Awesome! Please sign me up as a committer - I've been working with Mu on MXNet (Amazon) and would love to get more involved with project!
GitHub ID: jspisak Sent from Joe's iPhone On 2017-01-06 08:44 (-0800), Henri Yandell wrote: > Understood. I saw that Greg had recently approved another podling to do> > this. Though, assuming approved, there will still need to be some infra> > headscratching on the 3,000 issues currently on the main dmlc/mxnet repo> > and how imports are best done :) The simplest would be to transfer the> > current repo as is over at GitHub - not sure if that's been done before.> > > On Thu, Jan 5, 2017 at 11:32 PM, Henry Saputra > > wrote:> > > > This is great news and I am looking forward to it =)> > >> > > According to proposal, the community want to stick with Github issues for> > > tracking issues and bugs?> > > I suppose this needs a nod by Greg Stein as rep from Apache Infra to> > > confirm that this is ok for incubation and how would it impact during> > > graduation.> > >> > > - Henry> > >> > > On Thu, Jan 5, 2017 at 9:12 PM, Henri Yandell 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 %u201Cvolunteer%u201D 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%u2018s internal format from > > > MXNet,> > > that> > > > would allow users to run ETL/preprocessing in Spark, save the results in> > > > Arrow%u2019s 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.> > > >> > >> > Sent from Joe's iPhone --------------------------------------------------------------------- To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org For additional commands, e-mail: general-h...@incubator.apache.org
