1. Deploying ML Models on Cloudflare Workers


    To deploy machine learning (ML) models on Cloudflare Workers, you'll typically need to follow these steps:

    1. Prepare the ML Model: Your ML model needs to be in a format compatible with the serverless execution environment provided by Cloudflare Workers. This often means using a lightweight model saved in a format like TensorFlow Lite or ONNX.

    2. Write the Worker Script: You'll write a JavaScript or TypeScript script that loads and runs the ML model. Depending on the model's format, you might use libraries like ONNX.js or TensorFlow.js that are capable of running in the Workers' v8 environment.

    3. Deploy the Worker: Using Pulumi's Cloudflare provider, you'll define infrastructure as code that sets up your Worker script, and deploys it to Cloudflare's network.

    4. Set up Routes: Define routes that determine which requests should be served by the Worker.

    Let's write a Pulumi program to deploy a simple ML model to Cloudflare Workers:

    import pulumi import pulumi_cloudflare as cloudflare # Assume your ML model and associated worker script is packaged in `ml_worker.js` # and you have already set the account_id and zone_id via pulumi config. # The content of your Worker script that contains the ML model. with open('ml_worker.js', 'r') as file: worker_script_content = file.read() # Define the Worker script. worker_script = cloudflare.WorkerScript("ml-model-worker-script", name="ml-model-worker", # Name your worker script content=worker_script_content, account_id="your-account-id") pulumi.export('worker_script_name', worker_script.name) # Define a route pattern that your worker will listen for # e.g., all requests to `https://your-domain.com/api/ml-model/*` worker_route = cloudflare.WorkerRoute("ml-model-worker-route", pattern="your-domain.com/api/ml-model/*", script_name=worker_script.name, zone_id="your-zone-id") # Export the Worker route ID pulumi.export('worker_route_id', worker_route.id)

    Before running this code:

    • You need to have the ml_worker.js file in the same directory as your Pulumi program. This file should be the JavaScript code for your Cloudflare Worker, which includes the logic for invoking the ML model.
    • You'll need to replace your-account-id with the actual account ID from Cloudflare, and your-zone-id with the zone ID of the domain you're setting this up for.
    • your-domain.com should be replaced with the domain you've set up in Cloudflare.

    This Python program uses Pulumi's Cloudflare provider to deploy a machine learning model onto Cloudflare Workers. It defines two main resources:

    • WorkerScript: This resource uploads your worker script to Cloudflare, ready to respond to incoming requests. The script must be written in a way that it can make inferences based on the input it receives from the HTTP request.

    • WorkerRoute: This resource specifies the route pattern your Cloudflare Worker will listen for. Every time a request is made to the specified pattern, the Worker is invoked to handle the request.

    After executing this code with pulumi up, Pulumi will provision the necessary resources on Cloudflare, and your ML model will be up and running, capable of serving inference requests.

    If you have specific requirements for the ML model or additional configurations for the Cloudflare Worker (such as KV Storage bindings, secret bindings, or environment variables), these will need to be incorporated into the Pulumi program as well.