1. Deploying AI Model Serving on DigitalOcean App


    To deploy AI model serving on DigitalOcean, you can use the DigitalOcean App Platform, which is a platform as a service (PaaS) offering that allows you to deploy, manage, and scale apps quickly. With Pulumi's DigitalOcean provider, you can define your app infrastructure in code, which includes configuration for services, databases, and other components.

    Below is a Pulumi program in Python to deploy an AI model serving app as a service in DigitalOcean using the digitalocean.App component from the Pulumi DigitalOcean provider. We will focus on deploying a service that serves a machine learning model. The service can be a Docker container with a pre-trained model and server code that exposes an endpoint for making predictions.

    Before running this Pulumi program, ensure that you have the following prerequisites in place:

    1. Pulumi CLI installed and authenticated with DigitalOcean.
    2. Docker image of your AI model serving app pushed to a container registry accessible by DigitalOcean (e.g., Docker Hub, DigitalOcean Container Registry).

    Here is the Pulumi program that defines the deployment:

    import pulumi import pulumi_digitalocean as digitalocean # Configuration app_name = "ai-model-serving-app" docker_image = "your-docker-image" # Replace with your Docker image e.g. "user/ai-model-serving:v1" region = "nyc" # Choose the region closest to your users # DigitalOcean App definition app = digitalocean.App(f"{app_name}-app", spec=digitalocean.AppSpecArgs( name=app_name, region=region, services=[ digitalocean.AppServiceSpecArgs( name=f"{app_name}-service", image=digitalocean.AppImageSpecArgs( repository=docker_image ), http_port=80, # The port your app listens on inside the container instance_count=1, # The number of instances of the service to run instance_size_slug="basic-xxs", # The size of the instances to run routes=[ digitalocean.AppRouteSpecArgs( path="/" # Path to serve the app at the root of the domain ) ], envs=[ # Environment variables required by the service # For example, you might include model configuration parameters here digitalocean.AppVariableArgs( key="MODEL_NAME", value="my-ai-model" # Replace with the actual model name ) ] ) ] )) # Export the live URL of the app to access it once deployed pulumi.export("live_url", app.live_url)

    In this program:

    • We import pulumi and the pulumi_digitalocean module to use DigitalOcean-related resources.
    • We set up the basic configuration variables like the app name, Docker image URL, and region where the app should be deployed.
    • We declare a digitalocean.App resource with a specification that describes the app, including the service and its properties, such as name, image, port mappings, instance count and size, and routes.
    • An environment variable MODEL_NAME is set, which you'll need to replace with actual configuration your service requires.
    • We export the app's live URL so you can access the deployed service in a web browser or through API calls.

    Replace the placeholder your-docker-image with the actual Docker image URL for your AI model serving app. To get a live URL, the Docker image must contain a web server or an API that handles incoming HTTP requests and uses your AI model to make predictions or serve results.