Root Cause (Actual):
The actual nested session issue was in training_service.py, not just in
the trainer methods. The flow was:
1. training_service.py creates outer session (line 173)
2. Updates training_log at line 235-237 (uncommitted)
3. Calls trainer.train_tenant_models() at line 239
4. Trainer creates its own session at line 93
5. DEADLOCK: Outer session has uncommitted UPDATE, inner session can't proceed
Fix:
Added explicit session.commit() after the ml_training progress update
(line 241) to ensure the UPDATE is committed before trainer creates
its own session. This prevents the deadlock condition.
Related to previous commit caff497 which fixed nested sessions in
prophet_manager and hybrid_trainer, but missed the actual root cause
in training_service.py.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Critical fixes for training session logging:
1. Training log race condition fix:
- Add explicit session commits after creating training logs
- Handle duplicate key errors gracefully when multiple sessions
try to create the same log simultaneously
- Implement retry logic to query for existing logs after
duplicate key violations
- Prevents "Training log not found" errors during training
2. Audit event async generator error fix:
- Replace incorrect next(get_db()) usage with proper
async context manager (database_manager.get_session())
- Fixes "'async_generator' object is not an iterator" error
- Ensures audit logging works correctly
These changes address race conditions in concurrent database
sessions and ensure training logs are properly synchronized
across the training pipeline.
This commit addresses all identified bugs and issues in the training code path:
## Critical Fixes:
- Add get_start_time() method to TrainingLogRepository and fix non-existent method call
- Remove duplicate training.started event from API endpoint (trainer publishes the accurate one)
- Add missing progress events for 80-100% range (85%, 92%, 94%) to eliminate progress "dead zone"
## High Priority Fixes:
- Fix division by zero risk in time estimation with double-check and max() safety
- Remove unreachable exception handler in training_operations.py
- Simplify WebSocket token refresh logic to only reconnect on actual user session changes
## Medium Priority Fixes:
- Fix auto-start training effect with useRef to prevent duplicate starts
- Add HTTP polling debounce delay (5s) to prevent race conditions with WebSocket
- Extract all magic numbers to centralized constants files:
- Backend: services/training/app/core/training_constants.py
- Frontend: frontend/src/constants/training.ts
- Standardize error logging with exc_info=True on critical errors
## Code Quality Improvements:
- All progress percentages now use named constants
- All timeouts and intervals now use named constants
- Improved code maintainability and readability
- Better separation of concerns
## Files Changed:
- Backend: training_service.py, trainer.py, training_events.py, progress_tracker.py
- Backend: training_operations.py, training_log_repository.py, training_constants.py (new)
- Frontend: training.ts (hooks), MLTrainingStep.tsx, training.ts (constants, new)
All training progress events now properly flow from 0% to 100% with no gaps.
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
