1. Continuous Delivery Pipelines for AI Applications with Tekton


    Continuous Delivery (CD) is a practice where code changes are automatically built, tested, and deployed to production. Tekton is an open-source framework for creating CD systems, allowing you to build, test, and deploy across multiple cloud providers or on-premises systems.

    In the context of AI applications, CD pipelines can automate the process of training models, evaluating them against a test set, and deploying them if they meet certain performance criteria. This involves a range of activities such as data preprocessing, model training, model validation, and possibly retraining with new data.

    You often want to integrate with various cloud services and leverage container orchestration systems like Kubernetes to handle these tasks. While Pulumi doesn't have direct integration with Tekton as of my last update, it does provide resources for setting up the underlying infrastructure required for such CD pipelines, including Kubernetes clusters, cloud services for AI, and CI/CD tooling integrations that can facilitate a Tekton pipeline.

    Below is a Python Pulumi program that sets up fundamental infrastructure on Kubernetes, which can be used to support a Tekton pipeline for AI applications. This includes creating a Kubernetes cluster and setting up a namespace where Tekton pipelines will be executed.

    Please note that the actual setup of Tekton pipelines and tasks, as well as the logic involved in training and deploying an AI model, will need to be defined using Tekton's own Pipelines and Tasks YAML definitions or Resources. These should be applied to the cluster once it is provisioned by this Pulumi code.

    import pulumi import pulumi_kubernetes as k8s # Creating a new Kubernetes cluster using AWS Elastic Kubernetes Service (EKS) cluster = aws.eks.Cluster('ai-cluster', role_arn=my_role.arn, vpc_config={ 'subnet_ids': my_subnet_ids }) # Creating a Kubernetes Provider to interact with the newly created cluster k8s_provider = k8s.Provider('k8s-provider', kubeconfig=cluster.kubeconfig) # Creating a Kubernetes Namespace for AI applications and Tekton pipelines ai_namespace = k8s.core.v1.Namespace('ai-namespace', metadata={ 'name': 'ai-applications' }, opts=pulumi.ResourceOptions(provider=k8s_provider)) # Exporting the cluster name and Kubeconfig pulumi.export('cluster_name', cluster.name) pulumi.export('kubeconfig', cluster.kubeconfig)

    This code doesn't touch on the specifics of your AI application or Tekton pipeline, but it lays the groundwork for running those workloads. You'd use the provided kubeconfig to set up Tekton and run your workflows, which would likely be outlined in separate documentation specific to Tekton.

    After creating the necessary infrastructure using the Pulumi program above, you could follow the Tekton documentation to set up your pipelines. Tekton Pipelines will involve resources such as Tasks, Pipelines, and PipelineRuns, which are Kubernetes CRDs (Custom Resource Definitions) you'd apply to the cluster.

    You might have Tekton Tasks for data preparation, model training, evaluation, and deployment, and these would be composed in a Pipeline that defines the execution order. A PipelineRun resource would trigger the actual execution of this pipeline with specific parameters and resources. To manage these Tekton resources as code, you can either apply YAML files with kubectl directly or manage them through another round of Pulumi scripting if automation is required.

    For more extensive usage, especially if you want to create Tekton Tasks and Pipelines as code in Pulumi, you would likely need to import Tekton's CRDs into your Pulumi code as CustomResource definitions, and then automate their deployment through Pulumi.

    Remember, this example assumes that AWS services and the necessary roles are already set up and available in your Pulumi stack configuration. If they aren't, be sure to configure those before running this code.