1. AI Workload Orchestration with Argo Workflows on Kubernetes


    To orchestrate AI workloads using Argo Workflows on Kubernetes with Pulumi, you will first need a Kubernetes cluster. Once you have a cluster, you can install Argo Workflows into it. Argo Workflows is a Kubernetes-native workflow engine for orchestrating parallel jobs on Kubernetes.

    The program I will guide you through accomplishes the following:

    1. Creates a Kubernetes Namespace for Argo Workflows to keep its resources isolated.
    2. Installs Argo Workflows into the namespace using Helm, a package manager for Kubernetes which simplifies deployment of applications and services.

    For this task, you'll need to have the following prerequisites in place:

    • Pulumi CLI installed and configured with a Pulumi account.
    • Kubernetes cluster up and running (can be a cloud-managed cluster like GKE, EKS, AKS, or a local one like minikube).
    • kubectl command-line tool configured to interact with your Kubernetes cluster.
    • Helm command-line tool installed.

    Now let's start with the Pulumi program. Make sure you are in a directory where you want to create your Pulumi project.

    import pulumi import pulumi_kubernetes as k8s from pulumi_kubernetes.helm.v3 import Chart, ChartOpts # Create a Kubernetes provider instance to interact with your cluster. # The kubeconfig can be set explicitly in the provider, or it will use the default kubeconfig location. k8s_provider = k8s.Provider('k8s-provider') # Create a Kubernetes Namespace specifically for Argo. argo_namespace = k8s.core.v1.Namespace('argo-namespace', metadata={'name': 'argo'}, opts=pulumi.ResourceOptions(provider=k8s_provider)) # Install Argo Workflows using the Helm Chart. # This assumes Helm is installed and configured on your system. argo_helm_chart = Chart('argo-workflows', ChartOpts( chart='argo-workflows', version='0.16.7', # Specify the version of Argo Workflows Helm chart you wish to deploy. fetch_opts={'repo': 'https://argoproj.github.io/argo-helm'}, # Repository where the chart is stored. namespace=argo_namespace.metadata['name'], values={ 'singleNamespace': True, # This confines Argo Workflows to only the namespace it is installed in. # You can set additional values here to customize your Argo Workflows installation. }, ), pulumi.ResourceOptions(provider=k8s_provider, depends_on=[argo_namespace])) # Export the namespace name created for Argo pulumi.export('argo_namespace', argo_namespace.metadata.apply(lambda metadata: metadata['name'])) # Export the Argo Workflows URL by assuming it is exposed via an Ingress or LoadBalancer. # Note that this is only an example URL structure and may differ based on your actual Ingress setup. argo_workflows_url = pulumi.Output.concat('https://argo-workflows.', argo_namespace.metadata['name'], '.svc.cluster.local') pulumi.export('argo_workflows_url', argo_workflows_url)

    In the above program, we perform the following actions:

    • First, we create a Kubernetes provider, which allows us to interact with our Kubernetes cluster using Pulumi.
    • We then define a Kubernetes Namespace named argo.
    • Next, we use the Pulumi Kubernetes Helm Chart package to deploy Argo Workflows. We specify the Helm chart details and configurations we want, including setting singleNamespace to True, which confines Argo to only operate within the namespace we created.
    • Finally, we export the namespace and a generated URL for Argo Workflows. Note that the URL is just an example and would need to be tailored to your actual Ingress or LoadBalancer setup.

    To run this Pulumi program:

    1. Save it in a file named __main__.py.
    2. Run pulumi up in the command line within the same directory.
    3. Confirm the preview looks as expected and select yes to deploy the changes.

    Remember to customize the namespace, Argo Workflows chart version, and values as needed for your specific requirements. After deploying, you can access the Argo Workflows UI via the exposed URL (again, based on your Ingress or LoadBalancer configuration) and begin orchestrating your AI workloads.