Apache Logo
The Apache Way Contribute ASF Sponsors

This was extracted (@ 2018-08-17 02:10) from a list of minutes which have been approved by the Board.
Please Note The Board typically approves the minutes of the previous meeting at the beginning of every Board meeting; therefore, the list below does not normally contain details from the minutes of the most recent Board meeting.

2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 | 1999 | Pre-organization meetings

MXNet

18 Jul 2018

Apache MXNet is a lean, flexible, and ultra-scalable deep learning framework
that supports state of the art in deep learning models, including
convolutional neural networks (CNNs) and long short-term memory networks
(LSTMs). The framework has its roots in academia and came about through the
collaboration and contributions of researchers at several top universities.
It has been designed to excel at computer vision, speech, language
processing and understanding, generative models, concurrent neural networks,
and recurrent neural networks. MXNet allows you to define, train, and deploy
networks across a wide array of use cases from massive cloud infrastructure
to mobile and connected devices. It provides a very flexible environment
with support for many common languages and the ability to utilize both
imperative and symbolic programming constructs. MXNet also very lightweight.
This allows it to scale across multiple GPUs and multiple machines very
efficiently, which is beneficial when conducting training on large datasets
in the cloud.

Apache MXNet has been incubating since 23-Jan, 2017.

Four most important issues to address in the move towards graduation:

1. Establish a predictable release process consistent with Apache Way
-- ESTABLISHED.
2. Grow the community -- ONGOING.
3. Bring website up to Apache standard – COMPLETED.
4. Identify remaining ICLAs or SGAs that need signing – NEAR COMPLETION.

Any issues that the Incubator PMC (IPMC) or ASF Board wish/need to be aware
of?

  None

How has the community developed since the last report?

a) Various Slack channels, dev@ mailing lists, and user discussion
forums (http://discuss.mxnet.io) are being used actively. The contributors
have been working on having all discussions on the public dev@ mailing list
as much as possible. At least one of the discussions infringed on Apache
code of conduct and mentors had to step in and provide guidance to the
community.

b) After lengthy discussion the community agreed to establish
user@mxnet.incubator.apache.org with the goal to increase users as well as
grow the contributor community.

c) Community events:
- In April the community organized the first MXNet meetup in Seattle. We had
over 100 attendees, 15% from outside Amazon.
- In May the community organized a virtual hangout with two sessions to
collect feedback and ideas from the community.
- Korean MXNet community is growing well and most of contributors are
outside of Amazon:
https://www.meetup.com/ko-KR/MXNet-Korea-User-Group/events/248531181/ . The
Facebook group has 530 members in https://www.facebook.com/groups/mxnetkr
- Community members presented at
https://www.meetup.com/Artificial-Intelligence-in-Practice/events/250882931/?
_xtd=gqFyqDI5NDE5MTUyoXCkaXBhZA&from=ref
- Community members presented at meetups in Vancouver (link see under
Webinars)
- Community members presented Intro to Amazon SageMaker with a sentiment
analysis demo using MXNet and Gluon at AWS Loft ML Day in SF on 6-19 to ~150
attendees
- Community members presented Model serving with Model Server for MXNet at
AWS Loft ML Day in SF on 6-19 to ~150 attendees
- A committer presented Distributed inference with Spark and MXNet at Apache
Roadshow in Berlin, 13-14 June (talk abstract -
https://foss-backstage.de/session/distributed-inference-using-apache-mxnet-an
d-apache-spark)
- A committer presented Distributed inference with Spark and MXNet at
Spark+AI in SF,  ~100 attendees (https://databricks.com/speaker/naveen-swamy)
- Community members presented Model serving with MXNet at dotAI conference
in Paris, France, ~300 attendees, May 30
(https://www.dotconferences.com/2018/05/hagay-lupesko-model-serving-for-deep-
learning)
- Community members presented ONNX with MXNet demo at prepareAI conference
in St Louis, May 8, ~100 attendees
(http://prepare.ai/conference/conference-agenda-details/)
- Community members presented Model serving with MXNet at prepareAI
conference in St Louis, May 8, ~100 attendees
(http://prepare.ai/conference/conference-agenda-details/)
- Community members presented Introduction to deep learning with MXNet and
Gluon, distinguished lecture, at BGU university in Israel, April 10, ~100
attendees (lhttps://www.cs.bgu.ac.il/~frankel/Lupesko/Lupesko.pdf)
- Community members presented Model serving with MXNet at AI IL meetup in
Tel Aviv, Israel, ~50 attendees
(https://www.meetup.com/artificial-intelligence-il/events/249312879/)

d) Blogs about MXNet: We established MXNet blog:
https://medium.com/apache-mxnet. As of today, the blog has 634 followers and
16 blogs published. Additionally, blogs have been publish related or about
MXNet at:
-
https://aws.amazon.com/blogs/machine-learning/category/artificial-intelligenc
e/apache-mxnet-on-aws/
- https://blogs.technet.microsoft.com/machinelearning/tag/mxnet
- https://zh.mxnet.io/blog/
- https://blogs.apache.org/mxnet
- https://github.com/chinakook/Awesome-MXNet
-
https://medium.com/mlreview/10-deep-learning-projects-based-on-apache-mxnet-8
231109f3f64
-
https://medium.com/datreeio/training-with-keras-mxnet-on-amazon-sagemaker-43a
34bd668ca
-
https://medium.com/@julsimon/a-quick-look-at-the-swish-activation-function-in
-apache-mxnet-1-2-79d9ff9d1673
- http://gigasquidsoftware.com/blog/2018/06/03/meet-clojure-mxnet-ndarray/
-
https://dzone.com/articles/ingesting-apache-mxnet-gluon-deep-learning-results
-
https://becominghuman.ai/an-introduction-to-the-mxnet-api-part-1-848febdcf8ab
- https://cosminsanda.com/posts/counting-object-with-mxnet-and-sagemaker/
- CVPR 2018 paper – Relation Networks for Object Detection -
https://arxiv.org/abs/1711.11575 and
https://github.com/msracver/Relation-Networks-for-Object-Detection
- Gluon-CV related blogpost -
https://dzone.com/articles/using-apache-mxnet-gluoncv-with-apache-nifi
-
https://aws.amazon.com/blogs/machine-learning/maximize-training-performance-w
ith-gluon-data-loader-workers/
-
https://medium.com/apache-mxnet/accelerating-deep-learning-on-cpu-with-intel-
mkl-dnn-a9b294fb0b9
-
https://medium.com/apache-mxnet/mxnet-for-pytorch-users-in-10-minutes-a735386
3406a
-
https://medium.com/apache-mxnet/gluoncv-deep-learning-toolkit-for-computer-vi
sion-9218a907e8da
-
https://medium.com/apache-mxnet/mxboard-mxnet-data-visualization-2eed6ae31d2c
- https://medium.com/apache-mxnet/mxnet-gluon-in-60-minutes-3d49eccaf266
- https://medium.com/apache-mxnet/announcing-apache-mxnet-1-2-0-d94f56da154b
-
https://medium.com/apache-mxnet/mxnet-1-2-adds-built-in-support-for-onnx-e2c7
450ffc28
-
https://aws.amazon.com/blogs/machine-learning/the-importance-of-hyperparamete
r-tuning-for-scaling-deep-learning-training-to-multiple-gpus/
-
https://medium.com/apache-mxnet/image-classification-with-mxnet-scala-inferen
ce-api-8ab6ce1bbccf
-
https://medium.com/apache-mxnet/object-detection-with-mxnet-scala-inference-a
pi-9049230c77fd
-
https://medium.com/apache-mxnet/scala-api-for-deep-learning-inference-now-ava
ilable-with-mxnet-v1-2-bcb13235db95
-
https://medium.com/apache-mxnet/train-using-keras-mxnet-and-inference-using-m
xnet-scala-api-49476a16a46a
- https://medium.com/apache-mxnet/page-segmentation-with-gluon-dcb4e5955e2
- https://medium.com/apache-mxnet/announcing-keras-mxnet-v2-2-4b8404568e75
- https://devblogs.nvidia.com/tensor-core-ai-performance-milestones/
-
https://aws.amazon.com/blogs/machine-learning/use-pre-trained-models-with-apa
che-mxnet/

e) Webinars, Technical talks and lectures about MXNet:
- Thomas Delteil presented at meetups in Vancouver:
https://www.youtube.com/watch?v=RgIa3_BjGyk&t=163s ,
https://www.youtube.com/watch?v=mN15vKIyfoA and
https://www.youtube.com/watch?v=K120xBnY6OA
- Gluon debugging: https://www.youtube.com/watch?v=6-dOoJVw9_0
- TVM stack: https://www.youtube.com/watch?v=DaCPJrTwT00
- https://mxnet.incubator.apache.org/tutorials/vision/cnn_visualization.html
- visual search: https://www.youtube.com/watch?v=9a8MAtfFVwI
- Cifar 10 super convergence: https://www.youtube.com/watch?v=O0XTkQPkUio
- Sparse Tensors: https://www.youtube.com/watch?v=smZfsYhDFkY
- Mixed precission training with MXNet:
https://www.youtube.com/watch?v=pR4KMh1lGC0
- See all videos on https://www.youtube.com/apachemxnet -
- Thomas Delteil presented a lecture on MXNet Gluon and Deep Learning at the
Machine Learning Summer School in Buenos Aires in front of 170 students.
http://mlss2018.net.ar/

How has the project developed since the last report?

a) The community released MXNet 1.2 with significant feature
enhancements:
1. Scala Inference API
2. ONNX model import
3. Model Quantization with calibration
4. MKL-DNN Integration
5. Improved exception handling for operators
6. Enhanced FP16 support
7. Profiling enhancements
Detailed release notes are provided on Apache Wiki and Github.
See
https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+%28incubating%
29+1.2.0+Release+Notes

b) Github: In April the project had 523 contributors. Excluding merges,
62 authors have pushed 199 commits to master and 258 commits to all
branches. In May the project had 531 contributors. Excluding merges, 55
authors have pushed 141 commits to master and 177 commits to all branches.
In June the project had 550 contributors. Excluding merges, 66 authors have
pushed 171 commits to master and 221 commits to all branches. We are working
on encourage more contributors to the project.
b) The community voted to adopted Jira for issue management. The change
is methodology is partially adopted. On June 8th a committer started a vote
to stop using Jira which created a passionate debate and is still ongoing.
c) List of design proposals published on MXNet Apache Wiki : All design
proposals have been or are being discussed on dev@mxnet.apache.org. Four
design proposals originated from external contributors. See
https://cwiki.apache.org/confluence/display/MXNET/Design+Proposals.`

d) Ecosystem development:
1. MXNet Model Serving:
- Repo: https://github.com/awslabs/mxnet-model-server
-
https://medium.com/apache-mxnet/model-server-for-apache-mxnet-adds-support-fo
r-gluon-models-dd398a710f94

2. ONNX support
- Repo: https://github.com/onnx/model-zoo
- Repo: https://github.com/onnx/models
-
https://medium.com/apache-mxnet/mxnet-1-2-adds-built-in-support-for-onnx-e2c7
450ffc28

3. Keras: The community release MXNet backend for Keras, a high level
API for deep learning.
- Repo - https://github.com/awslabs/keras-apache-mxnet/
-
https://medium.com/@julsimon/apache-mxnet-as-a-backend-for-keras-2-9993f97843
e7
- https://medium.com/apache-mxnet/announcing-keras-mxnet-v2-2-4b8404568e75
-
https://aws.amazon.com/blogs/machine-learning/apache-mxnet-incubating-adds-su
pport-for-keras-2/
-
https://medium.com/apache-mxnet/keras-gets-a-speedy-new-backend-with-keras-mx
net-3a853efc1d75

e) Documentation: We continue to improve the documentation on
Architecture guides, How To’s, Tutorials, and APIs continue to be improved.
43 new public tutorials related and about MXNet have been published YTD.
- https://thomasdelteil.github.io/CNN_NLP_MXNet/
- https://gluon-crash-course.mxnet.io/
-
https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/python/t
ypes_of_data_augmentation.md
-
https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/python/d
ata_augmentation_with_masks.md
-
https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/gluon/da
tasets.md
-
https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/onnx/inf
erence_on_onnx_model.md
-
https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/onnx/fin
e_tuning_gluon.md
-
https://aws.amazon.com/blogs/machine-learning/speeding-up-apache-mxnet-using-
the-nnpack-library/
-
https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/python/d
ata_augmentation.md
-
https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/gluon/da
ta_augmentation.md
-
https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/gluon/na
ming.md
-
https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/gluon/cu
stom_layer.md
- https://github.com/ThomasDelteil/VisualSearch_MXNet
-
https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/gluon/pr
etrained_models.md
- https://mxnet.incubator.apache.org/tutorials/gluon/save_load_params.html
- http://mxnet.incubator.apache.org/tutorials/python/profiler.html
-

How would you assess the podling's maturity?

  Podling is still having difficulties to grow the contributor and
committer community. Maturity == Low to Medium.

Please feel free to add your own commentary.

 [  ] Initial setup
 [  ] Working towards first release
 [X] Community building
 [  ] Nearing graduation
 [  ] Other

Date of last release: (latest to oldest releases)
1. Apache MXNet-incubating 1.2.0 (major release) was published on May
21 2018 - https://github.com/apache/incubator-mxnet/releases/tag/1.2.0

When were the last committers or PPMC members elected?

During the reporting period 1 contributor was elected as committer. The PPMC
elected Jim Jagielski as mentor.


Signed-off-by:

 [X](mxnet) Sebastian Schelter
    Comments:
 [ ](mxnet) Suneel Marthi
    Comments:
 [ ](mxnet) Markus Weimer
    Comments:
 [ ](mxnet) Henri Yandell
   Comments:
 [X](mxnet) Jim Jagielski
    Comments:

IPMC/Shepherd notes:

18 Oct 2017


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. 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.


Apache MXNet has been incubating since 2017-01-23.

The most important issues to address in the move towards graduation:

  1. Establish a predictable release process consistent with Apache Way -- Ongoing.
  2. Grow the Community - Ongoing
  3. Update project website to Apache standards - In Progress
  4. Identify remaining ICLAs and SGAs that need to be addressed - in progress

Any issues that the Incubator PMC (IPMC) or ASF Board wish/need to be
aware of?

  None

How has the community developed since the last report?

 a)  Various Slack channels and dev@ mailing lists are being used actively. New user support and discussion forum created with the guidance of Apache members at: http://discuss.mxnet.io

 b) O’Reilly  published a series of blogs about MXNet, including ones with step-by-step instructions to implement a convolutional neural network to classify traffic signs with Apache MXNet:
https://www.oreilly.com/ideas/classifying-traffic-signs-with-mxnet-an-introduction-to-computer-vision-with-neural-networks
https://www.oreilly.com/ideas/apache-mxnetthe-fruit-of-cross-institutional-collaboration
https://www.oreilly.com/ideas/self-driving-trucks-enter-the-fast-lane-using-deep-learning

 c)  A new blog post published on 28-July showing users how to exploit the unique features of Apache MXNet with a cheat sheet:       https://aws.amazon.com/blogs/ai/exploiting-the-unique-features-of-the-apache-mxnet-deep-learning-framework-with-a-cheat-sheet/

d) New Blog series by Viacheslav Kovalevskyi on Apache MxNet in Depth published on Medium https://blog.kovalevskyi.com/mxnet-distributed-training-explained-in-depth-part-1-b90c84bda725

e) Members of the community have conducted open meetups to share information on Apache MXNet: https://www.meetup.com/Apache-MXNet-learning-group/

f) Talks on Apache MXNet have been held in various universities and conferences across the world including US, China, etc.:
https://www.youtube.com/watch?v=GBkOMtc9BIk
https://www.youtube.com/watch?v=kGktiYF5upk

g) Presently working towards an upcoming 0.12.0 release targeted for October or November 2017.


How has the project developed since the last report?

a) The code base was migrated from http://github.com/dmlc/mxnet to https://github.com/apache/incubator-mxnet on 28-July, 2017. The website has also been migrated to this repository.

b) From a statistics perspective, in July 2017, 54 authors pushed 140 commits to master, with updates to 358 files including 22K additions and 3K deletions. In Sep 2017, 62 authors pushed 171 commits to master, with updates to 467 files including 26K additions and 7K deletions.

c) Documentation- Architecture guides, How To’s, Tutorials, and APIs continue to be improved.

d) More features (e.g. operators, algorithms) and bug-fixes requested by the user community continue to be added.


How would you assess the podling's maturity?

Podling is still being established in Apache - hence maturity == Low, but the project has a very diverse set of contributors.

Please feel free to add your own commentary.

  [ ] Initial setup
  [ ] Working towards first release
  [X] Community building
  [ ] Nearing graduation
  [ ] Other:

Date of last release:

 A maintenance release Apache MXNet-incubating 0.11.0 with few bug-fixes was released on 05-Sep, 2017.
   https://github.com/apache/incubator-mxnet/releases/tag/0.11.0

 A maintenance release Apache MXNet-incubating 0.10.0 Post 2 with few bug-fixes was released on 17-July, 2017.
  https://github.com/apache/incubator-mxnet/releases/tag/0.10.0.post2


When were the last committers or PPMC members elected?

  Ly Nguyen, Haibin Lin and Madan Jampani were added as committers and PPMC members in June 2017.

Signed-off-by:

  [X](mxnet) Sebastian Schelter
     Comments: Second Suneel's comment that the project should work towards more communication on its mailinglists.
  [X](mxnet) Suneel Marthi
     Comments: Plenty of activity on the project, would love to see more discussions happening on mail lists or Slack.
  [ ](mxnet) Markus Weimer
     Comments:
  [X](mxnet) Henri Yandell
     Comments: Noting acknowledgement that Infra JIRA requests will be going via mentors due to both the reported
               volume of requests and the requests coming from contributors and not committers. Also noting that
               I've been active on legal-discuss@ regarding MXNet ICLA/source headers, and in discussion offline
               with an employer of contributors who has concerns with ICLA signing.

IPMC/Shepherd notes:

  johndament: The podling has come under fire recently for what is perceived to be an over abundant amount of requests coming from non-committers.  If nothing else, it's a sign that they are anxious to get going here at Apache and perhaps they need to look at their communication model, moving more discussion on list, as well as potentially voting in new committers.

19 Jul 2017

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.


MXNet has been incubating since 2017-01-23.

Three most important issues to address in the move towards graduation:

  1. Migrate code (GitHub) and website to Apache Infra.
  2. Grow the community
  3. Establish a reliable Release process consistent with Apache Way.

Any issues that the Incubator PMC (IPMC) or ASF Board wish/need to be
aware of?

   None

How has the community developed since the last report?

 a)  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.

 b)  The PMC voted in a new committer 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 after that.

 c)  Slack and dev@ are being used more actively.

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

 e)  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/

How has the project developed since the last report?

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

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

 c) More features (e.g. operators) requested by the user community have been added.

 d) A new Perl language binding for MXNet was added.

How would you assess the podling's maturity?

   Podling's still getting established in Apache - so maturity == Low.

Please feel free to add your own commentary.

  [X] Initial setup
  [ ] Working towards first release
  [ ] Community building
  [ ] Nearing graduation
  [ ] Other:

Date of last release:

  No Release yet, project is still getting established in Apache.

When were the last committers or PPMC members elected?

  Ly Nguyen added as a committer and PPMC member in June 2017.

Signed-off-by:

  [X](mxnet) Sebastian Schelter
     Comments:
  [X](mxnet) Suneel Marthi
     Comments:
  [X](mxnet) Markus Weimer
     Comments:
  [X](mxnet) Henri Yandell
     Comments:

17 May 2017

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.

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

1.  Migrate code(GitHub) and website to Apache.
2.  Grow the community:
2.1 Improving documentation including APIs & Tutorials.
2.2 Improving user-experience, for example improved error messages.
3   Establish a dependable, Apache-way consistent release process.
3.1 Features: New operators requested by user community.
             Accelerate performance on CPUs and GPUs.

*   Any issues that the Incubator PMC or ASF Board might wish/need to be
aware of:

Code and website have taken time to get moving on. The plan is to discuss
these with Apache Infra at ApacheCon and then get the code migrated in May.

*   How has the community developed since the last report.

1. The community was engaged for contributions to API documentation and
tutorials.
2. Slack channels have been created for the community to contribute
discussions to (though they need to move to the ASF Slack channel that
was recently created).
3. In the last month, excluding merges, 51 authors have pushed 165 commits
to master and 180 commits to all branches. 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.

*   How has the project developed since the last report.

On GitHub:

1. The API Documentation has improved.
2. More features (e.g. operators) requested by the user community has been added.
3. Hardware acceleration like cuDDN6 integration and MKL ML package
integration was completed.
4. A new Perl language binding for MXNet was added.
5. Apache MxNet talk at Apache BigData North America on May 18, 2017

*   How does the podling rate their own maturity.

Maturity = Low. Initial setup and adjusting community to Apache-style interactions.

Date of last release:

 No @Apache release yet.

When were the last committers or PPMC members elected?

 No new committers added yet.

Signed-off-by:

 [ ](mxnet) Sebastian Schelter
    Comments:
 [X](mxnet) Suneel Marthi
    Comments:
 [ ](mxnet) Markus Weimer
    Comments:
 [X](mxnet) Henri Yandell
    Comments: Starting to see movement on moving things to Apache.
              The dev@ list is beginning to see traffic.

19 Apr 2017

 Signed-off-by:

 [ ](mxnet) Sebastian Schelter
    Comments:
 [ ](mxnet) Suneel Marthi
    Comments:
      Its been 4 months since this project has been proposed for Apache
      Incubator and so far nothing's been done to move the project to Apache.
      All activity is still happening on the original project github -
      http://github.com/dmlc/mxnet and all conversations still happen on the
      project's Gitter channel. Since the last report from March 2017 (which
      btw was drafted and filed by me) there's been ZERO traction on moving
      the project to Apache. There are upcoming talks in various confs like -
      https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/detail/58186
      that make no reference to the fact that the project is now 'Apache'.
      The reason me and Hen have not filed a report yet for April 2017 is due
      to the fact we would rather one of the committers on the project took
      the initiative to do it as opposed to a mentor covering for the
      project.
 [ ](mxnet) Markus Weimer
    Comments:
 [ ](mxnet) Henri Yandell
    Comments:

IPMC/Shepherd notes:

 Justin Mclean:

   No report yet. Slow to start up but at least one Mentor active.

15 Mar 2017

A Flexible and Efficient Library for Deep Learning

MXNet has been incubating since 2017-01-23.

Three most important issues to address in the move towards graduation:

  1. Move the code and website to Apache Infrastructure.
  2. Establish a formal release process and schedule, allowing for dependable
     release cycles in line with Apache development process.
  3.

Any issues that the Incubator PMC (IPMC) or ASF Board wish/need to be
aware of?

  PPMC to discuss adding in some community members who asked to join while
  the Incubator vote was in progress.

How has the community developed since the last report?

  Project is still getting set up in Incubator.

How has the project developed since the last report?

  Project is still getting set up, all proposed committers have submitted
  their ICLAs.

How would you assess the podling's maturity?
Please feel free to add your own commentary.

  [X] Initial setup
  [ ] Working towards first release
  [ ] Community building
  [ ] Nearing graduation
  [ ] Other:

Date of last release:

 No Release yet

When were the last committers or PPMC members elected?

  Project is being set up with the initial set of committers.

Signed-off-by:

  [X](mxnet) Sebastian Schelter
     Comments:
  [X](mxnet) Suneel Marthi
     Comments:
  [X](mxnet) Markus Weimer
     Comments:
  [X](mxnet) Henri Yandell
     Comments:

        As project already exists in the public, this incubation is about
        moving the development over without stopping the momentum of the
        project, and then learning about the Apache development processes.
        Previous conversations were Slack/GitHub-issue based, so making
        decisions on the email list will be the first likely adaptation. A
        dependency on ZeroMQ will be the primary discussion point for a first
        release at Apache.  PPMC list is still low on subscriptions, with
        less than half of the PPMC members subscribed.

27 Feb 2017

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 has been incubating since 2017-01-23.

Three most important issues to address in the move towards graduation:

 1. Getting ICLAs signed/private@ populated.
 2. Sharing documentation/mentor thoughts on being an Apache committer.
 3. Migrating the code/issues over to Apache.

Any issues that the Incubator PMC (IPMC) or ASF Board wish/need to be
aware of?

 None

How has the community developed since the last report?

 1. This is our first report.
 2. The mailing lists are created; with the dev@ list being well populated.
 3. ICLAs are being signed.

How has the project developed since the last report?

 1. Trademark review seems complete.
 2. A vendor, AWS, worked with Sally on a blog posting[1].

Date of last release:

 No Apache release yet; still being setup.

When were the last committers or PPMC members elected?

 At Proposal time. We have a few folk who asked to join the incubation
 proposal after the vote started and will be discussed once private@
 is populated.

[1] - https://aws.amazon.com/blogs/aws/excited-about-mxnet-joining-apache/

Signed-off-by:

 [X](mxnet) Henri Yandell
 [X](mxnet) Markus Weimer
 [X](mxnet) Sebastian Schelter
 [X](mxnet) Suneel Marthi

Shepherd/Mentor notes:

 John D. Ament:

   I find it a bit odd that the linked article uses "MXNet" repeatedly but
   only refers to it as "Apache MXNet" once near the end of the page.