1. Serving High-Volume AI Models from Azure File Share


    Serving high-volume AI models typically involves storing the model files in a location that is accessible to the services that will be running the AI inferences. Since you're interested in leveraging Azure File Share for this purpose, we will be creating an Azure File Share using Pulumi. This file share can be used to store and share your AI models across your services for high-volume processing.

    Firstly, it is important to understand the Azure File Share resource:

    • Azure File Share provides a fully managed cloud file share on Microsoft Azure. Files can be accessed via the SMB or NFS protocols, making it convenient for compatibility with existing applications and services.

    We will be using the azure-native.storage.FileShare resource to create the file share, and for simplicity, we will assume that the storage account has already been created and configured for access. If not, you may also need to create a storage account with azure-native.storage.StorageAccount.

    The following Pulumi program in Python sets up an Azure File Share. Remember to replace placeholders with your specific values, such as the resource group name and storage account name. The share_quota property sets the maximum size of the file share in GB, which you can adjust based on your needs.

    import pulumi import pulumi_azure_native.storage as storage # Create an Azure Resource Group if it doesn't exist # Replace `resource_group_name` with your desired Resource Group name resource_group = storage.ResourceGroup("resource_group_name") # Create an Azure File Share on an existing storage account # Replace `storage_account_name` with your existing storage account name file_share = storage.FileShare("file_share_name", account_name="storage_account_name", # Resource group name where the storage account is located resource_group_name=resource_group.name, # Specify the size of the file share (in GB) share_quota=100 ) # Export the file share name and the primary location pulumi.export("file_share_name", file_share.name) pulumi.export("primary_location", file_share.primary_location)

    In the above program:

    • We create a file share with a 100GB size, which will be the location where you can store your AI model files.
    • We export the file share name and primary location to easily access this information after deployment. These outputs can be used for configuring your AI services to use this file share for model storage.

    You will need to interact with your AI services or applications and configure them to use the Azure File Share for accessing model files. Depending on the architecture of your application, this might involve setting up mount points on virtual machines or configuring file paths in your application settings.

    To execute this Pulumi program, save the code to a __main__.py file, ensure you have the appropriate Azure credentials configured, and then run pulumi up from the command line in the same directory. Pulumi will handle the provisioning of the resources as per the code.

    Remember, Pulumi stores its state remotely (by default in the Pulumi SaaS backend), allowing you to collaborate with your team on infrastructure updates. You may also wish to integrate this script into a larger Pulumi deployment that provisions not just the file share but also the rest of your infrastructure that will use this file share.