programmable, flexible and super fast open-source platform. The recent >> > success of deep learning models especially for vision and speech >> > recognition tasks has generated interests in both applying existing deep >> > learning models and in developing new ones. Thus, an open-source platform >> > for deep learning backed by some of the top industry and academic players >> > will be able to attract a large community of users and developers. MXNet >> is >> > a complex system needing many iterations of design, implementation and >> > testing. Apache's collaboration framework which encourages active >> > contribution from developers will inevitably help improve the quality of >> > the system, as shown in the success of Hadoop, Spark, etc. Equally >> > important is the community of users which helps identify real-life >> > applications of deep learning, and helps to evaluate the system's >> > performance and ease-of-use. We hope to leverage ASF for coordinating and >> > promoting both communities, and in return benefit the communities with >> > another useful tool. >> > >> > == Known Risks == >> > >> > === Orphaned products === >> > >> > Given the current level of investment in MXNet and the stakeholders using >> > it - the risk of the project being abandoned is minimal. Amazon, for >> > example, is in active development to use MXNet in many of its services >> and >> > many large corporations use it in their production applications. >> > >> > === Inexperience with Open Source === >> > >> > MXNet has existed as a healthy open source project for more than a year. >> > During that time, the project has attracted 200+ contributors. >> > >> > === Homogenous Developers === >> > >> > The initial list of committers and contributors includes developers from >> > several institutions and industry participants (see above). >> > >> > === Reliance on Salaried Developers === >> > >> > Like most open source projects, MXNet receives a substantial support from >> > salaried developers. A large fraction of MXNet development is supported >> by >> > graduate students at various universities in the course of research >> degrees >> > - this is more a “volunteer” relationship, since in most cases students >> > contribute vastly more than is necessary to immediately support research. >> > In addition, those working from within corporations are devoting >> > significant time and effort in the project - and these come from several >> > organizations. >> > >> > === A Excessive Fascination with the Apache Brand === >> > >> > We choose Apache not for publicity. We have two purposes. First, we hope >> > that Apache's known best-practices for managing a mature open source >> > project can help guide us. For example, we are feeling the growing pains >> > of a successful open source project as we attempt a major refactor of the >> > internals while customers are using the system in production. We seek >> > guidance in communicating breaking API changes and version revisions. >> > Also, as our involvement from major corporations increases, we want to >> > assure our users that MXNet will stay open and not favor any particular >> > platform or environment. These are some examples of the know-how and >> > discipline we're hoping Apache can bring to our project. >> > >> > Second, we want to leverage Apache's reputation to recruit more >> developers >> > to create a diverse community. >> > >> > === Relationship with Other Apache Products === >> > >> > Apache Mahout and Apache Spark's MLlib are general machine learning >> > systems. Deep learning algorithms can thus be implemented on these two >> > platforms as well. However, in practice, the overlap will be minimal. >> Deep >> > learning is so computationally intensive that it often requires >> specialized >> > GPU hardware to accomplish tasks of meaningful size. Making efficient >> use >> > of GPU hardware is complex because the hardware is so fast that the >> > supporting systems around it must be carefully optimized to keep the GPU >> > cores busy. Extending this capability to distributed multi-GPU and >> > multi-host environments requires great care. This is a critical >> > differentiator between MXNet and existing Apache machine learning >> systems. >> > >> > Mahout and Spark ML-LIB follow models where their nodes run >> synchronously. >> > This is the fundamental difference to MXNet who follows the parameter >> > server framework. MXNet can run synchronously or asynchronously. In >> > addition, MXNet has optimizations for training a wide range of deep >> > learning models using a variety of approaches (e.g., model parallelism >> and >> > data parallelism) which makes MXNet much more efficient (near-linear >> > speedup on state of the art models). MXNet also supports both imperative >> > and symbolic approaches providing ease of programming for deep learning >> > algorithms. >> > >> > Other Apache projects that are potentially complimentary: >> > >> > Apache Arrow - read data in Apache Arrow‘s internal format from MXNet, >> > that would allow users to run ETL/preprocessing in Spark, save the >> results >> > in Arrow’s format and then run DL algorithms on it. >> > >> > Apache Singa - MXNet and Singa are both deep learning projects, and can >> > benefit from a larger deep learning community at Apache. >> > >> > == Documentation == >> > >> > Documentation has recently migrated to http://mxnet.io. We continue to >> > refine and improve the documentation. >> > >> > == Initial Source == >> > >> > We currently use Github to maintain our source code, >> > https://github.com/MXNet >> > >> > == Source and Intellectual Property Submission Plan == >> > >> > MXNet Code is available under Apache License, Version 2.0. We will work >> > with the committers to get CLAs signed and review previous contributions. >> > >> > == External Dependencies == >> > >> > * required by the core code base: GCC or CLOM, Clang, any BLAS library >> > (ATLAS, OpenBLAS, MKL), dmlc-core, mshadow, ps-lite (which requires >> > lib-zeromq), TBB >> > * required for GPU usage: cudnn, cuda >> > * required for python usage: Python 2/3 >> > * required for R module: R, Rcpp (GPLv2 licensing) >> > * optional for image preparation and preprocessing: opencv >> > * optional dependencies for additional features: torch7, numba, cython >> > (in NNVM branch) >> > >> > Rcpt and lib-zeromq are expected to be licensing discussions. >> > >> > == Cryptography == >> > >> > Not Applicable >> > >> > == Required Resources == >> > >> > === Mailing Lists === >> > >> > There is currently no mailing list. >> > >> > === Issue Tracking === >> > >> > Currently uses GitHub to track issues. Would like to continue to do so. >> > >> > == Committers and Affiliations == >> > >> > * Tianqi Chen (UW) >> > * Mu Li (AWS) >> > * Junyuan Xie (AWS) >> > * Bing Xu (Apple) >> > * Chiyuan Zhang (MIT) >> > * Minjie Wang (UYU) >> > * Naiyan Wang (Tusimple) >> > * Yizhi Liu (Mediav) >> > * Tong He (Simon Fraser University) >> > * Qiang Kou (Indiana U) >> > * Xingjian Shi (HKUST) >> > >> > == Sponsors == >> > >> > === Champion === >> > >> > Henri Yandell (bayard at apache.org) >> > >> > === Nominated Mentors === >> > >> > Sebastian Schelter (s...@apache.org) >> > >> > >> > === Sponsoring Entity === >> > >> > We are requesting the Incubator to sponsor this project. >> > >> > >>
-- Yizhi Liu DMLC member Technical Manager Qihoo 360 Inc, Shanghai, China --------------------------------------------------------------------- To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org For additional commands, e-mail: general-h...@incubator.apache.org
