Re: Podling Report Reminder - July 2017

[Sebastian]

Also signed off!


On 06.07.2017 00:34, Markus Weimer wrote:

On Wed, Jul 5, 2017 at 3:30 PM, Suneel Marthi  wrote:

Thanks Dom, the report has been filed.

Mentors, please sign-off on the report.


Thanks and done!

Markus

Re: Podling Report Reminder - July 2017

[Markus Weimer]

On Wed, Jul 5, 2017 at 3:30 PM, Suneel Marthi  wrote:
> Thanks Dom, the report has been filed.
>
> Mentors, please sign-off on the report.

Thanks and done!

Markus

Re: Podling Report Reminder - July 2017

[Suneel Marthi]

Thanks Dom, the report has been filed.

Mentors, please sign-off on the report.

On Wed, Jul 5, 2017 at 6:10 PM, Dominic Divakaruni <
dominic.divakar...@gmail.com> wrote:

> Please see below. Can one of our mentor's help upload this report to
> https://wiki.apache.org/incubator/July2017
>
> 1. Your project name: Apache MXNet
>
>
>
> 2. A brief description of your project, which assumes no knowledge of
> the project or necessarily of its field
>
> 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.
>
>
>
> 3. A list of the three most important issues to address in the move
> towards graduation.
>
> 3.1.  Migrate code (GitHub) and website to Apache.
>
> 3.2.  Grow the community:
>
> 3.2.1. Expand reference material including – new machine learning
> research published based on MXNet, tutorials, documented use cases and
> architecture documentation.
>
> 3.2.2. Improving user-experience –for example improved error messages
>
> 3.2.3. Improved support for various programming languages
>
> 3.2.4. Establish a dependable, Apache-way consistent release process.
>
> 3.3.  Features:
>
> 3.3.1. Capability (such as low precision support and quantization) that
> allows models to run efficiently on mobile and edge devices. Integrations
> with mobile and edge device acceleration drivers.
>
> 3.3.2. Accelerate performance on CPUs and GPUs.
>
>
>
> 4. Any issues that the Incubator PMC or ASF Board might wish/need to be
> aware of: None.
>
>
>
> 5. How has the community developed since the last report.
>
> 5.1.  On 5/27 MXNet published a comprehensive edit and makeover of the
> documentation including tutorials, how-to’s, APIs and architecture guides.
> This was a broad effort that involved over 40 contributors.
>
> 5.2.  The PMC voted in a new contributor who has been helping with the code
> migration and setup of the test infrastructure. We are making slow but
> steady progress towards getting the GitHub code migrated. The target date
> for migration is 7/17. Website migration will happen next.
>
> 5.3.  Slack and dev@ are being used more actively.
>
> 5.4.  Two presentations/workshops on Apache MXNet at the O’Reilly AI Conf
> on 6/27 and 6/28
>
> 5.5.  A new blog post published on 6/23 showing users how to Build a
> Real-time Object Classification System with ApacheMXNet on Raspberry Pi.
> https://aws.amazon.com/blogs/ai/build-a-real-time-object-
> classification-system-with-apache-mxnet-on-raspberry-pi/
>
> 6. How has the project developed since the last report.
>
> 6.1.  Since the last report 42 authors have pushed 326 commits to master.
>
> 6.2.  Documentation- Architecture guides, How To’s, Tutorials, and APIs
> have been improved.
>
> 6.3.  More features (e.g. operators) requested by the user community has
> been added.
>
> 6.4.   A new Perl language binding for MXNet was added.
>
>
>
> 7. How does the podling rate their own maturity. Maturity = Low.
>
>
>
>
>
> On Mon, Jul 3, 2017 at 3:49 AM,  wrote:
>
> > Dear podling,
> >
> > This email was sent by an automated system on behalf of the Apache
> > Incubator PMC. It is an initial reminder to give you plenty of time to
> > prepare your quarterly board report.
> >
> > The board meeting is scheduled for Wed, 19 July 2017, 10:30 am PDT.
> > The report for your podling will form a part of the Incubator PMC
> > report. The Incubator PMC requires your report to be submitted 2 weeks
> > before the board meeting, to allow sufficient time for review and
> > submission (Wed, July 05).
> >
> > Please submit your report with sufficient time to allow the Incubator
> > PMC, and subsequently board members to review and digest. Again, the
> > very latest you should submit your report is 2 weeks prior to the board
> > meeting.
> >
> > Thanks,
> >
> > The Apache Incubator PMC
> >
> > Submitting your Report
> >
> > --
> >
> > Your report should contain the following:
> >
> > *   Your project name
> > *   A brief description of your project, which assumes no knowledge of
> > the project or necessarily of its field
> > *   A list of the three most important issues to address in the move
> > towards graduation.
> > *   Any issues that the Incubator PMC or ASF Board might wish/need 

Re: Podling Report Reminder - July 2017

[Dominic Divakaruni]

Please see below. Can one of our mentor's help upload this report to
https://wiki.apache.org/incubator/July2017

1. Your project name: Apache MXNet



2. A brief description of your project, which assumes no knowledge of
the project or necessarily of its field

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.



3. A list of the three most important issues to address in the move
towards graduation.

3.1.  Migrate code (GitHub) and website to Apache.

3.2.  Grow the community:

3.2.1. Expand reference material including – new machine learning
research published based on MXNet, tutorials, documented use cases and
architecture documentation.

3.2.2. Improving user-experience –for example improved error messages

3.2.3. Improved support for various programming languages

3.2.4. Establish a dependable, Apache-way consistent release process.

3.3.  Features:

3.3.1. Capability (such as low precision support and quantization) that
allows models to run efficiently on mobile and edge devices. Integrations
with mobile and edge device acceleration drivers.

3.3.2. Accelerate performance on CPUs and GPUs.



4. Any issues that the Incubator PMC or ASF Board might wish/need to be
aware of: None.



5. How has the community developed since the last report.

5.1.  On 5/27 MXNet published a comprehensive edit and makeover of the
documentation including tutorials, how-to’s, APIs and architecture guides.
This was a broad effort that involved over 40 contributors.

5.2.  The PMC voted in a new contributor who has been helping with the code
migration and setup of the test infrastructure. We are making slow but
steady progress towards getting the GitHub code migrated. The target date
for migration is 7/17. Website migration will happen next.

5.3.  Slack and dev@ are being used more actively.

5.4.  Two presentations/workshops on Apache MXNet at the O’Reilly AI Conf
on 6/27 and 6/28

5.5.  A new blog post published on 6/23 showing users how to Build a
Real-time Object Classification System with ApacheMXNet on Raspberry Pi.
https://aws.amazon.com/blogs/ai/build-a-real-time-object-classification-system-with-apache-mxnet-on-raspberry-pi/

6. How has the project developed since the last report.

6.1.  Since the last report 42 authors have pushed 326 commits to master.

6.2.  Documentation- Architecture guides, How To’s, Tutorials, and APIs
have been improved.

6.3.  More features (e.g. operators) requested by the user community has
been added.

6.4.   A new Perl language binding for MXNet was added.



7. How does the podling rate their own maturity. Maturity = Low.





On Mon, Jul 3, 2017 at 3:49 AM,  wrote:

> Dear podling,
>
> This email was sent by an automated system on behalf of the Apache
> Incubator PMC. It is an initial reminder to give you plenty of time to
> prepare your quarterly board report.
>
> The board meeting is scheduled for Wed, 19 July 2017, 10:30 am PDT.
> The report for your podling will form a part of the Incubator PMC
> report. The Incubator PMC requires your report to be submitted 2 weeks
> before the board meeting, to allow sufficient time for review and
> submission (Wed, July 05).
>
> Please submit your report with sufficient time to allow the Incubator
> PMC, and subsequently board members to review and digest. Again, the
> very latest you should submit your report is 2 weeks prior to the board
> meeting.
>
> Thanks,
>
> The Apache Incubator PMC
>
> Submitting your Report
>
> --
>
> Your report should contain the following:
>
> *   Your project name
> *   A brief description of your project, which assumes no knowledge of
> the project or necessarily of its field
> *   A list of the three most important issues to address in the move
> towards graduation.
> *   Any issues that the Incubator PMC or ASF Board might wish/need to be
> aware of
> *   How has the community developed since the last report
> *   How has the project developed since the last report.
> *   How does the podling rate their own maturity.
>
> This should be appended to the Incubator Wiki page at:
>
> https://wiki.apache.org/incubator/July2017
>
> Note: This is manually populated. You may need to wait a little before
> this page is created 

Re: New Apache MXNet content

[Sally Khudairi]

Thanks so much, Chris. I've cleared this with Cynthya last week, so
we're in synch.

Warmly,
Sally

- - - 
Vice President Marketing & Publicity
The Apache Software Foundation

Tel +1 617 921 8656
Skype sallykhudairi

On Wed, Jul 5, 2017, at 14:21, Chris Mattmann wrote:
> Dear Cynthya,
> 
> Thanks for reaching out to the list.
> 
> I think it’s important for the community here at the ASF to make sure
> that
> as you are publishing those blogs/posts/tutorials and so forth that they
> are 
> in coordination with the Apache Press/Marketing guidelines and that they
> respect the attribution/branding/marketing requirements around Apache
> projects. I have CC’ed our VP, Press & Marketing and our VP, Brands & 
> Trademarks to ensure that these are coordinated. As these material are 
> being developed, bringing them here to the list for feedback and
> including 
> those VPs and their committees, it will help to ensure that the right
> people
> are looped in and that the materials adhere to Apache requirements.
> 
> Thanks,
> Chris
> 
> 
> 
> 
> On 7/5/17, 11:17 AM, "Cynthya Peranandam"  wrote:
> 
> Hi,
> 
> I am a product marketing manager working on AI at AWS. More
> specifically, I
> have been working on marketing and educational content for Apache
> MXNet. It
> was suggested by one of the project mentors that I reach out to the
> dev@
> list to share an update of what we're working on.
> 
> 
> 
> My team at AWS has been working with O’Reilly Media for some time. We
> contracted O'Reilly to develop educational, technical content for
> Apache
> MXNet to increase awareness of the deep learning framework and help
> drive
> adoption. O’Reilly will be developing a variety of content
> deliverables—articles, blogs, Jupyter notebooks, tutorials and
> videos—in
> the next 12 months, to be published on the O’Reilly website:
> https://www.oreilly.com/   This content will range from getting
> started
> material for developers to advanced topics.
> 
> 
> 
> I will share new content from O'Reilly on this list as it becomes
> available
> in the July/August timeframe. In the meantime, please let me know if
> you
> have any questions related to content, or if there are any specific
> MXNet
> topics you’d be interested in seeing more content about. As well, if
> you
> are interested in potentially authoring content, please let me know.
> 
> 
> Look forward to collaborating on education content for Apache MxNet.
> 
> 
> 
> Thanks.
> 
> *Cynthya Peranandam*
> 
> 
> 

Re: New Apache MXNet content

[Chris Mattmann]

Dear Cynthya,

Thanks for reaching out to the list.

I think it’s important for the community here at the ASF to make sure that
as you are publishing those blogs/posts/tutorials and so forth that they are 
in coordination with the Apache Press/Marketing guidelines and that they
respect the attribution/branding/marketing requirements around Apache
projects. I have CC’ed our VP, Press & Marketing and our VP, Brands & 
Trademarks to ensure that these are coordinated. As these material are 
being developed, bringing them here to the list for feedback and including 
those VPs and their committees, it will help to ensure that the right people
are looped in and that the materials adhere to Apache requirements.

Thanks,
Chris




On 7/5/17, 11:17 AM, "Cynthya Peranandam"  wrote:

Hi,

I am a product marketing manager working on AI at AWS. More specifically, I
have been working on marketing and educational content for Apache MXNet. It
was suggested by one of the project mentors that I reach out to the dev@
list to share an update of what we're working on.



My team at AWS has been working with O’Reilly Media for some time. We
contracted O'Reilly to develop educational, technical content for Apache
MXNet to increase awareness of the deep learning framework and help drive
adoption. O’Reilly will be developing a variety of content
deliverables—articles, blogs, Jupyter notebooks, tutorials and videos—in
the next 12 months, to be published on the O’Reilly website:
https://www.oreilly.com/   This content will range from getting started
material for developers to advanced topics.



I will share new content from O'Reilly on this list as it becomes available
in the July/August timeframe. In the meantime, please let me know if you
have any questions related to content, or if there are any specific MXNet
topics you’d be interested in seeing more content about. As well, if you
are interested in potentially authoring content, please let me know.


Look forward to collaborating on education content for Apache MxNet.



Thanks.

*Cynthya Peranandam*

New Apache MXNet content

[Cynthya Peranandam]

Hi,

I am a product marketing manager working on AI at AWS. More specifically, I
have been working on marketing and educational content for Apache MXNet. It
was suggested by one of the project mentors that I reach out to the dev@
list to share an update of what we're working on.



My team at AWS has been working with O’Reilly Media for some time. We
contracted O'Reilly to develop educational, technical content for Apache
MXNet to increase awareness of the deep learning framework and help drive
adoption. O’Reilly will be developing a variety of content
deliverables—articles, blogs, Jupyter notebooks, tutorials and videos—in
the next 12 months, to be published on the O’Reilly website:
https://www.oreilly.com/   This content will range from getting started
material for developers to advanced topics.



I will share new content from O'Reilly on this list as it becomes available
in the July/August timeframe. In the meantime, please let me know if you
have any questions related to content, or if there are any specific MXNet
topics you’d be interested in seeing more content about. As well, if you
are interested in potentially authoring content, please let me know.


Look forward to collaborating on education content for Apache MxNet.



Thanks.

*Cynthya Peranandam*

Re: Board report due

[Dominic Divakaruni]

I've made some updated and also posted to this
https://docs.google.com/document/d/1PGhs96klZB6DXhpK9_biPh4-aCm8-bWwFzexnOW_GMA/edit?usp=sharing

1. Your project name: Apache MXNet

2. A brief description of your project, which assumes no knowledge of
the project or necessarily of its field

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.

3. A list of the three most important issues to address in the move
towards graduation.

3.1.  Migrate code (GitHub) and website to Apache.

3.2.  Grow the community:

3.2.1. Expand reference material including – new machine learning
research published based on MXNet, tutorials, documented use cases and
architecture documentation.

3.2.2. Improving user-experience –for example improved error messages

3.2.3. Improved support for various programming languages

3.2.4. Establish a dependable, Apache-way consistent release process.

3.3.  Features:

3.3.1. Capability (such as low precision support and quantization) that
allows models to run efficiently on mobile and edge devices. Integrations
with mobile and edge device acceleration drivers.

3.3.2. Accelerate performance on CPUs and GPUs.

4. Any issues that the Incubator PMC or ASF Board might wish/need to be
aware of: None.

5. How has the community developed since the last report.

5.1.  On 5/27 MXNet published a comprehensive edit and makeover of the
documentation including tutorials, how-to’s, APIs and architecture guides.
This was a broad effort that involved over 40 contributors.

5.2.  The PMC voted in a new contributor who has been helping with the code
migration and setup of the test infrastructure. We are making slow but
steady progress towards getting the GitHub code migrated. The target date
for migration is 7/17. Website migration will happen next.

5.3.  Slack and dev@ are being used more actively.

5.4.  Two presentations/workshops on Apache MXNet at the O’Reilly AI Conf
on 6/27 and 6/28

5.5.  A new blog post published on 6/23 showing users how to Build a
Real-time Object Classification System with ApacheMXNet on Raspberry Pi.
https://aws.amazon.com/blogs/ai/build-a-real-time-object-classification-system-with-apache-mxnet-on-raspberry-pi/



6. How has the project developed since the last report.

6.1.  Since the last report 42 authors have pushed 326 commits to master.

6.2.  Documentation- Architecture guides, How To’s, Tutorials, and APIs
have been improved.

6.3.  More features (e.g. operators) requested by the user community has
been added.

6.4.   A new Perl language binding for MXNet was added.

7. How does the podling rate their own maturity. Maturity = Low.


On Wed, Jul 5, 2017 at 7:24 AM, Suneel Marthi  wrote:

> Dom,
>
> Its much easier to comment/modify if u created a google doc and send a
> editable link out. Please do that.
>
> On Tue, Jul 4, 2017 at 6:06 PM, Dominic Divakaruni <
> dominic.divakar...@gmail.com> wrote:
>
> > Hello all,
> > Hope those of us in the US are having a great 4th of July! I've taken a
> > stab at a draft of the report. Section 6 needs to be updated. Please
> pitch
> > in with your updates
> >
> > 1. Your project name: Apache MXNet
> >
> >
> >
> > 2. A brief description of your project, which assumes no knowledge of
> > the project or necessarily of its field: 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.
> >
> > 3. A list of the three most important issues to address in the move
> > towards graduation.
> >
> > 3.1.  Migrate code (GitHub) and website to 

Re: Board report due

[Suneel Marthi]

Dom,

Its much easier to comment/modify if u created a google doc and send a
editable link out. Please do that.

On Tue, Jul 4, 2017 at 6:06 PM, Dominic Divakaruni <
dominic.divakar...@gmail.com> wrote:

> Hello all,
> Hope those of us in the US are having a great 4th of July! I've taken a
> stab at a draft of the report. Section 6 needs to be updated. Please pitch
> in with your updates
>
> 1. Your project name: Apache MXNet
>
>
>
> 2. A brief description of your project, which assumes no knowledge of
> the project or necessarily of its field: 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.
>
> 3. A list of the three most important issues to address in the move
> towards graduation.
>
> 3.1.  Migrate code (GitHub) and website to Apache.
>
> 3.2.  Grow the community:
>
> 3.2.1. Expand reference material including – new machine learning
> research published based on MXNet, tutorials, documented use cases and
> architecture documentation.
>
> 3.2.2. Improving user-experience –for example improved error messages
>
> 3.2.3. Improved support for various programming languages
>
> 3.2.4. Establish a dependable, Apache-way consistent release process.
>
> 3.3.  Features:
>
> 3.3.1. Capability (such as low precision support and quantization) that
> allows models to run efficiently on mobile and edge devices. Integrations
> with mobile and edge device acceleration drivers.
>
> 3.3.2. Accelerate performance on CPUs and GPUs.
>
>
>
> 4. Any issues that the Incubator PMC or ASF Board might wish/need to be
> aware of:
>
> None.
>
>
>
> 5. How has the community developed since the last report.
>
> 5.1.  On 5/27 MXNet published a comprehensive edit and makeover of the
> documentation including tutorials, how-to’s, APIs and architecture guides.
> This was a broad effort that involved over 40 contributors.
>
> 5.2.  The PMC voted in a new contributor who has been helping with the code
> migration and setup of the test infrastructure. We are making slow but
> steady progress towards getting the GitHub code migrated. The target date
> for migration is 7/17. Website migration will happen next.
>
> 5.3.  Slack and dev@ are being used more actively.
>
> 6. How has the project developed since the last report.
>
> 6.1.  Since the last report 42 authors have pushed 326 commits to master.
>
> 6.2.  (Previous Update)  On master, 502 files have changed and there have
> been 26,246 additions and 12,188 deletions. Count of Closed Issues = 62,
> Count of new Issues = 146, Count of Merged Pull Requests = 161, Count of
> Proposed Pull Requests = 27.
>
> 6.3.  (Previous Update) The API Documentation has improved.
>
> 6.4.  (Previous Update) More features (e.g. operators) requested by the
> user community has been added.
>
> 6.5.  (Previous update) Hardware acceleration like cuDDN6 integration and
> MKL ML package integration was completed.
>
> 6.6.  (Previous Update) A new Perl language binding for MXNet was added.
>
>
>
> 7. How does the podling rate their own maturity: Maturity = Low.
>
>
> On Mon, Jul 3, 2017 at 11:39 PM, Henri Yandell  wrote:
>
> > In case the relentless automated pinging hasn't given it away, we've a
> > board report due.
> >
> > Hen
> >
>
>
>
> --
>
>
> Dominic Divakaruni
> 206.475.9200 Cell
>