1. OCI CPE for Robust Hybrid Cloud AI Workflows

    Python

    To create a robust hybrid cloud AI workflow using Oracle Cloud Infrastructure (OCI), we will need to provision several resources that can work together to support our AI applications. One key component in this infrastructure is the Customer Premises Equipment (CPE), which represents a physical or virtual device in your on-premises network that connects to the OCI virtual network via a VPN or FastConnect. This allows secure communication between your on-premises network and your cloud resources in OCI.

    The following program in Python utilizes Pulumi with the OCI provider to create a CPE. The example assumes you want to create an OCI CPE and connect it to a virtual cloud network (VCN) where you could set your AI workflows, perhaps using OCI's Data Science and AI services.

    In this program, we will:

    1. Create a CPE instance.
    2. Set up a VCN (Virtual Cloud Network).
    3. Create an Ai Vision Project that could be the starting point for our AI workflows.

    Here's a Pulumi program written in Python to accomplish the setup:

    import pulumi import pulumi_oci as oci # Configure the Oracle Cloud Provider with the right Compartment ID compartment_id = 'YOUR_OCI_COMPARTMENT_ID' # Create a Customer Premises Equipment (CPE). # While creating a CPE, you will provide an IP address which is the public IP address of the on-premises router. cpe = oci.core.Cpe("myCpe", compartment_id=compartment_id, display_name="MyCpeDisplay", # A user-friendly name for the CPE ip_address="123.45.67.89") # Public IP address of the on-premises router # Create a Virtual Cloud Network (VCN) for your cloud resources vcn = oci.core.Vcn("myVcn", compartment_id=compartment_id, cidr_block="10.0.0.0/16", # The CIDR IP range for the VCN display_name="MyVcnDisplay") # Create an AI Vision Project which will serve as a container for your AI models and jobs. ai_vision_project = oci.aivision.Project("myAiVisionProject", compartment_id=compartment_id, display_name="MyAIVisionProjectDisplay", description="Project description for AI Vision") # Exports pulumi.export("cpe_id", cpe.id) # Exports the CPE ID which you may need for other operations pulumi.export("vcn_id", vcn.id) # Exports the VCN ID for further configuration pulumi.export("ai_vision_project_id", ai_vision_project.id) # Exports the AI Vision Project ID # To follow along with the rest of setting up a robust hybrid cloud workflow, you can refer to the # documentation of other OCI services like the AI Vision service, the Data Science service, # and additional network components like subnets and gateways which you could also provision using Pulumi.

    In the presented program:

    • We initialize a new OCI CPE resource. The IP address provided should be the public IP address of your on-premises router. Replace '123.45.67.89' with the actual IP address.
    • We create a VCN that provides a virtual network for your cloud-based resources. The cidr_block specifies the IP address range for the VCN. You may choose a different CIDR block based on your requirements.
    • We set up an AI Vision project, intending to use OCI's AI Vision capabilities within our hybrid cloud setup.
    • Lastly, we export the IDs of the resources created, which could be used later on in other Pulumi programs or scripts to reference these resources.

    Please replace YOUR_OCI_COMPARTMENT_ID with your actual OCI compartment ID where you want these resources to reside.

    After running this program with Pulumi, you would have a foundational setup on OCI for a hybrid cloud AI workflow. From here, you can continue to define other resources that are part of your workflow, such as compute instances for running AI models, object storage buckets for storing datasets, or additional network components like gateways for enhanced connectivity.

    Remember to refer to the Pulumi OCI Documentation for more details on the resources and properties available.


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