se Python <3.11 together with the GCP integration.The GCS Artifact Store flavor is provided by the GCP ZenML integration, you need to install it on your local machine to be able to register a GCS Artifact Store and add it to your stack:

zenml integration install gcp -y

The only configuration parameter mandatory for registering a GCS Artifact Store is the root path URI, which needs to point to a GCS bucket and take the form gs://bucket-name. Please read the Google Cloud Storage documentation on how to configure a GCS bucket.

With the URI to your GCS bucket known, registering a GCS Artifact Store can be done as follows:

# Register the GCS artifact store

zenml artifact-store register gs_store -f gcp --path=gs://bucket-name

# Register and set a stack with the new artifact store

zenml stack register custom_stack -a gs_store ... --set

Depending on your use case, however, you may also need to provide additional configuration parameters pertaining to authentication to match your deployment scenario.

Infrastructure Deployment

A GCS Artifact Store can be deployed directly from the ZenML CLI:

zenml artifact-store deploy gcs_artifact_store --flavor=gcp --provider=gcp ...

You can pass other configurations specific to the stack components as key-value arguments. If you don't provide a name, a random one is generated for you. For more information about how to work use the CLI for this, please refer to the dedicated documentation section.

Authentication Methods

Integrating and using a GCS Artifact Store in your pipelines is not possible without employing some form of authentication. If you're looking for a quick way to get started locally, you can use the Implicit Authentication method. However, the recommended way to authenticate to the GCP cloud platform is through a GCP Service Connector. This is particularly useful if you are configuring ZenML stacks that combine the GCS Artifact Store with other remote stack components also running in GCP.