1. Serverless AI Model Inference with Cloudflare Workers


    To deploy a serverless AI model inference endpoint using Cloudflare Workers, you'll make use of the Cloudflare Workers runtime. You'll write code that runs directly on Cloudflare's edge locations around the world, which can respond to HTTP requests by executing your function. This environment is suitable for lightweight, high-performance applications like AI inference endpoints when latency is a critical factor.

    Here's a program that deploys a Cloudflare worker for AI model inference. The worker script would typically load a machine learning model and accept data via HTTP requests to perform predictions and return them in the response. The model loading and inference logic would depend on the specifics of your AI model and are typically handled via a WASM module or an external call if supported.

    The following program includes:

    • cloudflare.WorkerScript: To deploy your actual server logic.
    • cloudflare.WorkerRoute: To determine which routes (URL patterns) will trigger your worker.

    Let's walk through the Pulumi Python code.

    import pulumi import pulumi_cloudflare as cloudflare # Configuration variables for the Cloudflare account and zone # These should be preconfigured in your Pulumi setup or fetched from a secure location cloudflare_account_id = "your-account-id" cloudflare_zone_id = "your-zone-id" # AI model inference worker script content. # This is where you define your AI inference logic. # Typically, you load your machine learning model and expose an endpoint for inference. # For portability and performance, consider compiling your model into WebAssembly (WASM) # and then load it within your worker script. The following content is a placeholder. ai_inference_worker_script = """ addEventListener('fetch', event => { event.respondWith(handleRequest(event.request)) }) async function handleRequest(request) { // TODO: Load and interact with a machine learning model here. // Respond with the results of the model inference return new Response('AI Inference Results will be here', {status: 200}) } """ # Deploy the Worker script to Cloudflare worker_script = cloudflare.WorkerScript( "ai-inference-worker-script", account_id=cloudflare_account_id, name="ai-inference-worker", content=ai_inference_worker_script, ) # Configure the Worker route to specify which incoming requests should be handled by your Worker worker_route = cloudflare.WorkerRoute( "ai-inference-worker-route", account_id=cloudflare_account_id, zone_id=cloudflare_zone_id, pattern="your-custom-domain.com/api/infer", script_name=worker_script.name, ) # Export the Worker script URL so you can access it pulumi.export("worker_url", f"https://your-custom-domain.com/api/infer")

    This program sets up the foundation for deploying a serverless AI inference service using Cloudflare Workers. You would need to populate the ai_inference_worker_script variable with the actual JavaScript (or WASM) that performs the AI model loading and inference based on HTTP requests.

    Ensure you have the necessary Cloudflare credentials and permissions to deploy the worker, and replace placeholders with actual account and zone IDs, as well as domain patterns.

    The Cloudflare Workers runtime environment is designed for serverless applications where you want to minimize latency by running the code on geographically distributed servers - close to where the requests are originating.

    For more details on how to use Pulumi with Cloudflare, you can refer to the Pulumi Cloudflare documentation. Additionally, Cloudflare has a rich set of documentation for Workers that can assist you in scripting the serverless function.