Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Terry Chen]

Hello, 

I would like to be committer of MxNet

Github ID:   terrychenism
Email:  terrychen2...@live.com
Affiliations:  Novumind


Best,
Terry

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Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Terry Chen]

Hello, 

I would like to be committer of MxNet

Github ID:   terrychenism
Email:  terrychen2...@live.com
Affiliations:  Novumind

Best,
Terry

-
To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org
For additional commands, e-mail: general-h...@incubator.apache.org

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Terry Chen]

Hello, 

I would like to be committer of MxNet

Github ID:   terrychenism
Email:  terrychen2...@live.com
Affiliations:  Novumind


Best,
Terry


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 (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