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From "Terry Chen"<>
Subject Re: [DISCUSS] Proposing MXNet for the Apache Incubator
Date Thu, 12 Jan 2017 09:34:09 GMT


I would like to be committer of MxNet

Github ID:   terrychenism
Affiliations:  Novumind


On 2017-01-05 21:12 (-0800), Henri Yandell <> wrote: 
> Hello Incubator,
> I'd like to propose a new incubator Apache MXNet podling.
> The existing MXNet project ( - 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:
> 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 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  We continue to
> refine and improve the documentation.
> == Initial Source ==
> We currently use Github to maintain our source code,
> == 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
> === Nominated Mentors ===
> Sebastian Schelter (
> === Sponsoring Entity ===
> We are requesting the Incubator to sponsor this project.

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