# Kubernetes

The ZenML Kubernetes Orchestrator allows you to run your ML pipelines on a Kubernetes cluster without writing Kubernetes code. It's a lightweight alternative to more complex orchestrators like Airflow or Kubeflow.

{% hint style="info" %}
If you only want to run individual steps of your pipeline in Kubernetes, check out our [Kubernetes Step Operator](https://docs.zenml.io/stacks/step-operators/kubernetes).
{% endhint %}

## Prerequisites

To use the Kubernetes Orchestrator, you'll need:

* ZenML `kubernetes` integration installed (`zenml integration install kubernetes`)
* Docker installed and running
* `kubectl` installed
* A remote artifact store and container registry in your ZenML stack
* A deployed Kubernetes cluster
* A configured `kubectl` context pointing to the cluster (optional, see below)

## Deploying the Orchestrator

The Kubernetes orchestrator requires a Kubernetes cluster in order to run. There are many ways to deploy a Kubernetes cluster using different cloud providers or on your custom infrastructure, and we can't possibly cover all of them, but you can check out our [production guide](https://docs.zenml.io/user-guides/production-guide).

## Configuring the Orchestrator

There are two ways to configure the orchestrator:

1. Using a [Service Connector](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/service-connectors-guide) to connect to the remote cluster. This is the recommended approach, especially for cloud-managed clusters. No local `kubectl` context is needed.

```bash
zenml orchestrator register <ORCHESTRATOR_NAME> --flavor kubernetes
zenml service-connector list-resources --resource-type kubernetes-cluster -e
zenml orchestrator connect <ORCHESTRATOR_NAME> --connector <CONNECTOR_NAME>
zenml stack register <STACK_NAME> -o <ORCHESTRATOR_NAME> ... --set
```

2. Configuring `kubectl` with a context pointing to the remote cluster and setting the `kubernetes_context` in the orchestrator config:

```bash
zenml orchestrator register <ORCHESTRATOR_NAME> \
    --flavor=kubernetes \
    --kubernetes_context=<KUBERNETES_CONTEXT>

zenml stack register <STACK_NAME> -o <ORCHESTRATOR_NAME> ... --set
```

## Running a Pipeline

Once configured, you can run any ZenML pipeline using the Kubernetes Orchestrator:

```bash
python your_pipeline.py
```

This will create a Kubernetes pod for each step in your pipeline. You can interact with the pods using `kubectl` commands.

For more advanced configuration options and additional details, refer to the [full Kubernetes Orchestrator documentation](https://docs.zenml.io/stacks/orchestrators/kubernetes).

<figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>