1. OCI Container Engine for Kubernetes as a Serving Layer for ML Models


    OCI (Oracle Cloud Infrastructure) Container Engine for Kubernetes (OKE) is a fully managed, scalable, and highly available service that you can use to deploy your containerized applications to the cloud. When serving machine learning (ML) models, it's common to containerize the prediction service so that it can be efficiently scaled and managed.

    In the context of Pulumi, we will define our infrastructure as code, meaning we'll write a script that describes the desired state of our infrastructure, and Pulumi will make it so. This includes our OKE cluster where our models will be served, as well as any other necessary resources such as networks and storage.

    Below is a Pulumi program that could serve as the infrastructure set up for an OCI Container Engine for Kubernetes cluster to serve machine learning models.

    We will use the oci Pulumi provider to create the necessary resources. Specifically, we'll create a VCN (Virtual Cloud Network) and subnets for our Kubernetes cluster. Then we'll set up the OKE cluster itself.

    Here's a breakdown of steps our script will perform:

    1. VCN and Subnets: These are foundational network components that provide a virtual network for your OKE cluster.

    2. OKE Cluster: This is the Kubernetes cluster where your containers will run. You'll need to specify details like the Kubernetes version and the type of nodes you want in your cluster.

    3. Node Pool: This is a group of nodes within your Kubernetes cluster. Each node is a worker machine (virtual machine) that runs your containerized applications.

    import pulumi import pulumi_oci as oci # Create a VCN for our Kubernetes cluster vcn = oci.core.Vcn("my_vcn", cidr_blocks=[""], compartment_id=oci.config.require("compartment_id"), display_name="My VCN") # Create a subnet for the Kubernetes nodes subnet = oci.core.Subnet("my_subnet", compartment_id=oci.config.require("compartment_id"), display_name="My Subnet", vcn_id=vcn.id, cidr_block="") # Create an OCI Container Engine for Kubernetes cluster cluster = oci.containerengine.Cluster("my_cluster", compartment_id=oci.config.require("compartment_id"), vcn_id=vcn.id, kubernetes_version="v1.20.8", # Use an appropriate version for your needs options=oci.containerengine.ClusterOptionsArgs( service_lb_subnet_ids=[subnet.id] )) # Create a node pool for the Kubernetes cluster node_pool = oci.containerengine.NodePool("my_node_pool", cluster_id=cluster.id, compartment_id=oci.config.require("compartment_id"), kubernetes_version="v1.20.8", # Match the version used for the cluster node_shape="VM.Standard2.1", # Specify the shape of the nodes subnet_ids=[subnet.id], quantity_per_subnet=2, # Specify the number of nodes node_source_details=oci.containerengine.NodePoolNodeSourceDetailsArgs( source_type="IMAGE", image_id="ocid1.image.oc1..exampleuniqueID" # Specify the id of the node image )) # Export the Kubernetes cluster id and kubeconfig pulumi.export("kubeconfig", cluster.kube_config) pulumi.export("cluster_id", cluster.id)

    Please replace oci.config.require("compartment_id") with the actual compartment ID and fill in the required fields such as image_id with appropriate values for your specific OCI account and requirements.

    The above code is just a starting point. In a production environment, you would need to enhance the security, add storage options for persistence, configure proper networking, and potentially integrate a CI/CD pipeline for deployment.

    To apply this Pulumi program, save it to a file __main__.py, run pulumi up in the same directory, and follow the instructions to create the resources.

    Exporting kubeconfig at the end of the program allows you to interact with your Kubernetes cluster using kubectl after your Pulumi program has finished running. Exporting cluster_id can be useful for automating other parts of your infrastructure setup or maintenance scripts.