Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Henri Yandell]

The vote has started, so the proposal text is frozen. Assuming the vote is
successful, adding more folk can be an early order of business for the
newly created PPMC :)

Thanks,

Hen




On Mon, Jan 16, 2017 at 7:27 PM, tornadomeet wuwei 
wrote:

> hello,
>
>  Please sign me up as a committer for MXNet. i've been contributed some
> `operators` and examples to MXNet, and i'll continue contribute to MXNet in
> the furture.
>
>  GitHub ID: tornadomeet
>
> Thanks,
>
> Wei Wu
>
> -
> 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

[Henri Yandell]

The vote has started, so the proposal text is frozen. Assuming the vote is
successful, adding more folk can be an early order of business for the
newly created PPMC :)

Thanks,

Hen

On Tue, Jan 17, 2017 at 2:47 PM, Yihe Tang  wrote:

> Hi Henri,
>
> I am Larry Tang, working with Minjie Wang (@jermainewang) on imperative
> programming part of MXNet. Please add me to the list of committers for
> MXNet project. I will work intensively on merging a NumPy interface into
> MXNet as its imperative subsystem in the next few months.
>
> My GitHub ID is: lryta
> Affiliation: University of Michigan.
>
> Best,
> Larry
>
> On 2017-01-06 00:12 (-0500), 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>
> >  

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Yihe Tang]

Hi Henri,

I am Larry Tang, working with Minjie Wang (@jermainewang) on imperative 
programming part of MXNet. Please add me to the list of committers for MXNet 
project. I will work intensively on merging a NumPy interface into MXNet as its 
imperative subsystem in the next few months.

My GitHub ID is: lryta
Affiliation: University of Michigan.

Best,
Larry

On 2017-01-06 00:12 (-0500), 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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[tornadomeet wuwei]

hello,

 Please sign me up as a committer for MXNet. i've been contributed some 
`operators` and examples to MXNet, and i'll continue contribute to MXNet in the 
furture.

 GitHub ID: tornadomeet

Thanks,

Wei Wu

-
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

[tornadomeet wuwei]

hello,

 Please sign me up as a committer for MXNet. i've been contributed some 
`operators` and examples to MXNet, and i'll continue contribute to MXNet in the 
furture.

 GitHub ID: tornadomeet

Thanks,

Wei Wu

-
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

[tornadomeet wuwei]

hello,

 Please sign me up as a committer for MXNet. i've been contributed some 
`operators` and examples to MXNet, and i'll continue contribute to MXNet in the 
furture.

 GitHub ID: tornadomeet

Thanks,

Wei Wu

-
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

[Henri Yandell]

Added. Apologies if Liang DePeng is the incorrect anglicization of your
name.

Hen

On Sat, Jan 14, 2017 at 12:08 AM, 梁德澎  wrote:

> Hi,
>
> I’ve been working on the MXNet-ScalaPkg for a while with Yizhi Liu
> (@javelinjs).
> Please sign me up as a committer of MxNet.
>
> GitHub ID: Ldpe2G
> Email: liangdep...@gmail.com
> Affiliations: Sun Yat-sen University
>
> 2017-01-14 13:49 GMT+08:00 Henri Yandell :
>
> > Thanks for all the feedback and interested parties :)
> >
> > My aim is to propose a vote on Monday, unless someone raises an issue
> > before then.
> >
> > Hen
> >
> > 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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Henri Yandell]

Added :)

On Fri, Jan 13, 2017 at 4:37 PM, sandeep krishnamurthy <
sandeep.krishn...@gmail.com> wrote:

> Hi,
> Please sign me up as a committer of MxNet
>
> Github ID: sandeep-krishnamurthy
> Email: sandeep.krishn...@gmail.com
> Affiliations: AWS
>
> Best,
> Sandeep Krishnamurthy
>
> -
> 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

[sandeep krishnamurthy]

Hi, 
Please sign me up as a committer of MxNet 

Github ID: sandeep-krishnamurthy
Email: sandeep.krishn...@gmail.com
Affiliations: AWS

Best, 
Sandeep Krishnamurthy

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Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[sandeep krishnamurthy]

Please sign me up as a committer - I've been contributing to MXNet and
working with Mu at work on MXNet (Amazon) and would love to get more
involved in the project.

Github ID: sandeep-krishnamurthy
Gmail: sandeep.krishn...@gmail.com

-- 
Sandeep Krishnamurthy

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[sandeep krishnamurthy]

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 the documentation and code to help new
> developers get started quickly.
> 
> === Community ===
> 
> Acceptance into the Apache foundation would 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[sandeep krishnamurthy]

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 the documentation and code to help new
> developers get started quickly.
> 
> === Community ===
> 
> Acceptance into the Apache foundation would 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[YiZhi Liu]

Confirmed, and please update my affiliation to 'Qihoo 360'. Thanks.

2017-01-14 16:08 GMT+08:00 梁德澎 :
> Hi,
>
> I’ve been working on the MXNet-ScalaPkg for a while with Yizhi Liu
> (@javelinjs).
> Please sign me up as a committer of MxNet.
>
> GitHub ID: Ldpe2G
> Email: liangdep...@gmail.com
> Affiliations: Sun Yat-sen University
>
> 2017-01-14 13:49 GMT+08:00 Henri Yandell :
>
>> Thanks for all the feedback and interested parties :)
>>
>> My aim is to propose a vote on Monday, unless someone raises an issue
>> before then.
>>
>> Hen
>>
>> 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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[梁德澎]

Hi,

I’ve been working on the MXNet-ScalaPkg for a while with Yizhi Liu
(@javelinjs).
Please sign me up as a committer of MxNet.

GitHub ID: Ldpe2G
Email: liangdep...@gmail.com
Affiliations: Sun Yat-sen University

2017-01-14 13:49 GMT+08:00 Henri Yandell :

> Thanks for all the feedback and interested parties :)
>
> My aim is to propose a vote on Monday, unless someone raises an issue
> before then.
>
> Hen
>
> 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 ==
> >
> > === 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Henri Yandell]

Thanks for all the feedback and interested parties :)

My aim is to propose a vote on Monday, unless someone raises an issue
before then.

Hen

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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Henri Yandell]

Added :)

On Fri, Jan 13, 2017 at 3:48 AM, Zhang Jian 
wrote:

> Hi,
> Please sign me up as a committer of MxNet
>
> Github ID: jzhang-zju
> Email: zhang.jian@gmail.com
> Affiliations: Horizon Robotics
>
> Best,
> Jian
>
> On 2017-01-06 13:12 (+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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Henri Yandell]

Thanks Yifeng :) Added.

On Fri, Jan 13, 2017 at 3:33 AM, yifeng.geng  wrote:

> Hello,
>
> I committed some code to MXNet and would like to get more involved in the
> project.
> Please sign me up as a committer.
>
> Github ID: gengyifeng
> Email: yifeng.g...@hobot.cc
> Affiliations: horizon robotics
>
> Best,
> Yifeng
>
> On 2017-01-13 00:54 (+0800), Henri Yandell  wrote:
> > Added :)>
> >
> > On Thu, Jan 12, 2017 at 1:37 AM, Terry Chen  wrote:>
> >
> > >>
> > > 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>
> > >>
> > >>
> >
> -
> 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

[Zhang Jian]

Hi,  
Please sign me up as a committer of MxNet  

Github ID: jzhang-zju
Email: zhang.jian@gmail.com
Affiliations: Horizon Robotics

Best, 
Jian

On 2017-01-06 13:12 (+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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[yifeng . geng]

Hello, 

I committed some code to MXNet and would like to get more involved in the 
project. 
Please sign me up as a committer.

Github ID: gengyifeng
Email: yifeng.g...@hobot.cc
Affiliations: horizon robotics

Best,
Yifeng

On 2017-01-13 00:54 (+0800), Henri Yandell  wrote: 
> Added :)> 
> 
> On Thu, Jan 12, 2017 at 1:37 AM, Terry Chen  wrote:> 
> 
> >> 
> > 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> 
> >> 
> >> 
> 
-
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

[Henri Yandell]

Thanks :) Updated.

On Thu, Jan 12, 2017 at 10:49 AM, Henry Saputra 
wrote:

> Hi Henri,
>
> Could you update the proposal for === Mailing Lists === section to list
> existing and required mailing lists from Incubator?
>
> Thanks,
>
> 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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Henry Saputra]

Hi Henri,

Could you update the proposal for === Mailing Lists === section to list
existing and required mailing lists from Incubator?

Thanks,

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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Henri Yandell]

Added :)

On Thu, Jan 12, 2017 at 1:37 AM, Terry Chen  wrote:

>
> 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

[Henri Yandell]

Added :)

On Thu, Jan 12, 2017 at 1:43 AM, Tsuyoshi Ozawa  wrote:

> Hi Henri,
>
> My previous comment was just a review comment against the proposal,
> but I forgot to mentioning importance thing.
>
> > 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!
>
> I'm also interested in working on MXNet :-)
>
> Thanks,
> - Tsuyoshi
>
> On Thu, Jan 12, 2017 at 3:43 PM, 项亮  wrote:
> > I would like to volunteer as a committer for MXNet
> >
> > github id: xlvector
> > email: xlvec...@gmail.com
> >
> > Liang Xiang from Toutiao Lab
> >
> > On 2017-01-06 13:12 (+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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Henri Yandell]

Added :)

On Wed, Jan 11, 2017 at 10:43 PM, 项亮  wrote:

> I would like to volunteer as a committer for MXNet
>
> github id: xlvector
> email: xlvec...@gmail.com
>
> Liang Xiang from Toutiao Lab
>
> On 2017-01-06 13:12 (+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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Henri Yandell]

Added :)

On Wed, Jan 11, 2017 at 10:57 PM, Zihao Zheng  wrote:

> Hi, could you please add me to this group? I’ve been working on a
> visualization component(tensorboard )
> for MXNet for a while with Eric Junyuan Xie, and would like to contribute
> more on this direction.
>
> GitHub ID: zihaolucky
> Email: zihaolu...@gmail.com 
> Company: Alibaba Group
>
> Thank you!
>
>
> On 2017-01-06 13:12 (+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 

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

-
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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Tsuyoshi Ozawa]

Hi Henri,

My previous comment was just a review comment against the proposal,
but I forgot to mentioning importance thing.

> 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!

I'm also interested in working on MXNet :-)

Thanks,
- Tsuyoshi

On Thu, Jan 12, 2017 at 3:43 PM, 项亮  wrote:
> I would like to volunteer as a committer for MXNet
>
> github id: xlvector
> email: xlvec...@gmail.com
>
> Liang Xiang from Toutiao Lab
>
> On 2017-01-06 13:12 (+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
>> 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[项亮]

I would like to volunteer as a committer for MXNet

github id: xlvector
email: xlvec...@gmail.com

Liang Xiang from Toutiao Lab

On 2017-01-06 13:12 (+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 the documentation 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Zihao Zheng]

Hi, could you please add me into the mailing list of this group? I’ve been 
working on a visualization component(tensorboard 
) for MXNet for a while with Eric Junyuan 
Xie, and would like to contribute more on this direction.

GitHub ID: zihaolucky
Email: zihaolu...@gmail.com
Company: Alibaba Group

Thank you!


On 2017-01-06 13:12 (+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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[项亮]

I would like to be committer of Mxnet

Github ID: xlvector
xlvector#gmail.com

Affiliations: Toutiao

-
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

[Zihao Zheng]

Hi, could you please add me to this group? I’ve been working on a visualization 
component(tensorboard ) for MXNet for a 
while with Eric Junyuan Xie, and would like to contribute more on this 
direction.

GitHub ID: zihaolucky
Email: zihaolu...@gmail.com 
Company: Alibaba Group

Thank you!


On 2017-01-06 13:12 (+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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[shiwen hu]

Github ID: yajiedesign
Email: yajiedes...@gmail.com

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Hongliang Liu]

Hello Henri,

Please sign me up as a committer - I've worked with Mu and others on MXNet,
mostly about maintaining the Awesome-MXNet page as well as other
tutorials/blogs of teaching MXNet etc.

name: Hongliang Liu
github : phunterlau
Affiliations: Nominum

Thank you.

Regards,
Hongliang Liu
On 2017-01-06 10:59 (-0800), "Chris ol...@gmail.com> wrote:
> I would like to volunteer as a committer for MXNet.>
>
> -Chris Olivier>
> cjolivie...@gmail.com>
>
> ->
> 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

[Henri Yandell]

Thanks Ziheng :)

I've added you to the page.

Hen

On Wed, Jan 11, 2017 at 9:35 PM, ziheng  wrote:

> Hello
>
> Please sign me up as a committer for MXNet - I've been working on MXNet
> Profiler and NNVM-Fusion, also will work with Mu as an intern at Amazon AWS
> team in the next months, and I would love to get more involved in the
> project.
>
> GitHub ID:ZihengJiang
> Affiliations: AWS
>
>
> Thanks
>
>
>
> --
> View this message in context: http://apache-incubator-
> general.996316.n3.nabble.com/DISCUSS-Proposing-MXNet-for-
> the-Apache-Incubator-tp53250p53335.html
> Sent from the Apache Incubator - General mailing list archive at
> Nabble.com.
>
> -
> 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

[ziheng]

Hello 

Please sign me up as a committer for MXNet - I've been working on MXNet
Profiler and NNVM-Fusion, also will work with Mu as an intern at Amazon AWS
team in the next months, and I would love to get more involved in the
project. 

GitHub ID:ZihengJiang
Affiliations: AWS


Thanks



--
View this message in context: 
http://apache-incubator-general.996316.n3.nabble.com/DISCUSS-Proposing-MXNet-for-the-Apache-Incubator-tp53250p53335.html
Sent from the Apache Incubator - General mailing list archive at Nabble.com.

-
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

[Henri Yandell]

Added :)

On Fri, Jan 6, 2017 at 1:11 PM, Indhu Bharathi 
wrote:

> Please sign me up as a committer - I've been working with Mu at work on
> MXNet (Amazon) and would love to get more involved in the project.
> GitHub ID:  indhub
>
> Thanks,
> Indu
>
> 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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Henri Yandell]

Added :)

On Fri, Jan 6, 2017 at 10:59 AM, Naveen Swamy  wrote:

> Hello
>
> Please sign me up as a committer for MXNet - I've been working with Mu at
> work on MXNet (Amazon) and would love to get more involved in the project.
>
> *GitHub ID: nswamy*
>
>
> Thanks, Naveen
>
>
>
> 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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Joe Spisak]

Please sign me up as a committer - I've been working with Mu at work on MXNet 
(Amazon) and would love to get more involved in the project.

Github ID: jspisak

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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Mu Li]

Thanks, Henri.

The correct github link is https://github.com/dmlc/mxnet

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 the documentation and code to help new
> developers get started quickly.

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Indhu Bharathi]

Please sign me up as a committer - I've been working with Mu at work on MXNet 
(Amazon) and would love to get more involved in the project.
GitHub ID:  indhub

Thanks,
Indu

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
> 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Naveen Swamy]

Hello 

Please sign me up as a committer for MXNet - I've been working with Mu at work 
on MXNet (Amazon) and would love to get more involved in the project.

GitHub ID: nswamy

Thanks, Naveen 


-
To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org
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Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Naveen Swamy]

Hello

Please sign me up as a committer for MXNet - I've been working with Mu at
work on MXNet (Amazon) and would love to get more involved in the project.

*GitHub ID: nswamy*


Thanks, Naveen



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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Sebastian]

Great to see you join our efforts!

On 06.01.2017 18:20, Markus Weimer wrote:

On 2017-01-05 9:12 PM, Henri Yandell wrote:

I'd like to propose a new incubator Apache MXNet podling.


Awesome! If you still need a mentor, feel free to sign me up!

Markus

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Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Henri Yandell]

Thanks Joe - I've added you to the commiter list :)

On Fri, Jan 6, 2017 at 12:31 PM, Joe Spisak  wrote:

> 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
> -
> 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

[Henri Yandell]

Thanks Markus - I've added you to the proposal :)

On Fri, Jan 6, 2017 at 9:20 AM, Markus Weimer  wrote:

> On 2017-01-05 9:12 PM, Henri Yandell wrote:
>
>> I'd like to propose a new incubator Apache MXNet podling.
>>
>
> Awesome! If you still need a mentor, feel free to sign me up!
>
> Markus
>
>
> -
> 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

[Joe Spisak]

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> 
> > 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Joe Spisak]

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 



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Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Markus Weimer]

On 2017-01-05 9:12 PM, Henri Yandell wrote:

I'd like to propose a new incubator Apache MXNet podling.


Awesome! If you still need a mentor, feel free to sign me up!

Markus

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Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Henri Yandell]

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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Henri Yandell]

On Fri, Jan 6, 2017 at 3:52 AM, John D. Ament  wrote:

> There seem to be some discrepancies in the proposal vs what they currently
> have.  That and some comments in line.
>
> On Fri, Jan 6, 2017 at 12:12 AM 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.
> >
>
> This seems to be a broad gap between committers and contributors.  Of the
> remaining 220 not included as committer, are they being considered?
>

Mu's contacting (and has been contacting) contributors to invite them; for
the proposal I went with the notion of the current active folk with write
access and having others introduce themselves. I suspect some are already
awaiting general@ moderators to pass their email through :)

>
> > The wiki proposal page is located here:
> >
> >   https://wiki.apache.org/incubator/MXNetProposal
> >
> >
> I've added it to the project proposals page.
>
>
*doh* Thank you :)


>
> > 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 =
> >
>
>



> >
> > === 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)
> >
> >
> AFAIK, we still expect email addresses, not github accounts.
>
>
The GitHubs were just as an FYI :)


>
> >
> > == Initial Source ==
> >
> > We currently use Github to maintain our source code,
> > https://github.com/MXNet
>
>
> This doesn't look right.  This github organization has a single repo that
> contains a mobile app that appears to been 4 years old.  The website points
> to https://github.com/dmlc/mxnet which looks more correct.  Which is it?
> I'll also note that if it is the latter, there are git submodules in the
> codebase.  Please include references to those modules as well.  Please also
> include the website source code, if available.
>

*DOH* thank you :)

There will be more than dmlc/mxnet as some of the other dmlc repos relate
to mxnet too.


>
> I'll point out that since the github name is taken, it may be cause to say
> that MXNet isn't a viable name.  But that can be worked out later.
>

Yup. I did a preliminary name search of the various TESSA type places and
didn't see any blocking concerns.


>
> >
> > == 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)
> >
>
> Before we go any further, I think we need to address the incompatible
> licenses.  Knowing that R is a core component of MXNet, how will you
> replace it?
>

I don't expect R itself to be an issue. Rcpp I expect to be an issue and
there are options here - either hosting the R module separately or
switching to using Rcpp11 (MIT license).

ZeroMQ is perhaps a larger issue given it's a transitive dependency of the
core codebase. That community has a licensing exception in addition to its
LGPL which can be discussed, it has expressed a desire to move to MPL and
is so prevalent in the ecosystem that I feel it's something to be figured
out rather than avoided. Solutions would involve either approval for the
exception text, rewriting to not use ps-lite, or rewriting ps-lite to not
use zmq (given ps-lite is a dmlc project).


> >
> > == 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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Tsuyoshi Ozawa]

Apache SystemML(incubating) is also a flexible, scalable machine
learning system[4].

> Apache SystemML provides an optimal workplace for machine learning using big 
> data. It can be run on top of Apache Spark, where it automatically scales 
> your data, line by line, determining whether your code should be run on the 
> driver or an Apache Spark cluster.

[4] https://systemml.apache.org/

Thanks,
- Tsuyoshi

On Fri, Jan 6, 2017 at 6:11 PM, Tsuyoshi Ozawa  wrote:
> Hi Henri,
>
> It's a great news! Looking forwarding to MXNet's coming to Apache Incubator 
> :-)
>
> Two minor comments:
>
>> We currently use Github to maintain our source code,
>> https://github.com/MXNet
>
> In my understanding, the following url is correct one.
> https://github.com/dmlc/mxnet
>
>> === Relationship with Other Apache Products ===
>
> As far as I know, there are 2 additional machine learning libraries in
> addition to the projects you mentioned.
> Apache MADlib(incubating)[1] is a machine learning library, which can
> run on SQL system(Greenplum/Apache HAWQ(incubating)/PostgreSQL).
> Apache Hivemall(incubating)[2] are also machine learning library,
> which can run on Hadoop ecosystem: Apache Spark/Apache Hive/Apache
> Pig. Especially for Hivemall project, it has MIX server, one kind of
> parameter server to exchange parameters between mappers[3].
>
> This is just a sharing, and I don't mean you should add these projects
> to your comment.
>
> [1] http://madlib.incubator.apache.org/
> [2] https://hivemall.incubator.apache.org/
> [3] https://hivemall.incubator.apache.org/userguide/tips/mixserver.html
>
> Thanks,
> - Tsuyoshi
>
> On Fri, Jan 6, 2017 at 4:32 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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Tsuyoshi Ozawa]

Hi Henri,

It's a great news! Looking forwarding to MXNet's coming to Apache Incubator :-)

Two minor comments:

> We currently use Github to maintain our source code,
> https://github.com/MXNet

In my understanding, the following url is correct one.
https://github.com/dmlc/mxnet

> === Relationship with Other Apache Products ===

As far as I know, there are 2 additional machine learning libraries in
addition to the projects you mentioned.
Apache MADlib(incubating)[1] is a machine learning library, which can
run on SQL system(Greenplum/Apache HAWQ(incubating)/PostgreSQL).
Apache Hivemall(incubating)[2] are also machine learning library,
which can run on Hadoop ecosystem: Apache Spark/Apache Hive/Apache
Pig. Especially for Hivemall project, it has MIX server, one kind of
parameter server to exchange parameters between mappers[3].

This is just a sharing, and I don't mean you should add these projects
to your comment.

[1] http://madlib.incubator.apache.org/
[2] https://hivemall.incubator.apache.org/
[3] https://hivemall.incubator.apache.org/userguide/tips/mixserver.html

Thanks,
- Tsuyoshi

On Fri, Jan 6, 2017 at 4:32 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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Henry Saputra]

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. 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Henri Yandell]

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  wrote:

> I would like to sign up as mentor for MxNet.
>
> On Fri, Jan 6, 2017 at 12:12 AM, 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 

Re: [DISCUSS] Proposing MXNet for the Apache Incubator

[Suneel Marthi]

I would like to sign up as mentor for MxNet.

On Fri, Jan 6, 2017 at 12:12 AM, 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