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From Thejas Nair <thejas.n...@gmail.com>
Subject Re: [Fwd: Re: [DISCUSS] [PROPOSAL] Singa for Apache Incubator]
Date Fri, 27 Feb 2015 23:44:03 GMT
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
>> >>>>>>
>> >>>>>
>> >>>>> ---------------------------------------------------------------------
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>> >>>>> For additional commands, e-mail: general-help@incubator.apache.org
>> >>>>>
>> >>>
>> >>> ---------------------------------------------------------------------
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>> >>> For additional commands, e-mail: general-help@incubator.apache.org
>> >>>
>> >>
>> >>
>> >>
>> >
>>
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