1. OCI Node Pools for Scalable AI Model Training

    Python

    To deploy Node Pools in Oracle Cloud Infrastructure (OCI) capable of handling scalable AI Model training, we would typically use OCI's Container Engine for Kubernetes (OKE) to create a managed Kubernetes cluster and configure Node Pools according to the AI workloads' specific requirements, such as high CPU, GPU, or memory resources.

    OCI Container Engine for Kubernetes integrates with the Compute, Networking, and Storage services in OCI, providing a foundation to deploy and manage containers using Kubernetes.

    Node Pools in Kubernetes are a collection of worker machines, called nodes, that run containerized applications. Every node in the Node Pool belongs to a single Kubernetes cluster and contains the services necessary to run pods.

    Below is a Pulumi Python program that creates a Kubernetes cluster in OCI and then configures a Node Pool with specific resources tailored for AI training. This example assumes you want to create a new VCN and subnets for the cluster and Node Pool. For real-world scenarios, you would likely have a more complex network setup, with additional considerations for security, connectivity, and possibly existing infrastructure.

    In the program, change the placeholders (like the compartment ID, subnet IDs, ssh public key, etc.) with your actual OCI environment values.

    import pulumi import pulumi_oci as oci # Replace these variables with the actual values from your OCI environment. compartment_id = 'ocid1.compartment.oc1..xxxxxx' ssh_public_key = 'ssh-rsa AAAA...' kubernetes_version = 'v1.21.0' node_shape = 'VM.Standard2.1' # Choose the appropriate shape based on the AI workload. node_image_id = 'ocid1.image.oc1..xxxxxx' # Use a specific image OCID or provide a custom image. # Create a new VCN vcn = oci.core.Vcn("myVcn", cidr_block="10.0.0.0/16", compartment_id=compartment_id, display_name="myVcn") # Create a subnet for the Kubernetes cluster cluster_subnet = oci.core.Subnet("clusterSubnet", compartment_id=compartment_id, display_name="Cluster Subnet", vcn_id=vcn.id, cidr_block="10.0.10.0/24") # Create an internet gateway for the VCN internet_gateway = oci.core.InternetGateway("internetGateway", compartment_id=compartment_id, vcn_id=vcn.id, display_name="Internet Gateway", is_enabled=True) # Create a route table for the internet gateway route_table = oci.core.RouteTable("routeTable", compartment_id=compartment_id, vcn_id=vcn.id, display_name="Route Table", route_rules=[oci.core.RouteTableRouteRuleArgs( cidr_block=None, destination="0.0.0.0/0", destination_type="CIDR_BLOCK", network_entity_id=internet_gateway.id, )]) # Create a security list for the VCN security_list = oci.core.SecurityList("securityList", compartment_id=compartment_id, vcn_id=vcn.id, display_name="Security List", egress_security_rules=[oci.core.SecurityListEgressSecurityRuleArgs( destination="0.0.0.0/0", protocol="all", )], ingress_security_rules=[oci.core.SecurityListIngressSecurityRuleArgs( protocol="all", source="0.0.0.0/0", )]) # Create a Kubernetes cluster cluster = oci.containerengine.Cluster("myCluster", compartment_id=compartment_id, vcn_id=vcn.id, kubernetes_version=kubernetes_version, options=oci.containerengine.ClusterOptionsArgs( admission_controller_options=oci.containerengine.ClusterOptionsAdmissionControllerOptionsArgs( is_pod_security_policy_enabled=True, ), service_lb_subnet_ids=[cluster_subnet.id], )) # Create a Node Pool for the Kubernetes cluster node_pool = oci.containerengine.NodePool("myNodePool", cluster_id=cluster.id, compartment_id=compartment_id, kubernetes_version=kubernetes_version, node_shape=node_shape, node_source_details=oci.containerengine.NodePoolNodeSourceDetailsArgs( image_id=node_image_id, source_type="IMAGE", ), node_config_details=oci.containerengine.NodePoolNodeConfigDetailsArgs( size=3, # Desired number of nodes in the Node Pool placement_configs=[oci.containerengine.NodePoolNodeConfigDetailsPlacementConfigArgs( availability_domain="IAD-AD-1", subnet_id=cluster_subnet.id, )], ), ssh_public_key=ssh_public_key) # Export relevant outputs pulumi.export('vcn_id', vcn.id) pulumi.export('cluster_subnet_id', cluster_subnet.id) pulumi.export('cluster_id', cluster.id) pulumi.export('node_pool_id', node_pool.id)

    This program begins by setting up a new Virtual Cloud Network (VCN) with the necessary subnets, route table, internet gateway, and security list. It then creates a Kubernetes cluster and a Node Pool associated with that cluster.

    Explanations of key sections:

    • VCN & Subnet Creation: Creates a virtual networking environment with a specific CIDR block and an associated subnet for the Kubernetes cluster. Your application's pods will be placed within this subnet.

    • Internet Gateway & Route Table: Sets up a route to allow traffic to flow from the VCN to the broader internet, which is necessary for downloading images and updates.

    • Cluster Creation: Provisions a managed Kubernetes cluster (oci.containerengine.Cluster) within the VCN. You can specify the Kubernetes version to ensure compatibility with your AI ML workloads or tools.

    • Node Pool Creation: Adds a Node Pool (oci.containerengine.NodePool) with worker nodes configured to use a specific image and shape suitable for AI ML tasks. Node shapes determine the amount of CPU, memory, and other resources allocated to each node. The node_config_details specifies the number of nodes and their placement within the availability domain and subnet.

    • SSH Key: The provided SSH public key allows you to securely access the nodes within the node pool if needed.

    • Exports: The final part of the code exports the IDs of created resources for easy referencing and potential integration with other parts of your Infrastructure as Code.

    You should replace placeholders with actual OCI values, and you might need to adjust the number of nodes, pod capacity, images, desired kubernetes version, or other parameters based on your specific AI model training requirements.

    To run this Pulumi program:

    1. Ensure that Pulumi and OCI CLI are installed and configured on your machine.
    2. Save this code to a file named __main__.py.
    3. Run the program with pulumi up, following the prompts to create the resources.

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