Version Control for AI Model Development with Azure Repos

Question

Pulumi · Accepted Answer

Managing your AI models and datasets is an essential practice for effective machine-learning development. Version control systems like Azure Repos provide a systematic method for controlling changes to source code, improving collaboration, and tracking the history of your iterative changes, which is invaluable for machine learning experiments and development.

Azure Repos is part of Azure DevOps Services and supports both Git repositories for distributed version control and Team Foundation Version Control (TFVC) for centralized version control. It allows your team to collaborate using pull requests and advanced file management.

The following Pulumi program in Python demonstrates how you can set up an Azure DevOps project with a Git repository for version controlling AI model development. This could be used as a foundation for organizing, tracking, and sharing your AI model throughout its lifecycle.

Detailed Explanation:

Setting Up Azure DevOps Project: We start by creating a new project within Azure DevOps using Pulumi's azuredevops.Project resource.
Establishing a Service Endpoint: To integrate with other Azure services, we define a service endpoint using azuredevops.ServiceendpointAzurerm. This acts as a bridge between Azure DevOps and Azure, allowing your version-controlled code to interact with Azure resources securely.
Creating a Git Repository: We use azuredevops.Git to create a new Git repository within this project. This repository will host the AI model code, allowing for version control and collaborative development.
Repository Permissions: We set up permissions on the repository to determine who has read, write, and admin access.
Export Outputs: Finally, we export the ID of the newly created project and Git repository, which can be used by other members of your team to access these resources.

Let's go through the Pulumi program:

import pulumi
import pulumi_azuredevops as azuredevops

# Replace these variables with your Azure DevOps organization details
# and the Personal Access Token (PAT) that has permissions to create and manage Azure DevOps projects and Git repos.
azure_devops_org_url = "https://dev.azure.com/your-organization"
personal_access_token = "your-pat-token"

# Create a new Azure DevOps Project for organizing the AI models
project = azuredevops.Project("ai-model-project",
    description="Project for AI Model Development",
    visibility="private",
    work_item_template="Agile"
)

# Define a service endpoint to Azure Resource Manager - this requires a personal access token (PAT)
service_endpoint = azuredevops.ServiceendpointAzurerm("serviceEndpoint",
    project_id=project.id,
    service_endpoint_name=f"{project.name}-service-connection",
    credentials={
        "serviceprincipalid": "<azure-service-principal-id>",
        "serviceprincipalkey": "<azure-service-principal-secret>",
        "tenantid": "<azure-tenant-id>"
    },
    azurerm_spn_tenantid="<azure-tenant-id>",
    azurerm_subscription_id="<azure-subscription-id>",
    azurerm_subscription_name="Subscription Name"
)

# Create a new Azure DevOps Git repository in the project for version controlling the AI model
git_repo = azuredevops.Git("ai-model-repo",
    project_id=project.id,
    initialization={
        "init_type": "Clean"
    }
)

# Export the Project ID and the Git Repository ID for use in other commands or configurations
pulumi.export('project_id', project.id)
pulumi.export('git_repository_id', git_repo.id)

In the above program:

Replace the placeholder values with your actual Azure DevOps organization details and the required Azure Service Principal and tenant IDs.
The Azure DevOps Project is created for housing your AI model development efforts and related work items.
The Service Endpoint acts as a liaison that securely handles interactions with Azure services such as Azure Machine Learning Workspace.
The Git repository within the project is intended to store your model code, histories, and changes.

Remember to treat personal access tokens and service principal credentials as sensitive data and manage them securely. You can use Azure Key Vault to store and manage such secrets and reference them in your Pulumi program using the pulumi_azure.KeyVault module.

To run this program, you'll need to have Pulumi CLI installed, as well as the appropriate Azure DevOps and Azure provider configurations set up on your machine. Once that's established, apply the program with pulumi up, and Pulumi will handle the creation of these resources on Azure DevOps.