### Configure the Databricks workspace You're sending traces to a Databricks workspace (`MLFLOW_TRACKING_URI={{ tracking_uri }}`). Before instrumenting the app, verify that Databricks auth is configured. The Databricks SDK resolves credentials from env vars, `~/.databrickscfg` profiles, OAuth, and other sources, so don't hard-require any specific env var: just confirm the SDK can authenticate. ```python from databricks.sdk import WorkspaceClient WorkspaceClient({{workspace_client_args}}).current_user.me() ``` If that call raises, stop and ask the user to configure auth (for example via `databricks auth login`, a `~/.databrickscfg` profile, or by exporting `DATABRICKS_HOST` and `DATABRICKS_TOKEN`). Never write secrets into files in the repo. Pin the active experiment by ID (tracking URI itself is wired in step 2 below, so don't repeat that here): ```python import mlflow mlflow.set_experiment(experiment_id="{{ experiment_id }}") ``` **Optional: store traces in Unity Catalog.** If the user wants traces backed by a UC Delta table (requires `mlflow>=3.11` and a SQL warehouse), ask for the catalog, schema, table prefix, and SQL warehouse ID, then: ```python import mlflow from mlflow.entities.trace_location import UnityCatalog mlflow.set_experiment( experiment_id="{{ experiment_id }}", trace_location=UnityCatalog( catalog_name="", schema_name="", table_prefix="", ), ) ``` Skip this block entirely if the user does not ask for UC-backed traces. #### References If anything in this section is ambiguous, consult the authoritative Databricks docs before guessing: - MLflow tracing from a local IDE: https://docs.databricks.com/aws/en/mlflow3/genai/getting-started/tracing/tracing-ide - Storing traces in Unity Catalog: https://docs.databricks.com/aws/en/mlflow3/genai/tracing/trace-unity-catalog - Workspace experiment paths: https://docs.databricks.com/aws/en/mlflow/experiments - Databricks SDK authentication: https://docs.databricks.com/aws/en/dev-tools/auth/index.html