> For the complete documentation index, see [llms.txt](https://docs.zingg.ai/latest/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zingg.ai/latest/running/aws.md).

# 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](https://blog.infostrux.com/identity-resolution-with-zingg-ai-snowflake-and-aws-emr-for-the-canadian-football-league-22cf0850ab53). 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](https://aws.amazon.com/emr/features/notebooks/)