Machine Learning Jobs

Determines the authorization credentials that are used to connect Control-M to Amazon SageMaker, as described in Machine Learning Connection Profiles.

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 runs only once. After it runs successfully, this ID cannot be reused.

To allow a re-execution 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 Amazon 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 job status.

Default: 30

Determines the authorization credentials that are used to connect to Control-M to Azure Machine Learning, as described in Azure Machine Learning Connection Profile Parameters.

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 host 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 host or a cluster.

• Stop: Stops a host or a cluster.

• Restart: Restarts a host or a cluster.

• Delete: Deletes a host or a cluster.

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

Default: 15

Determines the authorization credentials that are used to connect to Control-M to GCP Vertex AI, as described in GCP Vertex AI Connection Profile Parameters.

Rules:

• Characters: 1−30

• Case Sensitive: Yes

• Invalid Characters: Blank spaces.

Determines one of the following GCP Vertex AI actions to perform:

• Run a Pipeline

• Start Notebook Instance

• Stop Notebook Instance

Defines the name of the predefined Google Cloud project with configured APIs, authentication information, billing details, and job resources.

Defines the notebook instance when you run the job.

Defines a unique name of a single execution of a pipeline.

Defines the Google service account.

Defines the specifications of the pipeline that you run, which is usually a JSON file.

Defines the path to a GCP Cloud Storage bucket that serves as the root output directory of the pipeline.

Determines whether to include Pipeline Runtime Parameters.

Defines the parameters of the pipeline that you run in JSON format.

Defines the location of the notebook resources.

Default: us-central1-a

Determines the authorization credentials that are used to connect Control-M to OCI Data Science Services, as described in OCI Data Science Connection Profile Parameters.

Rules:

• Characters: 1−30

• Case Sensitive: Yes

• Invalid Characters: Blank spaces.

Variable Name: %%INF-ACCOUNT

Determines one of the following OCI Data Science actions:

• Start Job Run: Runs the OCI Data Science job.

• Start Pipeline Run: Activates the OCI Data Science pipeline.

• Create Model Deployment: Creates a Model Deployment.

• Create Notebook Session: Creates a Notebook Session.

• Delete Model Deployment: Deletes a Model Deployment.

• Delete Notebook Session: Deletes a Notebook Session.

Defines the parameters for the following actions, in JSON format, for example:

• Start Job Run

  Copy

  {
     "projectId":"ocid1.datascienceproject.oc1.phx.amaaaghfyu65876kjh345j35uagkr6es4j5txq2ehjqq4ioyqygq",
     "compartmentId":"ocid1.compartment.oc1..aaaaaaaahjo7g63l5dhmgepb7xjk34kj5k3j5dybd4wywxuz5aziyqpkvq",
     "jobId":"ocid1.datasciencejob.oc1.phx.amaaaaaatdg3y3qakj345kj34ed6v5kfuovupwaqeao3js2mcmcrk673w3fysq",
     "definedTags":{},
     "displayName":"test234",
     "freeformTags":{},
     "jobConfigurationOverrideDetails":{"jobType":"DEFAULT"},
     "jobLogConfigurationOverrideDetails":{"enableAutoLogCreation":false,"enableLogging":false}
  }

• Start Pipeline Run

  Copy

  {
     "pipelineId": "ocid1.datasciencepipeline.oc1.phx.amaaaaaatdg3y3qaqevmdwhe7kjhk345j345h6xiinyyj6rdkvj2l2lkzdvmwoq",
     "compartmentId": "ocid1.compartment.oc1..aaaaaaaahjo7g63l5dhmgepb7ljh3453uby4rdybd4wywxuz5aziyqpkvq"
  }

• Create Model Deployment

  Copy

  {
     "displayName": "Control_M",
     "projectId": "ocid1.datascienceproject.oc1.phx.amaaaahi34h5kj43h562ticili6ovduagkr6es4j5txq2ehjqq4ioyqygq",    
     "compartmentId": "ocid1.compartment.oc1..aaaaaaaahjo7g63l5dhmgepkjl5kj34lk5j34ikuby4rdybd4wywxuz5aziyqpkvq",
     "modelDeploymentConfigurationDetails": 
     {
        "deploymentType": "SINGLE_MODEL",
        "modelConfigurationDetails": 
        {
           "modelId": "ocid1.datasciencemodel.oc1.phx.amaaaaaatdg3y3qabio6hdklkj34l534kyfbllazy7n462yqidp3tjwqc7ga",
           "instanceConfiguration": 
           {
              "instanceShapeName": "VM.Standard.E4.Flex",
              "modelDeploymentInstanceShapeConfigDetails":
              {
                 "ocpus": 1.0,
                 "memoryInGBs": 16.0,
                 "cpuBaseline": null
              },
              "subnetId": null
           },            
        "scalingPolicy": 
           {                
              "policyType": "FIXED_SIZE",
              "instanceCount": 1
           },
        "bandwidthMbps": 10,
        "maximumBandwidthMbps": null
        }
     }
  }

• Create Notebook Session

  Copy

  {
     "displayName": "Demo",
     "projectId": "ocid1.datascienceproject.oc1.phx.amaaaaaathfgdh6rdejfghjpw62ticili6ovduagkr6es4j5txq2ehjqq4ioyqygq",
     "compartmentId": "ocid1.compartment.oc1..aaaaaaaahjo7g63l5dhmgepb7xfszhpgik6456fhfghywxuz5aziyqpkvq",
     "notebookSessionConfigDetails":
     {
        "shape": "VM.Standard.E4.Flex",
        "blockStorageSizeInGBs": 100,
        "subnetId": null,
        "privateEndpointId": null,
        "notebookSessionShapeConfigDetails": 
        {
           "ocpus": 4.0,
           "memoryInGBs": 64.0
        }
     }
  }

  For more information about the action parameters, see Oracle API documentation.

(Delete Model Deployment) Determines the OCID of the model deployment that is deleted.

(Delete Notebook Session) Determines the OCID of the notebook session that is deleted.

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

Default: 60