1. Orchestrating AI Pipelines with Kubeflow on EKS


    To orchestrate AI pipelines with Kubeflow on Amazon Elastic Kubernetes Service (EKS), we'll set up an EKS cluster and install Kubeflow on it. Kubeflow is an open-source project which aims to make deployments of machine learning (ML) workflows on Kubernetes simple, portable, and scalable.

    Here's an overview of the steps we'll take in the Python program:

    1. Create an EKS cluster: We'll use the pulumi_eks module to create an EKS cluster. It simplifies EKS deployment by abstracting some of the underlying resources needed.
    2. Deploy Kubeflow: Once the EKS cluster is up and running, you would typically use kubectl to deploy Kubeflow to the cluster. This part is not covered by Pulumi directly, as Pulumi focuses on infrastructure. However, one can script the deployment process of Kubeflow using Pulumi Automation API or manual post-deployment steps.

    The following program sets up an EKS cluster that you can then use to deploy Kubeflow:

    import pulumi import pulumi_eks as eks # Create an EKS cluster with default settings. Note that we might need to configure # specific settings like the VPC, subnet IDs, and IAM roles for a production environment. # This example uses the default VPC and default worker node configurations. cluster = eks.Cluster("ai-eks-cluster") # Export the cluster's kubeconfig and the AWS provider reference. pulumi.export("kubeconfig", cluster.kubeconfig)

    After running this program with Pulumi, you'll have an EKS cluster ready. To deploy Kubeflow, you can follow these high-level steps:

    1. Get Kubeconfig: Use the kubeconfig exported by the Pulumi program to authenticate your kubectl client.
    2. Install Kubeflow: Follow Kubeflow's installation instructions for EKS, which will usually involve applying several Kubernetes manifests to your cluster.

    Remember that to successfully apply these configs, your kubectl client needs to authenticate with the EKS cluster using the kubeconfig that was exported by the Pulumi program. You may also want to consider customizing your Kubeflow deployment to match the particular needs of your ML workflows or organization.

    Deploying Kubeflow and setting up pipelines would largely be an operation within the Kubernetes cluster, likely involving several Kubeflow CRDs (Custom Resource Definitions) that define your pipeline's structure, including datasets, training jobs, model serving, and more. This goes beyond infrastructure setup and falls into the category of application deployment and configuration within the cluster.