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, <oo...@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.sapu...@gmail.com> >> Date: Thu, February 5, 2015 2:57 pm >> To: "general@incubator.apache.org" <general@incubator.apache.org> >> Cc: oo...@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.n...@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.n...@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.sapu...@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.n...@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 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) - 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-unsubscr...@incubator.apache.org >>>>>> For additional commands, e-mail: general-h...@incubator.apache.org >>>>>> >>>>> >>>>> --------------------------------------------------------------------- >>>>> To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org >>>>> For additional commands, e-mail: general-h...@incubator.apache.org >>>>> >>> >>> --------------------------------------------------------------------- >>> To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org >>> For additional commands, e-mail: general-h...@incubator.apache.org >>> >> >> >> > --------------------------------------------------------------------- To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org For additional commands, e-mail: general-h...@incubator.apache.org
