1. Deploying AI Models on Azure Container Instances


    Deploying AI models on Azure Container Instances (ACI) is a straightforward process that allows you to run containers without having to manage servers or clusters of virtual machines. Azure Container Instances provide a flexible, on-demand, and fast way to run your containerized applications, including AI models. Here, you'll see a Pulumi program that allows you to deploy an AI model as a Docker container on ACI.

    To achieve this, we'll use the azure-native.containerinstance.ContainerGroup resource from Pulumi's Azure Native provider. This resource allows you to define a group of containers that share lifecycles, resources, local networks, and storage volumes.

    Our AI model container will need a Docker image to run. Usually, this image would be built from your trained model and all the necessary code and dependencies it requires to run. The image should be pushed to a container registry like Azure Container Registry or Docker Hub before this Pulumi program is executed.

    First, we define the container group, specify the image to use, and define the resources (like CPU and memory) necessary for our AI model to run. We also specify the ports that the container will open to communicate with the outside world if needed.

    Here is an example Pulumi program that deploys an AI model on Azure Container Instances:

    import pulumi import pulumi_azure_native as azure_native # Set your resource group name and container group name resource_group_name = 'my-ai-rg' container_group_name = 'my-ai-container-group' # Define a resource group where your resources will live resource_group = azure_native.resources.ResourceGroup('resource_group', resource_group_name=resource_group_name) # Define a container group with a single container container_group = azure_native.containerinstance.ContainerGroup('container_group', resource_group_name=resource_group.name, container_group_name=container_group_name, os_type='Linux', # AI model containers generally run on Linux containers=[ azure_native.containerinstance.ContainerArgs( name='ai-model', # Name of the container image='myacr.azurecr.io/my-ai-model:latest', # Replace with your AI model image resources=azure_native.containerinstance.ResourceRequirementsArgs( requests=azure_native.containerinstance.ResourceRequestsArgs( cpu=1.0, # Assign CPU requirements memory_in_gb=1.5, # Assign memory requirements ), ), ports=[azure_native.containerinstance.ContainerPortArgs( port=80, # Port that the application inside the container will listen on )], ), ], ip_address=azure_native.containerinstance.IpAddressArgs( type='Public', # Use public IP to expose the container to the internet ports=[azure_native.containerinstance.PortArgs( port=80, # Match the port from the container definition protocol='TCP', )], ), location='eastus', # Choose the Azure location to deploy the resources ) # Export the IP address of the container group pulumi.export('container_ip', container_group.ip_address.apply(lambda ip: ip.ip if ip else ''))

    In this program:

    • We provision a new resource group named my-ai-rg where our container instance will exist.
    • We define the ContainerGroup named my-ai-container-group, which will host our AI model's container named ai-model.
    • The Docker image my-ai-model is specified to contain the AI model and the necessary runtime. You would replace myacr.azurecr.io/my-ai-model:latest with the path to the Docker image for your AI model.
    • The os_type is specified as Linux, which is frequently used for running AI models.
    • We provide CPU and memory resources for our container; these values will depend on the requirements of the AI model you are running.
    • The port on which the container accepts requests is set to 80, and the container's IP address is set to be publicly accessible.
    • Finally, we export the IP address of the deployed container group, so you can easily access the running AI model.

    Remember that before deploying AI models, it is essential to test the Docker container locally to ensure that it runs correctly.

    To run this Pulumi program, save the code in a file (for example, deploy_ai_model.py), then execute the following commands using the Pulumi CLI:

    1. Initialize a new Pulumi project (only needed the first time, skip if already done):

      pulumi new python
    2. Install the needed Pulumi packages:

      pulumi plugin install resource azure-native 2.11.0 pip install pulumi-azure-native
    3. Replace the existing __main__.py with the code from deploy_ai_model.py.

    4. Run pulumi up to preview and deploy the changes:

      pulumi up

    After running these commands, your AI model will be deployed on Azure Container Instances, and you will receive the public IP address which you can use to interact with your deployed AI model.