━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━┛$ zenml orchestrator connect <ORCHESTRATOR_NAME> --connector aws-iam-multi-us
Running with active workspace: 'default' (repository)
Running with active stack: 'default' (repository)
Successfully connected orchestrator `<ORCHESTRATOR_NAME>` to the following resources:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━┓
┃             CONNECTOR ID             │ CONNECTOR NAME   │ CONNECTOR TYPE │ RESOURCE TYPE         │ RESOURCE NAMES   ┃
┠──────────────────────────────────────┼──────────────────┼────────────────┼───────────────────────┼──────────────────┨
┃ ed528d5a-d6cb-4fc4-bc52-c3d2d01643e5 │ aws-iam-multi-us │ 🔶 aws         │ 🌀 kubernetes-cluster │ zenhacks-cluster ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━┛

# Add the orchestrator to the active stack
$ zenml stack update -o <ORCHESTRATOR_NAME>

if you don't have a Service Connector on hand and you don't want to register one , the local Kubernetes kubectl client needs to be configured with a configuration context pointing to the remote cluster. The kubernetes_context stack component must also be configured with the value of that context:Copyzenml orchestrator register <ORCHESTRATOR_NAME> \
    --flavor=kubeflow \
    --kubernetes_context=<KUBERNETES_CONTEXT>

# Add the orchestrator to the active stack
zenml stack update -o <ORCHESTRATOR_NAME>

ZenML will build a Docker image called <CONTAINER_REGISTRY_URI>/zenml:<PIPELINE_NAME> which includes your code and use it to run your pipeline steps in Kubeflow. Check out this page if you want to learn more about how ZenML builds these images and how you can customize them.

You can now run any ZenML pipeline using the Kubeflow orchestrator:

python file_that_runs_a_zenml_pipeline.py

Kubeflow UI