Containers as AI Development Environments on Azure

Question

Pulumi · Accepted Answer

Creating AI development environments on Azure using containers is a common practice to ensure a consistent, reproducible, and isolated development space. With Azure, you can leverage various services to create and manage these containerized environments. Here we will use Azure Container Instances (ACI) as the platform to run our containers, which is a simple, lightweight option to quickly spin up containers without the need for orchestration, which makes it suitable for development purposes.

Resources

Azure Container Group: This represents a group of containers that you can deploy and manage together on Azure Container Instances.

What You'll Do

Create a container group that hosts your AI development environment.
Configure the container with necessary environments and dependencies for AI development, such as TensorFlow, PyTorch, Jupyter, etc.

Program Explanation

We'll import required Pulumi libraries for Azure.
Define a resource group under which our resources will reside.
Define an Azure Container Group with a single container that represents our AI development environment. This container will have the necessary settings like the image to use, resources (CPU and memory), environment variables, and exposed ports.
Export the FQDN (Fully Qualified Domain Name) of the container group, so you can access the services running inside the container, like Jupyter Notebook.

Now let's jump into the code:

import pulumi
import pulumi_azure_native.containerinstance as aci
import pulumi_azure_native.resources as resources

# Create a new resource group
resource_group = resources.ResourceGroup('ai-development-rg')

# Define the container group with a single container for our AI environment
container_group = aci.ContainerGroup('ai-container-group',
    resource_group_name=resource_group.name,
    containers=[aci.ContainerArgs(
        name='ai-dev-container',
        image='tensorflow/tensorflow:latest-gpu-jupyter', # Example image with TensorFlow and Jupyter
        resources=aci.ResourceRequirementsArgs(
            requests=aci.ResourceRequestsArgs(
                cpu=2.0,
                memory_in_gb=3.5
            )
        ),
        ports=[aci.ContainerPortArgs(port=8888)] # Usually, Jupyter runs on port 8888
    )],
    os_type=aci.OperatingSystemTypes.LINUX,
    restart_policy=aci.ContainerGroupRestartPolicy.ALWAYS,
    ip_address=aci.IpAddressArgs(
        ports=[aci.PortArgs(
            port=8888,
            protocol=aci.ContainerGroupIpAddressType.TCP
        )],
        type=aci.ContainerGroupIpAddressType.PUBLIC,
    )
)

# Export the FQDN of the container group, which can be used to access the Jupyter Notebook
pulumi.export('container_group_fqdn', container_group.ip_address.apply(lambda ip: ip.fqdn if ip else ''))

After running this program with Pulumi, it will output the FQDN that you can use to visit your Jupyter environment, typically through http://<FQDN>:8888. Just replace <FQDN> with the actual exported value. From there, you can start developing your AI models using the tools and libraries provided in the container image.

Remember to replace the container image with the one that contains your preferred AI tools and libraries, and adjust the resources depending on your workload.