Apache Mahout
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| Apache Mahout | |
|---|---|
| Developer | Apache Software Foundation |
| Initial release | 7 April 2009; 16 years ago (2009年04月07日)[1] |
| Stable release | |
| Repository | Mahout Repository |
| Written in | Java, Scala |
| Operating system | Cross-platform |
| Type | Machine Learning |
| License | Apache License 2.0 |
| Website | mahout |
Apache Mahout is a project of the Apache Software Foundation to produce free implementations of distributed or otherwise scalable machine learning algorithms focused primarily on linear algebra. In the past, many of the implementations use the Apache Hadoop platform, however today it is primarily focused on Apache Spark.[3] [4] Mahout also provides Java/Scala libraries for common math operations (focused on linear algebra and statistics) and primitive Java collections. Mahout is a work in progress; a number of algorithms have been implemented.[5]
Features
[edit ]Samsara
[edit ]Apache Mahout-Samsara refers to a Scala domain specific language (DSL) that allows users to use R-Like syntax as opposed to traditional Scala-like syntax. This allows user to express algorithms concisely and clearly.
valG=B%*%B.t-C-C.t+(ksidotksi)*(s_qcrosss_q)
Backend agnostic
[edit ]Apache Mahout's code abstracts the domain-specific language from the engine where the code is run. While active development is done with the Apache Spark engine, users are free to implement any engine they choose- H2O and Apache Flink have been implemented in the past and examples exist in the code base.
GPU/CPU accelerators
[edit ]The JVM has notoriously slow computation. To improve speed, "native solvers" were added which move in-core, and by extension, distributed BLAS operations out of the JVM, offloading to off-heap or GPU memory for processing via multiple CPUs and/or CPU cores, or GPUs when built against the ViennaCL library.[6] ViennaCL is a highly optimized C++ library with BLAS operations implemented in OpenMP, and OpenCL. As of release 14.1, the OpenMP build considered to be stable, leaving the OpenCL build is still in its experimental POC phase.
Recommenders
[edit ]Apache Mahout features implementations of Alternating Least Squares, Co-Occurrence, and Correlated Co-Occurrence, a unique-to-Mahout recommender algorithm that extends co-occurrence to be used on multiple dimensions of data.
History
[edit ]Transition from Map Reduce to Apache Spark
[edit ]While Mahout's core algorithms for clustering, classification and batch based collaborative filtering were implemented on top of Apache Hadoop using the map/reduce paradigm, it did not restrict contributions to Hadoop-based implementations. Contributions that run on a single node or on a non-Hadoop cluster were also welcomed. For example, the 'Taste' collaborative-filtering recommender component of Mahout was originally a separate project and can run stand-alone without Hadoop.
Starting with the release 0.10.0, the project shifted its focus to building a backend-independent programming environment, code named "Samsara".[7] [8] [9] The environment consists of an algebraic backend-independent optimizer and an algebraic Scala DSL unifying in-memory and distributed algebraic operators. Supported algebraic platforms are Apache Spark, H2O, and Apache Flink.[citation needed ] Support for MapReduce algorithms started being gradually phased out in 2014.[10]
Release history
[edit ]| Version | Release date | Notes |
|---|---|---|
| 0.1 | 2009年04月07日 | |
| 0.2 | 2009年11月18日 | |
| 0.3 | 2010年03月17日 | |
| 0.4 | 2010年10月31日 | |
| 0.5 | 2011年05月27日 | |
| 0.6 | 2012年02月06日 | |
| 0.7 | 2012年05月16日 | |
| 0.8 | 2013年07月25日 | |
| 0.9 | 2014年02月01日 | |
| 0.10.0 | 2015年04月11日 | Samsara DSL |
| 0.10.1 | 2015年05月31日 | |
| 0.10.2 | 2015年08月06日 | |
| 0.11.0 | 2015年08月07日 | |
| 0.11.1 | 2015年11月06日 | |
| 0.11.2 | 2016年03月11日 | |
| 0.12.0 | 2016年04月11日 | Added Apache Flink engine |
| 0.12.1 | 2016年05月19日 | |
| 0.12.2 | 2016年06月13日 | |
| 0.13.0 | 2017年04月17日 | |
| 0.14.0 | 2019年03月07日 | Source only (no binaries) |
| 14.1 | 2020年10月07日 |
Developers
[edit ]Apache Mahout is developed by a community. The project is managed by a group called the "Project Management Committee" (PMC). The current PMC is Andrew Musselman, Andrew Palumbo, Drew Farris, Isabel Drost-Fromm, Jake Mannix, Pat Ferrel, Paritosh Ranjan, Trevor Grant, Robin Anil, Sebastian Schelter, Stevo Slavić.[11]
References
[edit ]- ^ "Apache Mahout: First release 0.1 released".
- ^ "Apache Mahout: Scalable machine learning and data mining" . Retrieved 6 March 2019.
- ^ "Introducing Apache Mahout". ibm.com. 2011. Retrieved 13 September 2011.
- ^ "InfoQ: Apache Mahout: Highly Scalable Machine Learning Algorithms". infoq.com. 2011. Retrieved 13 September 2011.
- ^ "Algorithms - Apache Mahout - Apache Software Foundation". cwiki.apache.org. 2011. Archived from the original on 22 December 2013. Retrieved 13 September 2011.
- ^ "Extending Mahout Samsara to GPU Clusters". Archived from the original on 3 November 2020. Retrieved 29 October 2020.
- ^ "Mahout-Samsara's In-Core Linear Algebra DSL Reference". Archived from the original on 2 August 2016. Retrieved 29 February 2016.
- ^ "Mahout-Samsara's Distributed Linear Algebra DSL Reference". Archived from the original on 2 August 2016. Retrieved 29 February 2016.
- ^ "Mahout 0.10.x: first Mahout release as a programming environment". www.weatheringthroughtechdays.com. Archived from the original on 9 October 2016. Retrieved 29 February 2016.
- ^ "MAHOUT-1510 ("Good-bye MapReduce")".
- ^ "Apache Committee Information".