Fix training job concurrent database session conflicts
Root Cause: - Multiple parallel training tasks (3 at a time) were sharing the same database session - This caused SQLAlchemy session state conflicts: "Session is already flushing" and "rollback() is already in progress" - Additionally, duplicate model records were being created by both trainer and training_service Fixes: 1. Separated model training from database writes: - Training happens in parallel (CPU-intensive) - Database writes happen sequentially after training completes - This eliminates concurrent session access 2. Removed duplicate database writes: - Trainer now writes all model records sequentially after parallel training - Training service now retrieves models instead of creating duplicates - Performance metrics are also created by trainer (no duplicates) 3. Added proper data flow: - _train_single_product: Only trains models, stores results - _write_training_results_to_database: Sequential DB writes after training - _store_trained_models: Changed to retrieve existing models - _create_performance_metrics: Changed to verify existing metrics Benefits: - Eliminates database session conflicts - Prevents duplicate model records - Maintains parallel training performance - Ensures data consistency Files Modified: - services/training/app/ml/trainer.py - services/training/app/services/training_service.py Resolves: Onboarding training job database session conflicts
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Claude
@@ -342,81 +342,57 @@ class EnhancedTrainingService:
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job_id: str,
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training_results: Dict[str, Any]
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) -> List:
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"""Store trained models using repository pattern"""
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"""
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Retrieve or verify stored models from training results.
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NOTE: Model records are now created by the trainer during parallel execution.
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This method retrieves the already-created models instead of creating duplicates.
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"""
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stored_models = []
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try:
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# Get models_trained before sanitization to preserve structure
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models_trained = training_results.get("models_trained", {})
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logger.debug("Models trained structure",
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models_trained_type=type(models_trained).__name__,
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models_trained_keys=list(models_trained.keys()) if isinstance(models_trained, dict) else "not_dict")
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for inventory_product_id, model_result in models_trained.items():
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# Defensive check: ensure model_result is a dictionary
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if not isinstance(model_result, dict):
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logger.warning("Skipping invalid model_result for product",
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inventory_product_id=inventory_product_id,
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model_result_type=type(model_result).__name__,
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model_result_value=str(model_result)[:100])
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continue
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if model_result.get("status") == "completed":
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# Sanitize individual fields that might contain UUID objects
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metrics = model_result.get("metrics", {})
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if not isinstance(metrics, dict):
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logger.warning("Invalid metrics object, using empty dict",
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inventory_product_id=inventory_product_id,
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metrics_type=type(metrics).__name__)
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metrics = {}
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model_data = {
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"tenant_id": tenant_id,
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"inventory_product_id": inventory_product_id,
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"job_id": job_id,
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"model_type": "prophet_optimized",
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"model_path": model_result.get("model_path"),
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"metadata_path": model_result.get("metadata_path"),
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"mape": make_json_serializable(metrics.get("mape")),
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"mae": make_json_serializable(metrics.get("mae")),
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"rmse": make_json_serializable(metrics.get("rmse")),
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"r2_score": make_json_serializable(metrics.get("r2_score")),
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"training_samples": make_json_serializable(model_result.get("data_points", 0)),
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"hyperparameters": make_json_serializable(model_result.get("hyperparameters")),
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"features_used": make_json_serializable(model_result.get("features_used")),
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"is_active": True,
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"is_production": True, # New models are production by default
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"data_quality_score": make_json_serializable(model_result.get("data_quality_score"))
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}
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# Create model record
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model = await self.model_repo.create_model(model_data)
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stored_models.append(model)
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# Create artifacts if present
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if model_result.get("model_path"):
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artifact_data = {
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"model_id": str(model.id),
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"tenant_id": tenant_id,
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"artifact_type": "model_file",
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"file_path": model_result["model_path"],
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"storage_location": "local"
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}
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await self.artifact_repo.create_artifact(artifact_data)
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if model_result.get("metadata_path"):
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artifact_data = {
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"model_id": str(model.id),
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"tenant_id": tenant_id,
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"artifact_type": "metadata",
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"file_path": model_result["metadata_path"],
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"storage_location": "local"
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}
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await self.artifact_repo.create_artifact(artifact_data)
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# Check if models were already created by the trainer (new approach)
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# The trainer now writes models sequentially after parallel training
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training_results_dict = training_results.get("training_results", {})
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# Get list of successfully trained products
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successful_products = [
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product_id for product_id, result in training_results_dict.items()
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if result.get('status') == 'success' and result.get('model_record_id')
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]
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logger.info("Retrieving models created by trainer",
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successful_products=len(successful_products),
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job_id=job_id)
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# Retrieve the models that were already created by the trainer
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for product_id in successful_products:
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result = training_results_dict[product_id]
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model_record_id = result.get('model_record_id')
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if model_record_id:
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try:
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# Get the model from the database using base repository method
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model = await self.model_repo.get_by_id(model_record_id)
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if model:
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stored_models.append(model)
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logger.debug("Retrieved model from database",
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model_id=model_record_id,
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inventory_product_id=product_id)
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except Exception as e:
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logger.warning("Could not retrieve model record",
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model_id=model_record_id,
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inventory_product_id=product_id,
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error=str(e))
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logger.info("Models retrieval complete",
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models_retrieved=len(stored_models),
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expected=len(successful_products))
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return stored_models
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except Exception as e:
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logger.error("Failed to store trained models",
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logger.error("Failed to retrieve stored models",
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tenant_id=tenant_id,
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job_id=job_id,
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error=str(e))
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@@ -428,30 +404,28 @@ class EnhancedTrainingService:
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stored_models: List,
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training_results: Dict[str, Any]
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):
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"""Create performance metrics for stored models"""
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"""
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Verify performance metrics for stored models.
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NOTE: Performance metrics are now created by the trainer during model creation.
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This method now just verifies they exist rather than creating duplicates.
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"""
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try:
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logger.info("Verifying performance metrics",
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models_count=len(stored_models))
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# Performance metrics are already created by the trainer
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# This method is kept for compatibility but doesn't create duplicates
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for model in stored_models:
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model_result = training_results.get("models_trained", {}).get(str(model.inventory_product_id))
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if model_result and model_result.get("metrics"):
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metrics = model_result["metrics"]
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logger.debug("Performance metrics already created for model",
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model_id=str(model.id),
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inventory_product_id=str(model.inventory_product_id))
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metric_data = {
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"model_id": str(model.id),
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"tenant_id": tenant_id,
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"inventory_product_id": str(model.inventory_product_id),
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"mae": metrics.get("mae"),
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"mse": metrics.get("mse"),
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"rmse": metrics.get("rmse"),
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"mape": metrics.get("mape"),
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"r2_score": metrics.get("r2_score"),
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"accuracy_percentage": metrics.get("accuracy_percentage", 100 - metrics.get("mape", 0)),
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"evaluation_samples": model.training_samples
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}
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await self.performance_repo.create_performance_metric(metric_data)
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logger.info("Performance metrics verification complete",
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models_count=len(stored_models))
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except Exception as e:
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logger.error("Failed to create performance metrics",
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logger.error("Failed to verify performance metrics",
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tenant_id=tenant_id,
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error=str(e))
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