Model checkpoint loading silent failure#272
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- Add proper error handling to load_checkpoint in deep_svdd_trainer.py - Add proper error handling to load_checkpoint in temporal.py - Add validation for required checkpoint keys - Add explicit error messages for different failure scenarios - Add return value to indicate success/failure - Use weights_only=True for security (addresses test_security.py concerns) - Add comprehensive tests for checkpoint loading error cases Fixes silent failures where checkpoint loading would fail without clear error messages, making debugging difficult.
- Resolved merge conflict in deep_svdd_trainer.py - Combined security fix (weights_only=True) with comprehensive metadata validation - Added return value to indicate success/failure - Maintained detailed error messages from both versions
- Create ClaimService with async background retry mechanism - Implement exponential backoff with optional jitter for retries - Add claim status tracking (pending, submitted, approved, rejected, failed, expired) - Add claim expiration handling - Add max retry limit with proper error handling - Implement database integration for claim status updates - Add comprehensive tests for retry logic and edge cases - Support loading pending claims from database for recovery Features: - Configurable retry parameters (max_retries, backoff, jitter) - Async background loop for automatic retry processing - Claim expiration detection and handling - Database status updates for claim tracking - Recovery of pending claims after restart
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Summary
This PR addresses an issue where model checkpoint loading could fail silently without providing meaningful feedback to users or developers. The update introduces proper error handling, validation checks, and detailed logging to ensure checkpoint loading failures are detected and reported promptly.
Changes Made
Added explicit exception handling during checkpoint loading.
Implemented checkpoint file existence and integrity validation.
Added descriptive error messages for loading failures.
Improved logging for checkpoint initialization and loading processes.
Prevented application startup from continuing with an invalid or unloaded model state.
Added unit tests covering corrupted, missing, and incompatible checkpoint scenarios.
Problem
Previously, checkpoint loading failures could occur without raising visible errors, making debugging difficult and potentially causing the application to operate with an uninitialized or incorrect model state.
Solution
The checkpoint loading workflow now validates checkpoints before loading, raises clear exceptions when issues occur, and logs detailed diagnostic information to aid troubleshooting. This ensures failures are surfaced immediately and handled safely.
Testing
Verified successful loading of valid checkpoints.
Tested behavior with missing checkpoint files.
Tested corrupted checkpoint files.
Tested incompatible checkpoint formats and versions.
Confirmed appropriate error messages and logs are generated in all failure scenarios.
Impact
Improved reliability and observability of model initialization.
Faster diagnosis of deployment and configuration issues.
Reduced risk of running inference or training with an invalid model state..Closed #188