1. Blob Storage for Distributed Machine Learning Checkpoints


    Understanding Blob Storage for Machine Learning Checkpoints

    In machine learning, checkpoints are an essential part of the training process as they allow you to save the state of your model at regular intervals. This can help with recovery in case of failures and can also be used for analysis or to continue training at a later time.

    Blob storage is an ideal place to store these checkpoints because it's designed to store large amounts of unstructured data such as the binary files used for machine learning model checkpoints. By using blob storage, you can ensure that your data is stored in a durable, highly available, and scalable way.

    For such use cases, Microsoft Azure Blob Storage is a common choice. It's a service that stores unstructured data in the cloud as blobs. It's highly scalable and accessible, which makes it perfect for storing large files like machine learning model checkpoints.

    In this Pulumi program, we will create an Azure Blob Storage container where you can store your distributed machine learning checkpoints. Pulumi allows us to define, deploy, and manage cloud infrastructure using familiar programming languages.

    Here's how we will proceed:

    1. Set up an Azure resource group.
    2. Create a Blob Storage account.
    3. Create a container in the Blob Storage account.

    Pulumi Program to Create Blob Storage for Machine Learning Checkpoints

    import pulumi import pulumi_azure_native as azure_native # Step 1: Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup("ml_checkpoints_resource_group") # Step 2: Create a Storage Account # The storage account is where all blobs (or files) will be stored. storage_account = azure_native.storage.StorageAccount("mlcheckpointsaccount", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.storage.SkuArgs(name=azure_native.storage.SkuName.STANDARD_LRS), # LRS = Locally Redundant Storage kind=azure_native.storage.Kind.STORAGE_V2) # Step 3: Create a Blob Container # This container will hold our machine learning checkpoints. container = azure_native.storage.BlobContainer("ml_checkpoints_container", account_name=storage_account.name, resource_group_name=resource_group.name, public_access=azure_native.storage.PublicAccess.NONE) # Export the Azure Storage Account connection string storage_account_connection_string = storage_account.primary_connection_string.apply( lambda connection_string: connection_string) pulumi.export('resource_group_name', resource_group.name) pulumi.export('storage_account_name', storage_account.name) pulumi.export('container_name', container.name) pulumi.export('storage_account_connection_string', storage_account_connection_string)

    This is a Python program using Pulumi that provisions blob storage suitable for storing machine learning checkpoints. Let's break down what the code does:

    1. Create an Azure Resource Group: A resource group is a container that holds related resources for an Azure solution. In this code, we create a new resource group named ml_checkpoints_resource_group.

    2. Create a Storage Account: Next, we create an Azure Storage Account within the resource group. The storage account provides a unique namespace for your Azure Storage data, which is accessible from anywhere in the world over HTTP or HTTPS. We are using the "Standard_LRS" SKU, which stands for Standard performance tier with Locally Redundant Storage.

    3. Create a Blob Container: Within the created storage account, we create a blob container named ml_checkpoints_container. This container will hold all blobs, each of which can be a machine learning checkpoint file. We set the public access level to NONE for privacy and security reasons.

    Finally, we use Pulumi’s export feature to output the names of our created resources and the connection string for the storage account, which can be used to access the storage from your application or services.

    To deploy this infrastructure, save the above code into a file named __main__.py, and then run pulumi up from the same directory. Pulumi will present you with a summary of the changes before performing them when you confirm.