Sagemaker

In this section, we provide guides and references to use the Sagemaker connector.

Configure and schedule Sagemaker metadata and profiler workflows from the OpenMetadata UI:

If you don't want to use the OpenMetadata Ingestion container to configure the workflows via the UI, then you can check the following docs to connect using Airflow SDK or with the CLI.

OpenMetadata 0.12 or later

To deploy OpenMetadata, check the Deployment guides.

To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with custom Airflow plugins to handle the workflow deployment.

OpenMetadata retrieves information about models and tags associated with the models in the AWS account. The user must have following policy set to ingest the metadata from Sagemaker.

For more information on Sagemaker permissions visit the AWS Sagemaker official documentation.

The first step is ingesting the metadata from your sources. Under Settings, you will find a Services link an external source system to OpenMetadata. Once a service is created, it can be used to configure metadata, usage, and profiler workflows.

To visit the Services page, select Services from the Settings menu.

Visit Services Page

Find Dashboard option on left panel of the settings page

Click on the 'Add New Service' button to start the Service creation.

Create a new service

Add a new Service from the Dashboard Services page

Select Sagemaker as the service type and click Next.

Select Service

Select your service from the list

Provide a name and description for your service as illustrated below.

OpenMetadata uniquely identifies services by their Service Name. Provide a name that distinguishes your deployment from other services, including the other {connector} services that you might be ingesting metadata from.

Add New Service

Provide a Name and description for your Service

In this step, we will configure the connection settings required for this connector. Please follow the instructions below to ensure that you've configured the connector to read from your sagemaker service as desired.

Configure service connection

Configure the service connection by filling the form

  • AWS Access Key ID & AWS Secret Access Key: When you interact with AWS, you specify your AWS security credentials to verify who you are and whether you have permission to access the resources that you are requesting. AWS uses the security credentials to authenticate and authorize your requests (docs).

Access keys consist of two parts: An access key ID (for example, AKIAIOSFODNN7EXAMPLE), and a secret access key (for example, wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY).

You must use both the access key ID and secret access key together to authenticate your requests.

You can find further information on how to manage your access keys here.

  • AWS Region: Each AWS Region is a separate geographic area in which AWS clusters data centers (docs).

As AWS can have instances in multiple regions, we need to know the region the service you want reach belongs to.

Note that the AWS Region is the only required parameter when configuring a connection. When connecting to the services programmatically, there are different ways in which we can extract and use the rest of AWS configurations.

You can find further information about configuring your credentials here.

  • AWS Session Token (optional): If you are using temporary credentials to access your services, you will need to inform the AWS Access Key ID and AWS Secrets Access Key. Also, these will include an AWS Session Token.

You can find more information on Using temporary credentials with AWS resources.

  • Endpoint URL (optional): To connect programmatically to an AWS service, you use an endpoint. An endpoint is the URL of the entry point for an AWS web service. The AWS SDKs and the AWS Command Line Interface (AWS CLI) automatically use the default endpoint for each service in an AWS Region. But you can specify an alternate endpoint for your API requests.

Find more information on AWS service endpoints.

  • Profile Name: A named profile is a collection of settings and credentials that you can apply to a AWS CLI command. When you specify a profile to run a command, the settings and credentials are used to run that command. Multiple named profiles can be stored in the config and credentials files.

You can inform this field if you'd like to use a profile other than default.

Find here more information about Named profiles for the AWS CLI.

  • Assume Role Arn: Typically, you use AssumeRole within your account or for cross-account access. In this field you'll set the ARN (Amazon Resource Name) of the policy of the other account.

A user who wants to access a role in a different account must also have permissions that are delegated from the account administrator. The administrator must attach a policy that allows the user to call AssumeRole for the ARN of the role in the other account.

This is a required field if you'd like to AssumeRole.

Find more information on AssumeRole.

  • Assume Role Session Name: An identifier for the assumed role session. Use the role session name to uniquely identify a session when the same role is assumed by different principals or for different reasons.

By default, we'll use the name OpenMetadataSession.

Find more information about the Role Session Name.

  • Assume Role Source Identity: The source identity specified by the principal that is calling the AssumeRole operation. You can use source identity information in AWS CloudTrail logs to determine who took actions with a role.

Find more information about Source Identity.

Once the credentials have been added, click on Test Connection and Save the changes.

Test Connection

Test the connection and save the Service

In this step we will configure the metadata ingestion pipeline, Please follow the instructions below

Configure Metadata Ingestion

Configure Metadata Ingestion Page

  • Name: This field refers to the name of ingestion pipeline, you can customize the name or use the generated name.

  • Mark Deleted Ml Models (toggle):: Set the Mark Deleted Ml Models toggle to flag ml models as soft-deleted if they are not present anymore in the source system.

Scheduling can be set up at an hourly, daily, or weekly cadence. The timezone is in UTC. Select a Start Date to schedule for ingestion. It is optional to add an End Date.

Review your configuration settings. If they match what you intended, click Deploy to create the service and schedule metadata ingestion.

If something doesn't look right, click the Back button to return to the appropriate step and change the settings as needed.

After configuring the workflow, you can click on Deploy to create the pipeline.

Schedule the Workflow

Schedule the Ingestion Pipeline and Deploy

Once the workflow has been successfully deployed, you can view the Ingestion Pipeline running from the Service Page.

View Ingestion Pipeline

View the Ingestion Pipeline from the Service Page

If there were any errors during the workflow deployment process, the Ingestion Pipeline Entity will still be created, but no workflow will be present in the Ingestion container.

  • You can then edit the Ingestion Pipeline and Deploy it again.

  • From the Connection tab, you can also Edit the Service if needed.

Workflow Deployment Error

Edit and Deploy the Ingestion Pipeline