Vertex AI Experiments is supported by the Agent Platform SDK for Python and Cloud de Confiance console. Vertex AI Experiments requires and depends on Vertex ML Metadata.
Set up
-
In the Cloud de Confiance console, on the project selector page, select or create a Cloud de Confiance project.
Roles required to select or create a project
- Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
-
Create a project: To create a project, you need the Project Creator role
(
roles/resourcemanager.projectCreator), which contains theresourcemanager.projects.createpermission. Learn how to grant roles.
-
Verify that billing is enabled for your Cloud de Confiance project.
Enable the required API, if it is not already enabled.
Roles required to enable APIs
To enable APIs, you need the
serviceusage.services.enablepermission. If you created the project, then you likely already have this permission through the Owner role (roles/owner). Otherwise, you can get this permission through the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). Learn how to grant roles.- Create a Service account. See Create a service account with required permissions.
- Install the Agent Platform SDK for Python.
- Check for existence of the
defaultMetadata Store in your project. (required)- To see if your project has the
defaultMetadata Store, go to theMetadatapage in the Cloud de Confiance console. - If the
defaultMetadata Store doesn't exist, it's created when- you run the first PipelineJob,
- or, create your first experiment in the Agent Platform SDK for Python.
Optional: To configure with CMEK, see Configure your project's metadata store.
- To see if your project has the
Supported Locations
The Feature availability table lists the available locations for Vertex AI Experiments. When using Agent Platform Pipelines or Vertex AI TensorBoard, they must be in the same location as your Agent Platform experiment.