Thank you Edward Capriolo for willingness to be a mentor and contributor.

We look forward to building a wider community and rev up activity. More 
contributors and mentors are welcome.
'Scalar' project proposal is listed in mail chain below.

Thanks,
Sachin Ghai

https://drive.google.com/file/d/0B7mbLUemi6LFbzFQLXB1Z1p2dm8/view?usp=sharing

-----Original Message-----
From: Edward Capriolo [mailto:edlinuxg...@gmail.com]
Sent: 02 March 2017 09:05 PM
To: general@incubator.apache.org
Subject: Re: Proposal for an Apache Hama sub-project

On Mon, Feb 27, 2017 at 7:13 PM, Edward J. Yoon <edward.y...@samsung.com>
wrote:

> Thanks for your proposal.
>
> I of course think Apache Hama can be used for scheduling sync and
> async communication/computation networks with various topologies and
> resource allocation. However, I'm not sure whether this approach is
> also fit for modern microservice architecture? In my opinion, this can
> be discussed and cooked in Hama community as a sub-project until it's
> mature enough (CC'ing general@i.a.o. I'll be happy to read more
> feedbacks from ASF incubator community).
>
> P.S., It seems you referred to incubation proposal template. There's
> no need to add me as initial committer (I don't have much time to
> actively contribute to your project). And, I recently quit Samsung
> Electronics and joined to $200 billion sized O2O e-commerce company as
> a CTO.
>
> -----Original Message-----
> From: Sachin Ghai [mailto:sachin.g...@impetus.co.in]
> Sent: Monday, February 27, 2017 5:16 PM
> To: d...@hama.apache.org
> Subject: Proposal for an Apache Hama sub-project
>
> Hama Community,
>
> I would like to propose a sub-project for Apache Hama and initiate
> discussion around the proposal. The proposed sub-project named
> 'Scalar' is a scalable orchestration, training and serving system for
> machine learning and deep learning. Scalar would leverage Apache Hama
> to automate the distributed training, model deployment and prediction
> serving.
>
> More details about the proposal are listed below as per Apache project
> proposal template:
> Abstract
> Scalar is a general purpose framework for simplifying massive scale
> big data analytics and deep learning modelling, deployment, serving
> with high performance.
> Proposal
> It is a goal of Scalar to provide an abstraction framework which
> allows user to easily scale the functions of training a model,
> deploying a model and serving the prediction from underlying machine
> learning or deep learning framework. It is also the characteristic of
> its execution framework to orchestrate heterogeneous workload graphs
> utilizing Apache Hama, Apache Hadoop, Apache Spark and TensorFlow
> resources.
> Background
> The initial Scalar code was developed in 2016 and has been
> successfully beta tested for one of the largest insurance
> organizations in a client specific PoC. The motivation behind this
> work is to build a framework that provides abstraction on
> heterogeneous data science frameworks and helps users leverage them in
> the most performant way.
> Rationale
> There is a sudden deluge of machine learning and deep learning
> frameworks in the industry. As an application developer, it becomes a
> hard choice to switch from one framework to another without rewriting
> the application.
> Also, there is additional plumbing to be done to retrieve the
> prediction results for each model in different frameworks. We aim to
> provide an abstraction framework which can be used to seamlessly train
> and deploy the model at scale on multiple frameworks like TensorFlow,
> Apache Horn or Caffe.
> The abstraction further provides a unified layer for serving the
> prediction in the most performant, scalable and efficient way for a
> multi-tenant deployment. The key performance metrics will be reduction
> in training time, lower error rate and lower latency time for serving models.
> Scalar consists of a core engine which can be used to create flows
> described in terms of state, sequences and algorithms. The engine
> invokes execution context of Apache Hama to train and deploy models on
> target framework.
> Apache Hama is used for a variety of functions including parameter
> tuning and scheduling computations on a distributed cluster. A data
> object layer provides access to data from heterogeneous sources like
> HDFS, local, S3 etc.
> A REST API layer is utilized for serving the prediction functions to
> client applications. A caching layer in the middle acts as a latency
> improver for various functions.
> Initial Goals
> Some current goals include:
>
>   *   Build community.
>   *   Provide general purpose API for machine learning and deep learning
> training, deployment and serving.
>   *   Serve the predictions with low latency.
>   *   Run massive workloads via Apache Hama on TensorFlow, Apache Spark and
> Caffe.
>   *   Provide CPU and GPU support on-premise or on cloud to run the
> algorithms.
> Current Status
> Meritocracy
> The core developers understand what it means to have a process based
> on meritocracy. We will provide continuous efforts to build an
> environment that supports this, encouraging community members to
> contribute.
> Community
> A small community has formed within the Apache Hama project community
> and companies such as enterprise services and product company and
> artificial intelligence startup. There is a lot of interest in data
> science serving systems and Artificial intelligence simplification
> systems. By bringing Scalar into Apache, we believe that the community will 
> grow even bigger.
> Core Developers
> Edward J. Yoon, Sachin Ghai, Ishwardeep Singh, Rachna Gogia, Abhishek
> Soni, Nikunj Limbaseeya, Mayur Choubey Known Risks Orphaned Products
> Apache Hama is already a core open source component being utilized at
> Samsung Electronics, and Scalar is already getting adopted by major
> enterprise organizations. There is no direct risk for Scalar project
> to be orphaned.
> Inexperience with Open Source
> All contributors have experience using and/or working on Apache open
> source projects.
> Homogeneous Developers
> The initial committers are from different organizations such as
> Impetus, Chalk Digital, and Samsung Electronics.
> Reliance on Salaried Developers
> Few will be working as full-time open source developer. Other
> developers will also start working on the project in their spare time.
> Relationships with Other Apache Products
>
>   *   Scalar is being built on top of Apache Hama
>   *   Apache Spark is being used for machine learning.
>   *   Apache Horn is being used for deep learning.
>   *   The framework will run natively on Apache Hadoop and Apache Mesos.
> An Excessive Fascination with the Apache Brand Scalar itself will
> hopefully have benefits from Apache, in terms of attracting a
> community and establishing a solid group of developers, but also the
> relation with Apache Hadoop, Spark and Hama. These are the main
> reasons for us to send this proposal.
> Documentation
> Initial design of Scalar can be found at this
> link<https://drive.google.com/file/d/0B7mbLUemi6LFVHlFSzhONm
> Z4aU0/view?usp=s
> haring>.
> Initial Source
> Impetus Technologies (Impetus) will contribute the initial
> orchestration code base to create this project. Impetus plans to
> contribute the Scalar code base, test cases, build files, and
> documentation to the ASF under the terms specified in the ASF
> Corporate Contributor License and further develop it with wider
> community. Once at Apache, the project will be licensed under the ASF
> license.
> Cryptography
> Not applicable.
> Required Resources
> Mailing Lists
>
>   *   scalar-dev
>   *   scalar-pmc
> Subversion Directory
>
>   *   Git is the preferred source control system:
> git://git.apache.org/scalar
> Issue Tracking
>
>   *   a JIRA issue tracker, SCALAR
> Initial Committers
>
>   *   Sachin Ghai (sachin.ghai AT impetus DOT co DOT in)
>   *   Edward J. Yoon (edwardyoon AT apache DOT org)
>   *   Abhishek Soni (abhishek.soni AT impetus DOT co DOT in)
>   *   Ishwardeep Singh ( ishwardeep AT chalkdigital DOT com )
>   *   Nikunj Limbaseeya (nikunj.limbaseeya AT impetus DOT co DOT in)
>   *   Rachna Gogia (rachna AT hadoopsphere DOT org)
>   *   Mayur Choubey (mayur.choubey AT impetus DOT co DOT in)
> Affiliations
>
>   *   Sachin Ghai (Impetus)
>   *   Edward J. Yoon (Samsung Electronics)
>   *   Abhishek Soni (Impetus)
>   *   Ishwardeep Singh ( Chalk Digital)
>   *   Nikunj Limbaseeya (Impetus)
>   *   Rachna Gogia (HadoopSphere)
>   *   Mayur Choubey (Impetus)
> Sponsors
> <proposed>
> Champion
>
>   *   Edward J. Yoon <ASF member, Samsung Electronics >
> Nominated Mentors
>
>   *   Edward J. Yoon <ASF member, Samsung Electronics >
> Sponsoring Entity
> The Apache Hama project
>
> -- End of proposal --
>
> Thanks,
> Sachin Ghai
>
> ________________________________
>
>
>
>
>
>
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>
I do not believe the the Hama project has had activity for a long time. 1 + 
year. For example, have attempted to broach this discussion and got no official 
reply: https://issues.apache.org/jira/browse/HAMA-998.

I am interested in Scalar and I would like to take time and familiarize myself 
with it.  I do not believe I am the right champion but I can possibly be a 
mentor/contributor.

Thanks,
Edward

________________________________






NOTE: This message may contain information that is confidential, proprietary, 
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