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From Hitesh Shah <hit...@apache.org>
Subject Re: [DISCUSS] SystemML Incubator Proposal
Date Sat, 24 Oct 2015 02:22:18 GMT
Hi Luciano, 

If you need any additional mentors, let me know. I would be interested in helping out. 

thanks
— Hitesh 


On Oct 23, 2015, at 4:34 PM, Luciano Resende <luckbr1975@gmail.com> wrote:

> We would like to start a discussion on accepting SystemML as an Apache
> Incubator project.
> 
> The proposal is available at :
> https://wiki.apache.org/incubator/SystemM
> 
> And it's contents is also copied below.
> 
> Thanks in Advance for you time reviewing and providing feedback.
> 
> ==============
> 
> = 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 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 Hadoop or Spark clusters.
> 
> == Background ==
> 
> SystemML started as a research project in the IBM Almaden Research Center
> around 2010 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 Spark or Hadoop. 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 Hadoop or
> 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 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
> * Alpine: Holden Karau
> * 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 and Mike Dusenberry.
> 
> == 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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