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From Henri Yandell <bay...@apache.org>
Subject Re: [DISCUSS] Proposing MXNet for the Apache Incubator
Date Fri, 06 Jan 2017 05:44:40 GMT
Brilliant - thank you Suneel :)

I see you're on the Incubator PMC and have added you to the wiki page.

On Thu, Jan 5, 2017 at 9:25 PM, Suneel Marthi <smarthi@apache.org> wrote:

> I would like to sign up as mentor for MxNet.
>
> On Fri, Jan 6, 2017 at 12:12 AM, Henri Yandell <bayard@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 (ssc@apache.org)
> >
> >
> > === Sponsoring Entity ===
> >
> > We are requesting the Incubator to sponsor this project.
> >
>

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