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The following deployment patterns are supported for WSO2 ML.

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ValueDescription
localRuns Spark locally with one worker thread. There will be no multiple threads running in parallel.
local[k]Runs Spark locally with k number of threads. (K is ideally the number of cores in your machine).
local[*]Runs Spark locally with a number of worker threads that equals the number of logical cores in your machine.


With external Spark cluster
Anchor
External Spark Cluster
External Spark Cluster

By default, WSO2 ML runs with an inbuilt Apache Spark instance. However, when working with big data, you can handle those large data sets in a distributed environment through WSO2 ML. You can carry out data pre-processing and model building processes on an Apache Spark cluster to share the workload between the nodes of the cluster. Using a Spark cluster optimizes the performance and reduces the time consumed to build and train a machine learning model for a large data set.

Follow the steps below to run the ML jobs by connecting WSO2 ML to an external Apache Spark cluster.

Info
  • When following the instructions below you need to use Apache Spark version 1.4.1 with Apache Hadoop version 2.6 and later in the Apache Spark cluster.
  • The Spark deployment pattern can be Standalone, Yarn or Mesos.
  • WSO2 ML is unaware of the underlying configuration of the Spark cluster. It only interacts with the Spark master to which the jobs are submitted.

  1. Press Ctrl+C keys to shutdown the WSO2 ML server. For more information on shutting down WSO2 ML server, see Running the Product .

  2. In the <ML_HOME>/repository/conf/etc/spark-config.xml file, enter the Spark master URL as the value of the < spark.master> property as shown in the example below.

    Tip

    You can find the Spark Master URL in the Apache Spark Web UI as shown below.

    Image Removed

    Code Block
    languagexml
    <property name="spark.master">spark://{SPARK_MASTER_URL}:7077</property>
  3. Create a directory named <SPARK_HOME>/ml/ and copy the following jar files into it. These jar files can be found in the <ML_HOME>/repository/components/plugins directory.
    • org.wso2.carbon.ml.core_1.0.12.jar
    • org.wso2.carbon.ml.commons_1.0.12.jar
    • org.wso2.carbon.ml.database_1.0.12.jar
    • kryo_2.24.0.wso2v1.jar

  4. Create a file named spark-env.sh in the <SPARK_HOME>/conf/ directory and add the following entries.

    Code Block
    languagetext
    SPARK_MASTER_IP={SPARK_MASTER_URL}127.0.0.1
SPARK_CLASSPATH={SPARK_HOME}/ml/org.wso2.carbon.ml.core_1.0.12.jar:{SPARK_HOME}/ml/org.wso2.carbon.ml.commons_1.0.12.jar:{SPARK_HOME}/ml/org.wso2.carbon.ml.database_1.0.12.jar:{SPARK_HOME}/ml/kryo_2.24.0.wso2v1.jar

  5. Restart the external Spark cluster using the following commands:

    Code Block
    {SPARK_HOME}$ ./sbin/stop-all.sh
{SPARK_HOME}$ ./sbin/start-all.sh

  6. In the <ML_HOME>/repository/conf/etc/spark-config.xml file, enter the Spark master URL as the value of the < spark.master> property as shown in the example below.

    Tip

    You can find the Spark Master URL in the Apache Spark Web UI as shown below.

    Image Added

    Code Block
    languagexml
    <property name="spark.master">{SPARK_MASTER_URL}</property>

  7. Restart the WSO2 ML server. For more information on restarting WSO2 ML server, see Running the Product.

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  1. Setup DAS cluster using Carbon clustering. Configure it to have at least one worker node. For more information on setting up a DAS cluster, see Clustering Data Analytics Server.

  2. Install the following ML features in each DAS node from the P2 repository of your ML version. For more information on installing features, see Installing and Managing Features. 

    • Machine Learner Commons

    • Machine Learner Core

    • Machine Learner Database Service

  3. Stop all DAS nodes. For more information on stopping DAS nodes, see Running the Product in DAS documentation.

  4. Start DAS cluster again without initializing Spark contexts with CarbonAnalytics and ML features. Use the following options when starting the cluster.

    OptionPurpose
    -DdisableAnalyticsSparkCtx=trueTo disable CarbonAnalytics Spark context.
    -DdisableMLSparkCtx=trueTo disable ML Spark context.

  5. To configure ML to use DAS as the Spark cluster, set the following property in the <ML_HOME>/repository/conf/etc/spark-config.xml file.

    Code Block
    languagexml
     <property name="spark.master">{SPARK_MASTER}</property>


    Add the jars to Spark executor extra class path.

    • org.wso2.carbon.ml.commons_1.0.12.jar
    • org.wso2.carbon.ml.core_1.0.12.jar
    • org.wso2.carbon.ml.database_1.0.12.jar
    • spark-mllib_2.10_1.4.1.wso2v1.jar
    • arpack_combined_0.1.0.wso2v1.jar
    • breeze_2.10_0.11.1.wso2v1.jar
    • core_1.1.2.wso2v1.jar
    • jblas_1.2.3.wso2v1.jar
    • spire_2.10_0.7.4.wso2v1.jar 


    These should also be added to the Spark driver extra class path as Spark configuration properties in the <ML_HOME>/repository/conf/etc/spark-config.xml file as shown below.

    Code Block
    languagexml
    <property name="spark.driver.extraClassPath">{ML_HOME}/repository/components/plugins/org.wso2.carbon.ml.commons_1.0.12.jar:{ML_HOME}/repository/components/plugins/org.wso2.carbon.ml.core_1.0.12.jar:{ML_HOME}/repository/components/plugins/org.wso2.carbon.ml.database_1.0.12.jar:{ML_HOME}/repository/components/plugins/spark-mllib_2.10_1.4.1.wso2v1.jar:{ML_HOME}/repository/components/plugins/arpack_combined_0.1.0.wso2v1.jar:{ML_HOME}/repository/components/plugins/breeze_2.10_0.11.1.wso2v1.jar:{ML_HOME}/repository/components/plugins/core_1.1.2.wso2v1.jar:{ML_HOME}/repository/components/plugins/jblas_1.2.3.wso2v1.jar:{ML_HOME}/repository/components/plugins/spire_2.10_0.7.4.wso2v1.jar
</property>
    Code Block
    languagexml
    <property name="spark.executor.extraClassPath">{ML_HOME}/repository/components/plugins/org.wso2.carbon.ml.commons_1.0.12.jar:{ML_HOME}/repository/components/plugins/org.wso2.carbon.ml.core_1.0.12.jar:{ML_HOME}/repository/components/plugins/org.wso2.carbon.ml.database_1.0.12.jar:{ML_HOME}/repository/components/plugins/spark-mllib_2.10_1.4.1.wso2v1.jar:{ML_HOME}/repository/components/plugins/arpack_combined_0.1.0.wso2v1.jar:{ML_HOME}/repository/components/plugins/breeze_2.10_0.11.1.wso2v1.jar:{ML_HOME}/repository/components/plugins/core_1.1.2.wso2v1.jar:{ML_HOME}/repository/components/plugins/jblas_1.2.3.wso2v1.jar:{ML_HOME}/repository/components/plugins/spire_2.10_0.7.4.wso2v1.jar
</property>
  6. Enter values that are less than or equal to the allocated resources for Spark workers in the DAS cluster for the following two properties in the <ML_HOME>/repository/conf/etc/spark-config.xml file. This ensures that the ML does not call for unsatisfiable resources from the DAS Spark cluster.

    • spark.executor.memory: 

      Code Block
      languagexml
      <property name="spark.executor.memory">{memory_in_m/g}</property>

       

    • spark.executor.cores:

      Code Block
      languagexml
      <property name="spark.executor.cores">{number_of_cores}</property>

       

  7. Start the ML server. For more information on starting WSO2 ML server, see Running the Product.

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