1. Containerized AI Workload Orchestration with Buildkite


    To orchestrate a containerized AI workload using Buildkite, you will first need to set up a Buildkite pipeline that can handle the tasks required for the AI workload. This includes setting up the necessary infrastructure, which typically involves a container runtime environment (like Docker), an orchestration platform (like Kubernetes or Amazon ECS), and Buildkite agents that can run the CI/CD pipeline.

    The Pulumi program I'll provide will demonstrate how to set up a Buildkite pipeline and agents using the Pulumi Buildkite provider, which will be responsible for running your AI workload. It assumes that you have a container with your AI application and that you're ready to deploy and manage this container as part of your CI/CD process.

    In this program, we will:

    1. Set up a new Buildkite pipeline that references your source control repository where your AI application code exists.
    2. Configure the Buildkite pipeline to include steps that outline how to build, test, and deploy your containerized application.
    3. Connect the pipeline to a cluster where your Buildkite agents are running, and these agents will execute the pipeline jobs.
    4. Assuming you have Docker and any necessary credentials configured, the Buildkite pipeline steps will be able to orchestrate the tasks needed to manage the lifecycle of your containerized AI workload.
    import pulumi import pulumi_buildkite as buildkite # Initialize a new Buildkite organization resource. # Note: The organization must already exist in Buildkite. organization = buildkite.Organization("my-organization", # You can specify settings like allowed API IP addresses if needed. allowed_api_ip_addresses=[""] # This is just an example. You should limit to your known IPs. ) # Define a new Buildkite pipeline that connects to your code repository. pipeline = buildkite.Pipeline("ai-workload-pipeline", repository="git://github.com/your-org/your-repo.git", # Replace with your repository link. steps=""" steps: - label: ":docker: Build" command: "docker build -t my-app ." agents: queue: "my-queue" - wait - label: ":docker: Run Tests" command: "docker run my-app /tests" agents: queue: "my-queue" - label: ":rocket: Deploy" command: "./deploy.sh" agents: queue: "my-queue" """, description="Pipeline for containerized AI workload orchestration", default_branch="main" ) # Export the pipeline URL to access it later easily. pipeline_url = pulumi.Output.concat("https://buildkite.com/", organization.slug, "/", pipeline.slug) pulumi.export("pipeline_url", pipeline_url)

    In this program, we define a Pipeline resource with steps for building a Docker image, running tests, and deploying the application. The command values should be adjusted to fit your actual build, test, and deployment commands for your AI workload. Similarly, you would need to provide the correct Docker image names and any scripts you want to run. The agents key specifies which Buildkite agents (and therefore, which queue) should run the step.

    Remember that the infrastructure to support this process – like the Docker registry, any Kubernetes config, or cloud service setup – needs to be handled outside of this script or in additional Pulumi scripts. The program above assumes you've already set up Buildkite agents in your cluster that can process the jobs dispatched by the Buildkite pipeline.

    After you apply this Pulumi program, a new Buildkite pipeline will be configured and ready to be triggered either manually or by code changes in your repository. You can navigate to the URL exported by the program to check the status and manage the pipeline in the Buildkite UI.