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From Thejas Nair <thejas.n...@gmail.com>
Subject Re: [Fwd: Re: [DISCUSS] [PROPOSAL] Singa for Apache Incubator]
Date Tue, 03 Mar 2015 18:56:55 GMT
I have added Ted as a mentor, so we now have some diversity in mentor
affiliations. (Thanks Ted!)
I also reached out to few other people in mahout community who I
thought might be potentially interested, but I didn't hear from them.

I am planning to put this to a vote in 2 days. Meanwhile, please let
me know if anybody else would be willing to join as a mentor.

Thanks,
Thejas


On Fri, Feb 27, 2015 at 3:44 PM, Thejas Nair <thejas.nair@gmail.com> wrote:
> Thanks Ted. That helps a lot !
> I have also reached out to few other folks in Mahout community to see
> if they might also be interested.
>
>
> On Fri, Feb 27, 2015 at 8:06 AM, Ted Dunning <ted.dunning@gmail.com> wrote:
>> Thejas,
>>
>> Please add me as a mentor if it helps to have diversity.  I have enormous
>> trust based on previous experience with him that Alan Gates would act as a
>> highly impartial and effective mentor, but would be happy to help if there
>> is a concern that could be addressed by having another mentor from a
>> different company.
>>
>>
>>
>> On Thu, Feb 26, 2015 at 6:12 PM, Thejas Nair <thejas.nair@gmail.com> wrote:
>>
>>> The incubator proposal has been updated with the feedback so far.
>>> We have 3 mentors now, but I think it would be good to have additional
>>> mentors. Please let me know if anyone is able to help mentor this
>>> project.
>>>
>>> I am planning to start a vote on the proposal in a day or two.
>>>
>>>
>>> On Fri, Feb 6, 2015 at 5:21 PM,  <ooibc@comp.nus.edu.sg> wrote:
>>> >
>>> > Regarding the number of users using this project -- at this moment, the
>>> > community is not big.  A few local start-ups have been trying to use it
>>> > (mainly due to announcement in our seminar list), eg. one is using it for
>>> > image recognition (given a phone snapped by a user, it wants to be return
>>> > the same the product, and a list of similar products, such as a luxury
>>> bag
>>> > on a passerby).  Researchers from outside of NUS may have been using it
>>> > since we published an application paper on cross domain/modal retrieval
>>> in
>>> > VLDB 2014.
>>> >
>>> > We have not announced the project to the outside community yet -- we
>>> would
>>> > announce it in dbworld etc in due course.
>>> >
>>> > Thanks and have a good weekend.
>>> >
>>> > regards
>>> > beng chin
>>> >
>>> >>
>>> >> Thanks for the comments and suggestions.
>>> >> With permission from Thejas, I would like to respond to point 2.
>>> >>
>>> >> We have a huge team down at NUS (National University of Singapore) --
>>> >> we have about seven database/data mining data professors (not including
>>> >> those in systems, networking, and machine learning).
>>> >> I myself have nine PhD students in a steady state, and I have a few
>>> large
>>> >> grants, with a total budget of about 15 million S$ (~12 million USD),
>>> that
>>> >> allows me to hire a number of research fellows and research assistants
>>> for
>>> >> the next few years.  In a constant state, I have about 20 people (PhD
>>> >> students/RA/RF) working with me alone.  Other professors have their
own
>>> >> grants (unlike other countries, it is relatively easy to get large
>>> grants
>>> >> in Singapore; many overseas Universities, including UIUC, MIT, ETH etc
>>> >> have research labs funded by Singapore Research Foundation [equivalent
>>> of
>>> >> NSF]).
>>> >>
>>> >> SINGA is a long term project for us -- while it is a platform as it
is,
>>> we
>>> >> are using it for healthcare predictive analytics (by working with a
>>> >> hospital associated with the University).  Therefore, we will be working
>>> >> on SINGA, not solely as a distributed DL platform, but as a tool that
>>> will
>>> >> enable us to do data analytics on some business domains (eg. healthcase,
>>> >> consumer etc)
>>> >>
>>> >> For the initial set of committers, three are tenured professors, five
>>> are
>>> >> students, with 2-5 years to go before they complete their PhD.  Quite
>>> >> often, some would stay back as a research fellow for a couple of years
>>> >> before they start looking for a job outside.  We will work with mentors
>>> >> and new developers (from outside of NUS or Zhejiang University) in
>>> >> enhancing the system.
>>> >>
>>> >> The project should survive in that sense.
>>> >>
>>> >> (I have an on-going project CIIDAA that has been around since 2008;
it
>>> was
>>> >> started as another project, epiC,  with a different grant, and then
we
>>> >> continue the development with a new grant for CIIDAA --
>>> >> http://www.comp.nus.edu.sg/~ciidaa/
>>> >> )
>>> >>
>>> >> Thanks.
>>> >>
>>> >> regards
>>> >> beng chin
>>> >> ps: i am not sure if my email will get through to the group.
>>> >>
>>> >>
>>> >> ---------------------------- Original Message
>>> ----------------------------
>>> >> Subject: Re: [DISCUSS] [PROPOSAL] Singa for Apache Incubator
>>> >> From:    "Henry Saputra" <henry.saputra@gmail.com>
>>> >> Date:    Thu, February 5, 2015 2:57 pm
>>> >> To:      "general@incubator.apache.org" <general@incubator.apache.org>
>>> >> Cc:      ooibc@comp.nus.edu.sg
>>> >>
>>> --------------------------------------------------------------------------
>>> >>
>>> >> Several comments:
>>> >> -) How many users already using this project? I would reccomend to
>>> >> drop request for singa-user list at the beginning.
>>> >> -) All the initial committers come from university and seemed like
>>> >> some of them already ready to leave university. I am not too sure if
>>> >> this project go survive if all of the inital committers are from
>>> >> university as students.
>>> >> -) Need to solicit more mentors if this project ever get to Apache
>>> >> incubator.
>>> >>
>>> >> - Henry
>>> >>
>>> >> On Tue, Feb 3, 2015 at 3:58 PM, Thejas Nair <thejas.nair@gmail.com>
>>> wrote:
>>> >>> The "Relationship with Other Apache Products" section has been
>>> >>> updated. The reference to H2O in that section has been removed,
and
>>> >>> other projects have been added.
>>> >>>  Thanks for the feedback!
>>> >>>
>>> >>>
>>> >>> On Wed, Jan 28, 2015 at 10:27 AM, Thejas Nair <thejas.nair@gmail.com>
>>> >> wrote:
>>> >>>> Thanks for pointing that out Henry! Yes, looks like H20 is not
an
>>> >>>> apache project, I should have verified that.
>>> >>>> I will edit that, and revisit that section along with the folks
in
>>> >>>> Singa community.
>>> >>>>
>>> >>>>
>>> >>>> On Tue, Jan 27, 2015 at 6:55 PM, Henry Saputra
>>> >> <henry.saputra@gmail.com> wrote:
>>> >>>>> Quick immediate comment that "Apache H2O" is not really
Apache
>>> >>>>> project.
>>> >>>>>
>>> >>>>> I assume you are referring to https://github.com/h2oai/h2o
(or
>>> >>>>> https://github.com/h2oai/h2o-dev) ?
>>> >>>>>
>>> >>>>> - Henry
>>> >>>>>
>>> >>>>> On Tue, Jan 27, 2015 at 5:29 PM, Thejas Nair <thejas.nair@gmail.com>
>>> >> wrote:
>>> >>>>>> Hello everyone,
>>> >>>>>>
>>> >>>>>> I would like to propose the inclusion of Singa as an
Apache
>>> Incubator
>>> >> project.
>>> >>>>>>
>>> >>>>>> Here is the proposal -
>>> >>>>>> https://wiki.apache.org/incubator/SingaProposal
>>> >>>>>>
>>> >>>>>> Please review the proposal and give feedback. I am planning
to start
>>> >>>>>> a
>>> >>>>>> vote after 7 days if the proposal looks good.
>>> >>>>>> We are also seeking additional Apache mentors for the
project.
>>> >>>>>>
>>> >>>>>> Thanks,
>>> >>>>>> Thejas
>>> >>>>>> ==========================================================
>>> >>>>>> Singa Incubator Proposal
>>> >>>>>>
>>> >>>>>> Abstract
>>> >>>>>>
>>> >>>>>> SINGA is a distributed deep learning platform.
>>> >>>>>>
>>> >>>>>> Proposal
>>> >>>>>>
>>> >>>>>> SINGA is an efficient, scalable and easy-to-use distributed
platform
>>> >>>>>> for training deep learning models, e.g., Deep Convolutional
Neural
>>> >>>>>> Network and Deep Belief Network. It parallelizes the
computation
>>> >>>>>> (i.e., training) onto a cluster of nodes by distributing
the
>>> training
>>> >>>>>> data and model automatically to speed up the training.
Built-in
>>> >>>>>> training algorithms like Back-Propagation and Contrastive
Divergence
>>> >>>>>> are implemented based on common abstractions of deep
learning
>>> models.
>>> >>>>>> Users can train their own deep learning models by simply
customizing
>>> >>>>>> these abstractions like implementing the Mapper and
Reducer in
>>> >>>>>> Hadoop.
>>> >>>>>>
>>> >>>>>> Background
>>> >>>>>>
>>> >>>>>> Deep learning refers to a set of feature (or representation)
>>> learning
>>> >>>>>> models that consist of multiple (non-linear) layers,
where different
>>> >>>>>> layers learn different levels of abstractions (representations)
of
>>> >>>>>> the
>>> >>>>>> raw input data. Larger (in terms of model parameters)
and deeper (in
>>> >>>>>> terms of number of layers) models have shown better
performance,
>>> >>>>>> e.g.,
>>> >>>>>> lower image classification error in Large Scale Visual
Recognition
>>> >>>>>> Challenge. However, a larger model requires more memory
and larger
>>> >>>>>> training data to reduce over-fitting. Complex numeric
operations
>>> make
>>> >>>>>> the training computation intensive. In practice, training
large deep
>>> >>>>>> learning models takes weeks or months on a single node
(even with
>>> >>>>>> GPU).
>>> >>>>>>
>>> >>>>>> Rational
>>> >>>>>>
>>> >>>>>> Deep learning has gained a lot of attraction in both
academia and
>>> >>>>>> industry due to its success in a wide range of areas
such as
>>> computer
>>> >>>>>> vision and speech recognition. However, training of
such models is
>>> >>>>>> computationally expensive, especially for large and
deep models
>>> >>>>>> (e.g.,
>>> >>>>>> with billions of parameters and more than 10 layers).
Both Google
>>> and
>>> >>>>>> Microsoft have developed distributed deep learning systems
to make
>>> >>>>>> the
>>> >>>>>> training more efficient by distributing the computations
within a
>>> >>>>>> cluster of nodes. However, these systems are closed
source
>>> softwares.
>>> >>>>>> Our goal is to leverage the community of open source
developers to
>>> >>>>>> make SINGA efficient, scalable and easy to use. SINGA
is a full
>>> >>>>>> fledged distributed platform, that could benefit the
community and
>>> >>>>>> also benefit from the community in their involvement
in contributing
>>> >>>>>> to the further work in this area. We believe the nature
of SINGA and
>>> >>>>>> our visions for the system fit naturally to Apache's
philosophy and
>>> >>>>>> development framework.
>>> >>>>>>
>>> >>>>>> Initial Goals
>>> >>>>>>
>>> >>>>>> We have developed a system for SINGA running on a commodity
computer
>>> >>>>>> cluster. The initial goals include, * improving the
system in terms
>>> >>>>>> of
>>> >>>>>> scalability and efficiency, e.g., using Infiniband for
network
>>> >>>>>> communication and multi-threading for one node computation.
We would
>>> >>>>>> consider extending SINGA to GPU clusters later. * benchmarking
with
>>> >>>>>> larger datasets (hundreds of millions of training instances)
and
>>> >>>>>> models (billions of parameters). * adding more built-in
deep
>>> learning
>>> >>>>>> models. Users can train the built-in models on their
datasets
>>> >>>>>> directly.
>>> >>>>>>
>>> >>>>>> Current Status
>>> >>>>>>
>>> >>>>>> Meritocracy
>>> >>>>>>
>>> >>>>>> We would like to follow ASF meritocratic principles
to encourage
>>> more
>>> >>>>>> developers to contribute in this project. We know that
only active
>>> >>>>>> and
>>> >>>>>> excellent developers can make SINGA a successful project.
The
>>> >>>>>> committer list and PMC will be updated based on developers'
>>> >>>>>> performance and commitment. We are also improving the
documentation
>>> >>>>>> and code to help new developers get started quickly.
>>> >>>>>>
>>> >>>>>> Community
>>> >>>>>>
>>> >>>>>> SINGA is currently being developed in the Database System
Research
>>> >>>>>> Lab
>>> >>>>>> at the National University of Singapore (NUS) in collaboration
with
>>> >>>>>> Zhejiang University in China. Our lab has extensive
experience in
>>> >>>>>> building database related systems, including distributed
systems.
>>> Six
>>> >>>>>> PhD students and research assistants (Jinyang Gao, Kaiping
Zheng,
>>> >>>>>> Sheng Wang, Wei Wang, Zhaojing Luo and Zhongle Xie)
, a research
>>> >>>>>> fellow (Anh Dinh) and three professors (Beng Chin Ooi,
Gang Chen,
>>> >>>>>> Kian
>>> >>>>>> Lee Tan) have been working for a year on this project.
We are open
>>> to
>>> >>>>>> recruiting more developers from diverse backgrounds.
>>> >>>>>>
>>> >>>>>> Core Developers
>>> >>>>>>
>>> >>>>>> Beng Chin Ooi, Gang Chen and Kian Lee Tan are professors
who have
>>> >>>>>> worked on distributed systems for more than 20 years.
They have
>>> >>>>>> collaborated with the industry and have built various
large scale
>>> >>>>>> systems. Anh Dinh's research is also on distributed
systems, albeit
>>> >>>>>> with more focus on security aspects. Wei Wang's research
is on deep
>>> >>>>>> learning problems including deep learning applications
and large
>>> >>>>>> scale
>>> >>>>>> training. Sheng Wang and Jinyang are working on efficient
indexing,
>>> >>>>>> querying of large scale data and machine learning. Kaiping,
Zhaojing
>>> >>>>>> and Zhongle are new PhD students who jointed SINGA recently.
They
>>> >>>>>> will
>>> >>>>>> work on this project for a longer time (next 4-5 years).
While we
>>> >>>>>> share common research interests, each member also brings
diverse
>>> >>>>>> expertise to the team.
>>> >>>>>>
>>> >>>>>> Alignment
>>> >>>>>>
>>> >>>>>> ASF is already the home of many distributed platforms,
e.g., Hadoop,
>>> >>>>>> Spark and Mahout, each of which targets a different
application
>>> >>>>>> domain. SINGA, being a distributed platform for large-scale
deep
>>> >>>>>> learning, focuses on another important domain for which
there still
>>> >>>>>> lacks a robust and scalable open-source platform. The
recent success
>>> >>>>>> of deep learning models especially for vision and speech
recognition
>>> >>>>>> tasks has generated interests in both applying existing
deep
>>> learning
>>> >>>>>> models and in developing new ones. Thus, an open-source
platform for
>>> >>>>>> deep learning will be able to attract a large community
of users and
>>> >>>>>> developers. SINGA is a complex system needing many iterations
of
>>> >>>>>> design, implementation and testing. Apache's collaboration
framework
>>> >>>>>> which encourages active contribution from developers
will inevitably
>>> >>>>>> help improve the quality of the system, as shown in
the success of
>>> >>>>>> Hadoop, Spark, etc.. Equally important is the community
of users
>>> >>>>>> which
>>> >>>>>> helps identify real-life applications of deep learning,
and helps to
>>> >>>>>> evaluate the system's performance and ease-of-use. We
hope to
>>> >>>>>> leverage
>>> >>>>>> ASF for coordinating and promoting both communities,
and in return
>>> >>>>>> benefit the communities with another useful tool.
>>> >>>>>>
>>> >>>>>> Known Risks
>>> >>>>>>
>>> >>>>>> Orphaned products
>>> >>>>>>
>>> >>>>>> Four core developers (Anh, Wei Wang, Jinyang and Sheng
Wang) may
>>> >>>>>> leave
>>> >>>>>> the lab in two to four years time. It is possible that
some of them
>>> >>>>>> may not have enough time to focus on this project after
that. But,
>>> >>>>>> SINGA is part of our other bigger research projects
on building an
>>> >>>>>> infrastructure for data intensive applications, which
include
>>> >>>>>> health-care analytics and brain-inspired computing.
Beng Chin and
>>> >>>>>> Kian
>>> >>>>>> Lee would continue working on it and getting more people
involved.
>>> >>>>>> For
>>> >>>>>> example, three new developers (Kaiping, Zhaojing and
Zhongle) joined
>>> >>>>>> us recently. Individual developers are welcome to make
SINGA a
>>> >>>>>> diverse
>>> >>>>>> community that is robust and independent from any single
developer.
>>> >>>>>>
>>> >>>>>> Inexperience with Open Source
>>> >>>>>>
>>> >>>>>> All the developers are active users and followers of
open source
>>> >>>>>> projects. Our research lab has a strong commitment to
open source,
>>> >>>>>> and
>>> >>>>>> has released the source code of several systems under
open source
>>> >>>>>> license as a way of contributing back to the open source
community.
>>> >>>>>> But we do not have much real experience in open source
projects with
>>> >>>>>> large and well organized communities like those in Apache.
This is
>>> >>>>>> one
>>> >>>>>> reason we choose Apache which is experienced in open
source project
>>> >>>>>> incubation. We hope to get the help from Apache (e.g.,
champion and
>>> >>>>>> mentors) to establish a healthy path for SINGA.
>>> >>>>>>
>>> >>>>>> Homogenous Developers
>>> >>>>>>
>>> >>>>>> Although the current developers are researchers in the
universities,
>>> >>>>>> they have different research interests and project experiences,
as
>>> >>>>>> mentioned in the section that introduces the core developers.
We
>>> know
>>> >>>>>> that a diverse community is helpful. Hence we are open
to the idea
>>> of
>>> >>>>>> recruiting developers from other regions and organizations.
>>> >>>>>>
>>> >>>>>> Reliance on Salaried Developers
>>> >>>>>>
>>> >>>>>> As a research project in the university, SINGA's current
developing
>>> >>>>>> community consists of professors, PhD students, research
assistants
>>> >>>>>> and postdoctoral fellows. They are driven by their interests
to work
>>> >>>>>> on this project and have contributed actively since
the start of the
>>> >>>>>> project. The research assistants and fellows are expected
to leave
>>> >>>>>> when their contracts expire. However, they are keen
to continue to
>>> >>>>>> work on the project voluntarily. Moreover, as a long
term research
>>> >>>>>> project, new research assistants and fellows are likely
to join the
>>> >>>>>> project.
>>> >>>>>>
>>> >>>>>> A Excessive Fascination with the Apache Brand
>>> >>>>>>
>>> >>>>>> We choose Apache not for publicity. We have two purposes.
First, we
>>> >>>>>> want to leverage Apache's reputation to recruit more
developers to
>>> >>>>>> make a diverse community. Second, we hope that Apache
can help us to
>>> >>>>>> establish a healthy path in developing SINGA. Beng Chin
and Kian-Lee
>>> >>>>>> are established database and distributed system researchers,
and
>>> >>>>>> together with the other contributors, they sincerely
believe that
>>> >>>>>> there is a need for a widely accepted open source distributed
deep
>>> >>>>>> learning platform. The field of deep learning is still
at its
>>> >>>>>> infancy,
>>> >>>>>> and an open source platform will fuel the research in
the area.
>>> >>>>>> Moreover, such a platform will enable researchers to
develop new
>>> >>>>>> models and algorithms, rather than spending time implementing
a deep
>>> >>>>>> learning system from scratch. Furthermore, the need
for scalability
>>> >>>>>> for such a platform is obvious.
>>> >>>>>>
>>> >>>>>> Relationship with Other Apache Products
>>> >>>>>>
>>> >>>>>> Apache H2O implemented two simple deep learning models,
namely the
>>> >>>>>> Multi-Layer Perceptron and Deep Auto-encoders. There
are two
>>> >>>>>> significant differences between H2O and SINGA. First,
H2O adopts the
>>> >>>>>> Map-Reduce framework which runs a set of computing nodes
in parallel
>>> >>>>>> againsts of the training set. Model parameters trained
by all
>>> >>>>>> computing nodes are averaged as the final model parameters.
This
>>> >>>>>> training algorithm is different from the distributed
training
>>> >>>>>> algorithm used by DistBelief, Adam and SINGA, which
frequently
>>> >>>>>> synchronizes the parameters trained from different nodes.
SINGA
>>> >>>>>> adopts
>>> >>>>>> the parameter server framework to support a wide range
of
>>> distributed
>>> >>>>>> training algorithms and parallelization methods (e.g.,
data
>>> >>>>>> parallelism, model parallelism and hybrid parallelism.
H2O only
>>> >>>>>> support data parallelism) . Second, in H2O, users are
restricted to
>>> >>>>>> use the two built-in models. In SINGA, we provide simple
programming
>>> >>>>>> model to let users implement their own deep learning
models. A new
>>> >>>>>> deep learning model can be implemented by customizing
the base Layer
>>> >>>>>> class for each layer involved in the model. It is similar
to writing
>>> >>>>>> Hadoop programs where users only need to override the
base Mapper
>>> and
>>> >>>>>> Reducer. We also provide built-in models for users to
use directly.
>>> >>>>>>
>>> >>>>>> Documentation
>>> >>>>>>
>>> >>>>>> The project is hosted at
>>> >>>>>> http://www.comp.nus.edu.sg/~dbsystem/project/singa.html.
>>> >>>>>> Documentations can be found at the Github Wiki Page:
>>> >>>>>> https://github.com/nusinga/singa/wiki. We continue to
refine and
>>> >>>>>> improve the documentation.
>>> >>>>>>
>>> >>>>>> Initial Source
>>> >>>>>>
>>> >>>>>> We use Github to maintain our source code,
>>> >> https://github.com/nusinga/singa
>>> >>>>>>
>>> >>>>>> Source and Intellectual Property Submission Plan
>>> >>>>>>
>>> >>>>>> We plan to make our code base be under Apache License,
Version 2.0.
>>> >>>>>>
>>> >>>>>> External Dependencies
>>> >>>>>>
>>> >>>>>> required by the core code base: glog, gflags, google
protobuf,
>>> >>>>>> open-blas, mpich, armci-mpi.
>>> >>>>>> required by data preparation and preprocessing: opencv,
hdfs,
>>> python.
>>> >>>>>>
>>> >>>>>> Cryptography
>>> >>>>>>
>>> >>>>>> Not Applicable
>>> >>>>>>
>>> >>>>>> Required Resources
>>> >>>>>>
>>> >>>>>> Mailing Lists
>>> >>>>>>
>>> >>>>>> Currently, we use google group for internal discussion.
The mailing
>>> >>>>>> address is nusinga@googlegroup.com. We will migrate
the content to
>>> >>>>>> the
>>> >>>>>> apache mailing lists in the future.
>>> >>>>>>
>>> >>>>>> singa-dev
>>> >>>>>> singa-user
>>> >>>>>> singa-commits
>>> >>>>>> singa-private (for private discussion within PCM)
>>> >>>>>>
>>> >>>>>> Git Repository
>>> >>>>>>
>>> >>>>>> We want to continue using git for version control. Hence,
a git repo
>>> >>>>>> is required.
>>> >>>>>>
>>> >>>>>> Issue Tracking
>>> >>>>>>
>>> >>>>>> JIRA Singa (SINGA)
>>> >>>>>>
>>> >>>>>> Initial Committers
>>> >>>>>>
>>> >>>>>> Beng Chin Ooi (ooibc @comp.nus.edu.sg)
>>> >>>>>> Kian Lee Tan (tankl @comp.nus.edu.sg)
>>> >>>>>> Gang Chen (cg @zju.edu.cn)
>>> >>>>>> Wei Wang (wangwei @comp.nus.edu.sg)
>>> >>>>>> Dinh Tien Tuan Anh (dinhtta @comp.nus.edu.sg)
>>> >>>>>> Jinyang Gao (jinyang.gao @comp.nus.edu.sg)
>>> >>>>>> Sheng Wang (wangsh @comp.nus.edu.sg)
>>> >>>>>> Kaiping Zheng (kaiping @comp.nus.edu.sg)
>>> >>>>>> Zhaojing Luo (zhaojing @comp.nus.edu.sg)
>>> >>>>>> Zhongle Xie (zhongle @comp.nus.edu.sg)
>>> >>>>>>
>>> >>>>>> Affiliations
>>> >>>>>>
>>> >>>>>> Beng Chin Ooi, National University of Singapore
>>> >>>>>> Kian Lee Tan, National University of Singapore
>>> >>>>>> Gang Chen, Zhejiang University
>>> >>>>>> Wei Wang, National University of Singapore
>>> >>>>>> Dinh Tien Tuan Anh, National University of Singapore
>>> >>>>>> Jinyang Gao, National University of Singapore
>>> >>>>>> Sheng Wang, National University of Singapore
>>> >>>>>> Kaiping Zheng, National University of Singapore
>>> >>>>>> Zhaojing Luo, National University of Singapore
>>> >>>>>> Zhongle Xie, National University of Singapore
>>> >>>>>>
>>> >>>>>> Sponsors
>>> >>>>>>
>>> >>>>>> Champion
>>> >>>>>>
>>> >>>>>> Thejas Nair (thejas at apache.org) - Hortonworks
>>> >>>>>>
>>> >>>>>> Nominated Mentors
>>> >>>>>>
>>> >>>>>> Thejas Nair (thejas at apache.org) - Hortonworks
>>> >>>>>> Alan Gates (gates at apache dot org) - Hortonworks
>>> >>>>>> (Seeking more volunteers!)
>>> >>>>>>
>>> >>>>>> Sponsoring Entity
>>> >>>>>>
>>> >>>>>> We are requesting the Incubator to sponsor this project.
>>> >>>>>>
>>> >>>>>>
>>> ---------------------------------------------------------------------
>>> >>>>>> To unsubscribe, e-mail: general-unsubscribe@incubator.apache.org
>>> >>>>>> For additional commands, e-mail: general-help@incubator.apache.org
>>> >>>>>>
>>> >>>>>
>>> >>>>> ---------------------------------------------------------------------
>>> >>>>> To unsubscribe, e-mail: general-unsubscribe@incubator.apache.org
>>> >>>>> For additional commands, e-mail: general-help@incubator.apache.org
>>> >>>>>
>>> >>>
>>> >>> ---------------------------------------------------------------------
>>> >>> To unsubscribe, e-mail: general-unsubscribe@incubator.apache.org
>>> >>> For additional commands, e-mail: general-help@incubator.apache.org
>>> >>>
>>> >>
>>> >>
>>> >>
>>> >
>>>
>>> ---------------------------------------------------------------------
>>> To unsubscribe, e-mail: general-unsubscribe@incubator.apache.org
>>> For additional commands, e-mail: general-help@incubator.apache.org
>>>
>>>

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