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The following deployment patterns are supported for WSO2 ML.

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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.

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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.

   Code Block
   language xml
   <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_1.0.1.jar
   • org.wso2.carbon.ml.commons_1.0.1.jar
   • org.wso2.carbon.ml.database_1.0.1.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
   language text
   SPARK_MASTER_IP={SPARK_MASTER_URL}127.0.0.1 SPARK_CLASSPATH={SPARK_HOME}/ml/org.wso2.carbon.ml.core_1.0.1.jar:{SPARK_HOME}/ml/org.wso2.carbon.ml.commons_1.0.1.jar:{SPARK_HOME}/ml/org.wso2.carbon.ml.database_1.0.1.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. Restart the WSO2 ML server. For more information on restarting WSO2 ML server, see Running the Product.

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