1. AI/ML Workflow Management with Argo Workflows in Kubernetes


    To manage AI/ML workflows in Kubernetes with Argo Workflows, you'll need to have a Kubernetes cluster up and running. Argo Workflows is a Kubernetes-native workflow engine that allows you to orchestrate parallel jobs on Kubernetes. Pulumi enables you to define and manage your Kubernetes resources using code.

    Here, I'll guide you through creating a Kubernetes Namespace, installing the Argo Workflows with a CustomResourceDefinition (CRD), and deploying a simple workflow as a CustomResource in Python using Pulumi.

    Kubernetes Namespace

    A Kubernetes Namespace provides a mechanism to scope resources in a cluster. We will create a separate namespace for Argo Workflows to keep our workflows organized and isolated from other cluster resources.

    Argo Workflows Installation

    Argo Workflows operate via custom resources. Therefore, we need to install the necessary CRDs and the Argo Workflow controller that watches for Workflow resources and orchestrates them.

    Argo Workflow Resource

    After the CRDs and controller are in place, we can define a workflow using the Workflow kind. Workflows are defined in YAML or JSON format and they describe a sequence of tasks to run inside your Kubernetes cluster.

    Below is a Pulumi program that does the following:

    1. Creates a Kubernetes namespace for Argo Workflows.
    2. Applies CRDs needed for Argo Workflows.
    3. Deploys an example workflow that prints a "hello world" message.
    import pulumi import pulumi_kubernetes as kubernetes # Create a Kubernetes Namespace for Argo Workflows. argo_namespace = kubernetes.core.v1.Namespace("argo", metadata={"name": "argo"}) # Install Argo Workflows from a YAML manifest. This may include CRDs and the Argo Workflow controller deployment. # For simplicity, let's assume you already have the path to your manifest file. # In a real-world scenario, you might download or refer to a specific manifest URL or # your YAML configurations might be generated or modified in your code before applying. argo_workflows_manifest_path = './argo-workflows-installation-manifest.yaml' argo_workflows_resources = kubernetes.yaml.ConfigFile('argo-workflows', file=argo_workflows_manifest_path, opts=pulumi.ResourceOptions(namespace=argo_namespace.metadata['name'])) # Define an example Argo Workflow. # Typically, this is defined in YAML format, but Pulumi requires us to define it in Pythonic way. # This example illustrates an inline workflow definition. In practice, you might want to # separate this into its own YAML file or generate it programmatically. hello_world_workflow = kubernetes.apiextensions.CustomResource( "hello-world-workflow", api_version="argoproj.io/v1alpha1", kind="Workflow", metadata={ "namespace": argo_namespace.metadata["name"] }, spec={ "entrypoint": "whalesay", "templates": [ { "name": "whalesay", "container": { "image": "docker/whalesay", "command": ["cowsay"], "args": ["hello world"] } } ] }) # Export the name of the namespace and the workflow name. pulumi.export('namespace', argo_namespace.metadata['name']) pulumi.export('workflow_name', hello_world_workflow.metadata['name'])

    This program uses pulumi_kubernetes module to interact with the Kubernetes cluster. When you run the program with the Pulumi CLI, it will apply the configurations to your cluster.

    The argo_namespace variable creates a Kubernetes namespace, and the argo_workflows_resources variable applies the Argo Workflows installation YAML, which includes the necessary CRDs and the workflow controller. Finally, the hello_world_workflow variable creates a new workflow resource that runs a simple container that prints "hello world".

    You must make sure that the cluster's RBAC permissions allow Pulumi to create and manage these resources. Additionally, you need to have kubeconfig set up correctly so that Pulumi can communicate with your cluster.

    Make sure to replace './argo-workflows-installation-manifest.yaml' with the path to the actual Argo Workflows YAML manifest file, or use a remotely hosted file that Pulumi can download from within the script using an HTTP client and apply it accordingly.

    Before running this code with Pulumi, you should familiarize yourself with Argo Workflows to understand how to write and structure your workflow YAML definitions effectively. The official Argo Workflows documentation can provide a detailed guide on creating more complex workflows.