Tekton Orchestrator

Orchestrating your pipelines to run on Tekton.

Tekton is a powerful and flexible open-source framework for creating CI/CD systems, allowing developers to build, test, and deploy across cloud providers and on-premise systems.

This component is only meant to be used within the context of a remote ZenML deployment scenario. Usage with a local ZenML deployment may lead to unexpected behavior!

When to use it

You should use the Tekton orchestrator if:

• you're looking for a proven production-grade orchestrator.

• you're looking for a UI in which you can track your pipeline runs.

• you're already using Kubernetes or are not afraid of setting up and maintaining a Kubernetes cluster.

• you're willing to deploy and maintain Tekton Pipelines on your cluster.

How to deploy it

You'll first need to set up a Kubernetes cluster and deploy Tekton Pipelines:

AWS

• A remote ZenML server. See the deployment guide for more information.

• Have an existing AWS EKS cluster set up.

• Make sure you have the AWS CLI set up.

• Download and install kubectl and configure it to talk to your EKS cluster using the following command:

  aws eks --region REGION update-kubeconfig --name CLUSTER_NAME

• Install Tekton Pipelines onto your cluster.

GCP

Azure

If one or more of the deployments are not in the Running state, try increasing the number of nodes in your cluster.

ZenML has only been tested with Tekton Pipelines >=0.38.3 and may not work with previous versions.

How to use it

To use the Tekton orchestrator, we need:

• The ZenML tekton integration installed. If you haven't done so, run

  zenml integration install tekton -y

• Docker installed and running.

• Tekton pipelines deployed on a remote cluster. See the deployment section for more information.

• The name of your Kubernetes context which points to your remote cluster. Run kubectl config get-contexts to see a list of available contexts.

• A remote artifact store as part of your stack.

• A remote container registry as part of your stack.

• kubectl installed and the name of the Kubernetes configuration context which points to the target cluster (i.e. runkubectl config get-contexts to see a list of available contexts). This is optional (see below).

It is recommended that you set up a Service Connector and use it to connect ZenML Stack Components to the remote Kubernetes cluster, especially If you are using a Kubernetes cluster managed by a cloud provider like AWS, GCP or Azure, This guarantees that your Stack is fully portable on other environments and your pipelines are fully reproducible.

We can then register the orchestrator and use it in our active stack. This can be done in two ways:

1. If you have a Service Connector configured to access the remote Kubernetes cluster, you no longer need to set the kubernetes_context attribute to a local kubectl context. In fact, you don't need the local Kubernetes CLI at all. You can connect the stack component to the Service Connector instead:

   $ zenml orchestrator register <ORCHESTRATOR_NAME> --flavor tekton
   Running with active stack: 'default' (repository)
   Successfully registered orchestrator `<ORCHESTRATOR_NAME>`.
   
   $ zenml service-connector list-resources --resource-type kubernetes-cluster -e
   The following 'kubernetes-cluster' resources can be accessed by service connectors that you have configured:
   ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━┓
   ┃             CONNECTOR ID             │ CONNECTOR NAME        │ CONNECTOR TYPE │ RESOURCE TYPE         │ RESOURCE NAMES      ┃
   ┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────────────┼─────────────────────┨
   ┃ e33c9fac-5daa-48b2-87bb-0187d3782cde │ aws-iam-multi-eu      │ 🔶 aws         │ 🌀 kubernetes-cluster │ kubeflowmultitenant ┃
   ┃                                      │                       │                │                       │ zenbox              ┃
   ┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────────────┼─────────────────────┨
   ┃ ed528d5a-d6cb-4fc4-bc52-c3d2d01643e5 │ aws-iam-multi-us      │ 🔶 aws         │ 🌀 kubernetes-cluster │ zenhacks-cluster    ┃
   ┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────────────┼─────────────────────┨
   ┃ 1c54b32a-4889-4417-abbd-42d3ace3d03a │ gcp-sa-multi          │ 🔵 gcp         │ 🌀 kubernetes-cluster │ zenml-test-cluster  ┃
   ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━┛
   
   $ zenml orchestrator connect <ORCHESTRATOR_NAME> --connector aws-iam-multi-us
   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 ┃
   ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━┛
   
   # Register and activate a stack with the new orchestrator
   $ zenml stack register <STACK_NAME> -o <ORCHESTRATOR_NAME> ... --set

2. 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:

   zenml orchestrator register <ORCHESTRATOR_NAME> --flavor=tekton --kubernetes_context=<KUBERNETES_CONTEXT>
   
   # Register and activate a stack with the new orchestrator
   zenml stack register <STACK_NAME> -o <ORCHESTRATOR_NAME> ... --set

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 Tekton. 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 Tekton orchestrator:

python file_that_runs_a_zenml_pipeline.py

Tekton UI

Tekton comes with its own UI that you can use to find further details about your pipeline runs, such as the logs of your steps.

Tekton UI

To find the Tekton UI endpoint, we can use the following command:

kubectl get ingress -n tekton-pipelines  -o jsonpath='{.items[0].spec.rules[0].host}'

Additional configuration

For additional configuration of the Tekton orchestrator, you can pass TektonOrchestratorSettings which allows you to configure node selectors, affinity, and tolerations to apply to the Kubernetes Pods running your pipeline. These can be either specified using the Kubernetes model objects or as dictionaries.

from zenml.integrations.tekton.flavors.tekton_orchestrator_flavor import TektonOrchestratorSettings
from kubernetes.client.models import V1Toleration

tekton_settings = TektonOrchestratorSettings(
    pod_settings={
        "affinity": {
            "nodeAffinity": {
                "requiredDuringSchedulingIgnoredDuringExecution": {
                    "nodeSelectorTerms": [
                        {
                            "matchExpressions": [
                                {
                                    "key": "node.kubernetes.io/name",
                                    "operator": "In",
                                    "values": ["my_powerful_node_group"],
                                }
                            ]
                        }
                    ]
                }
            }
        },
        "tolerations": [
            V1Toleration(
                key="node.kubernetes.io/name",
                operator="Equal",
                value="",
                effect="NoSchedule"
            )
        ]
    }
)

If your pipelines steps have certain hardware requirements, you can specify them as ResourceSettings:

resource_settings = ResourceSettings(cpu_count=8, memory="16GB")

These settings can then be specified on either pipeline-level or step-level:

# Either specify on pipeline-level
@pipeline(
    settings={
        "orchestrator": tekton_settings,
        "resources": resource_settings,
    }
)
def my_pipeline():
    ...

# OR specify settings on step-level
@step(
    settings={
        "orchestrator": tekton_settings,
        "resources": resource_settings,
    }
)
def my_step():
    ...

Check out the SDK docs for a full list of available attributes and this docs page for more information on how to specify settings.

For more information and a full list of configurable attributes of the Tekton orchestrator, check out the SDK Docs .

Enabling CUDA for GPU-backed hardware

Note that if you wish to use this orchestrator to run steps on a GPU, you will need to follow the instructions on this page to ensure that it works. It requires adding some extra settings customization and is essential to enable CUDA for the GPU to give its full acceleration.