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Date: Sun, 31 May 2015 16:58:36 -0400
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Subject: [ANNOUNCE] Apache Mahout 0.10.1 Released
From: Suneel Marthi <smarthi@apache.org>
To: mahout <dev@mahout.apache.org>, dev@bigtop.apache.org,
 dev@flink.apache.org,
	"user@mahout.apache.org" <user@mahout.apache.org>, user@bigtop.apache.org,
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The Apache Mahout PMC is pleased to announce the release of Mahout 0.10.1.
Mahout's goal is to create an environment for quickly creating machine
learning applications that scale and run on the highest performance
parallel computation engines available. Mahout comprises an interactive
environment and library that supports generalized scalable linear algebra
and includes many modern machine learning algorithms.

The Mahout Math environment we call =E2=80=9CSamsara=E2=80=9D for its symbo=
l of universal
renewal. It reflects a fundamental rethinking of how scalable machine
learning algorithms are built and customized. Mahout-Samsara is here to
help people create their own math while providing some off-the-shelf
algorithm implementations. At its base are general linear algebra and
statistical operations along with the data structures to support them. It=
=E2=80=99s
written in Scala with Mahout-specific extensions, and runs most fully on
Spark.

To get started with Apache Mahout 0.10.1, download the release artifacts
and signatures from http://www.apache.org/dist/mahout/0.10.1/.


Many thanks to the contributors and committers who were part of this
release. Please see below for the Release Highlights.


RELEASE HIGHLIGHTS

This is an incremental minor release over Mahout 0.10.0 meant to fix a few
bugs and upgrade to Spark 1.2.2 or less.

Mahout 0.10.1

   1.

   This release fixes a major memory usage bug in co-occurrence analysis
   used by the driver spark-itemsimilarity MAHOUT-1707. This will now
   require far less memory in the executor.
   2.

   Support Spark 1.2.2 or less - due to a bug in Spark 1.2+ in the
   JavaSerializer (SPARK-6069) we removed the use of Guava from any code
   executed in Spark Executors. To do this we created a Scala Collections
   based BiMap so any example code showing how to use the old Guava
   collections is obsolete.
   3.

   Some minor fixes to Mahout-Samsara QR Decomposition and matrix ops.
   4.

   Trim down packages size to < 200MB - MAHOUT-1704.
   5.

   Minor testing indicates binary compatibility with Spark 1.3 except for
   the Mahout Shell, which does not run.


STATS

A total of 9 separate JIRA issues are addressed in this release [2] with 5
bugfixes.


Scope of Mahout 0.10.2 ~ targeted for June 28, 2015

   1.

   In-core transpose view rewrites. Modifiable transpose views (for (col <-
   a.t) col :=3D 5).
   2.

   Matrix structure flavor additions. (understand general matrix structure
   and stride direction).
   3.

   %*% optimization based on matrix flavors.
   4.

   In-core ::=3D sparse assignment functions.
   5.

   Assign :=3D optimization (do proper traversal based on matrix flavors,
   similarly to %*%).
   6.

   Adding in-place elementwise functional assignment (e.g. mxA :=3D exp _,
   mxA ::=3D exp _).
   7.

   Distributed and in-core version of simple elementwise analogues of
   scala.math._. for example, for log(x) the convention is dlog(drm),
   mlog(mx), vlog(vec). Unfortunately we cannot overload these functions ov=
er
   what is done in scala.math, i.e. scala would not allow log(mx) or log(dr=
m)
   and log(Double) at the same time, mainly because they are being defined =
in
   different packages.
   8.

   Distributed performance bug fixes. This relates mostly to (a) matrix
   multiplication deficiencies, and (b) handling parallelism.
   9.

   Distributed allreduceBlock predicate.
   10.

   Distributed optimizer operators for elementwise functions. Rewrites
   recognizing e.g. 1+ drmX * dexp(drmX) as a single fused elementwise
   physical operator.
   11.

   More cbind, rbind flavors (e.g. 1 cbind mxX, 1 cbind drmX or the other
   way around).


Mahout 0.11.0-snapshot (ongoing, but available)

   1.

   Support for Spark 1.3 sequence file write.
   2.

   Spark Shell (timing TBD).
   3.

   First release that would see integration of Apache Mahout with Apache
   Flink as a backend.


GETTING STARTED

Download the release artifacts and signatures at
http://www.apache.org/dist/mahout/0.10.1/
The examples directory contains several working examples of the core
functionality available in Mahout. These can be run via scripts in the
examples/bin directory. Most examples do not need a Hadoop cluster in order
to run.

FUTURE PLANS

We will continue bug fixes and enhancements on the 0.10.x branch, which
will remain dependent on Spark 1.2.x. Support for Spark 1.3 will be in the
master branch reflecting Mahout-0.11.0-SNAPSHOT. To see progress on this
branch look here: https://github.com/apache/mahout/commits/master.  As of
this writing it is not ready yet to build for Spark 1.3.

Integration with Apache Flink is in the works in collaboration with TU
Berlin and Data Artisans to add Flink as the 3rd execution engine to
Mahout. This would be in addition to existing Apache Spark and H2O engines.

CONTRIBUTING


If you are interested in contributing, please see our How to Contribute
<http://mahout.apache.org/developers/how-to-contribute.html>[3] page or
contact us via email at dev@mahout.apache.org.

CREDITS

As with any release, we wish to thank all of the users and contributors to
Mahout. Please see the CHANGELOG [1] and JIRA Release Notes [2] for
individual credits, as there are too many to list here.


[1] https://github.com/apache/mahout/blob/mahout-0.10.x/CHANGELOG

[2]
https://issues.apache.org/jira/browse/MAHOUT-1707?jql=3Dproject%20%3D%20MAH=
OUT%20AND%20status%20in%20%28Resolved%2C%20closed%29%20AND%20%28fixVersion%=
20%3D%200.10.1%29
<https://issues.apache.org/jira/browse/MAHOUT-1707?jql=3Dproject%20%3D%20MA=
HOUT%20AND%20status%20in%20%28Resolved%2C%20closed%29%20AND%20%28fixVersion=
%20%3D%200.10.1%20OR%20fixVersion%20%3D%200.11.0%29>

[3] http://mahout.apache.org/developers/how-to-contribute.html

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