1. Rapid Environment Duplication for AI Experimentation


    Creating a rapid environment duplication setup for AI experimentation often involves provisioning infrastructure where different versions of an AI environment can be spun up quickly for testing and experimentation. In the cloud, this often translates to setting up virtual machines or containers with necessary libraries and dependencies, along with data storage and possibly machine learning model registry for versioning.

    In the context of Azure, you can use Azure Machine Learning services to manage the experimentation environment. The EnvironmentVersion and Workspace resources are particularly useful. EnvironmentVersion allows you to define various environments with different sets of dependencies, which can be rapidly instantiated for AI experiments. Workspace acts as a centralized hub where all artifacts related to the experimentation (like datasets, models, environments, etc.) are stored.

    Here is a Pulumi program written in Python that sets up a simple Azure Machine Learning environment for AI experimentation:

    import pulumi import pulumi_azure_native.machinelearningservices as mls # This is your resource group where all the resources will be added. resource_group = mls.ResourceGroup( "rg", resource_group_name="my_ml_rg" ) # Create an Azure Machine Learning Workspace. # The workspace is the top-level resource for Azure Machine Learning, # providing a centralized place to work with all the artifacts you create. workspace = mls.Workspace( "ws", resource_group_name=resource_group.name, location="East US", workspace_name="my_ml_workspace" ) # Define an Azure Machine Learning environment version. # This specifies the Docker build context, including the Dockerfile used to build the image. environment_version = mls.EnvironmentVersion( "env-version", name="my-ml-environment", version="1", # You can iterate this version for different experiments. resource_group_name=resource_group.name, workspace_name=workspace.name, environment_version_properties={ "image": "mcr.microsoft.com/azureml/base:intelmpi2018.3-ubuntu16.04", "environment_type": "Docker", "docker": { "dockerfile_path": "Dockerfile", "context_uri": "https://path.to.your.docker.context.zip", "base_image_registry": { "address": "<container-registry-address>", "username": "<registry-username>", "password": "<registry-password>" } } } ) # The output that provides the URL of the newly created environment version. pulumi.export('environment_version_url', environment_version.id)

    This Pulumi program defines a machine learning environment within Azure Machine Learning Services:

    1. ResourceGroup is created to organize all resources in one place.
    2. Workspace acts as a central hub for managing the ML lifecycle, including environments, datasets, and more.
    3. EnvironmentVersion captures a versioned environment, here including base Docker image and the Docker build context for consistent and reproducible AI environments.

    Once this infrastructure is provisioned, AI experiments can be run inside this versioned environment, ensuring consistency across your experiments.

    Keep in mind, this code requires you to fill in your own details for the Docker context URI and the container registry credentials. Those details depend on your specific setup. The exported environment_version_url will give you a handle to refer to the environment version programmatically in other parts of your Pulumi code or in CI/CD pipelines for streamlined operations.