1. Checkpoint Storage for AI Model Training on Azure Blob

    Python

    To set up checkpoint storage for AI model training on Azure Blob Storage, you'll need to create a storage account and a container within it, where your model checkpoint data will be stored. Azure Blob Storage is a scalable and secure place to store such data.

    Below is a Pulumi program written in Python that accomplishes this task. The program does the following:

    1. Creates an Azure Resource Group to organize related resources.
    2. Provisions a new Azure Storage Account, which is a container that holds all of your Azure Storage data objects.
    3. Creates a Blob Container in the Storage Account where the model training checkpoint data will be stored.

    Here's the complete Pulumi program:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup('ai-model-training-rg') # Create an Azure Storage Account storage_account = azure_native.storage.StorageAccount( 'aimodelstorageaccount', resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.storage.SkuArgs(name=azure_native.storage.SkuName.STANDARD_LRS), kind=azure_native.storage.Kind.STORAGE_V2) # Create a Blob Container in the Storage Account blob_container = azure_native.storage.BlobContainer( 'modelcheckpointscontainer', account_name=storage_account.name, resource_group_name=resource_group.name, public_access=azure_native.storage.PublicAccess.NONE) # Export the primary connection string for the storage account primary_connection_string = pulumi.Output.all(resource_group.name, storage_account.name).apply( lambda args: azure_native.storage.list_storage_account_keys_output(args[0], args[1]).apply( lambda account_keys: f"DefaultEndpointsProtocol=https;EndpointSuffix=core.windows.net;AccountName={args[1]};AccountKey={account_keys.keys[0].value}")) pulumi.export('primary_storage_connection_string', primary_connection_string)

    Let's break this down step by step:

    • The ResourceGroup is a resource provided by Azure to organize a collection of assets. It's a container that holds related resources for an Azure solution.
    • The StorageAccount is the foundation of Azure Storage. It gives you a unique namespace to work with Azure Storage data objects.
    • The Skus (Stock-keeping units) represent the different storage types available, such as LRS (Locally Redundant Storage).
    • The Kind specifies that we're using the Storage V2 (general-purpose v2) type, which is recommended for most scenarios involving blobs, files, queues, and tables.
    • The BlobContainer within the storage account is where we store the AI model checkpoints. By setting public_access to None, we ensure that the blobs are private and can only be accessed by authorized accounts/users.
    • Finally, we export the primary_connection_string of the storage account, which can be used in your application or Azure services to access blobs within the container.

    After running the above Pulumi program, you will have an Azure Blob Storage container ready to hold your AI training checkpoints. This is a key part of setting up an automated training pipeline, allowing you to store and retrieve different versions of your models conveniently and securely.


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