1. Continuous Delivery for AI Models with Argo Rollouts

    Python

    To accomplish Continuous Delivery for AI Models using Argo Rollouts with Pulumi, you'll need to deploy a Kubernetes cluster, install Argo Rollouts, and set up a pipeline for continuous delivery. Although Pulumi doesn't directly manage Argo Rollouts, you would generally use Pulumi to create a Kubernetes cluster and then deploy Argo Rollouts within this cluster.

    Here’s a step-by-step approach:

    1. Create Kubernetes Cluster: First, you will create a Kubernetes cluster using a cloud provider of your choice (AWS EKS, Azure AKS, GCP GKE, etc.).

    2. Install Argo Rollouts: Once your Kubernetes cluster is ready, you can use the pulumi_kubernetes library to apply YAML files or Helm charts for Argo Rollouts.

    3. Set Up CI/CD: For Continuous Integration/Continuous Delivery, you could use Pulumi to manage external resources such as triggering a pipeline in GitLab, a webhook in GitHub, etc.

    Let's walk through a Pulumi Python program to set up an AWS EKS cluster, which then can be used to deploy Argo Rollouts manually. For the sake of simplicity, the following program outlines the steps to create a Kubernetes cluster.

    import pulumi import pulumi_aws as aws import pulumi_eks as eks # Create an EKS cluster. cluster = eks.Cluster('ai-models-cluster', instance_type="t2.medium", # Choose an appropriate instance type for your use case. desired_capacity=2, # Set desired number of worker nodes in the cluster. min_size=1, max_size=3, deploy_dashboard=False, ) # Export the cluster's kubeconfig. pulumi.export('kubeconfig', cluster.kubeconfig)

    In the code snippet above, we create an Amazon EKS cluster that will allow you to manage your AI models. Once you have the Kubernetes infrastructure, you can install Argo Rollouts. The eks.Cluster creates an AWS EKS cluster and exports the kubeconfig file so you can interact with your cluster using kubectl.

    To continue setting up Argo Rollouts, you would generally execute commands like kubectl create namespace argo-rollouts and kubectl apply -f https://raw.githubusercontent.com/argoproj/argo-rollouts/stable/manifests/install.yaml to deploy Argo Rollouts into your cluster.

    Unfortunately, we don't have an existing Pulumi resource for directly deploying Argo Rollouts, but this could be done using the pulumi_kubernetes module by creating a kubernetes.yaml.ConfigGroup resource with the desired Argo Rollouts YAML configurations, or ideally through the pulumi_kubernetes.helm.v3.Chart resource if there is a Helm chart available for Argo Rollouts.

    After installing Argo Rollouts, you would write deployments manifests utilizing Argo Rollouts CRDs for your AI models and manage updates and rollouts through Argo's system. This will involve understanding Argo Rollouts and Kubernetes concepts such as Deployments, Rollouts, Canary releases, etc.

    As Pulumi works to manage infrastructure through code, your CI pipeline could look something like this:

    1. Commit Code: Developers commit their AI model code and configurations into the repository.
    2. Build and Test: A CI process builds the models and runs tests.
    3. Push Images: Upon successful tests, the updated Docker images are pushed to a registry.
    4. Pulumi Up: Run pulumi up to update Kubernetes resources including Argo Rollouts configurations according to the updates in the AI models.

    The actual Continuous Delivery of AI Models is likely to encompass more specific steps pertaining to the model training, evaluation, and release process which can be customized according to your project needs.

    Remember to have your Pulumi CLI configured along with the necessary cloud provider credentials before you run your Pulumi program. You can consult Pulumi's EKS documentation for more details.


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