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### 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="<catalog>",
schema_name="<schema>",
table_prefix="<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