Vote has passed with 6 binding votes from : Luciano Resende, Henry Saputra, Chris A Mattmann Till Westmann Seetharam Venkatesh Julian Hyde
And two non-binding votes from: Arun Manoharan Madhawa Kasun Gunasekara and no other votes. Thanks On Wed, Oct 28, 2015 at 1:52 AM, Luciano Resende <luckbr1...@gmail.com> wrote: > After initial discussion, please vote on the acceptance of SystemML > Project for incubation at the Apache Incubator. The full proposal is > available at the end of this message and on the wiki at : > > https://wiki.apache.org/incubator/SystemML > <http://wiki.apache.org/incubator/Nuvem> > > Please cast your votes: > > [ ] +1, bring SystemML into Incubator > [ ] +0, I don't care either way > [ ] -1, do not bring SystemML into Incubator, because... > > The vote is open for the next 72 hours and only votes from the > Incubator PMC are binding. > > > = SystemML = > > == Abstract == > > SystemML provides declarative large-scale machine learning (ML) that aims > at flexible specification of ML algorithms and automatic generation of > hybrid runtime plans ranging from single node, in-memory computations, to > distributed computations on Apache Hadoop MapReduce and Apache Spark. ML > algorithms are expressed in an R-like syntax, that includes linear algebra > primitives, statistical functions, and ML-specific constructs. This > high-level language significantly increases the productivity of data > scientists as it provides (1) full flexibility in expressing custom > analytics, and (2) data independence from the underlying input formats and > physical data representations. Automatic optimization according to data > characteristics such as distribution on the disk file system, and sparsity > as well as processing characteristics in the distributed environment like > number of nodes, CPU, memory per node, ensures both efficiency and > scalability. > > == Proposal == > > The goal of SystemML is to create a commercial friendly, scalable and > extensible machine learning framework for data scientists to create or > extend machine learning algorithms using a declarative syntax. The machine > learning framework enables data scientists to develop algorithms locally > without the need of a distributed cluster, and scale up and scale out the > execution of these algorithms to distributed Apache Hadoop MapReduce or > Apache Spark clusters. > > == Background == > > SystemML started as a research project in the IBM Almaden Research Center > around 2007 aiming to enable data scientists to develop machine learning > algorithms independent of data and cluster characteristics. > > == Rationale == > > SystemML enables the specification of machine learning algorithms using a > declarative machine learning (DML) language. DML includes linear algebra > primitives, statistical functions, and additional constructs. This > high-level language significantly increases the productivity of data > scientists as it provides (1) full flexibility in expressing custom > analytics and (2) data independence from the underlying input formats and > physical data representations. > > SystemML computations can be executed in a variety of different modes. It > supports single node in-memory computations and large-scale distributed > cluster computations. This allows the user to quickly prototype new > algorithms in local environments but automatically scale to large data > sizes as well without changing the algorithm implementation. > > Algorithms specified in DML are dynamically compiled and optimized based > on data and cluster characteristics using rule-based and cost-based > optimization techniques. The optimizer automatically generates hybrid > runtime execution plans ranging from in-memory single-node execution to > distributed computations on Apache Spark or Apache Hadoop MapReduce. This > ensures both efficiency and scalability. Automatic optimization reduces or > eliminates the need to hand-tune distributed runtime execution plans and > system configurations. > > == Initial Goals == > > The initial goals to move SystemML to the Apache Incubator is to broaden > the community foster the contributions from data scientists to develop new > machine learning algorithms and enhance the existing ones. Ultimately, this > may lead to the creation of an industry standard in specifying machine > learning algorithms. > > == Current Status == > > The initial code has been developed at the IBM Almaden Research Center in > California and has recently been made available in GitHub under the Apache > Software License 2.0. The project currently supports a single node (in > memory computation) as well as distributed computations utilizing Apache > Hadoop MapReduce or Apache Spark clusters. > > === Meritocracy === > > We plan to invest in supporting a meritocracy. We will discuss the > requirements in an open forum. Several companies have already expressed > interest in this project, and we intend to invite additional developers to > participate. We will encourage and monitor community participation so that > privileges can be extended to those that contribute operating to the > standard of meritocracy that Apache emphasizes. > > === Community === > > The need for a generic scalable and declarative machine learning approach > in the open source is tremendous, so there is a potential for a very large > community. We believe that SystemML’s extensible architecture, declarative > syntax, cost based optimizer and its alignment with Spark will further > encourage community participation not only in enhancing the infrastructure > but also speed up the creation of algorithms for a wide range of use > cases. We expect that over time SystemML will attract a large community. > > === Alignment === > > The initial committers strongly believe that a generic scalable and > declarative machine learning approach for machine learning will gain > broader adoption as an open source, community driven project, where the > community can contribute not only to the core components, but also to a > growing collection of algorithms which will leverage the optimizations and > ease of scaling in SystemML. Our hope is that the Apache Spark, Apache > Hadoop and other communities will find tremendous value in SystemML and > this will foster further collaboration between these projects furthering > the already existing integration points. > > == Known Risks == > > To-date, development has been sponsored by IBM and coordinated mostly by > the core team of researchers at the IBM Almaden Research Center. > > For SystemML to fully transition to an "Apache Way" governance model, it > needs to start embracing the meritocracy-centric way of growing the > community of contributors. > > === Orphaned Products === > > The SystemML developers and previous sponsor have a long-term interest in > use and maintenance of the code and there is also hope that growing a > diverse community around the project will become a guarantee against the > project becoming orphaned. We feel that it is also important to put formal > governance in place both for the project and the contributors as the > project expands. We feel ASF is the best location for this. > > === Inexperience with Open Source === > > The current SystemML set of contributors are very diverse regarding > participation in Open Source. While some initial members are experiencing > an open source project for the first time, others have been contributing > and mentoring various Apache and non-Apache open source projects. > > === Reliance on Salaried Developers === > > SystemML currently receives substantial support from salaried developers. > However, they are all passionate about the project, and we are confident > that the project will continue even if no salaried developers contribute to > the project. We are committed to recruiting additional committers including > non-salaried developers. > > > === Relationships with Other Apache Products === > > Currently, SystemML integrates with Apache Hadoop MapReduce and Apache > Spark as underlying computational distributed runtimes. > > === An Excessive Fascination with the Apache Brand === > > SystemML solves a real need for generic scalable and declarative machine > learning approach for machine learning in the Apache Hadoop and Spark > ecosystems, something that has been addressed in a very ad hoc manner so > far by multiple Apache projects. Our rationale for developing SystemML as > an Apache project is detailed in the Rationale section. We believe that the > Apache brand and community process will help us attract more contributors > to this project, and help establish ubiquitous APIs. > > > == Documentation == > > Documentation regarding SystemML is available in the current GitHub > repository https://github.com/SparkTC/systemml/tree/master/system-ml/docs. > > > == Initial Source == > > Initial source is available on GitHub under the Apache License 2.0 > > https://github.com/SparkTC/systemml > > == Source and Intellectual Property Submission Plan == > > We know of no legal encumbrances in the transfer of source code and rights > to Apache. In fact, given the internal IBM due diligence performed on the > source code during open sourcing, we expect the code base to be free from > any IP issues. > > == External Dependencies == > > SystemML is written in Java and currently supports Apache Hadoop MapReduce > and Apache Spark runtimes. > > To the best of our knowledge, all dependencies of SystemML are distributed > under Apache compatible licenses. Upon acceptance to the incubator, we > would begin a thorough analysis of all transitive dependencies to verify > this fact and introduce license checking into the build and release process > (for instance integrating Apache Rat). > > Cryptography > N/A > > == Required Resources == > > === Mailing lists === > * priv...@sysml.incubator.apache.org (moderated subscriptions) > * comm...@sysml.incubator.apache.org > * d...@sysml.incubator.apache.org > > === Git Repository === > * https://git-wip-us.apache.org/repos/asf/incubator-sysml.git > > === Issue Tracking === > * JIRA (SYSML) > > == Initial Committers == > > * Luciano Resende (lresende AT apache DOT org) > * Berthold Reinwald (reinwald AT us DOT ibm DOT com) > * Matthias Boehm (mboehm AT us DOT ibm DOT com) > * Shirish Tatikonda (statiko AT us DOT ibm DOT com) > * Niketan Pansare (npansar AT us DOT ibm DOT com) > * Prithviraj Sen (senp AT us DOT ibm DOT com) > * Alexandre V Evfimievski (evfimi AT us DOT ibm DOT com) > * Fred Reiss (frreiss AT us DOT ibm DOT com) > * Deron Eriksson (deron AT us DOT ibm DOT com) > * Arvind Surve (asurve AT us DOT ibm DOT com) > * Mike Dusenberry (mwdusenb AT us DOT ibm DOT com) > * Reynold Xin (rxin AT apache DOT org) > * Xiangrui Meng (meng AT apache DOT org) > * Joseph Bradley (jkbradley AT apache DOT org) > * Patrick Wendell (pwendell AT apache DOT org) > * Holden Karau (holden AT apache DOT org) > * DB Tsai (dbtsai AT apache DOT org) > > == Affiliations == > > * DataBricks: Reynold Xin, Xiangrui Meng, Joseph Bradley, Patrick Wendell > * Netflix: DB Tsai > * IBM: Luciano Resende, Berthold Reinwald, Matthias Boehm, Shirish > Tatikonda, Niketan Pansare, Prithviraj Sen, Alexandre V Evfimievski, Fred > Reiss, Deron Eriksson, Arvind Surve, Mike Dusenberry and Holden Karau. > > == Sponsors == > > === Champion === > * Luciano Resende > > === Nominated Mentors === > * Luciano Resende > * Reynold Xin > * Patrick Wendell > * Rich Bowen > > === Sponsoring Entity === > We would like to propose the Apache Incubator to sponsor this project. > > > -- > Luciano Resende > http://people.apache.org/~lresende > http://twitter.com/lresende1975 > http://lresende.blogspot.com/ > -- Luciano Resende http://people.apache.org/~lresende http://twitter.com/lresende1975 http://lresende.blogspot.com/
