Scalable Machine Learning Workflows with Databricks Service Principal Roles

Question

Pulumi · Accepted Answer

To set up a scalable machine learning workflow in Databricks using Service Principal Roles, you will need to create a number of resources. Firstly, you'll need the Databricks Service Principal which acts as a type of identity in Azure Databricks, allowing you to automate, simplify, and secure resource management. Additionally, assigning Service Principal Roles to the principal will allow you to define a set of permissions to access and execute actions within an Azure Databricks workspace.

Here's what you need to set up:

Service Principal: This is the identity created for use with applications, hosted services, and automated tools to access Azure resources.
Service Principal Secret: The credential for authentication used by the service principal.
Service Principal Role: Defines the set of permissions for the service principal, ensuring it can only perform the actions needed for the machine learning workflows.
Databricks Cluster: The computing environment for running the workflows.
Model Serving: If you also want to deploy models as REST endpoints, you would also set up Model Serving.

Below is a Pulumi Python program that outlines how to provision these necessary resources. This program does not run a specific machine learning workflow but sets the stage for you to deploy your models and workflows with the necessary permissions and infrastructure.

import pulumi
import pulumi_databricks as databricks

# Before running this program, ensure that you have configured Pulumi for Databricks.
# You should have a Databricks workspace set up and the necessary credentials and configurations for Pulumi.

# Create a Databricks service principal. This acts like a user identity for your applications.
service_principal = databricks.ServicePrincipal("my-service-principal",
    active=True,
    display_name="MyMLServicePrincipal",
    # application_id, acls, and other properties to match your setup
)

# Create a secret scope for securely storing the secret associated with the service principal.
# In your real-world scenario, make sure to handle secrets with care following best practices.
secret_scope = databricks.SecretScope("my-secret-scope",
    initial_manage_principal="users", # Change this according to who should initially manage the scope.
)

# Create a service principal secret. This is the credentials used by the service principal to authenticate.
service_principal_secret = databricks.Secret("my-service-principal-secret",
    string_value="<YOUR_SECRET_HERE>",
    scope=secret_scope.name,
    key="service-principal-secret-key"
)

# Assign the service principal a role which defines permissions within the Databricks workspace.
# The role should be created to align with your security practices and the principle of least privilege.
service_principal_role_assignment = databricks.ServicePrincipalRole("my-service-principal-role",
    service_principal_id=service_principal.id,
    # Define the role to assign to the service principal.
    # Options include roles like "Admin", "Contributor", and so on, depending on your workspace and requirements.
    role="<ROLE_HERE>"
)

# Create a Databricks cluster for running machine learning jobs.
# The configuration can be adjusted based on the workload requirements.
cluster = databricks.Cluster("my-ml-cluster",
    # Define node types, scaling, and other properties to match the demands of your machine learning workload.
    num_workers=4,
    autoscale=databricks.ClusterAutoscaleArgs(
        min_workers=2,
        max_workers=8
    ),
    spark_version="9.1.x-scala2.12",
    node_type_id="Standard_D3_v2",
    driver_node_type_id="Standard_D3_v2",
    # Additional properties such as instance pools, libraries, and security settings can be added based on requirements.
)

# Optionally, if you are also serving models via REST endpoints, set up model serving.
model_serving = databricks.ModelServing("my-model-serving",
    config=databricks.ModelServingConfigArgs(
        served_models=[
            databricks.ModelServingConfigServedModelArgs(
                model_name="MyFirstModel",
                model_version="1",
                # Define environment, size, and other attributes as needed.
            )
        ]
        # Define traffic configs if necessary.
    ),
    name="MyModelServingEndpoint",
    # Add tags or other attributes as desired.
)

# Export the HTTP URL for the model serving endpoint if applicable.
serving_url = pulumi.Output.all(service_principal, model_serving).apply(
    lambda args: f"https://{args[0].workspace_url}/{args[1].name}"
)
pulumi.export("model_serving_url", serving_url)

Explanation:

This program assumes that you've already configured your Pulumi CLI with the Databricks provider and set up any required authentication.
The ServicePrincipal is being created with an active status and a display name.
A secret scope (SecretScope) is created where secrets will be stored.
The Secret resource defined here is where we would store the secret value that the service principal will use to authenticate; in practice, this should not be hardcoded, and you should use a secure method to manage this value.
ServicePrincipalRole assigns a defined role to the service principal, specifying the level of access control within Databricks.
A Databricks cluster (Cluster) is provisioned to execute machine learning jobs, with properties defined for number of workers, autoscaling, and node types.
As an optional step, ModelServing sets up a way to serve models through REST endpoints.

Remember to replace placeholders like "<YOUR_SECRET_HERE>", "<ROLE_HERE>", and configure the Cluster and ModelServing with the correct properties suitable for your specific machine learning workloads.