1. Multi-Cloud AI Model Training using Rancher Orchestrated Clusters


    To set up a multi-cloud AI model training environment using Rancher orchestrated clusters, we'll leverage several cloud providers, including the resources from the Pulumi Registry that fit our needs. Here's a rundown on how we might approach this:

    1. Provision Kubernetes Clusters: We will provision Kubernetes clusters across multiple clouds.
    2. Rancher Server: We will install and set up Rancher, which is a container management platform that makes it easier to manage Kubernetes clusters across various cloud providers.
    3. AI Training Model: Choose an AI model training service. For example, you might use Google Cloud's AI Platform or a similar service from another provider.
    4. Data Storage and Processing: Use a shared data service that is accessible from clusters in different clouds, like Google Cloud Storage or AWS S3.

    Below, you'll find a Pulumi program which accomplishes the following:

    • Creates a Kubernetes cluster on Google Cloud using Google Kubernetes Engine (GKE).
    • Creates a Kubernetes cluster on AWS using Amazon Elastic Kubernetes Service (EKS).
    • Deploys Rancher server on one of the clusters to manage both clusters.
    • Sets up a Google Cloud AI-Platform training job that can be managed via Rancher (assuming the appropriate integrations/extensions are in place).

    This program does not cover the complete setup but gives you a starting point to further expand and customize based on your specific requirements. Please ensure you have the appropriate Pulumi credentials configured for AWS and Google Cloud before running this program.

    import pulumi import pulumi_gcp as gcp import pulumi_aws as aws import pulumi_rancher2 as rancher2 # Create a GKE cluster to be managed by Rancher gke_cluster = gcp.container.Cluster("gke-cluster", initial_node_count=1, node_config={ "oauth_scopes": [ "https://www.googleapis.com/auth/compute", "https://www.googleapis.com/auth/devstorage.read_only", "https://www.googleapis.com/auth/logging.write", "https://www.googleapis.com/auth/monitoring" ], }) # Export the GKE Kubeconfig gke_kubeconfig = pulumi.Output.all(gke_cluster.name, gke_cluster.endpoint, gke_cluster.master_auth).apply( lambda args: gcp.container.get_kubeconfig(cluster_name=args[0], location=gke_cluster.location, project=gke_cluster.project) ) # Using AWS provider, create an EKS cluster to be managed by Rancher eks_cluster = aws.eks.Cluster("eks-cluster", role_arn=aws_iam_role["eks"]["arn"], vpc_config={ "security_group_ids": [aws_security_group["eks"]["id"]], "subnet_ids": aws_subnet_ids, }) # Export the EKS Kubeconfig eks_kubeconfig = pulumi.Output.all(eks_cluster.endpoint, eks_cluster.certificate_authority, eks_cluster.name).apply( lambda args: f''' apiVersion: v1 clusters: - cluster: server: {args[0]} certificate-authority-data: {args[1]["data"]} name: kubernetes contexts: - context: cluster: kubernetes user: aws name: aws current-context: aws kind: Config preferences: {{}} users: - name: aws user: exec: apiVersion: client.authentication.k8s.io/v1alpha1 command: aws-iam-authenticator args: - "token" - "-i" - "{args[2]}" ''' ) # Now we deploy Rancher in one of the clusters (for example, GKE) to manage both GKE and EKS. # This can be accomplished using Helm chart for Rancher or other custom deployment methods. # Placeholder for Rancher setup on the chosen cluster. The specific details of deploying # Rancher are beyond the scope of this code but should involve installing the Rancher helm chart # into the Kubernetes cluster, typically into a dedicated namespace like `cattle-system`. # Example command to add the Helm repo for Rancher: # helm repo add rancher-latest https://releases.rancher.com/server-charts/latest # Example command to install Rancher: # helm install rancher rancher-latest/rancher --namespace cattle-system --set hostname=rancher.my.org # (Setup the AI training job on GCP AI Platform - placeholder code) ai_platform_job = gcp.ml.EngineModel("ai-platform-job", name="my-ai-model", project=gke_cluster.project, online_prediction_logging=True, # More configuration related to the AI training job would be added here. ) # Export kubeconfig outputs that could be used to manage clusters via Rancher (after correct setup) pulumi.export("gke_kubeconfig", gke_kubeconfig) pulumi.export("eks_kubeconfig", eks_kubeconfig) # Note: This programmatically provides access to the kubeconfig, # which should be securely managed when using it in real implementations.

    Here are the key components to note in the program:

    • gcp.container.Cluster: This creates a Kubernetes cluster in GCP using GKE.
    • aws.eks.Cluster: This provisions an EKS cluster in AWS.
    • gcp.ml.EngineModel: Prepares for an AI model to be trained on Google's AI Platform. This is a placeholder and should be adapted to your specific AI training requirements.

    This program is the first step and would need additional steps like setting up Rancher through Helm and configuring networking, security, and data storage in a way that they are accessible across clouds while meeting your compliance and governance requirements. It is also important to note that multi-cloud setups can become complex to manage and should be approached with careful planning and consideration of multi-cloud networking, security, and data management strategies.