1. Real-time AI Inference with Azure Container Instances


    When setting up real-time AI inference using Azure Container Instances (ACI), you'll typically need to deploy a Docker container that includes your machine learning model and inference code. Azure Container Instances make it easy to deploy containers in Azure without having to manage VMs or higher-level services like Kubernetes.

    Below is a Pulumi program in Python that deploys an Azure Container Group with a single container. This container could be used to serve a machine learning model for real-time inference. The program assumes that you have a Docker image ready that contains your model and application ready for inference.

    Here's a step-by-step explanation:

    1. Set up an Azure Resource Group: An Azure Resource Group is a logical container into which your Azure resources will be deployed.
    2. Define a Container Group: You're creating a container group in ACI, which is essentially the deployment of your container(s).
    3. Configure the Container: This includes the Docker image you've created for inference, resource requests like CPU and memory, and environment variables if needed.
    4. Expose a Port: ACI allows you to expose ports for your container. If your application listens on a port, you need to expose it for HTTP/HTTPS traffic.
    5. Declare Outputs: At the end, it's helpful to export the fully qualified domain name (FQDN) of the container instance so you can use it to send inference requests.
    import pulumi import pulumi_azure_native.containerinstance as containerinstance import pulumi_azure_native.resources as resources # Step 1: Create a new Azure Resource Group resource_group = resources.ResourceGroup('ai-inference-rg') # Step 2: Define a Container Group container_group = containerinstance.ContainerGroup('ai-inference-cg', resource_group_name=resource_group.name, os_type=containerinstance.OperatingSystemTypes.LINUX, containers=[{ 'name': 'inference-container', 'image': 'your-docker-image', # Replace with your Docker image URL 'resources': { 'requests': { 'cpu': 1.0, 'memory_in_gb': 1.5, }, }, 'ports': [{ 'port': 80, # Replace with the port your application uses }], }], # Replace with the port(s) your application uses ip_address={ 'type': 'Public', 'ports': [{ 'protocol': 'TCP', 'port': 80, }], }, restart_policy=containerinstance.ContainerGroupRestartPolicy.ALWAYS, ) # Step 3: Declare an output for the FQDN of the container instance to be able to send requests to fqdn = pulumi.Output.all(resource_group.name, container_group.name).apply( lambda args: containerinstance.get_container_group(resource_group_name=args[0], container_group_name=args[1]) ).apply(lambda cg: cg.ip_address.fqdn) pulumi.export('container_group_fqdn', fqdn)

    In the provided code, you need to replace 'your-docker-image' with the URL or name of your Docker image that is hosted in a registry such as Azure Container Registry or Docker Hub. Also, make sure to change the port value inside ports to match the port your application listens on.

    This simple setup will get you a running container in the cloud, accessible via an FQDN, where you can send data for real-time AI inference. Make sure your Docker image contains all that's required to set up the environment for your AI model and that it's configured to start your app automatically upon launch.

    Remember, ACI is suitable for lightweight, stateless, single-container workloads. For more complex applications, consider orchestrators like Azure Kubernetes Service (AKS), especially if you require higher availability, scalability, or a multi-container setup.