Containerized AI Workflows Using Azure Container Apps

Question

Pulumi · Accepted Answer

Containerized AI workflows are an excellent way to ensure that your machine learning models and algorithms can be run consistently across different environments. Azure Container Apps is a service that allows you to deploy and manage containerized applications easily. This service is serverless, event-driven, and can dynamically scale your containers.

The resources relevant to deploying containerized AI workflows on Azure Container Apps are:

1. **[Azure Container Apps](https://www.pulumi.com/registry/packages/azure-native/api-docs/app/containerapp/)**: This is the main resource that is used to deploy a containerized application. It allows the configuration of various aspects such as the image to run, environment variables, scaling rules, and networking settings.

2. **[Azure Container Registry](https://www.pulumi.com/registry/packages/azure-native/api-docs/containerregistry/registry/)**: This service provides a Docker container registry for storing and managing private Docker container images. It is commonly used in combination with Azure Container Apps when you need to deploy from private container images.

3. **[Azure Container App Environment](https://www.pulumi.com/registry/packages/azure-native/api-docs/app/containerappenvironment/)**: It provides a way to specify settings and configurations that are common across multiple container apps. This can include network features, logging, and Dapr configurations.

Below is a program that sets up a containerized AI workflow using Azure Container Apps, including a private Azure Container Registry for storing and managing your container images and a Container App Environment for shared settings and configurations:

```python
import pulumi
import pulumi_azure_native as azure_native

# Create an Azure resource group for organizing the resources
resource_group = azure_native.resources.ResourceGroup("aiResourceGroup")

# Create an Azure Container Registry to store container images
container_registry = azure_native.containerregistry.Registry(
    "aiContainerRegistry",
    resource_group_name=resource_group.name,
    sku=azure_native.containerregistry.SkuArgs(
        name="Basic"  # Choosing the Basic SKU for this example
    ),
    admin_user_enabled=True,  # Enable admin user for simplicity in this example
)

# Create an Azure Container App Environment for common settings
container_app_environment = azure_native.app.ContainerAppEnvironment(
    "aiContainerAppEnvironment",
    resource_group_name=resource_group.name,
    location=resource_group.location,
)

# Create a containerized app using the Azure Container App service
container_app = azure_native.app.ContainerApp(
    "aiContainerApp",
    resource_group_name=resource_group.name,
    managed_environment_id=container_app_environment.id,
    configuration=azure_native.app.ContainerAppConfigurationArgs(
        ingress=azure_native.app.IngressConfigurationArgs(
            external=True,  # Enable external access
            target_port=80  # Typically HTTP traffic runs on port 80
        ),
    ),
    template=azure_native.app.ContainerTemplateArgs(
        containers=[azure_native.app.ContainerArgs(
            name="ai-container",
            image="mcr.microsoft.com/azureml/base:latest",  # Example image for AI workflows
            resources=azure_native.app.ContainerResourcesArgs(
                cpu=1.0,  # Assign CPU resources for the container
                memory="1.5Gi"  # Assign Memory resources for the container
            ),
        )]
    ),
    # Select the networking mode - Serverless offers dynamic scaling
    workload_profile_type="serverless",
)

# Export the URL of the Container App
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