1. Storing AI Model Snapshots in Azure Blob Storage


    To store AI model snapshots in Azure Blob Storage, you'll be using Azure's Blob Storage service which is designed to store large amounts of unstructured data, like files and binary data, which is ideal for AI model snapshots.

    In Pulumi, we can use the azure-native package to interact with Azure Blob Storage. This package provides a modern, idiomatic, and strongly-typed interface to Azure services from Pulumi programs.

    Here's how you will accomplish this task:

    1. Set up a new Azure Resource Group to organize all the resources used for this task.
    2. Create an Azure Storage Account which is the top-level object for accessing Azure Blob Storage.
    3. Create a Blob Container within the Storage Account where your snapshots will be stored.
    4. Finally, upload your snapshot file as a Blob in the Blob Container.

    Below is a Pulumi Python program that accomplishes this workflow:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup('ai-snapshots-rg') # Create an Azure Storage Account storage_account = azure_native.storage.StorageAccount('aisnapshotstorageaccount', resource_group_name=resource_group.name, sku=azure_native.storage.SkuArgs( name=azure_native.storage.SkuName.STANDARD_LRS, # Using Locally-Redundant Storage for cost efficiency ), kind=azure_native.storage.Kind.STORAGE_V2 # General-purpose v2 account type ) # Create a Blob Container in the Storage Account blob_container = azure_native.storage.BlobContainer('aisnapshotblobcontainer', account_name=storage_account.name, resource_group_name=resource_group.name, public_access=azure_native.storage.PublicAccess.NONE # No public access ) # Upload an AI Model Snapshot as a Blob (Assuming you have a local snapshot file 'model_snapshot.bin') blob_snapshot = azure_native.storage.Blob('aimodelsnapshot', resource_group_name=resource_group.name, account_name=storage_account.name, container_name=blob_container.name, source=pulumi.FileAsset('model_snapshot.bin'), blob_name='model_snapshot.bin' ) # Export the URL to access the AI Model Snapshot blob_url = pulumi.Output.concat( 'https://', storage_account.name, '.blob.core.windows.net/', blob_container.name, '/', blob_snapshot.name ) pulumi.export('ai_model_snapshot_url', blob_url)

    Let's break down what each step is doing:

    • ResourceGroup is a logical container into which Azure resources like hosting servers, databases, and storage accounts are deployed and managed.
    • StorageAccount is where all of Azure Storage's data objects, like blobs, files, queues, tables, and disks, are kept.
    • BlobContainer is a container that can store a set of blobs. All blobs must be in a container.
    • Blob represents the file that you're going to store. Here, pulumi.FileAsset has been used to reference a local file named 'model_snapshot.bin' which is your AI model snapshot.

    This program is intended to be run in an environment where Pulumi is installed and configured to connect to an Azure subscription. Once executed, it will deploy the necessary resources in Azure and output the blob URL which can be used to access your AI model snapshot.

    Remember to replace 'model_snapshot.bin' with the actual path to your model snapshot file to be uploaded. If your snapshots are in a different format or have different naming, adjust the source and blob_name accordingly.