1. Event-driven AI Workloads with Azure Container Apps


    Azure Container Apps is a fully managed serverless container service that enables you to run microservices and containerized applications without managing complex infrastructure. It's particularly well-suited for event-driven applications because you can scale dynamically based on HTTP traffic, events, or other custom metrics.

    To implement event-driven AI workloads with Azure Container Apps, you typically need to:

    1. Define a container that includes your AI application or workload, ensuring it can respond to events.
    2. Set up an event source such as Azure Event Hubs, Azure Service Bus, or Azure Queue Storage.
    3. Configure Azure Container Apps to scale based on the events coming from the event source.

    In this Pulumi program, I'll define a simple AI workload that's event-driven. It will be a containerized application that could, for instance, process images or analyze text when triggered by an event source. For demo purposes, we assume that the Docker container is already created and available in Azure Container Registry or Docker Hub.

    Here’s a step-by-step guide to creating a simple event-driven AI workload using Azure Container Apps:

    1. Import the necessary Pulumi libraries for Azure.
    2. Create an Azure Container App Environment, which provides the execution context for your container apps.
    3. Define the Azure Container App, including settings for the event source that triggers the workload.
    4. Set up scaling rules based on the event source to ensure the application scales according to incoming events.

    The following program is written in Python and uses Pulumi with the azure-native package. Make sure you have Pulumi set up and Azure CLI authenticated with the correct permissions before running this code.

    import pulumi import pulumi_azure_native as azure_native # Replace the following placeholder values with your specific container image, resource group, etc. container_image = 'your-container-registry/ai-workload:latest' resource_group_name = 'your-resource-group' container_app_environment_location = 'East US' # First, create an Azure Resource Group where all resources will be initialized. resource_group = azure_native.resources.ResourceGroup( 'resource_group', resource_group_name=resource_group_name, location=container_app_environment_location, ) # Create an Azure Container App Environment, which is required for the Azure Container App. container_app_env = azure_native.app.ContainerAppEnvironment( 'container_app_env', resource_group_name=resource_group.name, location=container_app_environment_location, # Configure additional environment settings as necessary. ) # Create an Azure Container App with event-driven scaling configured. ai_container_app = azure_native.app.ContainerApp( 'ai_container_app', resource_group_name=resource_group.name, container_app_environment_id=container_app_env.id, location=container_app_environment_location, configuration=azure_native.app.ContainerAppsConfigurationArgs( ingress=azure_native.app.ContainerAppIngressArgs( external=True # This configures the ingress to be public; adjust as needed. ), secrets=[], ), template=azure_native.app.ContainerAppTemplateArgs( containers=[ azure_native.app.ContainerArgs( name='ai-container', image=container_image, # Use your AI workload's container image. resources=azure_native.app.ContainerResourcesArgs( cpu=1.0, memory="1.5Gi" # Set appropriate values for CPU and memory requirements. ), # Add environment variables or other configurations as needed. ) ], scale=azure_native.app.ContainerAppScaleArgs( # Configure the scale rules to be event-driven based on Event Hubs, Azure Service Bus, etc. # For this example, I'm using a simple HTTP rule for a basic demonstration. # Replace this with the actual event-driven logic for your workload using the appropriate event source configuration. rules=[ azure_native.app.ScaleRuleArgs( http=azure_native.app.ScaleRuleHttpArgs( metadata={'concurrentRequests': '50'}, # Adjust concurrency as needed. ), name="http-scaling-rule", ), # Here is where you'd add more scaling rules based on event sources. ], max_replicas=5, # Set these values according to your workload requirements. min_replicas=1, ) ) ) # Lastly, export the URL of the deployment to access the AI workload. pulumi.export('ai_app_url', ai_container_app.configuration.apply(lambda c: c.ingress.fqdn))

    Run this Pulumi program by saving it as a Python file (e.g., main.py) and executing pulumi up in the console, which will provision the resources defined above. Once complete, Pulumi will output the Fully Qualified Domain Name (FQDN) of the Container App, which you can use to trigger and access your event-driven AI workload.

    This example demonstrates a basic setup and you would typically have more complex event sourcing and handling logic based on the specifics of your AI workloads and the events they respond to.

    If you have not created a Pulumi project or stack before, please reference the Pulumi documentation to get started: Create Pulumi Project and Stack. Additionally, you may want to refer to the Azure Container Apps documentation to understand various configurations and integration options: Azure Container Apps Documentation.

    Remember, this code does not create or configure the actual AI application or the event sources but gives you the template to start building the container app infrastructure on Azure with Pulumi. You’ll need to implement the event source and AI application logic separately.