Secure Multi-tenant ML Environments on Databricks

Question

Pulumi · Accepted Answer

Creating secure multi-tenant machine learning environments on Databricks involves setting up isolated workspaces with proper access controls and configurations to ensure that each tenant's data and processes are kept separate and inaccessible to others. In this Pulumi program, I will guide you through setting up such an environment on Azure Databricks, covering aspects like creating the Databricks workspace, clusters, and setting appropriate entitlements for user access control.

Here is what we want to accomplish:

1. Provision a Databricks workspace on Azure.
2. Set up Databricks clusters.
3. Configure security entitlements to control access for different tenants.

Let's start by provisioning an Azure Databricks workspace using the `azure_native.databricks.Workspace` resource. We'll specify the necessary details like the location, SKU, and encryption settings.

After setting up the workspace, we will proceed with creating a Databricks cluster using the `databricks.Cluster` resource. Clusters are the compute resource in Databricks where your data gets processed. We will configure it with autoscaling to optimize cost and performance.

Lastly, we will manage user entitlements within the Databricks workspace using the `databricks.Entitlements` resource, defining access permissions for workspace interactions and instance pool creation.

Let's begin writing the program:

```python
import pulumi
import pulumi_azure_native as azure_native
import pulumi_databricks as databricks

# Create an Azure Databricks Workspace
databricks_workspace = azure_native.databricks.Workspace("secureMLWorkspace",
    resource_group_name="my-resource-group",
    location="East US",
    sku=azure_native.databricks.SkuArgs(
        name="standard"
    ),
    managed_resource_group_id="/subscriptions/{subscription_id}/resourceGroups/{managed_resource_group_name}"
)

# Retrieve the workspace URL which will be used to manage Databricks resources
workspace_url = databricks_workspace.ui_url.apply(lambda url: url.split("adb-")[1].split(".")[0])

# Create a Databricks cluster with autoscaling enabled
ml_cluster = databricks.Cluster("mlCluster",
    cluster_name="ml-cluster",
    spark_version="7.3.x-scala2.12",
    autoscale=databricks.AutoscaleArgs(
        min_workers=1,
        max_workers=2
    ),
    node_type_id="Standard_D3_v2",
    driver_node_type_id="Standard_D3_v2",
)

# Configure security entitlements for a tenant's user
user_entitlements = databricks.Entitlements("userEntitlements",
    user_id="databricks-user-id",
    allow_cluster_create=True,
    allow_instance_pool_create=False
)

pulumi.export("DatabricksWorkspaceURL", databricks_workspace.ui_url)
pulumi.export("MLClusterId", ml_cluster.cluster_id)
```

In this program, we set up a Databricks environment with a cluster configuration that scales according to workload demands. The `Entitlements` resource defines what actions a certain user can perform within the workspace.

Keep in mind the following:

- Replace `"my-resource-group"` with your actual Azure Resource Group name.
- Replace `{subscription_id}`, `{managed_resource_group_name}` with the appropriate Azure subscription and managed resource group information.
- Use your account's user ID when initializing `Entitlements` (replacing `databricks-user-id`).
- This program uses simplified configurations; in real-world scenarios, you should also set up Virtual Network peering, proper network security groups, and other resources for a comprehensive environment.

When you run this program with Pulumi, it will interactively prompt you to log into your Azure account (unless you've done so already) and then carry out the steps defined. Remember to review the best practices for securing your credentials and managing access to your cloud resources appropriately.