our ZenML instance. See here for more information.Checkout the documentation on fetching runs for more information on the various ways how you can fetch and use the pipeline, pipeline run, step run, and artifact resources in code.

Stacks, Infrastructure, Authentication

Stack: The stacks registered in your ZenML instance.

Stack Components: The stack components registered in your ZenML instance, e.g., all orchestrators, artifact stores, model deployers, ...

Flavors: The stack component flavors available to you, including:Built-in flavors like the local orchestrator,Integration-enabled flavors like the Kubeflow orchestrator,Custom flavors that you have created yourself.

User: The users registered in your ZenML instance. If you are running locally, there will only be a single default user.

Secrets: The infrastructure authentication secrets that you have registered in the ZenML Secret Store.

Service Connectors: The service connectors that you have set up to connect ZenML to your infrastructure.

Client Methods

Reading and Writing Resources

List Methods

Get a list of resources, e.g.:

client.list_pipeline_runs(

stack_id=client.active_stack_model.id,  # filter by stack

user_id=client.active_user.id,  # filter by user

sort_by="desc:start_time",  # sort by start time descending

size=10,  # limit page size to 10

These methods always return a Page of resources, which behaves like a standard Python list and contains, by default, the first 50 results. You can modify the page size by passing the size argument or fetch a subsequent page by passing the page argument to the list method.