1. Orchestrating AI Workflows with Argo Workflows on Kubernetes


    Orchestrating AI workflows often requires a reliable and scalable platform. Kubernetes is a great choice for this as it provides the robustness and flexibility needed to manage complex workflows. To orchestrate these workflows on Kubernetes, we can use Argo Workflows, which is an open-source container-native workflow engine for orchestrating parallel jobs on Kubernetes.

    Argo Workflows runs on Kubernetes and is specifically designed to orchestrate job execution in a multi-step workflow, allowing you to create and manage jobs that run on Kubernetes and ensure that they are executed in the order you've defined.

    To use Argo Workflows, you will need to:

    1. Set up a Kubernetes cluster (if you don't already have one).
    2. Install Argo Workflows on your Kubernetes cluster.
    3. Define your workflow as an Argo Workflow custom resource definition (CRD).

    Below is a sample Pulumi program in Python that sets up a Kubernetes cluster and installs Argo Workflows. This example assumes that you want to use Amazon EKS (Elastic Kubernetes Service) as your Kubernetes cluster and that you have the necessary AWS credentials configured in your environment or your Pulumi stack.

    The steps we're about to follow in the code are:

    • Create an EKS cluster using Pulumi's EKS module.
    • Install Argo Workflows using the Helm chart, which is a package manager for Kubernetes that allows us to easily deploy applications like Argo Workflows.

    Let's look at the Pulumi program:

    import pulumi import pulumi_eks as eks import pulumi_kubernetes as kubernetes # Create an EKS cluster. cluster = eks.Cluster('ai-workflows-cluster') # Using a Kubernetes Provider to interact with the EKS cluster. # The provider uses the kubeconfig from our newly created EKS cluster. k8s_provider = kubernetes.Provider('eks-k8s', kubeconfig=cluster.kubeconfig) # Install Argo Workflows with Helm. # Helm charts are a way to define, install, and upgrade even the most complex Kubernetes applications. argo_chart = kubernetes.helm.v3.Chart( 'argo-workflows', kubernetes.helm.v3.ChartOpts( chart='argo', version='0.16.7', fetch_opts=kubernetes.helm.v3.FetchOpts( repo='https://argoproj.github.io/argo-helm' ), ), opts=pulumi.ResourceOptions(provider=k8s_provider) ) # Export the cluster's kubeconfig and the Argo Workflows UI service endpoint. pulumi.export('kubeconfig', cluster.kubeconfig) argo_workflows_ui_service = argo_chart.get_resource('v1/Service', 'argo-workflows-argo-ui') pulumi.export('argo_workflows_ui_endpoint', argo_workflows_ui_service.status.load_balancer.ingress[0].hostname)

    In this program, we start by importing the necessary Pulumi libraries. We use pulumi_eks to create an EKS cluster and pulumi_kubernetes to interact with Kubernetes resources.

    We create an EKS cluster named ai-workflows-cluster and then define a Pulumi Kubernetes provider named eks-k8s that uses the kubeconfig from the created EKS cluster.

    Next, we install the Argo Workflows using a Helm chart with a specific version number. We specify the Helm chart repository URL where the Argo Workflows Helm chart is located.

    Finally, we export the kubeconfig needed to interact with the Kubernetes cluster and the endpoint for the Argo Workflows UI service. The Argo Workflows UI is useful for visualizing and managing the workflows you define.

    This Pulumi program provides the necessary infrastructure to start working with Argo Workflows on Kubernetes for orchestrating AI workflows. Once set up, you would go on to define the actual workflow steps using Argo's CRDs, which are beyond the scope of infrastructure setup but critical for the actual AI workflow orchestration.