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This module sets up multi-workspace model registry between a Databricks AWS development (dev) workspace, staging workspace, and production (prod) workspace, allowing READ access from dev/staging workspaces to staging & prod model registries.

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databricks/terraform-databricks-mlops-aws-infrastructure

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MLOps AWS Infrastructure Module

This module sets up multi-workspace model registry between a development (dev) workspace, a staging workspace, and a production (prod) workspace, allowing READ access from dev/staging workspaces to staging & prod model registries.

The module performs this setup by creating service principals in the staging and prod workspaces with READ-only access to their respective model registries. It will also create secret scopes and store the necessary secrets in the dev and staging workspaces, and only give READ access to this secret scope to the "users" group and the generated service principals group. The output of this module will be the secret scope names and prefixes since these values are needed to be able to access the remote model registry.

NOTE:

  1. This module is in preview so it is still experimental and subject to change. Feedback is welcome!
  2. The Databricks providers that are passed into the module must be configured with workspace admin permissions.
  3. In order to create tokens for service principals, they are added to a group, which is then given token_usage permission. However, in order to set this permission, there must be at least 1 personal access token in the workspace, and this permission strictly overwrites existing permissions. Currently, running this module will overwrite permissions to allow token usage only for members of the generated service principals group in the staging and prod workspaces. If additional groups are desired to have token_usage permissions, they can be set via the additional_token_usage_groups input variable.
  4. The service principal tokens stored for remote model registry access are created with a default expiration of 100 days (8640000 seconds), and the module will need to be re-applied after this time to refresh the tokens.

Usage

provider "databricks" {
  alias = "dev"    # Authenticate using preferred method as described in Databricks provider
}

provider "databricks" {
  alias = "staging"     # Authenticate using preferred method as described in Databricks provider
}

provider "databricks" {
  alias = "prod"     # Authenticate using preferred method as described in Databricks provider
}

module "mlops_aws_infrastructure" {
  source = "databricks/mlops-aws-infrastructure/databricks"
  providers = {
    databricks.dev = databricks.dev
    databricks.staging = databricks.staging
    databricks.prod = databricks.prod
  }
  staging_workspace_id          = "123456789"
  prod_workspace_id             = "987654321"
  additional_token_usage_groups = ["users"]     # This field is optional.
}

Usage example with MLOps AWS Project Module

provider "databricks" {
  alias = "dev" # Authenticate using preferred method as described in Databricks provider
}

provider "databricks" {
  alias = "staging"     # Authenticate using preferred method as described in Databricks provider
}

provider "databricks" {
  alias = "prod"     # Authenticate using preferred method as described in Databricks provider
}

module "mlops_aws_infrastructure" {
  source = "databricks/mlops-aws-infrastructure/databricks"
  providers = {
    databricks.dev     = databricks.dev
    databricks.staging = databricks.staging
    databricks.prod    = databricks.prod
  }
  staging_workspace_id          = "123456789"
  prod_workspace_id             = "987654321"
  additional_token_usage_groups = ["users"]     # This field is optional.
}


module "mlops_aws_project" {
  source = "databricks/mlops-aws-project/databricks"
  providers = {
    databricks.staging = databricks.staging
    databricks.prod    = databricks.prod
  }
  service_principal_name = "example-name"
  project_directory_path = "/dir-name"
  service_principal_group_name = module.mlops_aws_infrastructure.service_principal_group_name 
  # The above field is optional, especially since in this case service_principal_group_name will be mlops-service-principals either way, 
  # but this also serves to create an implicit dependency. Can also be replaced with the following line to create an explicit dependency:
  # depends_on             = [module.mlops_aws_infrastructure]
}

Requirements

Name Version
terraform >=1.1.6
databricks >=0.5.8

Inputs

Name Description Type Default Required
staging_workspace_id Workspace ID of the staging workspace (can be often found in the URL) used for remote model registry setup. string N/A yes
prod_workspace_id Workspace ID of the prod workspace (can be often found in the URL) used for remote model registry setup. string N/A yes
additional_token_usage_groups List of groups that should have token usage permissions in the staging and prod workspaces, along with the created service principal group (mlops-service-principals). list(string) [] no

Outputs

Name Description Type Sensitive
dev_secret_scope_name_for_staging The name of the secret scope created in the dev workspace that is used for remote model registry access to the staging workspace. string no
dev_secret_scope_name_for_prod The name of the secret scope created in the dev workspace that is used for remote model registry access to the prod workspace. string no
staging_secret_scope_name_for_prod The name of the secret scope created in the staging workspace that is used for remote model registry access to the prod workspace. string no
dev_secret_scope_prefix_for_staging The prefix used in the dev workspace secret scope for remote model registry access to the staging workspace. string no
dev_secret_scope_prefix_for_prod The prefix used in the dev workspace secret scope for remote model registry access to the prod workspace. string no
staging_secret_scope_prefix_for_prod The prefix used in the staging workspace secret scope for remote model registry access to the prod workspace. string no
service_principal_group_name The name of the service principal group created in the staging and prod workspace. string no

Providers

Name Authentication Use
databricks.dev Provided by the user. Generate all resources in the dev workspace.
databricks.staging Provided by the user. Generate all resources in the staging workspace.
databricks.prod Provided by the user. Generate all resources in the prod workspace.

Resources

Name Type
databricks_group.staging_sp_group resource
databricks_permissions.staging_token_usage resource
databricks_group.prod_sp_group resource
databricks_permissions.prod_token_usage resource
aws-service-principal.staging_sp module
aws-service-principal.prod_sp module
remote-model-registry.remote_model_registry_dev_to_staging module
remote-model-registry.remote_model_registry_dev_to_prod module
remote-model-registry.remote_model_registry_staging_to_prod module

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This module sets up multi-workspace model registry between a Databricks AWS development (dev) workspace, staging workspace, and production (prod) workspace, allowing READ access from dev/staging workspaces to staging & prod model registries.

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