Machine Learning Jobs

Determines the authorization credentials that are used to connect Control-M to AWS SageMaker.

Rules:

• Characters: 1−30

• Case Sensitive: Yes

• Invalid Characters: Blank spaces.

Determines the name of the preexisting AWS SageMaker pipeline used in this job.

(Optional) Defines the unique ID (idempotency token) that guarantees the job is executed only once. After successful execution, this ID cannot be used again.

To allow a rerun of the job with a new token, replace the default value with a unique ID that has not been used before. Use the RUN_ID, which can be retrieved from the job output.

Default: Token_Control-M_for_SageMaker%%ORDERID.

Determines whether to add or change default parameters in the execution of the pipeline.

Defines the parameters, in JSON format, to add or change, according to the AWS SageMaker convention. The list of parameters must begin with the name of the parameter type.

{ "Name": "string", "Value": "string" }

Determines whether to retry the execution of a pipeline, which you might want to do if a previous execution fails or stops.

Defines the Amazon Resource Name (ARN) of the pipeline, which is required to retry the execution of the pipeline.

An ARN is a standardized AWS resource address.

Determines the number of seconds to wait before checking the status of the job.

Default: 30

Determines the authorization credentials that are used to connect to Control-M to Azure Machine Learning.

Rules:

• Characters: 1−30

• Case Sensitive: Yes

• Invalid Characters: Blank spaces.

Determines the name of the Azure Machine Learning workspace for the job.

Determines the Azure resource group that is associated with a specific Azure Machine Learning workspace.

A resource group is a container that holds related resources for an Azure solution. The resource group can include all the resources for the solution, or only those resources that you want to manage as a group.

Determines one of the following Azure Machine Learning actions to perform:

• Trigger Endpoint Pipeline: Starts a pipeline.

• Compute Management: Stops, starts, restarts, or deletes a computer or a cluster.

Determines the pipeline endpoint ID, which points to a published pipeline in Azure Machine Learning.

Defines additional parameters for the pipeline, in JSON format.

{
    "ExperimentName": "test",
    "DisplayName":"test1123"
}

For no parameters, type {}.

Defines the name of the compute function.

Determines one of the following compute actions to perform:

• Start: Starts a computer or a cluster.

• Stop: Stops a computer or a cluster.

• Restart: Restarts a computer or a cluster.

• Delete: Deletes a computer or a cluster.

Determines the number of seconds to wait before checking the status of the job.

Default: 15