This sounds very exciting and enriching! Look forward to the developments
on this front.
On Thu, Mar 13, 2014 at 11:16 AM, SriSatish Ambati <srisat...@0xdata.com
wrote:
We are excited at the possibilities of this convergence.
A fan of Mahout 's vision and how it captured the imagination of machine
learning enthusiasts over the years..
(Still fondly recollect Isabel's spirited talk at ApacheCon years ago!)
We found that a real product, hacker and an open source developer culture
was the need.
The R community has also been looking for a package that solved
distributed
frames (in-memory) & parallel packages for the algorithms behind.
Our team has executed on a lots of these inspirations fast & furiously in
open source over the past two years.
We hope to enrich & fulfill the day-to-day workflows of the Machine
Learning users world-wide through this. 0xdata built H2O, which is an
open
source community inspired by the vision of Google-scale Machine Learning
for the Enterprise using existing APIs like R. H2O has been Apache v2,
https://github.com/0xdata/h2o for quite sometime now.
http://jira.0xdata.com/ Underlying a fine-grain in-memory parallelism
is a
distributed k/v store (non-blocking hashmap) purpose built for doing
quantitative queries at scale while keeping consistency [1]
With help of legendary Scientific Advise from Stanford's Rob Tibshirani,
Trevor Hastie, Stephen Boyd, a tight-feedback based partnership with
customers and phenomenal distributed systems & math teams [0], 0xdata
built some of the fastest and accurate [4] distributed algorithms
in-Memory
- Generalized Logistic Modeling, Gradient Boosting Machine, Random Forest
http://docs.0xdata.com/resources/algoroadmap.html
For example, our billion row regression takes under 6 seconds on a
48-node
cluster! These and other math legos are accessible via R, Java, Python,
JavaScript and Scala. Our R package is headed to CRAN this week & users
can
drive from an R REPL. http://docs.0xdata.com/Ruser/Rpackage.html
H2O is also easy to install on or off Hadoop, on or off cloud[3] or on a
laptop. http://docs.0xdata.com/deployment/hadoop.html
We have been brewing an authentic product focused movement of systems,
math, ml and data scientists and spreading the word in meetups -
( http://0xdata.com/h2o-2/community/ )
The vision is that a convergence with the Mahout community would bring
fresh infusion of product & hacker culture needed to potentially make
Mahout NextGen the principal platform for machine learning at scale that
can run in-memory, on Hadoop, or on Spark. This will be part of the
ongoing
evolution of the Hadoop ecosystem.
And I'd love to get the grass roots excited, energized and have dev,
users
and founders (Grant & Isabel) corral much needed support and momentum.
Tactically speaking -
Like all software projects, we have to start small & in expansive spirals
to converge at each level given the resources at hand. So a call to
action
and JIRAs are upcoming.
Our earliest integration is going to be along the lines of getting
minimal
artifacts (think mvn) & make the Mahout Matrix API connect over
Distributed
Frames.
- SparseMatrix is a real requirement on the road ahead & simple
extensions
to our core Distributed Frame [1] will get us some of the way.
Together we can make this happen.
Thanks, Ted, Ellen and others for introducing us to the community!
Looking forward, Sri
Reference:
[0] Team H2O http://0xdata.com/about/
[1] http://www.infoq.com/presentations/api-memory-analytics
[2] http://www.slideshare.net/0xdata/
[3] http://docs.0xdata.com/newuser/ec2.html
[4] http://test.0xdata.com
On Wed, Mar 12, 2014 at 6:16 PM, Andrew Musselman <
andrew.mussel...@gmail.com> wrote:
Sounds like a large positive step; looking forward to hearing more!
On Mar 12, 2014, at 5:44 PM, Ted Dunning <ted.dunn...@gmail.com>
wrote:
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
--
ceo & co-founder, 0 <http://www.0xdata.com/>*x*data Inc
+1-408.316.8192
