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开源软件名称:Mahout开源软件地址:https://gitee.com/apache/mahout开源软件介绍:Welcome to Apache Mahout!The goal of the Apache Mahout™ project is to build an environment for quickly creating scalable, performant machine learning applications. For additional information about Mahout, visit the Mahout Home Page Setting up your EnvironmentWhether you are using the Mahout- shell, running command line jobs, or using it as a library to build apps, you will need to set-up several environment variables. Edit your environment in export MAHOUT_HOME=/path/to/mahoutexport MAHOUT_LOCAL=true # for running standalone on your dev machine, # unset MAHOUT_LOCAL for running on a cluster You will need Using Mahout as a LibraryRunning any application that uses Mahout will require installing a binary or source version and setting the environment.To compile from source:
To use Maven, add the appropriate setting to your pom.xml or build.sbt following the template below. To use the Samsara environment you'll need to include both the engine neutral math-scala dependency: <dependency> <groupId>org.apache.mahout</groupId> <artifactId>mahout-math-scala</artifactId> <version>${mahout.version}</version></dependency> and a dependency for back end engine translation, e.g: <dependency> <groupId>org.apache.mahout</groupId> <artifactId>mahout-spark</artifactId> <version>${mahout.version}</version></dependency> Building From SourcePrerequisites:Linux Environment (preferably Ubuntu 16.04.x) Note: Currently, only the JVM-only build will work on a Mac.gcc > 4.xNVIDIA Card (installed with OpenCL drivers alongside usual GPU drivers) DownloadsInstall java 1.7+ in an easily accessible directory (for this example, ~/java/)http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html Create a directory ~/apache/. Download apache Maven 3.3.9 and un-tar/gunzip to ~/apache/apache-maven-3.3.9/ .https://maven.apache.org/download.cgi Download and un-tar/gunzip Hadoop 2.4.1 to ~/apache/hadoop-2.4.1/ .https://archive.apache.org/dist/hadoop/common/hadoop-2.4.1/ Download and un-tar/gunzip spark-1.6.3-bin-hadoop2.4 to ~/apache/ .http://spark.apache.org/downloads.htmlChoose release: Spark-1.6.3 (Nov 07 2016)Choose a package type: Pre-Built for Hadoop 2.4 Install ViennaCL 1.7.0+If running Ubuntu 16.04+ sudo apt-get install libviennacl-dev Otherwise if your distribution’s package manager does not have a viennniacl-dev package >1.7.0, clone it directly into the directory which will be included in when being compiled by Mahout: mkdir ~/tmpcd ~/tmp && git clone https://github.com/viennacl/viennacl-dev.gitcp -r viennacl/ /usr/local/cp -r CL/ /usr/local/ Ensure that the OpenCL 1.2+ drivers are all installed (packed with most consumer-grade NVIDIA drivers). Not sure about higher-end cards. Clone mahout repository into git clone https://github.com/apache/mahout.git ConfigurationWhen building mahout for a spark backend, we need four System Environment variables set: export MAHOUT_HOME=/home/<user>/apache/mahout export HADOOP_HOME=/home/<user>/apache/hadoop-2.4.1 export SPARK_HOME=/home/<user>/apache/spark-1.6.3-bin-hadoop2.4 export JAVA_HOME=/home/<user>/java/jdk-1.8.121 Mahout on Spark regularly uses one more env variable, the IP of the Spark clusters' master node (usually, the node hosting the session user). To use four local cores (Spark master need not be running) export MASTER=local[4] To use all available local cores (again, Spark master need not be running) export MASTER=local[*] To point to a cluster with spark running: export MASTER=spark://master.ip.address:7077 We then add these to the path: PATH=$PATH$:MAHOUT_HOME/bin:$HADOOP_HOME/bin:$SPARK_HOME/bin:$JAVA_HOME/bin These get appended to the users' ~/.bashrc file. Building Mahout with Apache MavenCurrently, Mahout has three builds. From the $MAHOUT_HOME directory, we may issue the commands to build each using mvn profiles. JVM only: mvn clean install -DskipTests JVM with native OpenMP level 2 and level 3 matrix/vector Multiplication mvn clean install -Pviennacl-omp -Phadoop2 -DskipTests JVM with native OpenMP and OpenCL for Level 2 and level 3 matrix/vector Multiplication. (GPU errors fall back to OpenMP, and currently, only a single GPU/node is supported). mvn clean install -Pviennacl -Phadoop2 -DskipTests Testing the Mahout EnvironmentMahout provides an extension to the spark-shell that is good for getting to know the language, testing partition loads, prototyping algorithms, etc. To launch the shell in local mode with two threads - simply do the following: $ MASTER=local[2] mahout spark-shell After a very verbose startup, a Mahout welcome screen will appear: Loading /home/andy/sandbox/apache-mahout-distribution-0.13.0/bin/load-shell.scala...import org.apache.mahout.math._import org.apache.mahout.math.scalabindings._import org.apache.mahout.math.drm._import org.apache.mahout.math.scalabindings.RLikeOps._import org.apache.mahout.math.drm.RLikeDrmOps._import org.apache.mahout.sparkbindings._sdc: org.apache.mahout.sparkbindings.SparkDistributedContext = org.apache.mahout.sparkbindings.SparkDistributedContext@3ca1f0a4 _ __ __ ___ __ _| |__ ___ _ _| |_ '_ ` _ \ / _` | '_ \ / _ \| | | | __| | | | | (_| | | | | (_) | |_| | |__| |_| |_|\__,_|_| |_|\___/ \__,_|\__| version 0.13.0That file does not existscala> At the scala> prompt, enter: scala> :load /home/<andy>/apache/mahout/examples /bin/SparseSparseDrmTimer.mscala Which will load a matrix multiplication timer function definition. To run the matrix timer: scala> timeSparseDRMMMul(1000,1000,1000,1,.02,1234L) {...} res3: Long = 16321 Note the 14.1 release is missing a class required for this will be fixed in 14.2. We can see that the JVM only version is slow, thus our motive for GPU and Native Multithreading support. To understand the processes getting performed under the hood of the timer, we may examine the .mscala (mahout scala) code that is both fully functional scala and the Mahout R-Like DSL for tensor algebra: def timeSparseDRMMMul(m: Int, n: Int, s: Int, para: Int, pctDense: Double = .20, seed: Long = 1234L): Long = { val drmA = drmParallelizeEmpty(m , s, para).mapBlock(){ case (keys,block:Matrix) => val R = scala.util.Random R.setSeed(seed) val blockB = new SparseRowMatrix(block.nrow, block.ncol) blockB := {x => if (R.nextDouble < pctDense) R.nextDouble else x } (keys -> blockB) } val drmB = drmParallelizeEmpty(s , n, para).mapBlock(){ case (keys,block:Matrix) => val R = scala.util.Random R.setSeed(seed + 1) val blockB = new SparseRowMatrix(block.nrow, block.ncol) blockB := {x => if (R.nextDouble < pctDense) R.nextDouble else x } (keys -> blockB) } var time = System.currentTimeMillis() val drmC = drmA %*% drmB // trigger computation drmC.numRows() time = System.currentTimeMillis() - time time } For more information, please see the following references: http://mahout.apache.org/users/environment/in-core-reference.html http://mahout.apache.org/users/environment/out-of-core-reference.html http://mahout.apache.org/users/sparkbindings/play-with-shell.html http://mahout.apache.org/users/environment/classify-a-doc-from-the-shell.html Note that due to an intermittent out-of-memory bug in a Flink-based test, we have disabled it from the binary releases. To use Flink, please uncomment the line in the root pom.xml in the ExamplesFor examples of how to use Mahout, see the examples directory located in For information on how to contribute, visit the How to Contribute Page LegalPlease see the |
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