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This was extracted (@ 2020-09-29 22:10) 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.

Meeting times vary, the exact schedule is available to ASF Members and Officers, search for "calendar" in the Foundation's private index page (svn:foundation/private-index.html).

MXNet

15 Jul 2020

A Flexible and Efficient Library for Deep Learning

MXNet has been incubating since 2017-01-23.

### Three most important unfinished issues to address before graduating:

 1. Address licensing and trademark issues for the binary releases in the
 community.
 - ONGOING. See update in the next section.
 2. Address release issues. Successfully and smoothly make releases
 without WIP disclaimer.
 - ONGOING.
 3. Improve development process and tooling to help reduce the overhead of
 releases
 - ONGOING.

### Are there any issues that the IPMC or ASF Board need to be aware of?

 1. Binary Distribution Licensing Issue
 2. MXNet 2.0 first public beta, interoperable with NumPy, is on the way:
    https://github.com/apache/incubator-mxnet/projects/18
    RFC: https://github.com/apache/incubator-mxnet/issues/16167

#### Issues with releases and distributions

##### Background
 In May 2020 The MXNet PPMC has proactively initiated a ASF policy
 compliance
 review [1] and a license review [2] with the Apache Legal team.

 The license review uncovered that

 - Building unmodified MXNet release source code with the optional NVidia
 GPU
 support enabled results in a binary subject to restrictions of NVidia
 EULA.
 - PPMC members and committers uploaded convenience releases to
 repository.apache.org which contain Category-X components. Both GPL and
 NVidia EULA components were found.

 The policy review uncovered that:

 - Prior ASF guidance to the PPMC (December 2018 legal review [3]) was
 incomplete
 and did not include a reference to the "unwritten" rule that convenience
 binary distributions created by third-parties using ASF Trademarks must
 not
 include Category-X components. Based on this discovery, the Draft
 Downstream
 Distribution Branding Policy was updated in June 2020 to include the
 "unwritten" requirement. Based on the updated guidance, PPMC discovered
 various third-party trademark infringements.

 The policy review did not yet conclude on the questions if

 - The PPMC may create nightly development builds (audience restricted to
 dev
 list subscribers as per Release policy [4]) for the purpose of testing and
 developing MXNet;

##### List of issues and their status

 Justin classified the issues into 14 items.

 1) Source and convenance binary releases containing Category X licensed
 code.

 See summary from license review in Background section. Source code
 releases do
 not contain Category X code; Takedown of binary releases on
 repository.apache.org is pending on Apache Infra. (Trademark infringements
 of 3rd-parties such as on pypi are discussed separately)

 2. Website giving access to downloads of non released/unapproved code.

 Website contained links to nightly development builds which have been
 removed [5];
 Going forward the PPMC intends to begin periodical voting on Alpha and
 Beta Releases which will then be linked from the website.

 3. Website giving access to releases containing Category X licensed code.

 Website contained links to third-party distributions incorporating
 Category-X
 components (see summary from license review above). Disclaimers were added
 to the website clarifying the third-party status of the releases and their
 licenses. [5]

 4. Web site doesn't given enough warning to users of the issues with non
 (P)PMC releases or making it clear that these are not ASF releases.

 Website contained links to third-party distributions incorporating
 Category-X
 components (see summary from license review above). Disclaimers were added
 to the website clarifying the third-party status of the releases and their
 licenses. [5]

 5. Maven releases containing Category X licensed code.

 See summary from license review in Background section. Source code
 releases do
 not contain Category X code; Takedown of binary releases on
 repository.apache.org is pending on Apache Infra. [6] (Trademark
 infringements of 3rd-parties are discussed separately)

 6. PyPI releases containing Category X licensed code.

 There are no PiPy releases by the PPMC. Please refer to the trademark
 infringement section of the report.

 7. Docker releases containing Category X licensed code.

 There are no Docker releases by the PPMC. Please refer to the trademark
 infringement section of the report.

 8. Docker releases containing unreleased/unapproved code.

 There are no Docker releases by the PPMC. The existence of third-party
 releases
 containing unreleased code was approved in [3] and is also in line with
 the current Downstream Distribution Branding Draft Policy. ("using any
 particular revision from the development branch is OK" [3])

 9. Trademark and branding issues with PiPy and Docker releases.

 There are no PiPy releases by the PPMC. Please refer to the trademark
 infringement section of the report.

 10. Trademark and brand issues with naming of releases.

 There are no binary releases by the PPMC besides the repository.apache.org
 releases discussed above, which are being removed.
 Please refer to the trademark infringement section of
 the report.

 11. Developer releases available to users and public searchable
 https://repo.mxnet.io / https://dist.mxnet.io

 Links to the nightly development builds were removed from the MXNet
 website and
 a robot.txt file was added to prevent indexing of the sites. These
 websites are removed from Google search index.

 12. Releases and other nightly builds on
 https://repo.mxnet.io / https://dist.mxnet.io containing category X
 licensed code.

 Neither of the two site contains Releases. It is an open question of the
 policy
 review (see Background section above) if nightly development builds may or
 may not contain Category X components.

 13. Lack of clarity on all platforms for what is an ASF release and what
 is not.

 https://github.com/apache/incubator-mxnet/releases?after=1.2.0 previously
 did not distinguish MXNet releases prior to MXNet joining the Incubator.
 Disclaimers
 were added. Other PPMC platforms do not contain references to non-ASF
 releases
 (MXNet releases made prior to MXNet joining the ASF). The PPMC is aware of
 old third-party releases created prior to MXNet joining the ASF which are
 still
 available, but can be clearly separated from the ASF MXNet releases due to
 the lack of reference to the Apache foundation. PPMC was able to find an
 exemplar
 such release at [7]. If there are concerns from the Incubator, PPMC can
 request
 the third-parties to take down these releases, as editing their
 Description to include
 references to events (MXNet joining Apache) is not
 supported due to immutability constraints. [8]

 14. Branding and release of 3rd parties containing unreleased code.
 (e.g.


 https://docs.nvidia.com/deeplearning/frameworks/mxnet-release-notes/rel_20-0
 3.html)

 Please refer to the trademark infringement section of the report.

 [1]: https://issues.apache.org/jira/browse/LEGAL-515
 [2]: https://issues.apache.org/jira/browse/LEGAL-516
 [3]: https://s.apache.org/flvug
 [4]: http://www.apache.org/legal/release-policy.html#publication
 [5]:


 https://github.com/apache/incubator-mxnet/commit/b6b40878f0aba2ba5509f3f3a4c
 d517a654847ce#diff-19bc831c1dab6d92d2efc3b87ec5c740
 [6]: https://issues.apache.org/jira/browse/INFRA-20442
 [7]: https://pypi.org/project/mxnet/0.9.5/
 [8]:
 https://mail.python.org/pipermail/distutils-sig/2017-December/031826.html

### How has the community developed since the last report?

 * The number of GitHub contributors increased to currently 781
 (2019-03-28; +2.4% since last report)
 * Active discussions on user forums
     * https://discuss.mxnet.io/ (English, 2.8K registered users (+10.5%)
     and 8.1K posts (+5.2%))
     * https://discuss.gluon.ai/ (Chinese, 8.3K registered users (+2.5%)
     and 22.2K posts(+1.3% since last report))
 * Active blogs and social media presence
   * Medium (https://medium.com/apache-mxnet) w/ 1.8k followers
   * Twitter account (https://twitter.com/ApacheMXNet) w/ 2.6k followers
   (+8.3%)

 * Active video channels
   * YouTube channel  (https://www.youtube.com/apachemxnet) w/ 1.18k
   subscriber (+13.5% since last report)
   * Chinese YouTube channel
   (https://www.youtube.com/channel/UCjeLwTKPMlDt2segkZzw2ZQ) w/ 6.05k
    subscribers (+6.0%)
   * (in China) bilibili space (https://space.bilibili.com/209599371) w/
   24k subscriber (+9.0% since last report)

 * Highlights in MXNet ecosystem
   * Dive into Deep Learning has 73K 28-day active users, and has
     attracted 23.9K stars & 295 contributors on GitHub. It has been
     adopted as a textbook or reference book by 100+ universities from 27
     countries, such as Stanford, MIT, UC Berkeley, CMU.
   * GluonCV v0.7.0 release
     (https://github.com/dmlc/gluon-cv/releases/tag/v0.7.0)
   * GluonNLP v0.9.1 release
     (https://github.com/dmlc/gluon-nlp/releases/tag/v0.9.1)
   * GluonTS v0.5.0 release
     (https://github.com/awslabs/gluon-ts/releases/tag/v0.5.0)
   * Multi-Model Server v1.1.1 release
     (https://github.com/awslabs/mxnet-model-server/releases/tag/v1.1.1)
   * DeepInsight (https://github.com/deepinsight/insightface)
   * Sockeye (https://github.com/awslabs/sockeye)

### How has the project developed since the last report?

 1) 1.6.0 was released:
 https://github.com/apache/incubator-mxnet/releases/tag/1.6.0
 with over 830+ patches of new features, improvements, and fixes.
 2) 2.0 project: https://github.com/apache/incubator-mxnet/projects/18
 3) Github statistics of last month:
 * May 28, 2020 – June 28, 2020: Excluding merges, 59 authors have
   pushed 79 commits to master and 107 commits to all branches.
   On master, 2,010 files have changed and there have been 10,897
   additions and 274,406 deletions.

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

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

### Date of last release:

 2020-02-20

### When were the last committers or PPMC members elected?

 2020-03-02

### Have your mentors been helpful and responsive?

 Yes. In particular, Bob has provided guidance on the license and
 trademark issue.

### Is the PPMC managing the podling's brand / trademarks?

 PPMC notes that there are multiple trademark infringements based on both
 the redistribution of MXNet with addition of unreleased code and the
 redistribution of MXNet with Category-X GPL and Category-X NVidia
 components. PPMC intends to handle both issues separately.

#### Unauthorized redistribution of unreleased code by third-parties

 PPMC members have reached out to the offending third parties (Nvidia
 Corporation
 and Amazon Web Services) via inofficial channels and notified them of the
 problem. If the problem is not resolved by the end of July 2020, PPMC will
 request guidance from the Brand Management Team on how to formally notify
 the offenders of their trademark infringement.

#### Unauthorized redistribution of Category-X GPL and NVidia CUDA EULA
 components by third-parties

 PPMC members note that the issue of "NVidia CUDA EULA infecting any
 application
 built with CUDA support" is an industry-wide problem. PPMC is not aware of
 any individual or corporation correctly labeling their binary
 distributions
 subject
 to the NVidia CUDA EULA. Instead, PPMC found that for example Facebook
 claims distribution of PyTorch under BSD License (BSD-3) and Google claims
 distribution of Tensorflow under Apache 2.0 License, despite both being
 subject to the CUDA EULA. Thus, PPMC has contacted NVidia Corporation and
 requested
 NVidia Corporation to add clarifying language that applications based on
 the CUDA SDK with material additional functionality may be licensed under
 a license
 of the application owner's choice, consistent with existing industry
 "practice".
 The issue was also discussed with NVidia and other Deep Learning Framework
 implementers during the Nvidia Deep Learning Framework Developer Council
 meeting,
 during which NVidia promised to conclude their internal review and
 follow-up with the PPMC.

 PPMC thus recommends to give NVidia the chance to clarify and improve
 their
 license. As NVidia employs a team for working on MXNet, the PPMC is
 optimistic
 about receiving a detailed clarification and resolution from NVidia.

 If NVidia fails to clarify their license or the resolution is
 unsatisfactory
 within Q3 2020, the PPMC will notify any third-parties about their license
 infringement and ask them to take down or rename their redistributions
 containing Category-X pieces.

 Due the substantial overhead of trademark-infringement takedown notices
 for any
 involved party, PPMC is further awaiting NVidia's clarification prior to
 contacting third-parties about trademark infringement due to inclusion of
 GPL
 components. This is to avoid sending two separate takedown notices in case
 of an unsatisfactory response by NVidia.

 The following downstream software distributors are known to the PPMC to be
 using the name MXNet while redistributing Category-X components

 - pypi.org
 - hub.docker.com
 - ngc.nvidia.com
 - aws.amazon.com

### Signed-off-by:

 - [X] (mxnet) Henri Yandell
    Comments: Kudos to the project on the licensing review; respect to Bob
    and Justin for their work as well.
 - [ ] (mxnet) Markus Weimer
    Comments:
 - [X] (mxnet) Michael Wall
    Comments: Good progress on the issues.  They are complex but important.
 - [X] (mxnet) Bob Paulin
    Comments: Team is making thoughtful process on the issues.  Lots of
    good support and experiance coming from VPs of Brand and Incubator PMC.
 - [ ] (mxnet) Jason Dai
    Comments:

### IPMC/Shepherd notes:
 Justin Mclean: Good to see progress on these issues. But I have two concerns
 a) you may not have an active PPMC b) branding all release as "3rd party" may
 not be the best way to solve the issue. Hopefully the license discussion will
 be fruitful.

15 Jan 2020

A Flexible and Efficient Library for Deep Learning

MXNet has been incubating since 2017-01-23.

### Three most important unfinished issues to address before graduating:

 1. Address past release issues. Successfully and smoothly make releases
 - ONGOING.
 2. Improve development process and tooling to help reduce the overhead of
 releases
 - ONGOING.

### Are there any issues that the IPMC or ASF Board need to be aware of?

 1. MXNet 2.0 has started:
    https://github.com/apache/incubator-mxnet/projects/18
    RFC: https://github.com/apache/incubator-mxnet/issues/16167

### How has the community developed since the last report?

 * The number of GitHub contributors increased to currently 763
 (2019-12-31; +3.8% since last report)
 * Active discussions on user forums
     * https://discuss.mxnet.io/ (English, 2.3K registered users (+9.5%)
     and 7.4K posts (+7.2%))
     * https://discuss.gluon.ai/ (Chinese, 8.5K registered users (+10.4%)
     and 21.6K posts(+2.3% since last report))
 * Active blogs and social media presence
   * Medium (https://medium.com/apache-mxnet) w/ 1.7k followers (+6.2%)
   * Twitter account (https://twitter.com/ApacheMXNet) w/ 2.4k followers
   (+4.3%)
   * Meetup group
   (https://www.meetup.com/pro/deep-learning-with-apache-mxnet/) w/ 10
   groups in 8 countries, 2207 members

 * Active video channels
   * YouTube channel  (https://www.youtube.com/apachemxnet) w/ 949
   subscriber (+12.8% since last report)
   * Chinese YouTube channel
   (https://www.youtube.com/channel/UCjeLwTKPMlDt2segkZzw2ZQ) w/ 5.4k
    subscribers (+8.2%)
   * (in China) bilibili space (https://space.bilibili.com/209599371) w/
   21k subscriber (+15.4% since last report)

 * Highlights in MXNet ecosystem
   * MXNet Gluon book published (https://zh.d2l.ai/) first 34k hard copies
   * Dive into Deep Learning has 59K 28-day active users, and has
     attracted 15K stars & 210+ contributors on GitHub. It has been adopted
     as a textbook or reference book by 30+ universities in U.S., China,
     Spain, Brazil, India, and Australia, such as MIT, UC Berkeley, CMU,
     IIT Bombay, PKU, and SJTU.
   * GluonCV v0.6.0 release
     (https://github.com/dmlc/gluon-cv/releases/tag/v0.6.0)
   * GluonNLP v0.8.2 release
     (https://github.com/dmlc/gluon-nlp/releases/tag/v0.8.2)
   * GluonTS v0.4.2 release
     (https://github.com/awslabs/gluon-ts/releases/tag/v0.4.2)
   * MXNet Model Server v1.0.9 release
     (https://github.com/awslabs/mxnet-model-server/releases/tag/v1.0.9)
   * GluonFace (https://github.com/THUFutureLab/gluon-face)
   * DeepInsight (https://github.com/deepinsight/insightface)

### How has the project developed since the last report?

 1) 1.5.1 patch release and 1.6.0 in progress:
 https://github.com/apache/incubator-mxnet/releases/tag/1.5.1
 https://github.com/apache/incubator-mxnet/releases/tag/1.6.0
 with over 830+ patches of new features, improvements, and fixes.
 2) 2.0 project: https://github.com/apache/incubator-mxnet/projects/18
 3) Many ongoing projects:
     * numpy-compatbile deep learning:
       https://github.com/apache/incubator-mxnet/projects/14
     * CPU performance and quantization:
       https://github.com/apache/incubator-mxnet/projects/15
     * MKLDNN 1.0 upgrade:
       https://github.com/apache/incubator-mxnet/projects/16
     * New Website Launch:
       https://github.com/apache/incubator-mxnet/projects/17
 4) Github statistics of last month:
 * Nov 30, 2019 – Dec 30, 2019: Excluding merges, 45 authors have pushed
 109 commits to master and 131 commits to all branches. On master, 314
 files have changed and there have been 20,474 additions and 3,842
deletions.

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

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

### Date of last release:

 2019-10-01

### When were the last committers or PPMC members elected?

 2019-11-24

### Have your mentors been helpful and responsive?

 Yes, mentors have been responsive and helpful as usual

### Signed-off-by:

 - [ ] (mxnet) Henri Yandell
    Comments:
 - [ ] (mxnet) Markus Weimer
    Comments:
 - [x] (mxnet) Michael Wall
    Comments:
 - [ ] (mxnet) Bob Paulin
    Comments:
 - [ ] (mxnet) Jason Dai
    Comments:

### IPMC/Shepherd notes:

16 Oct 2019

A Flexible and Efficient Library for Deep Learning

MXNet has been incubating since 2017-01-23.

### Three most important unfinished issues to address before graduating:

  1. Revisit Apache Maturity Model Assessment — ONGOING.
  2. Address past release issues. Successfully and smoothly make releases -
  ONGOING.

### Are there any issues that the IPMC or ASF Board need to be aware of?

  1. Our community is planning for the long-term development of MXNet.
     RFC: https://github.com/apache/incubator-mxnet/issues/16167
     Discussion: https://github.com/apache/incubator-mxnet/issues/9686
  2. Our community established the process for conflict resolution and
     violation reporting.

  https://github.com/apache/incubator-mxnet/blob/master/CODE_OF_CONDUCT.md

### How has the community developed since the last report?

  * The number of GitHub contributors increased to currently 735
  (2019-06-24; +4.0% since last report)
  * Active discussions on user forums
      * https://discuss.mxnet.io/ (English, 2.1K registered users (+17%)
      and 9.4K posts (+23%))
      * https://discuss.gluon.ai/ (Chinese, 7.7K registered users (+18%)
      and 21.1K posts(+10% since last report))
  * Active blogs and social media presence
    * Medium (https://medium.com/apache-mxnet) w/ 1.6k followers (+14%)
    * Twitter account (https://twitter.com/ApacheMXNet) w/ 2.3k followers
    (+8.7%)
    * Meetup group
    (https://www.meetup.com/pro/deep-learning-with-apache-mxnet/) w/ 10
    groups in 8 countries, 2182 members
    * (in China) Zhihu w/ 7.9k followers (+4.0%), WeChat official account
    w/ 4.6k
    followers. (+9.5% since last report)

  * Active video channels
    * YouTube channel  (https://www.youtube.com/apachemxnet) w/ 841
    subscriber (+18.6% since last report)
    * Chinese YouTube channel
    (https://www.youtube.com/channel/UCjeLwTKPMlDt2segkZzw2ZQ) w/ 5.0k
     subscribers (+13.6%)
    * (in China) bilibili space (https://space.bilibili.com/209599371) w/
    18k subscriber (+20% since last report)

  * Highlights in MXNet ecosystem
    * MXNet Gluon book published (https://zh.d2l.ai/) first 34k hard copies
    * Dive into Deep Learning has 59K 28-day active users, and has
      attracted 15K stars & 210+ contributors on GitHub. It has been adopted
      as a textbook or reference book by 30+ universities in U.S., China,
      Spain, Brazil, India, and Australia, such as MIT, UC Berkeley, CMU,
      IIT Bombay, PKU, and SJTU.
    * GluonCV v0.5.0 release
      (https://github.com/dmlc/gluon-cv/releases/tag/v0.5.0)
    * GluonNLP v0.8.1 release
      (https://github.com/dmlc/gluon-nlp/releases/tag/v0.8.1)
    * GluonTS v0.3.3 release
      (https://github.com/awslabs/gluon-ts/releases/tag/v0.3.3)
    * MXNet Model Server v1.0.7 release
      (https://github.com/awslabs/mxnet-model-server/releases/tag/v1.0.7)
    * GluonFace (https://github.com/THUFutureLab/gluon-face)
    * DeepInsight (https://github.com/deepinsight/insightface)

### How has the project developed since the last report?

  1) 1.5.0 release and 1.5.1 patch release:
  https://github.com/apache/incubator-mxnet/releases/tag/1.5.0
  https://github.com/apache/incubator-mxnet/releases/tag/1.5.1
  with over 830+ patches of new features, improvements, and fixes.
  2) 2.0 Roadmap RFC published:

https://lists.apache.org/thread.html/9d344832757860c0ec897cb79b84f4d552c6c37
e52ae33f2c92b50f7@%3Cdev.mxnet.apache.org%3E
  3) Many ongoing projects:
      * numpy-compatbile deep learning:
        https://github.com/apache/incubator-mxnet/projects/14;
      * New Website Launch:
        https://mxnet.apache.org/
        https://github.com/apache/incubator-mxnet/projects/17
  4) Github statistics of last month:
  * Aug 30, 2019 – Sept 30, 2019: Excluding merges, 54 authors have pushed
  128 commits to master and 164 commits to all branches. On master, 1038
  files have changed and there have been 55,926 additions and 32,705
deletions.

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

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

### Date of last release:

  2019-10-01

### When were the last committers or PPMC members elected?

  2019-10-01

### Have your mentors been helpful and responsive?

Yes, mentors have been responsive and helpful as usual

### Signed-off-by:

  - [ ] (mxnet) Henri Yandell
     Comments:
  - [ ] (mxnet) Markus Weimer
     Comments:
  - [X] (mxnet) Michael Wall
     Comments:  PPMC is doing a good job of working through an issue with
     community interactions.
  - [X] (mxnet) Bob Paulin
     Comments:
  - [ ] (mxnet) Jason Dai
     Comments:

### IPMC/Shepherd notes:

--------------------

## SDAP

SDAP is an integrated data analytic framework for Big Science problems.

SDAP has been incubating since 2017-10-22.

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

 1. Make official SDAP (Incubating) Release
 2. Improve/create user guide documentation
 3. Improve committer participation

### Are there any issues that the IPMC or ASF Board need to be aware of?

  No.

### How has the community developed since the last report?

  Interest in the project has increased after presentation at ApacheCon.
  New `#sdap` channel created on the-asf.slack.com

### How has the project developed since the last report?

  Frank Greguska attended ApacheCon NA and presented an overview of SDAP.

  Discussion about what is necessary to produce a release were beneficial
  and the path forward is clear. Goal is to get a source release before the end
  of the year.

### How would you assess the podling's maturity?

  There are several deployments of SDAP actively being used and interest is
  high. However, active participation from project members is low. First
  source release should be coming soon.

Please feel free to add your own commentary.

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

### Date of last release:

  XXXX-XX-XX

### When were the last committers or PPMC members elected?

  Maya Debellis was elected as a committer on 2019-02-08

### Have your mentors been helpful and responsive?

  Yes, mentors have been helpful and responsive.

### Signed-off-by:

  - [ ] (sdap) Jörn Kottmann
     Comments:
  - [ ] (sdap) Trevor Grant
     Comments:
  - [ ] (sdap) Lewis John McGibbney
     Comments:

17 Jul 2019

A Flexible and Efficient Library for Deep Learning

MXNet has been incubating since 2017-01-23.

### Three most important unfinished issues to address before graduating:

 1. Increase diversity in contributors, committers, and PMC members — NEAR
 COMPLETION.
 2. Identify remaining ICLAs or SGAs that need signing – NEAR COMPLETION.
 3. Revisit Apache Maturity Model Assessment — TODO.

### Are there any issues that the IPMC or ASF Board need to be aware of?

 1. Based on usability feedback from users, community has started a
 redesign of the website.

### How has the community developed since the last report?

 * The number of GitHub contributors increased to currently 707
 (2019-06-24; +3.7% since last report)
 * Active discussions on user forums
     * https://discuss.mxnet.io/ (English, 1.8K registered users and 9.4K
     posts (+68% since last report))
     * https://discuss.gluon.ai/ (Chinese, 6.5K registered users and 37K
     posts(+93% since last report))
 * Active blogs and social media presence
   * Medium (https://medium.com/apache-mxnet) w/ 1.4k followers
   * Twitter account (https://twitter.com/ApacheMXNet) w/ 2.1k followers
   * Meetup group
   (https://www.meetup.com/pro/deep-learning-with-apache-mxnet/) w/ 10
   groups in 8 countries, 2141 members
   * (in China) Zhihu w/ 7.6k followers, WeChat official account w/ 4.2k
   followers. (+40% since last report)

 * Active video channels
   * YouTube channel  (https://www.youtube.com/apachemxnet) w/ 709
   subscriber (+13% since last report)
   * Chinese YouTube channel
   (https://www.youtube.com/channel/UCjeLwTKPMlDt2segkZzw2ZQ) w/ 4.4k
    subscribers
   * (in China) bilibili space (https://space.bilibili.com/209599371) w/
   15k subscriber (+15% since last report)

 * Highlights in MXNet ecosystem
   * MXNet Gluon book published (https://zh.d2l.ai/) first 21.5k hard
   copies
   * MXNet Gluon book (www.d2l.ai, Dive into Deep Learning/D2L) released
     * v1.0.0-rc0
     (https://github.com/d2l-ai/d2l-zh/releases/tag/v1.0.0-rc0)
     in Chinese
     * v0.6.0 (https://github.com/d2l-ai/d2l-en/releases/tag/v0.6.0) in
     English
 * Dive into Deep Learning has 47.5K 28-day active users, and has
 attracted 11.5K stars & 200+ contributors on GitHub. It has been adopted
 as a textbook or reference book by 15+ universities in U.S., China,
 Spain, and Australia, such as UC Berkeley.
 * GluonCV v0.4.0 release
 (https://github.com/dmlc/gluon-cv/releases/tag/v0.4.0)
 * GluonNLP v0.6.0 release
 (https://github.com/dmlc/gluon-nlp/releases/tag/v0.6.0)
 * GluonTS v0.1.4 release
 (https://github.com/awslabs/gluon-ts/releases/tag/v0.1.4)
 This is a new toolkit for deep-learning based time-series modeling.
 * MXNet Model Server v1.0.4 release
 (https://github.com/awslabs/mxnet-model-server/releases/tag/v1.0.4)
 * GluonFace (https://github.com/THUFutureLab/gluon-face)
 * DeepInsight (https://github.com/deepinsight/insightface)

### How has the project developed since the last report?

 1) 1.4.1 patch release:
 https://github.com/apache/incubator-mxnet/releases/tag/1.4.1
 2) 1.5.0 release in progress (pre-release v1.5.0.rc1):
 https://github.com/apache/incubator-mxnet/releases/tag/1.5.0.rc1
 with over 750 patches of new features, improvements, and fixes.
 3) Roadmap discussion on 2.0 in progress
 https://github.com/apache/incubator-mxnet/issues/9686
 4) Code donation from dmlc/mshadow in progress.
 5) Many ongoing projects:
     * numpy-compatbile deep learning:
 https://github.com/apache/incubator-mxnet/projects/14;
     * CPU performance and quantization:
 https://github.com/apache/incubator-mxnet/projects/15;
     * Mixed precision GPU training (AMP):
 https://github.com/apache/incubator-mxnet/pull/14173, etc.
 6) Github statistics of last month:
 * May 24, 2019 – June 24, 2019: Excluding merges, 16 authors have pushed
 88 commits to master and 140 commits to all branches. On master, 250 files
 have changed and there have been 12,939 additions and 9,919 deletions.

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

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

### Date of last release:

 2019-04-29

### When were the last committers or PPMC members elected?

 2019-05-20

### Have your mentors been helpful and responsive?

 Mentors have been providing helps per requests from community.

### Signed-off-by:

 - [ ] (mxnet) Henri Yandell
    Comments:
 - [ ] (mxnet) Markus Weimer
    Comments:
 - [x] (mxnet) Michael Wall
    Comments:
 - [ ] (mxnet) Bob Paulin
    Comments:
 - [ ] (mxnet) Jason Dai
    Comments:

### IPMC/Shepherd notes:

 - Drew Farris (shepherd): Very active project. One mentor observed on the
 mailing list, otherwise not a significant amount of mentor activity
 apparent - possibly due to the proximity toward graduation.
 - Justin Mclean: I'm not sure why the website redesign is an issue for the
 IPMC or board. Can you please give some more detail.

15 May 2019

A Flexible and Efficient Library for Deep Learning

MXNet has been incubating since 2017-01-23.

Three most important unfinished issues to address before graduating:

 1. Increase diversity in contributors, committers, and PMC members — NEAR
 COMPLETION.
 2. Identify remaining ICLAs or SGAs that need signing – NEAR COMPLETION.
 3. Revisit Apache Maturity Model Assessment — TODO

Any issues that the Incubator PMC (IPMC) or ASF Board wish/need to be
aware of?
 1. Community reached consensus on Gluon branding. In the context of the
 Gluon API in MXNet, it would be mentioned as MXNet Gluon.
 https://lists.apache.org/thread.html/af7dcb430e2cedf23d1531e79877e8bf2b40ec392e40853a2d7015da@%3Cdev.mxnet.apache.org%3E
 2. Community addressed the IP/licensing issues discovered during previous
 releases. Updated status can be found here:
 https://cwiki.apache.org/confluence/display/MXNET/MXNet+Source+Licenses
 3. MXNet has significant user presence in China that are worth optimizing
 for in terms of infrastructure. Setting up CDN in China requires ICP
 filing.

How has the community developed since the last report?
 * The number of GitHub contributors increased to currently 682
 (2019-04-22; +4.4% since last report)
 * Active discussions on user forums
 https://discuss.mxnet.io/ (English, 1.6K registered users and 5.6K posts)
 https://discuss.gluon.ai/ (Chinese, 6K registered users and 19.2K posts)
 * Active blogs and social media presence
 Medium (https://medium.com/apache-mxnet) w/ 1.3k followers (+30% since
 last report).
 Twitter account (https://twitter.com/ApacheMXNet) w/ 2k followers on
 (+16% since last report)
 Meetup group
 (https://www.meetup.com/pro/deep-learning-with-apache-mxnet/) w/ 10
 groups in 8 countries, 1998 members (+65% since last report)
 (in China) Zhihu w/ 7.5k followers, WeChat official account w/ 3k
 followers. (reported for the first time)
* Active video channels
 YouTube channel (https://www.youtube.com/apachemxnet) w/ 627 subscriber
 (+29% since last report)
 Chinese YouTube channel
 (https://www.youtube.com/channel/UCjeLwTKPMlDt2segkZzw2ZQ) w/ 4.1k
 subscribers (reported for the first time)
 (in China) bilibili space (https://space.bilibili.com/209599371) w/ 13k
 subscriber (reported for the first time)
* Highlights in MXNet ecosystem
 MXNet Gluon book (www.d2l.ai, Dive into Deep Learning/D2L) released
 v1.0.0-rc0 (https://github.com/d2l-ai/d2l-zh/releases/tag/v1.0.0-rc0) in
 Chinese and v0.6.0 (https://github.com/d2l-ai/d2l-en/releases/tag/v0.6.0)
 in English. Dive into Deep Learning has 47.5K 28-day active users, and
 has attracted 9.3K stars & 200+ contributors on GitHub. It has been adopted
 as a textbook or reference book by 15+ universities in U.S., China, Spain,
 and Australia, such as UC Berkeley.
 GluonCV v0.4.0 release
 (https://github.com/dmlc/gluon-cv/releases/tag/v0.4.0)
 GluonNLP v0.6.0 release
 (https://github.com/dmlc/gluon-nlp/releases/tag/v0.6.0)
 MXNet Model Server v1.0.2 release
 (https://github.com/awslabs/mxnet-model-server/releases/tag/v1.0.2)
 GluonFace (https://github.com/THUFutureLab/gluon-face)
 DeepInsight (https://github.com/deepinsight/insightface)

How has the project developed since the last report?
 1) Released v1.4.0:
 https://github.com/apache/incubator-mxnet/releases/tag/1.4.0;
 https://blogs.apache.org/mxnet/entry/apache-mxnet-1-4-is1
 2) Started v1.4.1 patch release
 https://lists.apache.org/thread.html/3bb49a1016fafd0840d14f099ce47c7a1822da45f7ca2187c0f03c64@%3Cdev.mxnet.apache.org%3E;
 Started roadmap discussion on
 short-term 1.5.0 release
 https://github.com/apache/incubator-mxnet/issues/14619; Started roadmap
 discussion on long-term 2.0 plan
 https://github.com/apache/incubator-mxnet/issues/9686;
 3) Code donation from dmlc/mshadow. Community expressed the desire to
 assimilate dmlc/mshadow code base into mxnet.

https://lists.apache.org/thread.html/c1ba34330b0eb52ef3a3a30da6d34964a35a01c
320e93067e94ed306@%3Cdev.mxnet.apache.org%3E.
 After discussion, DMLC
 reached agreement to donate dmlc/mshadow code to mxnet, which is its sole
 consumer.
 4) Many ongoing projects: numpy-compatbile deep learning:
 https://github.com/apache/incubator-mxnet/projects/14; CPU performance
 and quantization: https://github.com/apache/incubator-mxnet/projects/15;
 Mixed precision GPU training (AMP):
 https://github.com/apache/incubator-mxnet/pull/14173, etc.
 5) Github statistics of last month:
 * March 24, 2019 – April 24, 2019: Excluding merges, 53 authors have
 pushed  102 commits to master and 137 commits to all branches. On master,
 495 files have changed and there have been 14,977 additions and 5,391
 deletions.

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

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

Date of last release:
 2019-03-04 MXNet 1.4.0

When were the last committers or PPMC members elected?

 As recent as 2019-04-12.

 New committer since last report (+12):
 Iblis Lin, Da Zheng, Steffen Rochel, Lin Yuan, Nicolas Modrzyk, Jackie
 Wu, Aston Zhang, Ding Kuo, Patric Zhao, Kevin Qin, Jiajun Wu, Jeremie
 Desgagne-Bouchard

 New PPMC member (+1):
 Qing Lan

Have your mentors been helpful and responsive or are things falling
through the cracks? In the latter case, please list any open issues
that need to be addressed.
 Mentors continue providing guidance and support.

Signed-off-by:

 [x](mxnet) Henri Yandell
 Comments: Noting that Gluon is a trademark of Microsoft + Amazon's.
 [ ](mxnet) Markus Weimer
 Comments:
 [x](mxnet) Michael Wall
 Comments:
 [ ](mxnet) Bob Paulin
 Comments:
 [ ](mxnet) Jason Dai
 Comments:

IPMC/Shepherd notes:
 Justin Mclean: You might want to talk to eCharts as they are also setting
 up a CDN with infra's help.

16 Jan 2019

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. Establish a predictable release process consistent with Apache Way --
 ESTABLISHED.
 2. Grow the community -- ONGOING.
 3. 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?
 * The community voted on updated process becoming project committer and
 PPMC member, vote results:
https://lists.apache.org/thread.html/458f234120ed8fbf98ddd57aa42eafe398cdd7e
aeb89ac2be9d214c7@%3Cdev.mxnet.apache.org%3E
 * The community voted on separating PMC and Committership -
https://lists.apache.org/thread.html/5bbd3b6daf5e89d17f92aed3aa8acd192841f11
822dcd21627ad5389@%3Cdev.mxnet.apache.org%3E
 * We have active contributors from companies like Nvidia, Intel, Wolfram
 Design and more. The number of github contributors increased from 506
 (3/31/2018) to currently 653 (2019-01-02; +10% since last report)
 * Active discussions on https://discuss.mxnet.io/ (English) and
 https://discuss.gluon.ai/ (Chinese)
 * We developed an active blog on Medium
 (https://medium.com/apache-mxnet) and have now 1k followers (+12.2% since
 last report).
 * We established a YouTube channel (https://www.youtube.com/apachemxnet)
 and have 485 subscriber (+31% since last report).
 * We are tweeting to promote new content and community events such as
 meetups and conferences. Our Twitter account has 1768 followers on
 https://twitter.com/ApacheMXNet (17% since last report).
 * We established a global MXNet meetup group with 8 groups (+4) in 7 (+4)
 countries and 1209 member (+45% since last report).
 https://www.meetup.com/pro/deep-learning-with-apache-mxnet/
 * Highlights in MXNet ecosystem: GluonCV v0.3.0 release
 (https://github.com/dmlc/gluon-cv/releases/tag/v0.3.0), GluonNLP v0.5.0
 release
 (https://github.com/dmlc/gluon-nlp/releases/tag/v0.5.0), MXNet Model
 Server v1.0.1
 release
 (https://github.com/awslabs/mxnet-model-server/releases/tag/v1.0.1),
 Gluon-zh book v0.7 release
 (https://github.com/d2l-ai/d2l-zh/releases/tag/v0.7),
 and active developments in Gluon-en (https://github.com/d2l-ai/d2l-en),
 GluonFace (https://github.com/THUFutureLab/gluon-face) and DeepInsight
 (https://github.com/deepinsight/insightface).

How has the project developed since the last report?
 1) Released v1.3.1:
 https://github.com/apache/incubator-mxnet/releases/tag/1.3.1
 2) Completed vote for 1.4.0 release on dev@:
 https://github.com/apache/incubator-mxnet/releases/tag/1.4.0;

https://lists.apache.org/thread.html/236554041a412415df783d652830272beb21d36
660e8e0d5e0237f58@%3Cdev.mxnet.apache.org%3E
  Started vote on general@ on December 28, 2018:
https://lists.apache.org/thread.html/a645bdb72bc55e05ec57f5b07a95c2579971f0a
32ed257f31199e218@%3Cgeneral.incubator.apache.org%3E
3) Github statistics:
  * https://github.com/apache/incubator-mxnet/pulse/monthly - December 2,
  2018 – January 2, 2019
    Excluding merges, 56 authors have pushed 114 commits to master and 126
    commits to all branches.
    On master, 359 files have changed and there have been 15,054 additions
    and 3,531 deletions.
    65 issues got closed and 61 new issues created during the reporting
    period.
  * https://github.com/apache/incubator-mxnet/pulse/monthly - November 2,
    2018 - December 1, 2018
    Excluding merges, 67 authors have pushed 202 commits to master and 283
    commits to all branches.
    On master, 521 files have changed and there have been 21,810 additions
    and 8,161 deletions.
    145 issues got closed and 98 new issue created during the reporting
    period.
  * https://github.com/apache/incubator-mxnet/pulse/monthly - October 2,
    2018 - November 1, 2018
    Excluding merges, 55 authors have pushed 125 commits to master and 135
    commits to all branches.
    On master, 617 files have changed and there have been 37,194 additions
    and 7,424 deletions.
    119 issues got closed and 131 new issue created during the reporting
    period.

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

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

Date of last release:

 2018-11-29 MXNet 1.3.1

When were the last committers or PPMC members elected?
 New committer:
 Qin Lang (November 21, 2018;

 https://lists.apache.org/thread.html/27a2943240180666ce9e9219a049322966c851a
259d908e241103d7a@%3Cdev.mxnet.apache.org%3E)
 Thomas Delteil (November 20, 2018;

 https://lists.apache.org/thread.html/d4ad366e81852547a732b4c31b7f3f020132e20
ba9c4cae7fd908f31@%3Cdev.mxnet.apache.org%3E)
 Kellen Sunderland (November 21, 2018;

 https://lists.apache.org/thread.html/4f18f85e627c7c0db5ad9ea46b1c277ef304f9d
445be6b7e1e3ae3af@%3Cdev.mxnet.apache.org%3E)
 Tao Lv (Novmember 21, 2018;

 https://lists.apache.org/thread.html/3b14dadf32c84b0249758f25db52602527311ec
0efc7bf94c93aaeee@%3Cdev.mxnet.apache.org%3E)
 Rahul Huilgol (December 4, 2018:

 https://lists.apache.org/thread.html/1b30b460d4bbc2ddb7a0bc7113e8d064ba75241
4d7d97a6e54d442f3@%3Cdev.mxnet.apache.org%3E)
 Aaron Markham (December 4, 2018;

 https://lists.apache.org/thread.html/f807eaa4e6477c6133e4949b7b38428adff5726
8620343d4c7c1e7ce@%3Cdev.mxnet.apache.org%3E)
 Da Zhang (December 17, 2018;

 https://lists.apache.org/thread.html/ffaa9425c9f5da77176e0ad92d136e70179720e
4f485456daf6d7231@%3Cdev.mxnet.apache.org%3E)

 No new PPMC members got elected in the reporting period.

Have your mentors been helpful and responsive or are things falling
through the cracks? In the latter case, please list any open issues
that need to be addressed.

 Mentors continue providing guidance and support.

Signed-off-by:

 [ ](mxnet) Sebastian Schelter
    Comments:
 [ ](mxnet) Henri Yandell
    Comments:
 [X](mxnet) Markus Weimer
    Comments:
 [X](mxnet) Jim Jagielski
 [X](mxnet) Michael Wall
    Comments: Discussion on unapproved releases started, but needs
    resolution.  Good effort on community building.
 [ ](mxnet) Bob Paulin
    Comments:
 [X](mxnet) Jason Dai
    Comments:

IPMC/Shepherd notes:
 Justin Mclean: The issue with unapproved releases and issues around that
 should have been mentioned in this report.

17 Oct 2018

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. Establish a predictable release process consistent with Apache Way --
 ESTABLISHED.
 2. Grow the community -- ONGOING.
 3. 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?
 * We have active contributors from companies like Intel, Nvidia, Wolfram
 Design  and more. The number of github contributors increased from 506
 (3/31/2018) to currently 592 (9/28/2018, 2.4% MoM),
 * Active discussions on https://discuss.mxnet.io/ (English) and
 https://discuss.gluon.ai/ (Chinese)
 * We developed an active blog on Medium
 (https://medium.com/apache-mxnet). We have posted 28 blogs YTD
 (12.0% MoM), and have 891 (16.3% MoM) followers.
 * We established a YouTube channel (https://www.youtube.com/apachemxnet)
 and have 370 subscriber (+32.1% MoM) and 32 videos published.
 * We are tweeting to promote new content and community events such as
 meetups and conferences. Our Twitter account has 1,510 followers on
 https://twitter.com/ApacheMXNet (3.6% MoM).
 * 62 new public tutorials about MXNet have been published YTD in
 addition to the Medium blogs. Details available at
https://lists.apache.org/thread.html/52f88e9dc7a6a2a1dfa5ad41c469fe2cdd1209a
0be2eb345bc2f9a96@%3Cuser.mxnet.apache.org%3E
https://lists.apache.org/thread.html/dea9184350f2fe87ce450722ead28072f763196
045f39859190f83f8@%3Cuser.mxnet.apache.org%3E
 https://discuss.mxnet.io/t/apache-mxnet-digest-august-2018
 * We established a global MXNet meetup group with 4 groups in 3
 countries and 833 member.
 https://www.meetup.com/pro/deep-learning-with-apache-mxnet/
 * Community members participated at conferences and other venues,
 staffing booths and giving a total of nine presentations about MXNet:
 ACNA18 (Montréal, total 4 presentations about MXNet from community
 members), Linux Foundation Open Source Summit (Vancouver), AI Meetup
 (Toronto), MXNet Meetup (Seattle, San Francisco), Open Data Science
 Conference (London), Big Data Summit (Boston), AI  and Neural Networks
 on Arm Summit at Linaro conference (Vancouver),O'Reilly AI conference
 https://www.oreilly.com/ideas/machine-learning-in-the-cloudm

How has the project developed since the last report?
 1) v1.3 release notes:
 https://github.com/apache/incubator-mxnet/releases/tag/1.3.0
 2) Community updated MXNet backend for Keras to v2.2.2
 3) Github statistics
 Statistics are captured at the end of each month from
 https://github.com/apache/incubator-mxnet/pulse/monthly
 July 2018
 * Excluding merges, 62 authors have pushed 180 commits to master and 188
 commits to all branches. On master, 614 files have changed and there
 have been 29,908 additions and 26,648 deletions.
 * 273 issues got closed and 154 new issue created during the reporting
 period.
 August 2018:
 * Excluding merges, 68 authors have pushed 217 commits to master and 234
 commits to all branches. On master, 463 files have changed and there
 have been 20,882 additions and 6,404 deletions.
 * 200 issues got closed and 98 new issue created during the reporting
 period.
 September 2018:
 * Excluding merges, 47 authors have pushed 132 commits to master and 141
 commits to all branches. On master, 439 files have changed and there
 have been 13,797 additions and 5,871 deletions.
 * 124 issues got closed and 87 new issue created during the reporting
 period.

How would you assess the podling's maturity?
Please feel free to add your own commentary.
 Podling is still having difficulties to grow committer community.
 Maturity == Medium.

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

Date of last release:

 2018-09-19 (v1.3) Apache MXNet-incubating 1.3.0 (major release)
 was published on September 19, 2018
 https://github.com/apache/incubator-mxnet/releases/tag/1.3.0

When were the last committers or PPMC members elected?
 2018-08-10 Carin Meier
 2018-09-27 additional mentors joined to help with diversity
 among MXNet's mentors:
 Jason Dai; Jim Jagielski; Bob Paulin; and Michael Wall.
 2018-09-27 Suneel Marthi retired as mentor

Have your mentors been helpful and responsive or are things falling
through the cracks? In the latter case, please list any open issues
that need to be addressed.
 We thank the mentors for ongoing support and guidance.

Signed-off-by:

 [ ](mxnet) Sebastian Schelter
    Comments:
 [X](mxnet) Jason Dai
    Comments:
 [X](mxnet) Henri Yandell
    Comments: Needs to work on converting contributors to committers.
 [ ](mxnet) Markus Weimer
    Comments:
 [X](mxnet) Jim Jagielski
    Comments:
 [ ](mxnet) Bob Paulin
    Comments:
 [X](mxnet) Michael Wall
    Comments:

IPMC/Shepherd notes:

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 Apr 2018

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

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. This is an ongoing improvement process where the focus will be to
reduce the scope of private discussions to only a few individuals before it is
put on the public dev@ mailing list so that the Apache MXNet community gets a
fair chance in influencing the final outcome/decision of the discussion.

 b) O'Reilly published a series of blogs about MXNet:
https://www.oreilly.com/ideas/anomaly-detection-with-apache-mxnet
https://www.oreilly.com/ideas/logo-detection-using-apache-mxnet
https://www.oreilly.com/ideas/generative-model-using-apache-mxnet
https://www.oreilly.com/ideas/build-a-recurrent-neural-network-using-apache-mxnet

c) A new blog post published on 5-Feb about MXNet Model Server for Apache
MXNet introduces ONNX support and Amazon CloudWatch integration
https://aws.amazon.com/blogs/machine-learning/model-server-for-apache-mxnet-introduces-onnx-support-and-amazon-cloudwatch-integration/

d) A new blog post published on 13-Mar about MXNet - Deploy Gluon Models to
AWS DeepLens using a simple Python API
https://aws.amazon.com/blogs/machine-learning/deploy-gluon-models-to-aws-deeplens-using-a-simple-python-api/

e) Borealis AI (AI lab of Royal Bank of Canada) published a new blog post on
Feb about the comparison between PyTorch and MXNet – Standardizing a machine
learning framework for applied research – PyTorch vs MXNet
http://www.borealisai.com/2018/02/16/standardizing-a-machine-learning-framework-for-applied-research/

f) Microsoft AI research published a new blog post on Mar about the
performance comparison between multiple frameworks including MXNet – Comparing
deep learning frameworks: A Rosetta stone approach
https://blogs.technet.microsoft.com/machinelearning/2018/03/14/comparing-deep-learning-frameworks-a-rosetta-stone-approach/

g) Beeva Labs published a new blog post on Feb about MXNet – Accelerating the
training of deep neural networks with MXNet on AWS P3 instances
https://www.bbva.com/en/accelerate-training-deep-neural-networks-mxnet-aws-p3-instances/

h) Webinars and talks on Apache MXNet on the social media platform:
https://www.youtube.com/watch?v=DSNvm29kIAo
https://www.youtube.com/watch?v=kINQpQiee7g
https://www.youtube.com/watch?v=kWgCbjtsAAM
https://www.youtube.com/watch?v=YjDTL_Kjfww

i) Thomas Delteil spoke at Meetup in Vancouver -
https://www.meetup.com/LearnDataScience/events/248473200/?rv=ea1&_xtd=gatlbWFpbF9jbGlja9oAJDA0ODlhZGJjLWQ0YzYtNDJmOC04MDFiLTZiMWU1OWEwOWEwYw
- and talked about Convolutional Neural Networks for NLP

j) A public meeting is planned for April 24th in Seattle -
https://cwiki.apache.org/confluence/display/MXNET/Seattle

k) MXNet 1.2 release is in preparation and release notes are being reviewed at
https://cwiki.apache.org/confluence/display/MXNET/%5BWIP%5D+Apache+MXNet+%28incubating%29+1.2.0+Release+Notes

l) Community voted and adopted Jira for issue management -
https://issues.apache.org/jira/projects/MXNET/issues/MXNET-267?filter=allopenissues

m) Established MXNet youtube channel -
https://www.youtube.com/channel/UCQua2ZAkbr_Shsgfk1LCy6A. Please subscribe!

n) MXNet 1.1 was released on 19-Feb, 2018 with extensive support and help from
various community members and timely guidance from the Apache MXNet Mentors.

How has the project developed since the last report?

a. The community released MXNet 1.1 that makes MXNet more stable and
production ready: https://blogs.apache.org/mxnet/entry/1-1-0-release-makes

b. From a statistics perspective, based on the Github insights, found here:
https://github.com/apache/incubator-mxnet/pulse/monthly, in Mar 2018, 60
authors pushed 164 commits to master, with updates to 624 files including
23.1K additions and 5.6K deletions. Historically, in Dec 2017, 51 authors
pushed 115 commits to master, with updates to 489 files including 13K
additions and 9K deletions. Historically, in Sep 2017, 62 authors pushed 171
commits to master, with updates to 467 files including 26K additions and 7K
deletions. We are working on finding more contributors to the project.

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

d. More advanced features (e.g. ONNX support, mixed precission modeling, Scala
inference API, MKL-DNN integratino etc) 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.


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

Date of last release: (latest to oldest releases) A major release Apache
MXNet-incubating 1.1.0 was released on 19-Feb, 2018.
https://github.com/apache/incubator-mxnet/releases/tag/1.1.0

When were the last committers or PPMC members elected?

Jan 18 2018 - Jun Wu and Marco de Abreu elected as committer and PPMC member



Signed-off-by:

  [X](mxnet) Sebastian Schelter
     Comments:
  [ ](mxnet) Suneel Marthi
     Comments:
  [X](mxnet) Henri Yandell
     Comments: I have noted on list that the comment on
   reducing private conversation needs to go much further. The sentiment in
   item (a) does not reach Apache's minimum bar and 'fair chance' is
   insulting.
  [ ](mxnet) Markus Weimer
     Comments:

17 Jan 2018

Apache MXNet is an open-source, scalable, distributed and high-performance
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. Apache MXNet
allows you to mix symbolic and imperative programming flavors to maximize both
efficiency and productivity. Apache 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 Apache MXNet library is
portable and lightweight, and it scales to multiple GPUs and multiple
machines.


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

Three 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. Bring website up to Apache
  standard -- ONGOING 4. Identify remaining ICLAs or SGAs that need signing --
  ONGOING

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

 Request the Incubator PMC to add more mentors to the project, preferably
 mentors employed with Open Source companies like Hortonworks that have prior
 experience in handling multiple open source projects that were part of an
 ecosystem.

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. If some discussions happen in private, they are eventually brought
out on dev@ with all perspectives well-represented. This is an ongoing
improvement process where the discussion will be put on the public dev@
mailing list so that the Apache MXNet community gets a fair chance in
influencing the final outcome/decision of the discussion.


 b) O’Reilly published a series of blogs about MXNet, including ones with deep
 matrix factorization using Apache MXNet:
 https://www.oreilly.com/ideas/sentiment-analysis-with-apache-mxnet
 https://www.oreilly.com/ideas/deep-matrix-factorization-using-apache-mxnet
 https://www.oreilly.com/ideas/apache-mxnet-in-the-wolfram-language

 c) A blog post published on 25-Oct about MXNet – an open source binary neural
 network implementation based on MXNet:
https://aws.amazon.com/blogs/ai/research-spotlig ht-bmxnet-an-open-source-binary-neural-network-implementation-based-on-mxnet/

 d) A blog post published on 01-Nov about the availability of Nvidia Volta GPU
 support and Sparse Tensor support:
https://aws.amazon.com/blogs/ai/a pache-mxnet-release-adds-support-for-new-nvidia-volta-gpus-and-sparse-tensor/

 e) A new blog post published on 08-Nov showing MXNet 0.12 extends Gluon
 Functionality:
https://aws.amazon.com/blogs/ai/apache-mxn et-version-0-12-extends-gluon-functionality-to-support-cutting-edge-research/

 f) A blog post published on 08-Nov introducing Model Server for MXNet:
 https://aws.amazon.com/blogs/ai/introducing-model-server-for-apache-mxnet/

 g) A blog post published on 7-Nov demonstrating performance and scalability
 of MXNet:
h ttps://techburst.io/mxnet-the-real-world-deep-learning-framework-2690e56ef81f

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

i) 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=me1qOzSg8MU
https://www.youtube.com/watch?v=9IrvDHRQaaA
https://www.youtube.com/watch?v=4PbSZRYXa3o
https://www.youtube.com/watch?v=RRy-3VXA0nw

j)  MXNet 1.0 was released on 04-Dec, 2017 with extensive support and help
from various community members and timely guidance from the Apache MXNet
Mentors.

How has the project developed since the last report?

a.  The community released MXNet 1.0 that is production ready, simplifies deep
learning experience, and significantly improves performance with cutting-edge
features described here:
https://blogs.apache.org/mxnet/entry/milestone-v1-0-release-for

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

c. Support for Perl language bindings - contributed by Sergey Kolychev.

d. More advanced features (e.g. sparse tensor, advanced indexing, gradient
compression) and bug-fixes requested by the user community continue to be
added.

e. Community took complete end-to-end ownership of the continuous integration
process in order to enable reliable testing on a wide set of back ends (IoT
devices to GPU clusters).



How would you assess the podling's maturity?

  Podling is still being established in Apache and no efforts being made in
  increasing community - hence maturity == Low.

Please feel free to add your own commentary.

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

Date of last release:

  2017-12-04

When were the last committers or PPMC members elected?

Sergey Kolychev was elected as a committer and PPMC member in October 2017 for
contributing the Perl language bindings. There is a plan to convert more
contributors into committers in early 2018.


Signed-off-by:

  [X](mxnet) Sebastian Schelter
     Comments:
  [X](mxnet) Suneel Marthi
     Comments: 1. All design decisions and project
   roadmap seem to be done internally at team huddles and rarely the community
   is ever involved in making any decisions. 2. There is no effort being made
   to discuss roadmap or project issues on public mail lists. 3. All roadmap
   planning seems to happen internally. 4. No effort is being made to build a
   diverse community around the project. 5. Most committers (all of whom are
   employed by a single vendor) appear to be resistant in moving to using
   apache tools such as JIRA or adopting the Apache Way of growing diverse
   community.
  [X](mxnet) Henri Yandell
     Comments:
  [X](mxnet) Markus Weimer
     Comments:

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.