1. Containerized AI Workloads on DigitalOcean Kubernetes

    Python

    To deploy containerized AI workloads on DigitalOcean Kubernetes, we will use Pulumi to define and provision the necessary infrastructure. The basic setup involves creating a DigitalOcean Kubernetes (DOKS) cluster and ensuring that the necessary node pools are allocated for running your AI workloads.

    We'll use the digitalocean.KubernetesCluster resource to create the Kubernetes cluster itself. Additionally, we may want to define specific digitalocean.KubernetesNodePool resources if we require special configurations or capabilities for our AI workloads, such as GPU-enabled nodes or nodes with high memory capacity.

    The following sections provide a step-by-step guide to setting up a Kubernetes cluster with Pulumi, tailored for hosting AI workloads on DigitalOcean. The AI workloads themselves, which might be in the form of Docker containers, can be later deployed onto the cluster using Kubernetes artifacts like Deployments, Services, and Ingresses, but that is beyond the scope of infrastructure provisioning and would typically be done with Kubernetes-specific tools like kubectl or other CI/CD workflows that you may have in place.

    Step 1: Define the Pulumi Python program

    Below is a complete Pulumi Python program that defines the necessary infrastructure for containerized AI workloads:

    import pulumi import pulumi_digitalocean as digitalocean # Define a Kubernetes cluster in DigitalOcean ai_cluster = digitalocean.KubernetesCluster( "ai-cluster", region="nyc1", # Choose the region that is most appropriate for you version="latest", # Specify 'latest' or choose a specific version node_pool=digitalocean.KubernetesClusterNodePoolArgs( name="ai-node-pool", size="s-2vcpu-4gb", # Choose machine size based on AI workload needs node_count=3, # Number of nodes in the pool auto_scale=True, # Whether to allow the node pool to auto-scale min_nodes=1, max_nodes=5, ) ) # Export the cluster's kubeconfig file content pulumi.export('kubeconfig', ai_cluster.kube_configs.apply(lambda kube_configs: kube_configs[0].raw_config))

    Resources Used:

    1. digitalocean.KubernetesCluster: This resource is used to create and manage a DigitalOcean Kubernetes Cluster. You define the details of the cluster, such as the region, version, and the node pool configuration. We've named our cluster "ai-cluster" and targeted it for AI workloads specifying the machine size according to the expected workloads in the node pool configuration.

    2. pulumi.export: The pulumi.export line is used to output the kubeconfig when the cluster is created, allowing you to use this configuration to connect to your cluster with kubectl or other Kubernetes tools.

    Steps to Run the Program:

    1. Ensure you have Pulumi installed and configured with the necessary DigitalOcean Access Token.

    2. You need to have Python 3 installed on your system.

    3. Save the code to a file with a .py extension, say pulumi_ai_cluster.py.

    4. Run the program by executing pulumi up in the terminal in the directory where your Pulumi Python program is located.

    5. After successfully running the pulumi up command, you can get the kubeconfig by looking at the stack output.

    6. Use pulumi stack output kubeconfig to retrieve the kubeconfig and connect to your Kubernetes cluster.

    The program defines the desired state of the infrastructure for your containerized AI workloads. You may need to adjust the specifics such as the node size and count based on the demands of your AI applications. After deploying the Kubernetes cluster, you would typically proceed with the deployment of your AI workloads using Kubernetes manifests or Helm charts.


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