1. Hosting Large Datasets for AI with Azure Blob Storage


    To host large datasets for AI applications in Azure Blob Storage, you'll need to create a Storage Account and then a Container within that Storage Account. Azure Blob Storage is ideal for this purpose because it provides scalable and secure storage for large amounts of unstructured data like texts, binary data, documents, and media files.

    Here's what the process will generally look like:

    1. Create a Resource Group: Resource groups in Azure are a way to organize related resources. This is the first thing you need to set up to logically group your storage account and related resources.

    2. Create a Storage Account: The Storage Account is where all the blobs (or files) are stored. Think of this as the top-level namespace for your storage.

    3. Create a Blob Container: Within the Storage Account, Containers act like folders that organize your blobs.

    4. Upload Blobs (Data Files): Finally, you would upload your datasets as blobs into the container you created.

    In the Pulumi Python program below, I'll show you how to create each of these resources using Pulumi's azure-native SDK, which uses Azure's most up-to-date API features.

    Here's the step-by-step code to achieve this:

    import pulumi import pulumi_azure_native as azure_native # Create a resource group for our storage-related resources resource_group = azure_native.resources.ResourceGroup("ai_datasets_resource_group") # Create an Azure Storage Account for the datasets storage_account = azure_native.storage.StorageAccount("aidatasetsstorageaccount", resource_group_name=resource_group.name, sku=azure_native.storage.SkuArgs(name=azure_native.storage.SkuName.STANDARD_LRS), kind=azure_native.storage.Kind.STORAGE_V2) # Create a blob container within the storage account to hold our datasets container = azure_native.storage.BlobContainer("datasetscontainer", resource_group_name=resource_group.name, account_name=storage_account.name) # Export the primary storage account key primary_storage_key = pulumi.Output.all(resource_group.name, storage_account.name).apply( lambda args: azure_native.storage.list_storage_account_keys( resource_group_name=args[0], account_name=args[1] ).keys[0].value) # Export the connection string for the storage account to use with applications or clients connection_string = pulumi.Output.all(resource_group.name, storage_account.name, primary_storage_key).apply( lambda args: f"DefaultEndpointsProtocol=https;AccountName={args[1]};AccountKey={args[2]};EndpointSuffix=core.windows.net") # Finally, we export the IDs of the created resources, so we can reference them later on pulumi.export("resource_group_name", resource_group.name) pulumi.export("storage_account_name", storage_account.name) pulumi.export("container_name", container.name) pulumi.export("primary_storage_key", primary_storage_key) pulumi.export("storage_connection_string", connection_string)

    In this program:

    • We import the necessary Pulumi packages for handling Azure resources.
    • We create a new resource group which is a logical container where all our storage resources will live.
    • Then we setup a new storage account with a standard locally-redundant storage (LRS) to keep costs down. You may choose to use a different SKU based on your redundancy and performance requirements.
    • Inside the storage account, we create a new blob container where we will eventually upload our datasets. This container will be private by default but can be configured for different access levels according to need.
    • We retrieve the primary key of the storage account which will be used to access the storage account. It's important to keep this key secure.
    • We construct the storage account connection string which is useful when you need to access the Storage Account programmatically, for example, when uploading datasets.
    • Finally, we use pulumi.export to output the resource group name, storage account name, and container name. These exports are useful if you need to use these resource identifiers outside of Pulumi, such as in a CI/CD pipeline.

    Remember to keep sensitive information like your storage keys secure. Pulumi offers secret storage that automatically encrypts your sensitive data before it goes into your state file.