1. NAT Gateway Traffic Management for OCI Function-Based ML Inference

    Python

    To configure a NAT Gateway for traffic management in Oracle Cloud Infrastructure (OCI) to support function-based ML inference, you'll need to set up several components:

    1. NAT Gateway: This will enable private resources in your Virtual Cloud Network (VCN) to access the internet without exposing them to incoming internet connections.
    2. VCN and Subnets: These are necessary to host your OCI resources and provide network isolation.
    3. Route Table: Directs traffic from the subnet to the NAT Gateway.
    4. OCI Function: The serverless platform where your ML inference function will be deployed.

    Here's what each part of the setup does in the context of enabling ML inference:

    • NAT Gateway: Allows the ML function to access public internet resources (e.g., to download models or datasets) while maintaining the private nature of the function.
    • VCN and Subnets: Provide the networking layer for your OCI Functions to run on, including the subnet associated with the NAT Gateway.
    • Route Table: Ensures that requests from the function that need to access the internet are routed through the NAT Gateway.

    We will write a Pulumi program in Python that sets up the necessary infrastructure.

    The program does the following:

    • Creates a VCN and a public subnet.
    • Provisions a NAT Gateway.
    • Configures a route table to use the NAT Gateway for internet-bound traffic.
    • Deploys an OCI Function (a placeholder for your ML inference function) into the subnet.

    Below is a detailed Pulumi program that accomplishes this:

    import pulumi import pulumi_oci as oci # Replace with your own compartment ID compartment_id = "YOUR_OCI_COMPARTMENT_ID" # Create a Virtual Cloud Network vcn = oci.core.Vcn("myVcn", cidr_block="10.0.0.0/16", compartment_id=compartment_id, display_name="MyVcn") # Create a public subnet subnet = oci.core.Subnet("mySubnet", compartment_id=compartment_id, display_name="MySubnet", vcn_id=vcn.id, cidr_block="10.0.1.0/24") # Create a NAT Gateway nat_gateway = oci.core.NatGateway("myNatGateway", compartment_id=compartment_id, vcn_id=vcn.id, display_name="MyNatGateway") # Create a route table for the subnet to route traffic through the NAT Gateway route_table = oci.core.RouteTable("myRouteTable", compartment_id=compartment_id, vcn_id=vcn.id, route_rules=[oci.core.RouteTableRouteRuleArgs( destination="0.0.0.0/0", network_entity_id=nat_gateway.id )], display_name="MyRouteTable") # Associate the route table with the subnet subnet.route_table_id = route_table.id # Placeholder for OCI Function creation # Normally you would include details about your function, its configuration, and the code oci_function = oci.functions.Function("myFunction", display_name="MyFunction", image="phx.ocir.io/cloudnative-devrel/oke-functions/imagerecognition:latest", memory_in_mbs=1024, compartment_id=compartment_id, application_id="YOUR_OCI_APPLICATION_ID", # Replace with your application ID subnet_ids=[subnet.id]) # Output the NAT Gateway's IP address pulumi.export("nat_gateway_ip", nat_gateway.public_ip_id) # Output the OCI Function's invoke endpoint pulumi.export("function_invoke_endpoint", oci_function.invoke_endpoint)

    Before running this program, replace "YOUR_OCI_COMPARTMENT_ID" and "YOUR_OCI_APPLICATION_ID" with your actual OCI compartment and function application IDs.

    This Pulumi program sets up a network with a NAT Gateway allowing egress traffic for an OCI Function, ideal for a serverless ML inference function that requires access to external resources. The oci_function resource is a placeholder, and you would need to configure it with your actual ML function details.

    Please make sure you have the Pulumi CLI and OCI provider configured with appropriate access to your OCI account before running the program.


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