Scope MLflow experiment names to include user ID (Task 14.2)

- Updated FastAPI /training/start endpoint to extract X-User-ID header via get_user_id() dependency
- Modified _run_training_background to accept and use user_id parameter
- Added MLflow experiment setup with user scoping: experiments are named user_{user_id}_training when user_id is provided, falling back to default experiment name otherwise
- Updated database record insertion to store scoped experiment name
- Updated training/train.py train() function to accept user_id parameter and use it for experiment naming
- Mark task 14.2 as complete in tasks.md

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

openspec/changes/user-accounts/tasks.md

@@ -82,7 +82,7 @@
 ## 14. ML Service User Scoping
 
 - [x] 14.1 `[haiku]` Update FastAPI service to read `X-User-ID` header from incoming requests
-- [ ] 14.2 `[haiku]` Scope MLflow experiment names to include user ID (e.g., `user_{uuid}_training`)
+- [x] 14.2 `[haiku]` Scope MLflow experiment names to include user ID (e.g., `user_{uuid}_training`)
 - [ ] 14.3 `[sonnet]` Scope training run queries in FastAPI to filter by user ID
 
 ## 15. Documentation & Deployment

services/ml/app/main.py

@@ -1230,19 +1230,40 @@ def _run_training_background(
     model_type: str,
     config: PipelineConfig,
     chart_id: Optional[int] = None,
+    user_id: Optional[str] = None,
 ) -> None:
     """
     Background thread target: build dataset then train a model.
 
     Uses the pre-inserted TrainingRun record identified by ``run_id``.
+
+    Args:
+        run_id: Training run ID
+        model_type: Type of model to train
+        config: Pipeline configuration
+        chart_id: Optional chart ID to train on
+        user_id: Optional user ID for scoped experiment naming
     """
-    logger.info(f"Training thread started: run_id={run_id}, model_type={model_type}")
+    logger.info(f"Training thread started: run_id={run_id}, model_type={model_type}, user_id={user_id}")
 
     try:
         # Import training utilities here to avoid circular import issues
         from training.train import create_model, temporal_split
         from sklearn.metrics import accuracy_score, f1_score
 
+        # Set up MLflow experiment with user scoping
+        mlflow_config = config.stages.training.mlflow
+        mlflow.set_tracking_uri(mlflow_config.tracking_uri)
+
+        # Use user-scoped experiment name if user_id provided, otherwise use default
+        if user_id:
+            experiment_name = f"user_{user_id}_training"
+        else:
+            experiment_name = mlflow_config.experiment_name
+
+        mlflow.set_experiment(experiment_name)
+        logger.info(f"MLflow experiment set to: {experiment_name}")
+
         # Build dataset from database (feature engineering + annotation ingestion)
         logger.info("Building dataset from database...")
         build_dataset_from_db(config, chart_id=chart_id)
@@ -1407,12 +1428,16 @@ def _run_training_background(
 
 
 @app.post("/training/start", response_model=TrainingStartResponse, dependencies=[Depends(verify_api_key)])
-async def training_start(request: TrainingStartRequest):
+async def training_start(request: TrainingStartRequest, user_id: Optional[str] = Depends(get_user_id)):
     """
     Start a training run in a background thread.
 
     Returns immediately with run_id and status "running".
     Rejects concurrent runs with HTTP 409.
+
+    Args:
+        request: Training request parameters
+        user_id: Optional user ID from X-User-ID header for scoped experiments
     """
     # Validate model type
     if request.model_type not in SUPPORTED_MODEL_TYPES:
@@ -1455,11 +1480,18 @@ async def training_start(request: TrainingStartRequest):
 
     # Pre-insert the run record so callers can track it immediately
     try:
+        # Compute scoped experiment name
+        mlflow_config = config.stages.training.mlflow
+        if user_id:
+            experiment_name = f"user_{user_id}_training"
+        else:
+            experiment_name = mlflow_config.experiment_name
+
         with get_db() as db:
             training_run = TrainingRun(
                 run_id=run_id,
                 model_type=request.model_type,
-                experiment_name=config.stages.training.mlflow.experiment_name,
+                experiment_name=experiment_name,
                 pipeline_config_hash=config_hash,
                 status="running",
                 created_at=datetime.now(timezone.utc),
@@ -1479,13 +1511,13 @@ async def training_start(request: TrainingStartRequest):
     # Launch background thread (daemon so it doesn't block process exit)
     thread = threading.Thread(
         target=_run_training_background,
-        args=(run_id, request.model_type, config, request.chart_id),
+        args=(run_id, request.model_type, config, request.chart_id, user_id),
         daemon=True,
         name=f"training-{run_id[:8]}",
     )
     thread.start()
 
-    logger.info(f"Training started: run_id={run_id}, model_type={request.model_type}")
+    logger.info(f"Training started: run_id={run_id}, model_type={request.model_type}, user_id={user_id or 'default'}")
     return TrainingStartResponse(run_id=run_id, status="running")
 
 

services/ml/training/train.py

@@ -172,33 +172,40 @@ def compute_config_hash(config: PipelineConfig) -> str:
 def train(
     config: PipelineConfig,
     labeled_data_path: Path,
-    output_model_path: Optional[Path] = None
+    output_model_path: Optional[Path] = None,
+    user_id: Optional[str] = None
 ) -> str:
     """
     Main training function.
-    
+
     Loads labeled data, splits, trains model, evaluates, logs to MLflow,
     and stores metadata in PostgreSQL.
-    
+
     Args:
         config: Pipeline configuration
         labeled_data_path: Path to labeled CSV file
         output_model_path: Optional path to save model locally (for inference)
-        
+        user_id: Optional user ID for scoped experiment naming (e.g., user_{uuid}_training)
+
     Returns:
         MLflow run ID
     """
     training_config = config.stages.training
     mlflow_config = training_config.mlflow
-    
+
     # Initialize database
     init_db()
-    
+
     # Set MLflow tracking URI
     mlflow.set_tracking_uri(mlflow_config.tracking_uri)
-    
+
-    # Set experiment
+    # Set experiment with user scoping if user_id is provided
-    mlflow.set_experiment(mlflow_config.experiment_name)
+    if user_id:
+        experiment_name = f"user_{user_id}_training"
+    else:
+        experiment_name = mlflow_config.experiment_name
+
+    mlflow.set_experiment(experiment_name)
     
     logger.info(f"Loading labeled data from {labeled_data_path}")
     df = pd.read_csv(labeled_data_path)
@@ -247,7 +254,7 @@ def train(
             training_run = TrainingRun(
                 run_id=run_id,
                 model_type=training_config.model_type,
-                experiment_name=mlflow_config.experiment_name,
+                experiment_name=experiment_name,
                 pipeline_config_hash=compute_config_hash(config),
                 dataset_version=None,  # TODO: Add DVC hash if available
                 metrics_summary={},