1. Grouping Machine Learning Workspaces in Azure.


    To group Machine Learning Workspaces in Azure, you would typically organize them within a single Azure Resource Group. An Azure Resource Group is a container that holds related resources for an Azure solution. By placing Azure Machine Learning Workspaces in the same Resource Group, you can manage them as a single entity, applying common policies, access control, and shared resources.

    In Pulumi, you would define an Azure Resource Group and then create one or more Machine Learning Workspaces referencing that Resource Group. Here, we'll use the azure-native.machinelearningservices.Workspace resource to create Machine Learning Workspaces within the resource group.

    Below is a Pulumi program that demonstrates how to:

    1. Create a Resource Group to contain the workspaces.
    2. Create multiple Azure Machine Learning Workspaces within the same Resource Group.
    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup("resource_group") # Define a function to create an Azure Machine Learning Workspace within the resource group def create_ml_workspace(workspace_name, resource_group_name): return azure_native.machinelearningservices.Workspace( workspace_name, resource_group_name=resource_group_name, sku=azure_native.machinelearningservices.SkuArgs(name="Basic"), # Choose an appropriate SKU location=resource_group.location, description=f"{workspace_name} Machine Learning Workspace", ) # Create multiple Machine Learning Workspaces workspace_1 = create_ml_workspace("workspace1", resource_group.name) workspace_2 = create_ml_workspace("workspace2", resource_group.name) workspace_3 = create_ml_workspace("workspace3", resource_group.name) # Export the Resource Group and Workspace details pulumi.export("resource_group_name", resource_group.name) pulumi.export("workspace_1_name", workspace_1.name) pulumi.export("workspace_2_name", workspace_2.name) pulumi.export("workspace_3_name", workspace_3.name)

    Here's a breakdown of what this program does:

    1. Resource Group Creation: We start by creating an Azure Resource Group that will contain all the workspaces. The pulumi_azure_native.resources.ResourceGroup class is used for this purpose.

    2. Function to Create ML Workspaces: The create_ml_workspace function is a helper that simplifies the creation of Machine Learning Workspaces. It takes in a workspace_name and the resource_group_name as parameters.

    3. Creating Workspaces: We then invoke the create_ml_workspace function three times to create three different Machine Learning Workspaces. These workspaces will be created within the resource group we have defined.

    4. Exporting Resource Information: Finally, we export the names of the resource group and each workspace. In Pulumi, export is used to print out certain outputs after the Pulumi program has run successfully. You can use these names for reference or when integrating with other services.

    Please note that you will need to have your Azure account set up and configured with Pulumi for this code to execute successfully.

    To apply this Pulumi code, you would save it to a file (e.g., main.py), and then run pulumi up within the directory containing your file. Pulumi will handle the provisioning and updating of the resources defined in your program.