Deploying Large Language Models with Azure Container Apps

Question

Pulumi · Accepted Answer

To deploy Large Language Models (LLMs) with Azure Container Apps, we will approach this by creating a containerized application, where the Large Language Model can be hosted. This will involve setting up an Azure Container App environment, followed by deploying the container hosting the model itself.

The container will be packaged with all the necessary dependencies and code for running the LLM. This packaging will typically be done using a Dockerfile, which prepares an image that can be hosted inside Azure Container Apps.

Azure Container Apps is a serverless container service that enables you to run containers without managing the underlying infrastructure. It integrates with other Azure services such as Azure Logic Apps, Azure Functions, and Azure Event Grid for event-driven programming.

Below is a basic Pulumi program in Python which demonstrates how to set up an Azure Container App for deploying a Large Language Model:

- An Azure Resource Group is declared where all resources will reside.
- An Azure Container Apps Environment is created which provides the necessary settings for hosting containerized applications.
- An Azure Container App is set up to host the LLM.
- The image for the container is assumed to be available in a registry (this image contains the LLM).

```python
import pulumi
import pulumi_azure_native as azure_native

# Create an Azure Resource Group
resource_group = azure_native.resources.ResourceGroup("llm_resource_group")

# Create an Azure Container Apps Environment
containerapp_environment = azure_native.app.ContainerAppEnvironment("llm_environment",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    properties=azure_native.app.ContainerAppEnvironmentPropertiesArgs(
        app_logs_configuration=azure_native.app.AppLogsConfigurationArgs(
            destination="LogAnalytics",  # You can configure logging
        )
    )
)

# Create an Azure Container App with the Large Language Model
container_app = azure_native.app.ContainerApp("llm_container_app",
    resource_group_name=resource_group.name,
    container_app_environment_id=containerapp_environment.id,
    properties=azure_native.app.ContainerAppPropertiesArgs(
        configuration=azure_native.app.ConfigurationArgs(
            ingress=azure_native.app.IngressArgs(
                external=True,  # Expose the app to the internet
                target_port=80,  # The port your app listens on
            )
        ),
        template=azure_native.app.ContainerTemplateArgs(
            image="myregistry.azurecr.io/myllm:v1",  # Replace with your container image
            resources=azure_native.app.ContainerResourcesArgs(
                cpu=1.0,
                memory="1.5Gi",  # Calculate and set resources as required for the LLM
            ),
            env=[  # Set environment variables as needed
                azure_native.app.EnvironmentVarArgs(
                    name="MODEL_ENDPOINT",
                    value="http://localhost:5000",  # Example environment variable
                ),
            ],
        ),
    ),
)

# Export the URL of the container app once it's deployed.
pulumi.export("container_app_url", container_app.configuration.apply(lambda x: x.ingress.fqdn))
```

This program uses the Pulumi Azure Native Package (`pulumi_azure_native`) to declare the necessary Azure resources in a pythonic way. Each resource is declared as a class where you specify the required parameters such as names, properties, and relational aspects.

In this setup, you will need to replace the placeholder values with your specific details, such as the container image URL, which should point to the Docker image that contains your Large Language Model.

After deploying this Pulumi stack, the `container_app_url` will be outputted and you can use that URL to interact with your LLM that's running in the Azure Container App.

This program provides a scaffold and you may have to adjust it to fit the specific requirements of your LLM deployment, such as defining environment variables, adjusting scaling options, and setting up the correct ingress routes and ports that your application uses.