1. AI Model Deployment with OCI Functions and Object Storage

    Python

    To deploy an AI model with Oracle Cloud Infrastructure (OCI) Functions and Object Storage, you need to follow a series of steps within Pulumi to create and manage your cloud resources. The primary resources involved in this process include:

    • OCI Object Storage: This is used to store the AI model and any other associated files. Object Storage provides a scalable and secured means to manage this data.
    • OCI Functions: It's a serverless platform that allows you to run code without managing servers. You'll deploy your AI model as a function and it will be invoked with an HTTP request.
    • Other supporting OCI services: Depending on your needs, additional resources like IAM policies for securing and granting access, networking resources for secure connectivity, or monitoring and logging resources might be relevant.

    Here's a Python program using Pulumi to deploy an AI model using OCI Functions and Object Storage. The code assumes that you have already set up OCI with the necessary permissions and that you have Pulumi installed and configured.

    import pulumi import pulumi_oci as oci # First, upload your AI model to the OCI Object Storage. # For this example, let's assume you have a local file named 'ai_model.zip'. # Create a new Object Storage bucket to store the model. ai_model_bucket = oci.objectstorage.Bucket("aiModelBucket", compartment_id=oci.config.require("compartment_id"), name="ai-model-bucket") # Upload the AI model as an object to the bucket. ai_model_object = oci.objectstorage.Object("aiModelObject", bucket=ai_model_bucket.name, key="ai_model.zip", source=pulumi.FileAsset("path_to_your_local_ai_model/ai_model.zip"), content_type="application/zip", # If your model file is large, you might wish to include a `multipart=True` argument here. namespace=ai_model_bucket.namespace) # Now, create a function for the AI model deployment. # Functions require a Docker image that contains the function code, dependencies, and model. # Create an application for the function. This groups together multiple functions into a single logical group. ai_model_app = oci.functions.Application("aiModelApp", compartment_id=ai.config.require("compartment_id"), display_name="AI Model Application", subnet_ids=[oci.config.require("subnet_id")]) # Make sure to provide your VCN's subnet id # Now define the function, assuming you have a Docker image ready with the model and its dependencies. ai_model_function = oci.functions.Function("aiModelFunction", application_id=ai_model_app.id, image="<your-docker-image-uri>", # Replace with your Docker image URI display_name="AI Model Function", memory_in_mbs=1024, # Adjust the memory as necessary timeout_in_seconds=30, # Adjust the timeout as necessary invoke_endpoint=ai_model_app.invoke_endpoint) # Output the URL endpoint of the deployed function. pulumi.export("ai_model_function_invoke_endpoint", ai_model_function.invoke_endpoint) # Additionally, you might want to set up IAM policies to control access or define logging for the function calls. # Be sure to replace placeholder values (like "<your-docker-image-uri>") with actual values # from your OCI setup.

    In this program:

    • We create an OCI Object Storage bucket to store the AI model.
    • We upload the AI model file into this bucket. The file in this example is assumed to be a zip file named ai_model.zip.
    • We create an OCI Application, which is a logical entity that groups your functions together.
    • We define a OCI Function, which is the actual code that will be executed. The function references a Docker image that contains the AI model and any dependencies needed to run it.
    • We export the function’s invoke endpoint, which you can use to trigger model inference.

    There are indeed many more options and configuration settings you can apply, depending on the specific needs of your deployment. For instance, if your function needs to write logs, you would need to integrate OCI Logging. For networking or access, you would set up OCI IAM policies and network resources with the desired rules.

    Remember to replace placeholders and example values with actual data from your OCI environment. If you need detailed assistance on creating the Docker image or setting up IAM policies, you would need to refer to the specific guides or documentation that pertain to those tasks within the Oracle Cloud Infrastructure documentation.


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