1. High-Performance File Shares for Machine Learning with Azure NetApp Files


    Azure NetApp Files (ANF) is a high-performance, low-latency file storage service offered by Microsoft Azure, which makes it a suitable choice for machine learning workloads that require quick access to large datasets.

    To use Azure NetApp Files as part of a machine learning setup, you need to provision ANF resources and integrate them with Azure Machine Learning workspaces to ensure that your models have access to the necessary data.

    In the following program, we will use Pulumi to set up a volume in Azure NetApp Files that could be used as shared storage for machine learning computations. We will also create a simple machine learning workspace, which is a prerequisite for creating machine learning services and workflows on Azure, although we won't be configuring the full machine learning environment here.

    Here's the basic process that we're going to follow:

    1. Define the required Azure resources using Pulumi Python classes.
    2. Provision an Azure NetApp Files account and a pool.
    3. Create a volume within the pool to be used for high-performance file sharing.
    4. Set up an Azure Machine Learning workspace.

    Please make sure you have Pulumi and Azure CLI installed and configured with the necessary credentials to deploy resources on Azure.

    Below is the Pulumi program in Python:

    import pulumi import pulumi_azure_native as azure_native # Define the config values for the location and resource group config = pulumi.Config() location = config.require('location') resource_group_name = config.require('resourceGroupName') # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup('resource_group', resource_group_name=resource_group_name, location=location) # Create an Azure NetApp account netapp_account = azure_native.netapp.Account('netapp_account', account_name='myanfaccount', resource_group_name=resource_group.name, location=location) # Create a capacity pool within the Azure NetApp account capacity_pool = azure_native.netapp.CapacityPool('capacity_pool', pool_name='mypool', account_name=netapp_account.name, resource_group_name=resource_group.name, service_level='Premium', # Change to Standard or Ultra as needed size=4398046511104, # Pool size in bytes, equal to 4 TiB location=location) # Create a NetApp volume within the given capacity pool netapp_volume = azure_native.netapp.Volume('netapp_volume', volume_name='myvolume', account_name=netapp_account.name, pool_name=capacity_pool.name, resource_group_name=resource_group.name, location=location, creation_token='myuniquevolumetoken', service_level='Premium', # Make sure this matches the pool's service level subnet_id=pulumi.Output.concat( '/subscriptions/', config.require('subscriptionId'), '/resourceGroups/', resource_group.name, '/providers/Microsoft.Network/virtualNetworks/', config.require('virtualNetworkName'), '/subnets/', config.require('subnetName') ), usage_threshold=107374182400) # Volume size in bytes, equal to 100 GiB # Create an Azure Machine Learning workspace ml_workspace = azure_native.machinelearningservices.Workspace('ml_workspace', workspace_name='mymlworkspace', resource_group_name=resource_group.name, location=location, sku=azure_native.machinelearningservices.SkuArgs( name='Basic' )) # Output the Azure NetApp Files volume ID pulumi.export('netapp_volume_id', netapp_volume.id) # Output the Azure Machine Learning Workspace ID pulumi.export('ml_workspace_id', ml_workspace.id)

    In this program, you will need to provide some configuration parameters, such as the locations and names for the resource group, NetApp account, capacity pool, volume, and machine learning workspace.

    You can run this program with Pulumi using the pulumi up command. After provisioning these resources with Pulumi, your Azure environment will have a high-performance Azure NetApp Files volume ready to be used with a machine learning workspace, where you can deploy and train your models with faster data access.