1. Orchestrating AI Model Training Workflows with AWX on Kubernetes


    To orchestrate AI model training workflows with AWX (Ansible Tower) on Kubernetes, you need to set up a Kubernetes cluster and deploy AWX onto it. AWX will serve as the automation and scheduling tool to run your AI model training jobs. In this context, we are going to assume that you already have your AI models and corresponding Ansible Playbooks or AWX Templates prepared.

    Here's the high-level overview of what you need to do:

    1. Create a Kubernetes Cluster: This will be the platform on which you run AWX and your AI training jobs. For simplicity, we'll use a managed Kubernetes service provided by a cloud provider (e.g., Amazon EKS, Azure AKS, Google GKE).

    2. Deploy AWX to Kubernetes: AWX runs as a set of containerized services. You'll need to set up the necessary deployments, services, and other Kubernetes resources.

    3. Configure AWX: Once AWX is running, you'll need to add your model training playbooks or job templates, set up credentials, and configure any other necessary settings.

    4. Orchestrating Workflows: Use AWX's built-in facilities to orchestrate model training workflows, schedule jobs, and handle dependencies.

    The following is a program that uses Pulumi to set up a Kubernetes cluster on AWS using Amazon EKS and deploy a pre-built AWX Docker image onto it.

    Note: Deployment details such as the Docker image for AWX, storage configurations, domain name system (DNS) setup, and more are not covered in-depth, as they depend significantly on your specific requirements and existing infrastructure.

    import pulumi from pulumi_aws import eks from pulumi_kubernetes import Provider, helm # Step 1: Create an EKS cluster to run AWX eks_cluster = eks.Cluster("eks-cluster") # Create a Kubernetes provider instance using the kubeconfig from the generated EKS cluster k8s_provider = Provider("k8s-provider", kubeconfig=eks_cluster.kubeconfig.apply(lambda kc: kc) ) # Step 2: Deploy AWX to the Kubernetes cluster using the Helm chart # This assumes that there is an existing Helm chart for AWX. # Adjust the version and values according to your specific needs. awx_chart = helm.v3.Chart("awx", helm.v3.ChartOpts( chart="awx", version="19.0.0", # Specify the version of the AWX Helm chart you want to deploy fetch_opts=helm.v3.FetchOpts( repo="https://github.com/ansible/awx-helm", # Replace with the correct Helm chart repository for AWX ), values={ "serviceAccount": { "create": True, }, "postgresql": { "persistence": { "size": "50Gi", # Define the size of the persistent volume for PostgreSQL }, }, }, ), provider=k8s_provider, opts=pulumi.ResourceOptions(depend_on=[eks_cluster]) ) # Print the EKS cluster name and Kubeconfig once they are ready to be used. pulumi.export('eks_cluster_name', eks_cluster.eks_cluster.name) pulumi.export('kubeconfig', eks_cluster.kubeconfig)


    • We create an Amazon EKS cluster named eks-cluster. EKS is a managed Kubernetes service that makes it easier to run Kubernetes on AWS without needing to install and operate your own Kubernetes control plane or worker nodes.

    • Next, we set up a Kubernetes provider (k8s_provider) that will authenticate against our EKS cluster using its kubeconfig. This provider is used to interact with the Kubernetes API server of our EKS cluster.

    • We then define a Helm chart resource awx_chart to deploy AWX onto the Kubernetes cluster. We specify the AWX Helm chart version to use and repositories where the chart can be fetched.

    • The values dictionary in awx_chart specifies the configuration for the AWX Helm chart, instructing our preferences for the Kubernetes resources that the chart will create. This includes creating a service account for AWX and configuring persistent storage for the PostgreSQL instance used by AWX.

    • Finally, the program outputs the EKS cluster name and kubeconfig for your use in managing the Kubernetes cluster and the AWX instance.

    To proceed with this Pulumi program, you need to set up the Pulumi CLI, authenticate with your cloud provider, and then run this program using Pulumi's command-line tools. The output of this program will provide you with the credentials you need to access your Kubernetes cluster and manage AWX.

    Keep in mind, you must adjust the values and settings to fit your infrastructure and requirements for the AI training jobs. This includes setting up the proper networking, storage, computing resources, and any necessary AWX configurations. Additionally, you should manage sensitive information such as credentials securely, for instance, by using Kubernetes secrets or other secrets management tools.