1. Kubernetes GPU Clusters for AI Model Training on Civo


    To set up a Kubernetes cluster with GPU nodes for AI model training on Civo, you would generally need to follow these steps:

    1. Select a Kubernetes cluster resource for your cloud provider (Civo in your case).
    2. Configure node pools with GPU instance types to ensure your workers have GPU capabilities.
    3. Ensure that the Kubernetes cluster has access to the required GPU drivers and any other dependencies for AI model training.

    Since Civo is not directly supported as a provider in Pulumi, we cannot use a Civo-specific resource directly. However, Civo does offer Kubernetes clusters, and if they offer a managed service with support for GPU-based nodes, you would manage it outside of Pulumi and then configure your Pulumi program to interact with it.

    For the sake of demonstration, let's assume we want to set up a GPU-enabled Kubernetes cluster on a supported provider like Google Cloud Platform (GCP). This is how you would do it with pulumi_gcp, Google Cloud's Pulumi resource provider. We'll create a GKE (Google Kubernetes Engine) cluster and configure a node pool with GPUs.

    Here's a sample Pulumi program that creates a GKE cluster with a node pool that includes NVIDIA Tesla K80 GPUs:

    import pulumi import pulumi_gcp as gcp # Create a GKE cluster. gke_cluster = gcp.container.Cluster("gpu-cluster", initial_node_count=1, min_master_version="latest", node_version="latest", location="us-west1-a", node_config={ "machineType": "n1-standard-1", "oauthScopes": [ "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" ] } ) # Create a node pool with GPU-enabled nodes. gpu_node_pool = gcp.container.NodePool("gpu-node-pool", cluster=gke_cluster.name, location=gke_cluster.location, initial_node_count=1, node_config={ "machineType": "n1-standard-4", # Choose a machine type that is compatible with the desired GPU type. "oauthScopes": [ "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" ], "guest_accelerator": [{ # Configure the GPU type and count. "type": "nvidia-tesla-k80", "count": 1 }], }, autoscaling={ "minNodeCount": 1, "maxNodeCount": 2 }, management={ "autoRepair": True, "autoUpgrade": True }, ) # Export the cluster name and Kubeconfig pulumi.export("cluster_name", gke_cluster.name) pulumi.export("kubeconfig", pulumi.Output.all(gke_cluster.name, gke_cluster.endpoint, gke_cluster.master_auth).apply( lambda args: """apiVersion: v1 clusters: - cluster: certificate-authority-data: {0} server: https://{1} name: {2} contexts: - context: cluster: {2} user: {2} name: {2} current-context: {2} kind: Config preferences: {{}} users: - name: {2} user: auth-provider: config: cmd-args: config config-helper --format=json cmd-path: gcloud expiry-key: '{{.token_expiry}}' token-key: '{{.access_token}}' name: gcp """.format(args[2]["cluster_ca_certificate"], args[1], args[0])))

    In the above code, we have defined two main resources:

    1. gcp.container.Cluster: This resource is used to create a GKE cluster. The initial_node_count specifies the number of nodes for the pool created along with the cluster. We've set the min_master_version and node_version to "latest" for simplicity, but in a production setup, you would specify explicit versions. We've specified a minimal set of OAuth scopes that will allow the Compute Engine instances serving as Kubernetes nodes to interact with other GCP services.

    2. gcp.container.NodePool: This resource creates a node pool within the GKE cluster with GPU-enabled nodes. We specify the machineType that supports attaching GPUs and define guest_accelerator to configure the GPU type and quantity for each node in the pool. We also set the autoscaling parameters to allow the node pool to automatically scale the number of nodes based on workload.

    Finally, we export the cluster_name and kubeconfig for the created cluster. The kubeconfig allows you to interact with your cluster using the kubectl command-line tool or any Kubernetes-compatible tool.

    The types of GPUs and machine configurations available can vary based on the cloud provider and the availability in each region. Also, depending on your AI model training workloads, you may need to adjust the node pool configurations for optimal performance.

    In a production environment, ensure that you manage secrets such as the kubeconfig securely and follow the best practices for identity and access management provided by the cloud provider.