1. Global CDN for Model Deployment with Cloudflare Workers


    To set up a global content delivery network (CDN) to serve a machine learning model using Cloudflare Workers, you would first deploy the serverless worker script which acts as the serving function for your model. It will intercept HTTP requests, process them as needed (potentially running your machine learning inference), and return responses.

    Cloudflare Workers are essentially serverless JavaScript functions that run directly on Cloudflare's edge nodes worldwide. This means that they are located as close as possible to your users, which is ideal for latency-sensitive applications like machine learning model inference.

    Here's a step-by-step explanation followed by a Pulumi program in Python to set up a Cloudflare Worker:

    1. Define the Worker Script: This script will contain the logic for handling incoming requests and returning the result of the model inference.
    2. Define a Worker Route: This route specifies which incoming URL patterns should trigger the execution of your worker script.
    3. Deploy the Worker: By deploying the worker, you're making it live and accessible from the internet.

    Below is a Pulumi program that will create the necessary infrastructure on Cloudflare:

    import pulumi import pulumi_cloudflare as cloudflare # Configuration # The ID of your Cloudflare account account_id = 'your-account-id' # The ID of the zone that is to be utilized by the worker zone_id = 'your-zone-id' # The name/ID you want to assign to your Worker Script worker_script_name = 'model-serving-worker' # Content of the worker script # You can replace the code with your machine learning model serving logic worker_script_content = """ addEventListener('fetch', event => { event.respondWith(handleRequest(event.request)) }) async function handleRequest(request) { const response = 'Machine Learning Model Response'; return new Response(response, { status: 200 }) } """ # Define the Worker Script worker_script = cloudflare.WorkerScript(worker_script_name, name=worker_script_name, content=worker_script_content, account_id=account_id) # Define the Worker Route # Specify pattern to match for the route. Here, all requests to example.com/* would be handled by the worker. worker_route = cloudflare.WorkerRoute(f"{worker_script_name}-route", pattern="example.com/*", script_name=worker_script.name, zone_id=zone_id) # Export the worker's URL pulumi.export('worker_url', f"https://{worker_route.pattern}")

    To get started with this program:

    1. Replace 'your-account-id' and 'your-zone-id' with your actual Cloudflare account and zone IDs.
    2. Replace 'model-serving-worker' with a unique name for your worker script.
    3. Update the worker_script_content with the JavaScript code that serves your machine learning model.

    When you deploy this Pulumi stack, it will automatically create a Cloudflare Worker and a route in your Cloudflare account. This will ensure that any requests to the specified pattern will be intercepted by your Worker, where you can run your model serving logic.

    The pulumi.export statement is used to output the worker URL so that you can easily access it after the deployment is finished. This URL can be used to interact with your deployed model.

    This example assumes you're familiar with writing the logic for serving requests with a Cloudflare Worker in JavaScript and serving machine learning models. If you're not, you might need additional code to handle the processing and inference parts, which typically involves loading your pre-trained model and running inference with the data extracted from the request.

    Remember that the actual model serving logic is highly dependent on your specific requirements and the machine learning framework you're using. It would need to be coded in JavaScript (or compiled to WASM if using Rust or another language) as that is what Cloudflare Workers execute.