1. Continuous Integration/Continuous Deployment (CI/CD) for AI on AKS


    Continuous Integration/Continuous Deployment (CI/CD) is a method to frequently deliver apps to customers by introducing automation into the stages of app development. The main concepts attributed to CI/CD are continuous integration, continuous deployment, and continuous delivery. CI/CD bridges the gaps between development and operation activities and teams by enforcing automation in building, testing, and deployment of applications.

    Azure Kubernetes Service (AKS) is a managed container orchestration service, based on Kubernetes, which is available on the Microsoft Azure public cloud. An organization can use AKS to deploy, scale, and manage Docker containers and container-based applications across a cluster of container hosts.

    In the context of AKS, CI/CD typically involves:

    • Using source control repositories such as GitHub or GitLab to manage code.
    • Automating the testing of the code in the repository.
    • Using a CI/CD pipeline to build and test the code, then deploy it to an AKS cluster. Tools such as Azure Pipelines, Jenkins, GitHub Actions, or GitLab CI can be used for this purpose.

    To set up CI/CD for AI on AKS using Pulumi, you would need to:

    • Define the infrastructure for the AKS cluster using Pulumi's infrastructure-as-code.
    • Integrate CI/CD tooling with Pulumi to deploy changes to the AKS cluster.

    Here is a program written in Python using Pulumi that would establish the basis for a CI/CD setup by defining a simple AKS cluster. Post which you can integrate it with your preferred CI/CD tooling to deploy workloads:

    import pulumi import pulumi_azure_native as azure_native # Define the AKS cluster. managed_cluster = azure_native.containerservice.ManagedCluster( resource_name="my-aks-cluster", resource_group_name="my_resource_group", location="East US", # If you have more fine grained identity requirements, you'd configure them here. identity={ 'type': 'SystemAssigned', # Using a system assigned identity for simplicity. }, # Configure the agent pool with the desired VM size, number of replicas, etc. agent_pool_profiles=[{ 'name': 'agentpool', 'count': 2, 'vm_size': 'Standard_DS2_v2', 'os_type': 'Linux', 'mode': 'System', }], # Specify additional cluster settings as needed for your workload. # Including settings for networking, DNS, Linux profile, etc. dns_prefix="mydnsprefix", kubernetes_version="1.21.2", ) # Export the kubeconfig to be used by kubectl and CI/CD systems to deploy apps to the cluster. pulumi.export('kubeconfig', pulumi.Output.secret(managed_cluster.kube_config_raw))

    In this program:

    • We create an AKS cluster with a system-assigned identity.
    • We configure an agent pool with the Standard_DS2_v2 VM size, but you might want to choose something that matches your workload needs.
    • We're using a fixed version of Kubernetes, but you might want to make this customizable depending on how you manage updates.
    • We're exporting the Kubernetes configuration, which would be used by kubectl to interact with the cluster. This secret should be stored securely and used in your CI/CD pipelines.

    Next steps after creating an AKS cluster with Pulumi:

    1. Set up a CI/CD pipeline using a tool like GitHub Actions, GitLab CI, Jenkins, or Azure DevOps.
    2. Configure the pipeline to trigger on code repository changes.
    3. Use Pulumi within the pipeline to deploy updates to the AKS cluster.
    4. Add steps to the pipeline to build your AI application's Docker container, push it to a container registry, and then deploy it to the AKS cluster.

    It's important to note that running this Pulumi program deploys the basic AKS infrastructure, and it doesn't include the deployment of any specific application. The application deployment and CI/CD automation are generally handled outside the scope of this code and would be set up as part of the CI/CD pipeline configuration.

    Ensure that your CI/CD system has the necessary permissions to interact with Azure and your Kubernetes cluster. This will likely involve setting up service connections or secret variables with the required cloud provider credentials.