1. GitLab for Orchestrating AI Workflows with Kubernetes


    To orchestrate AI workflows with Kubernetes using GitLab, you'll want to:

    1. Set up a GitLab project to host your code and define your CI/CD pipelines.
    2. Leverage Kubernetes as the execution environment for your AI workflows.
    3. Enable a GitLab Runner in your Kubernetes cluster to execute the CI/CD jobs.

    With Pulumi, you can automate this setup. We'll write a Pulumi program in Python to:

    • Create a new GitLab project.
    • Configure a GitLab Runner in Kubernetes.
    • Enable the runner for your project.

    First, ensure you have GitLab and Kubernetes Pulumi providers configured. The GitLab provider will interact with your GitLab instance to create projects and manage settings. The Kubernetes provider will manage your Kubernetes resources, including setting up the GitLab Runner inside the cluster.

    Here's a Pulumi program that achieves this:

    import pulumi import pulumi_gitlab as gitlab import pulumi_kubernetes as k8s # Initialize a new GitLab project. project = gitlab.Project("ai-workflows-project", name="ai-workflows", description="Project for orchestrating AI workflows with Kubernetes", visibility_level="private") # GitLab Kubernetes Runner Configuration. # The following assumes you have a pre-existing Kubernetes cluster configured where Pulumi can deploy resources. # Variables `kubeconfig` and `runner_registration_token` should be set with actual values kubeconfig = "..." # Your Kubernetes config file. runner_registration_token = "..." # GitLab runner registration token. # Configure GitLab Runner on Kubernetes with a dedicated `Namespace` and `Deployment`. # We use the "gitlab/gitlab-runner:latest" image for the runner. runner_ns = k8s.core.v1.Namespace("gitlab-runner-ns", metadata={ "name": "gitlab-runner" }, opts=pulumi.ResourceOptions(provider=k8s_provider)) runner_deployment = k8s.apps.v1.Deployment("gitlab-runner-deployment", metadata={ "namespace": runner_ns.metadata["name"], }, spec={ "selector": { "matchLabels": { "app": "gitlab-runner" } }, "replicas": 1, "template": { "metadata": { "labels": { "app": "gitlab-runner" } }, "spec": { "containers": [{ "name": "gitlab-runner", "image": "gitlab/gitlab-runner:latest", "volumeMounts": [{ "name": "config", "mountPath": "/etc/gitlab-runner" }], "env": [{ "name": "GITLAB_RUNNER_REGISTRATION_TOKEN", "value": runner_registration_token }] }], "volumes": [{ "name": "config", "configMap": { "name": "gitlab-runner-config" } }] }, } }, opts=pulumi.ResourceOptions(provider=k8s_provider)) # Output the GitLab project details. pulumi.export("gitlab_project_id", project.id) pulumi.export("gitlab_project_web_url", project.web_url)

    This program does the following:

    1. It creates a new GitLab project using the gitlab.Project resource. The project is set to "private" ensuring only authorized users have access.
    2. It sets up a Kubernetes namespace and deployment for the GitLab Runner using Pulumi's pulumi_kubernetes package. This will create the necessary environment for a runner to execute CI/CD jobs.
    3. It uses a ConfigMap named gitlab-runner-config for the runner configuration. Make sure to create this ConfigMap with the correct configuration in your cluster.

    Please replace the placeholder values for kubeconfig and runner_registration_token with your actual Kubernetes configuration file and GitLab runner registration token.

    The GitLab Runner should be registered to your instance of GitLab and is responsible for running your CI/CD jobs in your Kubernetes cluster. Ensure you have kubectl configured with the same kubeconfig on the machine where you're running Pulumi.

    By running this program with Pulumi, you will have a GitLab project ready for orchestrating AI workflows with a GitLab Runner in a Kubernetes cluster.