+1 I am not nearly as worried about the committer diversity, certainly not relative to entry into incubator. This is a great project that has already shown some very strong willingness to work with others in the short time I have interacted with them.
On Tue, Mar 10, 2015 at 11:49 AM, Thejas Nair <thejas.n...@gmail.com> wrote: > Thanks for raising this issue. I agree that committer diversity is > important for long term success of a project. I think that should be a > criteria for graduation from incubator. > I think it is going to be more easier to find new contributors as an Apache > incubator project. > > > On Tue, Mar 10, 2015 at 9:09 AM, jan i <j...@apache.org> wrote: > > > > > +0 I am really concerned about the diversity of the initial committers, > > what happens if the university pulls the plug. I know we all say it will > > never happen, but it could happen. > > > > rgds > > jan i. > > > > > > On 10 March 2015 at 16:20, Alan Gates <alanfga...@gmail.com> wrote: > > > >> +1 > >> > >> Alan. > >> > >> Thejas Nair <thejas.n...@gmail.com> > >> March 10, 2015 at 7:33 > >> The Singa Incubator Proposal document has been updated based on > >> feedback in the proposal thread. > >> > >> This vote is proposing the inclusion of Apache Singa as incubator > project. > >> The vote will run for at least 72 hours. > >> > >> [ ] +1 Accept Apache Singa into the Incubator > >> [ ] +0 Don’t care. > >> [ ] -1 Don’t accept Apache Singa into the Incubator because.. > >> > >> Please vote ! > >> > >> Here is my +1 . > >> > >> Link to version of proposal being voted on : > >> https://wiki.apache.org/incubator/SingaProposal?action=recall&rev=10 > >> > >> The text is below > >> ---------------------------------------------- > >> > >> = 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 Mahout and Apache Spark's ML-LIB are general machine learning > >> systems. Deep > >> learning algorithm can thus be implemented on these two platforms as > >> well. However, the there are differences in training efficiency, > >> scalability and > >> usability. Mahout and Spark ML-LIB follow models where their > >> nodes run synchronously. This is the fundamental difference to Singa who > >> follows the parameter server framework (like Google Brain and Microsoft > >> Adam). Singa can run synchronously or asynchronously. The asynchronous > >> mode > >> is superior than the synchronous mode in terms of scalability. In > >> addition, Singa has some optimizations towards deep learning models > >> (e.g., model > >> parallelism, data parallelism and hybrid-parallelism) which make Singa > >> more efficient. We also provide ease of use programming model for deep > >> learning algorithms. > >> > >> There are also plans for integration with Apache Hadoop's HDFS as > >> storage, to handle large training data. > >> Specifically, we store the training data (e.g., images or raw features > of > >> images) in HDFS, then (pre-)fetch them online. > >> We will also explore integration with Hadoop's Yarn and Apache Mesos > >> to do resource management. > >> > >> > >> == 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 > >> nusi...@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) > >> > >> === Nominated Mentors === > >> * Thejas Nair (thejas at apache.org) > >> * Alan Gates (gates at apache dot org) > >> * Daniel Dai (daijy at apache dot org) > >> * Ted Dunning (tdunning at apache dot org) > >> > >> === Sponsoring Entity === > >> We are requesting the Incubator to sponsor this project. > >> > >> --------------------------------------------------------------------- > >> To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org > >> For additional commands, e-mail: general-h...@incubator.apache.org > >> > >> > > >
