ML Pipeline Orchestration with Kubernetes and Kubeflow

Question

Pulumi · Accepted Answer

In order to create an ML (Machine Learning) pipeline orchestration on Kubernetes and use Kubeflow for it, the following steps are generally involved:

1. **Provisioning a Kubernetes cluster**: We would need to spin up a Kubernetes cluster where our ML pipeline will run. This cluster could be on any cloud provider like AWS, GCP or Azure, or even on-premises.

2. **Setup Kubeflow**: Once we have our Kubernetes cluster, we install Kubeflow on it. Kubeflow is an ML toolkit for Kubernetes and is a great choice for running ML pipelines. It provides components for each step of the ML lifecycle from experiment to production.

3. **Creating ML pipelines**: Using Kubeflow Pipelines, we can build our ML pipelines.

Below we'll go though a Pulumi program on how to provision a Kubernetes cluster and setup Kubeflow on it. We'll take the case of AWS as the cloud provider and use `eks` module for creating an Elastic Kubernetes Service (EKS) cluster. After creating the cluster, we'll use the Kubernetes Python client to deploy Kubeflow to the cluster.

First, ensure you have the Pulumi CLI installed and configured for use with AWS.

Here's a Python program that uses Pulumi to accomplish the above steps:

```python
import pulumi
import pulumi_eks as eks

# STEP 1: Create an EKS cluster
cluster = eks.Cluster('my-kubeflow-cluster',
                      desired_capacity=2,
                      min_size=1,
                      max_size=3,
                      instance_type='t2.medium',
                      pulumi_program='install_kubeflow')

# Export the cluster's kubeconfig.
pulumi.export('kubeconfig', cluster.kubeconfig)

def install_kubeflow(cluster):
    """
    This function deploys Kubeflow to a given Kubernetes cluster.
    For the sake of brevity, we are using pseudo-code to represent the deployment steps.
    This would be replaced by actual Kubernetes manifests or Helm charts as required by Kubeflow.
    """
    # We would typically use `pulumi_kubernetes` provider here to deploy Kubeflow manifests.
    # For example:
    #   k8s = pulumi_kubernetes.Provider('k8s', kubeconfig=cluster.kubeconfig)
    #   kubeflow_app = pulumi_kubernetes.yaml.ConfigFile('kubeflow-app', 'kubeflow.yaml', provider=k8s)
    
    # Replace with real deployment code...
    pass # This is a placeholder

# Run the install_kubeflow function after the cluster is created
pulumi.runtime.run_in_stack(install_kubeflow, cluster)
```

### Explanations:

- **eks.Cluster**: This provisions an EKS cluster. We specify the desired capacity, sizes, instance types, and a unique name for our cluster.

- **pulumi.export**: The `kubeconfig` output is exported, which will be required to interact with the Kubernetes cluster.

- **install_kubeflow function**: This is a pseudo-function that represents the steps to install Kubeflow. In a real-world scenario, you would fetch the Kubeflow manifest or use a Helm chart to deploy Kubeflow on the Kubernetes cluster. The actual code would use `pulumi_kubernetes` provider to apply the manifests.

Note: This code does not actually deploy Kubeflow as the Kubeflow manifest setup is complex and cannot be covered in this simple example.

You will have to checkout the [Kubeflow documentation](https://www.kubeflow.org/docs/) for more details on how to deploy it, and adjust the `install_kubeflow` function to apply the correct Kubeflow configuration for your use case.

The above program should run out of the box provided your AWS credentials are set up correctly, and after deploying Kubeflow, you can create and run your ML workflows on your EKS cluster.