1. Ensuring Compliance in AI Model Training with Databricks Cluster Policies


    In order to ensure compliance when training AI models with Databricks, you would typically use the Databricks Cluster Policy resource. Cluster policies allow you to set specific limitations and configurations that clusters must adhere to, which is useful for maintaining standards and governance across teams or projects. For example, you might limit the maximum number of nodes in a cluster to control costs, or enforce the use of certain instance types to comply with regulatory requirements.

    With Pulumi, you can define infrastructure as code, including such policies. Below is a Pulumi program written in Python that creates a Databricks cluster policy with constraints on specific attributes.

    This program expects that you have the pulumi-databricks provider configured in your Pulumi settings. Also, make sure you have the appropriate access rights to create cluster policies in your Databricks workspace.

    Here's the step-by-step explanation of what the code below does:

    1. Imports and Initialization: We import the necessary Pulumi and databricks packages and start defining our stack.
    2. Cluster Policy Creation: A ClusterPolicy resource is defined, which specifies the constraints for clusters. We've set a simple policy that enforces a limit on the number of nodes to ensure cost compliance.
    3. Exporting Output: The last line of the program exports the ID of our cluster policy, which can be used to reference this policy when creating clusters or for other operations.
    import pulumi import pulumi_databricks as databricks # Create a Databricks cluster policy that enforces certain rules for compliance. # In this example, we enforce a maximum number of nodes to ensure cost control. cluster_policy = databricks.ClusterPolicy("compliance-cluster-policy", definition={ # You can define various constraints within this definition dictionary. # This example restricts the maximum number of workers for a cluster. "aws_attributes.instance_profile_arn": { "type": "fixed", "value": "<instance-profile-arn>" # replace with your specific instance profile ARN }, "node_type_id": { "type": "fixed", "value": "Standard_D3_v2" # Enforces use of specific node types }, "spark_version": { "type": "fixed", "value": "7.x-scala2.12" # Enforces a specific Spark version }, "num_workers": { "type": "range", # You can also specify ranges for numeric properties. "min_value": 1, "max_value": 10, # Replace with the maximum number of nodes allowed. } }, description="Policy to ensure clusters are compliant with cost and security policies." ) # The cluster policy ID can be exported and used to ensure that clusters adhere # to the policy when they are being created. pulumi.export("cluster_policy_id", cluster_policy.id)

    In the above code, the definition key takes a dictionary describing the constraints. Each entry in the dictionary corresponds to a property of a Databricks cluster that you want to enforce. The type key within each constraint can be either fixed, range, or allowlist, depending on the kind of restriction you want to impose.

    The fixed type enforces a specific value, while the range type allows any value within a specified range. The min_value and max_value keys specify this range.

    If you need to use this policy, simply reference the exported cluster_policy_id when creating a cluster, and Databricks will enforce the compliant configurations.

    This is a foundational step to ensuring that the resources created adhere to the policies that have been specified. As your needs grow more complex, you can expand your policies with additional restrictions to further guide the compliance of your Databricks clusters.