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From Ted Dunning <ted.dunn...@gmail.com>
Subject 0xdata interested in contributing
Date Thu, 13 Mar 2014 00:44:00 GMT
I have been working with a company named 0xdata to help them contribute
some new software to Mahout.  This software will give Mahout the ability to
do highly iterative in-memory mathematical computations on a cluster or a
single machine. This software also comes with high performance distributed
implementations of k-means, logistic regression, random forest and other
algorithms.

I will be starting a thread about this on the dev list shortly, but I
wanted the PMC members to have a short heads up on what has been happening
now that we have consensus on the 0xdata side of the game.

I think that this has a major potential to bring in an enormous amount of
contributing community to Mahout.  Technically, it will, at a stroke, make
Mahout the highest performing machine learning framework around.

*Development Roadmap*

Of the requirements that people have been talking about on the main mailing
list, the following capabilities will be provided by this contribution:

1) high performance distributed linear algebra

2) basic machine learning codes including logistic regression, other
generalized
linear modeling codes, random forest, clustering

3) standard file format parsing system (CSV, Lucene, parquet, other) x
    (continuous, constant, categorical, word-like, text-like)

4) standard web-based basic applications for common operations

5) language bindings (Java, Scala, R, other)

6) interactive + batch use

7) common representation/good abstraction over representation

8) platform diversity, localhost, with/without ( Hadoop, Yarn, Mesos, EC2,
GCE )


*Backstory*

I was recently approached by the Sri Satish, CEO and co-founder of 0xdata
who
wanted to explore whether they could donate some portion of the h2o
framework and technology to Mahout.  I was skeptical since all that I had
previously seen was the application level demos for this system and was not
at all familiar with the technology underneath. One of the co-founders of
0xdata, however, is Cliff Click who was one of the co-authors of the server
HotSpot compiler.  That alone made the offer worth examining.

Over the last few weeks, the technical team of 0xdata has been working with
me to work out whether this contribution would be useful to Mahout.

My strong conclusion is that the donation, with some associated shim work
that 0xdata is committing to doing will satisfy roughly 80% of the goals
that have emerged other the last week or so of discussion.  Just as
important, this donation connects Mahout to new communities who are very
actively working at the frontiers machine learning which is likely to
inject lots of new blood and excitement into the Mahout community.  This
has huge potential outside of Mahout itself as well since having a very
strong technical infrastructure that we can all use across many projects
has the potential to have the same sort of impact on machine learning
applications and products that Hadoop has had for file-based parallel
processing.  Coming together on a common platform has the potential to
create markets that would otherwise not exist if we don't have this
commonality.


*Technical Underpinnings*

At the lowest level, the h2o framework provides a way to have named objects
stored in memory across a cluster in directly computable form.  H2o also
provides a very fine-grained parallel execution framework that allows
computation to be moved close to the data while maintaining computational
efficiency with tasks as small as milliseconds in scale.  Objects live on
multiple machines and live until they are explicitly deallocated or until
the framework is terminated.

Additional machines can join the framework, but data isn't automatically
balanced, nor is it assumed that failures are handled within the framework.
 As might be expected given the background of the authors, some pretty
astounding things are done using JVM magic so coding at this lowest level
is remarkably congenial.

This framework can be deployed as a map-only Hadoop program, or as a bunch
of independent programs which borg together as they come up.  Importantly,
it is trivial to start a single node framework as well for easy development
and testing.

On top of this lowest level, there are math libraries which implement low
level
operations as well as a variety of machine learning algorithms.  These
include
high quality implementations of a variety of machine learning programs
including
generalized linear modeling with binomial logistic regression and good
regularization, linear regression, neural networks, random forests and so
on.
There are also parsing codes which will load formatted data in parallel from
persistency layers such as HDFS or conventional files.

At the level of these learning programs, there are web interfaces which
allow
data elements in the framework to be created, managed and deleted.

There is also an R binding for h2o which allows programs to access and
manage h2o objects.  Functions defined in an R-like language can be applied
in parallel to
data frames stored in the h2o framework.

*Proposed Developer User Experience*

I see several kinds of users.  These include numerical developers (largely
mathematicians), Java or Scala developers (like current Mahout devs), and
data
analysts.

- Local h2o single-node cluster
- Temporary h2o cluster
- Shared h2o cluster

All of these modes will be facilitated by the proposed development.

*Complementarity with Other Platforms*

I view h2o as complementary with Hadoop and Spark because it provides a
solid in-memory execution engine as opposed to a general out-of-core
computation model that other map-reduce engines like Hadoop and Spark
implement or more general dataflow systems like Stratosphere, Tez or Drill.

Also, h2o provides no persistence but depends on other systems for that
such as NFS, HDFS, NAS or MapR.

H2o is also nicely complimentary to R in that R can invoke operations and
move data to and from h2o very easily.

*Required Additional Work*

Sparse matrices
Linear algebra bindings
Class-file magic to allow off-the-cuff function definitions

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