Running On AWS

One option is to use the spark-submit option with the Zingg config and phase. Please note that the config.json should be available locally at the driver for Zingg to use it.

aws emr create-cluster --name "Add Spark Step Cluster" --release-label emr-6.2.0 --applications Name=Zingg \
--ec2-attributes KeyName=myKey --instance-type <instance type> --instance-count <num instances> \
--steps Type=Spark,Name="Zingg",ActionOnFailure=CONTINUE,Args=[--class,zingg.spark.client.SparkClient,<s3 location of zingg.jar>,--phase,<name of phase - findTrainingData,match etc>,--conf,<local location of config.json>] --use-default-roles````

A step-by-step is provided here. The guide mentions training locally using Zingg Docker, but the findTrainingData and label phases can be executed on EMR directly.

A second option is to run Zingg Python code in AWS EMR Notebooks