Added :) On Fri, Jan 6, 2017 at 1:11 PM, Indhu Bharathi <indhubhara...@gmail.com> wrote:
> Please sign me up as a committer - I've been working with Mu at work on > MXNet (Amazon) and would love to get more involved in the project. > GitHub ID: indhub > > Thanks, > Indu > > On 2017-01-05 21:12 (-0800), 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. > > > > --------------------------------------------------------------------- > To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org > For additional commands, e-mail: general-h...@incubator.apache.org > >
