d9fe8eb2f833b788e9d8a6ebde194765b42fe46 size: 3256It is also possible to update the local gcloud CLI configuration with credentials extracted from the GCP Service Connector:

zenml service-connector login gcp-user-account --resource-type gcp-generic

Example Command Output

Updated the local gcloud default application credentials file at '/home/user/.config/gcloud/application_default_credentials.json'

The 'gcp-user-account' GCP Service Connector connector was used to successfully configure the local Generic GCP resource client/SDK.

Stack Components use

The GCS Artifact Store Stack Component can be connected to a remote GCS bucket through a GCP Service Connector.

The Google Cloud Image Builder Stack Component, VertexAI Orchestrator, and VertexAI Step Operator can be connected and use the resources of a target GCP project through a GCP Service Connector.

The GCP Service Connector can also be used with any Orchestrator or Model Deployer stack component flavor that relies on Kubernetes clusters to manage workloads. This allows GKE Kubernetes container workloads to be managed without the need to configure and maintain explicit GCP or Kubernetes kubectl configuration contexts and credentials in the target environment or in the Stack Component itself.

Similarly, Container Registry Stack Components can be connected to a GCR Container Registry through a GCP Service Connector. This allows container images to be built and published to GCR container registries without the need to configure explicit GCP credentials in the target environment or the Stack Component.

End-to-end examples

This is an example of an end-to-end workflow involving Service Connectors that use a single multi-type GCP Service Connector to give access to multiple resources for multiple Stack Components. A complete ZenML Stack is registered and composed of the following Stack Components, all connected through the same Service Connector:

a Kubernetes Orchestrator connected to a GKE Kubernetes cluster

a GCS Artifact Store connected to a GCS bucket