1. Edge AI Inference with Cloudflare Workers


    Edge AI inference with Cloudflare Workers allows you to execute machine learning models and other computation at the edge, closer to the user, thereby reducing latency and improving performance. Cloudflare Workers are essentially serverless execution environments that run JavaScript or WebAssembly code in response to events, such as HTTP requests. You can deploy AI models as WebAssembly and run the inference directly on Cloudflare's network, which spans numerous locations around the world.

    To deploy an Edge AI Inference application using Cloudflare Workers via Pulumi in Python, you'll need:

    1. A Cloudflare Worker Script: This is where you'd include your code for performing AI inference. If you're using TensorFlow.js or ONNX.js, for instance, you can include the model files directly in your worker code, or load them from another location.

    2. A Cloudflare Worker Route: This is necessary to define the pattern of incoming requests that your worker should handle; essentially when to execute your worker script based on the URL pattern.

    3. Bindings and Resources Needed for Inference: Depending on your use case, you may need some stateful storage like Cloudflare KV for storing any model parameters or maintaining state, or Durable Objects for more complex state handling.

    Here's a high-level Pulumi program that demonstrates setting up these resources. Note that this is just a template; you'll need to include your actual inference code and model data:

    import pulumi import pulumi_cloudflare as cloudflare # Set up your Cloudflare configuration # Replace these with your actual account and zone IDs. cloudflare_account_id = 'your-account-id' cloudflare_zone_id = 'your-zone-id' # Create a new Cloudflare Worker script # You will need to include the actual inference logic and model data in `worker_script_content`. worker_script_content = """ addEventListener('fetch', event => { event.respondWith(handleRequest(event.request)) }) async function handleRequest(request) { // Your AI inference logic goes here. // For example, you might load a machine learning model, perform inference, // and return the result as the response to the request. return new Response('Hello World!') } """ worker_script = cloudflare.WorkerScript( "my-ai-inference-script", account_id=cloudflare_account_id, content=worker_script_content, name="ai-inference-script", # The name of the worker script ) # Create a route to specify which requests should trigger your worker # The pattern should match the requests which need AI inference. worker_route = cloudflare.WorkerRoute( "my-ai-inference-route", zone_id=cloudflare_zone_id, pattern="https://example.com/ai-inference-path/*", # The URL pattern to trigger the worker. script_name=worker_script.name, ) # (Optional) If you need to store and retrieve data, set up a Cloudflare KV Namespace kv_namespace = cloudflare.WorkersKvNamespace( "my-kv-namespace", title="my-kv-namespace", account_id=cloudflare_account_id, ) # (Optional) Bind the KV Namespace to your worker if needed # Update `worker_script_content` to use this KV Namespace for storing or retrieving data. kv_binding = { "name": "MY_KV_NAMESPACE", "namespace_id": kv_namespace.id } # Export the URLs to access the deployed worker script pulumi.export("worker_script_url", worker_route.pattern) # If using KV, you may want to export the KV Namespace ID as well pulumi.export("kv_namespace_id", kv_namespace.id)

    In this program, WorkerScript creates the worker script itself, where you'd integrate your AI model and inference code. The actual code for the AI inference should be provided within the worker_script_content variable.

    WorkerRoute specifies the route, i.e., what pattern of requests will trigger the execution of your worker script. You'll have to change "https://example.com/ai-inference-path/*" to the actual pattern you need.

    Additionally, if you plan to use a Key-Value (KV) store to maintain state or store model parameters, WorkersKvNamespace would be the resource to declare it. You can then bind this KV namespace to your worker using the kv_binding.

    Finally, the pulumi.export lines ensure that the URLs and IDs of the resources are outputted upon deployment, so you can easily locate and reference your deployed resources.

    Remember to replace placeholder values like your-account-id, your-zone-id, 'https://example.com/ai-inference-path/*', and the actual worker script content with appropriate values for your setup.