ake a look here for a guide on how to set that up.A remote artifact store as part of your stack. This is needed so that both your orchestration environment and SageMaker can read and write step artifacts. Check out the documentation page of the artifact store you want to use for more information on how to set that up and configure authentication for it.

An instance type that we want to execute our steps on. See here for a list of available instance types.

(Optional) An experiment that is used to group SageMaker runs. Check this guide to see how to create an experiment.

There are two ways you can authenticate your orchestrator to AWS to be able to run steps on SageMaker:

The recommended way to authenticate your SageMaker step operator is by registering or using an existing AWS Service Connector and connecting it to your SageMaker step operator. The credentials configured for the connector must have permissions to create and manage SageMaker runs (e.g. the AmazonSageMakerFullAccess managed policy permissions). The SageMaker step operator uses these aws-generic resource type, so make sure to configure the connector accordingly:

zenml service-connector register <CONNECTOR_NAME> --type aws -i

zenml step-operator register <STEP_OPERATOR_NAME> \

--flavor=sagemaker \

--role=<SAGEMAKER_ROLE> \

--instance_type=<INSTANCE_TYPE> \

#   --experiment_name=<EXPERIMENT_NAME> # optionally specify an experiment to assign this run to

zenml step-operator connect <STEP_OPERATOR_NAME> --connector <CONNECTOR_NAME>

zenml stack register <STACK_NAME> -s <STEP_OPERATOR_NAME> ... --set

If you don't connect your step operator to a service connector:

If using a local orchestrator: ZenML will try to implicitly authenticate to AWS via the default profile in your local AWS configuration file. Make sure this profile has permissions to create and manage SageMaker runs (e.g. the AmazonSageMakerFullAccess managed policy permissions).