en't done so, runCopyzenml integration install gcpDocker installed and running.

A remote artifact store as part of your stack.

A remote container registry as part of your stack.

GCP credentials with proper permissions

The GCP project ID and location in which you want to run your Vertex AI pipelines.

GCP credentials and permissions

This part is without doubt the most involved part of using the Vertex orchestrator. In order to run pipelines on Vertex AI, you need to have a GCP user account and/or one or more GCP service accounts set up with proper permissions, depending on whether you wish to practice the principle of least privilege and distribute permissions across multiple service accounts.

You also have three different options to provide credentials to the orchestrator:

use the gcloud CLI to authenticate locally with GCP

configure the orchestrator to use a service account key file to authenticate with GCP by setting the service_account_path parameter in the orchestrator configuration.

(recommended) configure a GCP Service Connector with GCP credentials and then link the Vertex AI Orchestrator stack component to the Service Connector.

This section explains the different components and GCP resources involved in running a Vertex AI pipeline and what permissions they need, then provides instructions for three different configuration use-cases:

use the local gcloud CLI configured with your GCP user account, including the ability to schedule pipelines

use a GCP Service Connector and a single service account with all permissions, including the ability to schedule pipelines

use a GCP Service Connector and multiple service accounts for different permissions, including the ability to schedule pipelines

Vertex AI pipeline components

To understand what accounts you need to provision and why, let's look at the different components of the Vertex orchestrator: