1. AI Model Hosting on Cloudflare Workers


    Cloudflare Workers provide a serverless execution environment that allows you to create entirely new applications or augment existing ones without configuring or maintaining infrastructure. For AI model hosting, this can be a very effective way to run inference at the edge, closer to end-users, which results in lower latency.

    To deploy an AI model on Cloudflare Workers, you'd typically have a pre-trained model that you've converted to a format compatible with JavaScript, such as TensorFlow.js if you're using TensorFlow models. You'd then write the logic of loading the model and handling inference requests in a worker script.

    In a Pulumi application, to set up an AI model hosting on Cloudflare Workers, we'd use the following resources:

    1. WorkerScript: To deploy the JavaScript or WebAssembly code that contains our AI model and inference code.
    2. WorkerRoute: To define the route at which our worker will be accessible.

    Below is a Pulumi program, written in Python, that demonstrates how you might set up an AI model hosting environment using Cloudflare Workers. The assumptions are:

    • You have already set up your Cloudflare global API key, email, and account ID as environment variables for Pulumi to use.
    • Your AI model is accessible within the Worker code, possibly included directly in the script or loaded from another source such as Cloudflare's KV (Key-Value) storage.
    import pulumi import pulumi_cloudflare as cloudflare # Define your Cloudflare account ID - replace with your actual account ID account_id = '<your-account-id>' # Define the Worker script content. This should contain the model initialization and # the event listener responding to fetch events (inference requests). worker_script_content = """ addEventListener('fetch', event => { event.respondWith(handleRequest(event.request)) }) async function handleRequest(request) { // Your AI model inference logic goes here return new Response('AI model response', {status: 200}) } """ # Create a new worker script in Cloudflare with the AI model inference logic. ai_worker_script = cloudflare.WorkerScript('ai-model-worker', account_id=account_id, content=worker_script_content) # Create a route for the worker to specify which requests should be directed to the worker. # The pattern will match the requests where you want AI model inferences to occur. ai_route = cloudflare.WorkerRoute('ai-model-worker-route', zone_id='<your-zone-id>', # Replace with your Zone ID pattern='*example.com/ai-model', # Replace with your desired route script_name=ai_worker_script.id) # Use Pulumi's export mechanism to output the URL of the deployed model so you can easily access it. pulumi.export('ai_model_worker_url', pulumi.Output.concat("https://example.com/ai-model"))

    In this program:

    • We define the AI model's worker script with logic to handle incoming requests. It's at this part you'll implement your AI model logic, whether it's using TensorFlow.js or another JavaScript compatible ML library.
    • We create a new WorkerScript resource, supplying the appropriate account ID and the script's content.
    • We define a route using WorkerRoute, which determines the path (URL pattern) where your worker will be executed.
    • We use pulumi.export to output the URL of the deployed model for easy access once deployment is complete.

    With this Pulumi setup, any HTTP request made to https://example.com/ai-model on your domain would be intercepted by the Cloudflare Worker, which in turn would use your hosted AI model to generate an inference result and respond.

    Remember to replace placeholders like <your-account-id> and <your-zone-id> with your actual Cloudflare account and zone ID values, and update the pattern to the correct domain and path where you want your AI model to be accessed.