1. Assigning Roles for Access Control in Azure Machine Learning Workspaces


    In Azure Machine Learning Workspaces, role-based access control (RBAC) is a mechanism that provides fine-grained access management for resources within the workspace. Assigning roles to users, groups, or service principals can help control who has access to the Azure Machine Learning Workspace and what they can do with it.

    To assign a role using Pulumi, you'll leverage the Azure Native provider, which exposes necessary resources to define and assign roles comprehensively. Specifically, you can use the RoleAssignment resource to assign a role to a user, group, or service principal at a specific scope.

    Below, I'll demonstrate how to use Pulumi to assign a Contributor role to a user in an Azure Machine Learning Workspace. First, you need to create the Machine Learning Workspace. Then, you identify the target user and assign the desired role to them within the scope of the just-created workspace. Here's how to accomplish that in Python:

    import pulumi import pulumi_azure_native as azure_native import pulumi_azure_native.authorization as authorization # Define configuration values # Here we define the configuration for the resource group and the ML Workspace. resource_group_name = 'my-resource-group' location = 'eastus' workspace_name = 'my-ml-workspace' user_principal_id = 'user-to-grant-access-object-id' # Object ID of the user, group, or service principal role_definition_id = '/subscriptions/{subscription_id}/providers/Microsoft.Authorization/roleDefinitions/{role_definition_id}' # The role definition id for the 'Contributor' role # Create a resource group if it doesn't exist resource_group = azure_native.resources.ResourceGroup('my-resource-group', resource_group_name=resource_group_name, location=location) # Create an Azure Machine Learning Workspace ml_workspace = azure_native.machinelearningservices.Workspace('my-ml-workspace', workspace_name=workspace_name, location=location, resource_group_name=resource_group.name, sku=azure_native.machinelearningservices.SkuArgs( name="Basic" # Choose the SKU for the workspace )) # Assign the 'Contributor' role to the user at the ML Workspace scope role_assignment = authorization.RoleAssignment('role-assignment', scope=ml_workspace.id, # The scope of the role assignment, which is the ML Workspace role_assignment_name='my-unique-guid', # A GUID for the role assignment properties=authorization.RoleAssignmentPropertiesArgs( principal_id=user_principal_id, role_definition_id=role_definition_id, )) # Export the ML Workspace ID and the Role Assignment ID pulumi.export('ml_workspace_id', ml_workspace.id) pulumi.export('role_assignment_id', role_assignment.id)

    Let's break down the code:

    1. We define configuration parameters at the top, which include the name of the resource group, the location of the resources, the name of the workspace, the user's object ID, and the role definition ID.

    2. We create a resource group using the ResourceGroup resource from the azure_native.resources module.

    3. We then proceed to create the Azure Machine Learning Workspace using the Workspace resource from the azure_native.machinelearningservices module.

    4. After we have the workspace in place, we assign a role to the user with a specified principal ID by creating a RoleAssignment resource from the azure_native.authorization module.

    5. Finally, we export the IDs of both the ML Workspace and the role assignment. These IDs can be used for reference or used in other Pulumi programs.

    Make sure you replace user-to-grant-access-object-id, {subscription_id}, and {role_definition_id} with the appropriate values for your setup. Additionally, the role_assignment_name must be a unique GUID which you can generate using tooling you prefer or an online service.

    Once you run this Pulumi program with the Pulumi CLI, the user specified by user_principal_id will have Contributor access to the Azure Machine Learning Workspace, enabling them to perform actions in accordance with that role.