1. Checkpoint Storage for Distributed Training on Azure


    In distributed machine learning, a checkpoint is a snapshot of the state of a model at a specific point in time during training. It includes the model weights, which are the parameters that have been learned so far, and other training states like the current epoch, optimizer state, and learning rate, among others. This is essential for long-running training jobs where you might want to resume training from a specific point due to failures or to avoid losing progress.

    On Azure, you can leverage several services to implement checkpoint storage for distributed training. One common approach is to use Azure Blob Storage for storing the checkpoint files. This service provides scalable and durable storage, which can be easily accessed from distributed training jobs running on Azure Machine Learning Service or Azure Kubernetes Service.

    To implement checkpoint storage, you need to create a storage account, a blob container within the storage account, and manage the permissions so that your training jobs can read and write checkpoint files to the container. You can use the Storage Account and Blob Container resources from the azure-native Pulumi package to automate this setup.

    Below is an example Pulumi program in Python that creates an Azure Blob Storage account and a container to be used for checkpoint storage for distributed training:

    import pulumi from pulumi_azure_native import storage from pulumi_azure_native import resources # Create an Azure Resource Group resource_group = resources.ResourceGroup("resource_group") # Create an Azure Storage Account for storing the checkpoints storage_account = storage.StorageAccount("storage_account", resource_group_name=resource_group.name, sku=storage.SkuArgs(name=storage.SkuName.STANDARD_LRS), kind=storage.Kind.STORAGE_V2) # Create a Blob Container in the storage account to store the checkpoints blob_container = storage.BlobContainer("blob_container", account_name=storage_account.name, resource_group_name=resource_group.name, public_access=storage.PublicAccess.NONE) # Export the connection string for the storage account which is needed for accessing it connection_string = pulumi.Output.all(resource_group.name, storage_account.name).apply( lambda args: storage.list_storage_account_keys(resource_group_name=args[0], account_name=args[1]) ).apply( lambda account_keys: f"DefaultEndpointsProtocol=https;AccountName={storage_account.name};AccountKey={account_keys.keys[0].value};EndpointSuffix=core.windows.net" ) pulumi.export("connection_string", connection_string) pulumi.export("storage_account_name", storage_account.name) pulumi.export("blob_container_name", blob_container.name)

    This program does the following:

    1. It creates an Azure Resource Group to organize related resources needed for storage provisioning.
    2. It provisions an Azure Storage Account where the actual data will be stored. Here, STANDARD_LRS specifies the performance tier Standard and the replication strategy Locally-redundant storage.
    3. It creates a Blob Container within the Storage Account with no public access for security. It’s where your checkpoints will be stored.
    4. It exports the connection string for the storage account, which will be used to authenticate the distributed training code to the storage resource.

    Please ensure you have the Pulumi CLI installed and configured with your Azure account before running this program. After running the program with pulumi up, it will give you the connection string output which needs to be securely stored and used in your training jobs for checkpoint saving and retrieval.