1. High Throughput File Storage for Machine Learning Training


    When setting up a cloud environment for machine learning training, high throughput file storage is essential, particularly when dealing with large datasets. This storage needs to be able to quickly read and write data to keep up with the computation speed of the training algorithms.

    For machine learning workloads on Azure, we can use Azure Machine Learning File System (Azure-native.storagecache.AmlFilesystem), which provides a high throughput and low latency file system designed for machine learning purposes. It's optimized for scenarios that require heavy read access to large datasets, which is typical in machine learning data processing and model training.

    The program below sets up an Azure Machine Learning File System instance. It creates a new resource within a specified location and with a specified size, making sure that the storage is encrypted for data security purposes. This file system will be mounted to a subnet (filesystemSubnet), which should be associated with your Azure Machine Learning workspace or compute resources. The SKU defines the performance tier of the cache, which should be selected based on your workload requirements.

    Please note that before running this Pulumi code, you need to have properly configured Pulumi with Azure credentials, and the resource_group_name and subnet_id should be predefined with your existing Azure resource group and subnet identifiers respectively.

    import pulumi import pulumi_azure_native.storagecache as storagecache # Define the configuration for the Azure Machine Learning File System. aml_filesystem_config = storagecache.AmlFilesystemArgs( sku=storagecache.SkuArgs( name="Standard_ZRS", # This is an example SKU; adjust this according to your needs ), tags={ "Environment": "MLTraining", }, encryption_settings=storagecache.EncryptionSettingsArgs( key_encryption_key=storagecache.KeyEncryptionKeyArgs( key_url="https://example-keyvault.vault.azure.net/keys/example-key/0123456789abcdef0123456789abcdef", source_vault=storagecache.SourceVaultArgs( id="/subscriptions/{subscription-id}/resourceGroups/{resource-group}/providers/Microsoft.KeyVault/vaults/{keyvault-name}", # Replace with your Vault resource ID. ), ), ), storage_capacity_tib=5, # Define storage capacity (in TiB) based on your needs. ) # Create an AmlFilesystem resource. aml_filesystem = storagecache.AmlFilesystem( "amlFilesystem", # Name your filesystem. resource_group_name=pulumi.Config("azure").get("resourceGroupName"), # Configure your Azure resource group name here. location="eastus", # Configure the desired location. filesystem_subnet="/subscriptions/{subscription-id}/resourceGroups/{resource-group}/providers/Microsoft.Network/virtualNetworks/{vnet-name}/subnets/{subnet-name}", # Replace with your Subnet resource ID. **aml_filesystem_config, ) # Export the ID of the AmlFilesystem so that it can be used elsewhere. pulumi.export("aml_filesystem_id", aml_filesystem.id)

    In this code:

    • We define the storage capacity, SKU, tags, and encryption settings.
    • We create the AmlFilesystem resource within a specified resource group and associated with a particular subnet.
    • At the end of the program, we export the filesystem ID, which you can use to reference this filesystem in other resources or outputs.

    Remember, this is a basic setup and you might need to adjust parameters like sku, storage_capacity_tib, or encryption_settings according to your specific requirements and existing Azure configuration. Additionally, you will need to ensure networking is set up such that your machine learning compute instances can access this file storage.