1. AI Development Environments with Azure Container Instances


    To create AI development environments with Azure Container Instances (ACI), you'll use the ContainerGroup resource from the azure-native provider. The environment will consist of container instances that can run AI development tools or frameworks such as TensorFlow, PyTorch, or Jupyter notebooks.

    The ContainerGroup represents a group of containers that you can manage as a single entity. For AI environments, you'll typically want to use containers with appropriate tools pre-installed. These can be custom images that you've created and stored in a registry, or public images that come with the necessary tools.

    Here’s a step-by-step guide on how to deploy an AI development environment using Azure Container Instances:

    1. Define the Container Group: You will define a ContainerGroup with properties like osType, image (Docker image to use), resources (to specify CPU and memory), and ports (to expose any required ports, such as for Jupyter notebook server).

    2. Environment Variables: If your container requires environment variables (like tokens, or configuration settings), you can specify them in the ContainerGroup.

    3. Volumes: If you need to persist data or share data between containers, you can define volumes and attach them to your containers.

    4. Diagnostics: Optionally, you can enable diagnostic settings to collect logs and metrics.

    Now, let’s look at the Pulumi code needed to deploy this setup:

    import pulumi import pulumi_azure_native.containerinstance as containerinstance # Define the resource group where the resources will be created. resource_group = containerinstance.ResourceGroup("aiResourceGroup") # Define the container group with an AI development environment. container_group = containerinstance.ContainerGroup( "aiContainerGroup", resource_group_name=resource_group.name, os_type="Linux", # Specify the operating system type. containers=[ # Specify the containers to run. containerinstance.ContainerArgs( name="ai-container", image="tensorflow/tensorflow:latest-gpu", # Replace with your preferred AI Docker image. resources=containerinstance.ResourceRequirementsArgs( requests=containerinstance.ResourceRequestsArgs( cpu=2.0, # Specify required CPU resources. memory_in_gb=4.0 # Specify required memory. ), ), ports=[containerinstance.ContainerPortArgs(port=8888)], # Open port for the Jupyter notebook. ), ], ip_address=containerinstance.IpAddressArgs( ports=[containerinstance.PortArgs(port=8888)], # Expose the same port at the group level. type="Public", # Make the container group accessible from the internet. ), ) # Export the IP address of the container group. pulumi.export("container_ip", container_group.ip_address.apply(lambda ip: ip.ip if ip else None))

    In this example, we've created a container group with a TensorFlow GPU-enabled image. We’ve requested sufficient CPU and memory resources for the AI computations and exposed port 8888 since this is the default port that Jupyter notebooks listen on when started.

    After deploying this Pulumi program, the TensorFlow environment will be accessible and you’ll be able to use it for AI development tasks.

    To run this program:

    1. Install Pulumi and set up the Azure CLI with your Azure account.
    2. Save the code in a file named __main__.py.
    3. Run pulumi up in the same directory as your code to launch the resources.

    Remember to replace tensorflow/tensorflow:latest-gpu with the Docker image containing the specific tools you want in your AI development environment. It could be a custom image that you have prepared with your AI development stack.

    Please refer to Pulumi's ContainerGroup documentation for more information on the resource properties and their descriptions to further customize your AI development environment.