1. File Shares for Dataset Versioning in Azure ML Workflows

    Python

    To create file shares for dataset versioning in Azure Machine Learning (Azure ML) Workflows, you can use Pulumi with the Azure Native provider. Below, I will walk you through a Python program that uses Pulumi to create and manage these Azure resources.

    Firstly, we'll need to define the necessary resources in our Pulumi program:

    1. Resource Group: All resources must belong to an Azure resource group. If you haven't created one, the program will define it.
    2. Azure Machine Learning Workspace: This is required for managing and orchestrating the ML training and deployment.
    3. Azure Storage Account and File Share: To store datasets, we'll create a Storage Account and then a File Share within that account. Azure File Share serves as the dataset's versioning system.
    4. Azure ML Dataset Configuration: This is to register the dataset inside the Azure ML workspace and link it to the file share.
    5. Data Versioning: The ability to track changes and maintain versions of the datasets can be facilitated by Azure ML services that support versioning.

    Let's build a Pulumi program to create these resources.

    import pulumi import pulumi_azure_native as azure_native # Define a resource group. resource_group = azure_native.resources.ResourceGroup("resource_group") # Create an Azure Machine Learning Workspace. aml_workspace = azure_native.machinelearningservices.Workspace( "amlWorkspace", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.machinelearningservices.SkuArgs(name="Basic"), identity=azure_native.machinelearningservices.IdentityArgs(type="SystemAssigned"), ) # Set up a storage account to store the datasets. storage_account = azure_native.storage.StorageAccount( "storageAccount", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.storage.SkuArgs(name=azure_native.storage.SkuName.STANDARD_LRS), kind=azure_native.storage.Kind.STORAGE_V2, ) # Create a file share within the storage account. file_share = azure_native.storage.FileShare( "fileShare", resource_group_name=resource_group.name, account_name=storage_account.name, share_name=f"datasets{pulumi.get_stack()}", ) # Register the file share as an Azure ML Dataset for versioning. # Note: Adjust dataset configuration as needed for your data source and scenario. ml_dataset = azure_native.machinelearningservices.MachineLearningDataset( "mlDataset", resource_group_name=resource_group.name, workspace_name=aml_workspace.name, parameters=azure_native.machinelearningservices.MachineLearningDatasetParametersArgs( path=azure_native.machinelearningservices.MachineLearningDatasetParametersArgsPathArgs( datastore_name=file_share.name, relative_path="/", # Specify the relative path within the file share. ) ) ) pulumi.export('resource_group_name', resource_group.name) pulumi.export('aml_workspace_name', aml_workspace.name) pulumi.export('storage_account_name', storage_account.name) pulumi.export('file_share_name', file_share.name) pulumi.export('ml_dataset_name', ml_dataset.name)

    This Pulumi program is structured as follows:

    • We create a ResourceGroup, which is required by Azure to manage the lifecycle of resources.
    • We then set up a Workspace specific to Azure Machine Learning, which is where the datasets will be registered and used.
    • Subsequently, we create a StorageAccount to persistently store the data, and within that, a FileShare is defined to hold the dataset files.
    • The MachineLearningDataset resource is then created, linking the file share to be used within our ML workflows, and facilitating dataset versioning.

    To use this code, make sure you have Pulumi installed and set up with Azure credentials. You can then place this code in a file (e.g. __main__.py), run pulumi up, and Pulumi will handle the provisioning of these resources on Azure.

    Remember, the resources here are the basic necessities for setting up dataset versioning with Azure ML and Azure Storage. Depending on the complexity of your ML workflows and your dataset requirements, you might need additional configuration and resources.

    You can find more information about the Azure-native Pulumi provider and Azure ML services in the Pulumi Azure Native documentation.


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