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From Henri Yandell <>
Subject [DISCUSS] Proposing MXNet for the Apache Incubator
Date Fri, 06 Jan 2017 05:12:21 GMT
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!


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

== 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

== 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

=== 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

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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