1. Operator-Driven Infrastructure Provisioning for AI


    To accomplish Operator-Driven Infrastructure Provisioning for AI, you would typically want to provision a range of resources that could include compute instances for running AI models, storage for datasets, networking to secure and connect your resources, and potentially specific AI services provided by cloud providers, such as machine learning pipelines or data processing services.

    When using Pulumi to perform such a provisioning task, your choice of resources will depend on your specific needs and the cloud provider you're using. Let's consider an example where you want to provision an AI application on Oracle Cloud Infrastructure (OCI), and you've decided to use Pulumi's OCI provider. This example aims to provision an AI service, store datasets, and set up a DevOps project to manage the application lifecycle.

    Here's a Pulumi program in Python that performs the following:

    1. AI Vision Project: Creates an AI Vision project in OCI to manage your AI datasets and models.
    2. Object Storage Bucket: Sets up an OCI object storage bucket to store your datasets.
    3. DevOps Project: Initializes an OCI DevOps project to automate the deployment and management of your AI application.
    import pulumi import pulumi_oci as oci # Note: Make sure you have configured your Pulumi for OCI provider with the appropriate credentials. # Define an AI Vision project within OCI ai_vision_project = oci.aivision.Project("aiVisionProject", compartment_id="ocid1.compartment.oc1..example", # Replace with your compartment ID display_name="MyAIVisionProject", description="An AI project for image analysis", defined_tags={ "TagNamespace": { "TagName": "TagValue" } }, freeform_tags={ "Environment": "Development" } ) # Define an OCI Object Storage bucket for storing datasets datasets_bucket = oci.objectstorage.Bucket("datasetsBucket", compartment_id=ai_vision_project.compartment_id, name="ai-datasets", storage_tier="Standard", defined_tags={ "TagNamespace": { "TagName": "TagValue" } }, freeform_tags={ "Purpose": "AI Datasets" } ) # Define an OCI DevOps project to manage the application lifecycle devops_project = oci.devops.Project("devOpsProject", compartment_id=ai_vision_project.compartment_id, name="AIApplicationLifecycle", description="DevOps project for our AI Application", defined_tags={ "TagNamespace": { "TagName": "TagValue" } }, freeform_tags={ "Service": "AI Application Management" }, notification_config={ "topic_id": "ocid1.onsnotificationtopic.oc1..example" # Replace with your notification topic ID } ) # Export the project, bucket, and DevOps project IDs pulumi.export("ai_vision_project_id", ai_vision_project.id) pulumi.export("datasets_bucket_name", datasets_bucket.name) pulumi.export("devops_project_id", devops_project.id)

    In the above program:

    • We begin by importing Pulumi and the OCI provider package.
    • We create an AI Vision project for managing AI-specific workflows within OCI.
    • We then create an object storage bucket to store our datasets.
    • Finally, we initialize a DevOps project to streamline application deployment and management.

    Each resource has its compartment ID, a uniquely identifying string for a compartment in OCI, where the resource belongs, and we set custom tags to categorize and manage these resources better.

    The pulumi.export lines at the end of the program will output the IDs of the created resources, which can be useful for integrating with other systems or resources not defined in this Pulumi program. This is particularly handy since the ID is determined by the cloud provider upon creation.

    Please replace placeholder strings like ocid1.compartment.oc1..example and ocid1.onsnotificationtopic.oc1..example with your actual OCI compartment and notification topic IDs.

    Upon running this Pulumi program, you should have the basic cloud infrastructure ready for starting with your AI application development and operations within the Oracle Cloud Infrastructure.