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From Henry Saputra <henry.sapu...@gmail.com>
Subject Re: [VOTE] Accept SystemML into Apache Incubator
Date Wed, 28 Oct 2015 06:57:43 GMT
+1 (binding)

Good luck guys!


On Tue, Oct 27, 2015 at 9:52 PM, Luciano Resende <luckbr1975@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 ===
>       * private@sysml.incubator.apache.org (moderated subscriptions)
>       * commits@sysml.incubator.apache.org
>       * dev@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/

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