Implement robust training pipeline

[martinnormark]

Overview

The current training pipeline needs a complete refactor to improve reliability, monitoring, and reproducibility. This issue focuses on implementing core training functionality before scaling to multi-GPU support.

Current Limitations

• Basic training loop without validation
• No model checkpointing or state management
• Missing progress tracking and metrics logging
• No early stopping or learning rate scheduling
• Limited error handling and recovery
• No experiment configuration management

Requirements

Training Loop Structure

• [ ] Implement proper train/validation split functionality
• [ ] Add epoch-level and batch-level progress tracking
• [ ] Implement validation loop with evaluation metrics
• [ ] Add support for early stopping based on validation metrics
• [ ] Implement learning rate scheduling

State Management

• [ ] Add model checkpointing (best model, latest model)
• [ ] Save/load training state for resume capability
• [ ] Track and save optimizer state
• [ ] Save training hyperparameters and configuration

Metrics and Logging

• [ ] Track and log training metrics:
  • Loss components (chamfer, surface, collision, etc.)
  • Learning rate
  • Gradient norms
  • Memory usage
• [ ] Track and log validation metrics:
  • Chamfer distance
  • Normal consistency
  • Edge preservation
  • Hausdorff distance
• [ ] Add per-epoch mesh quality visualization samples

Configuration

• [ ] Create training configuration system
  • Model hyperparameters
  • Training parameters (batch size, learning rate, etc.)
  • Loss weights
  • Validation frequency
  • Checkpoint frequency
• [ ] Add configuration validation and logging

Error Handling

• [ ] Add graceful handling of common training issues:
  • Out of memory errors
  • Invalid mesh inputs
  • NaN loss values
• [ ] Implement training state recovery after crashes

Implementation Notes

• Use PyTorch Lightning or similar framework for basic training structure
• Implement as a separate Trainer class for better organization
• Keep mesh-specific operations separate from general training logic
• Use proper type hints and documentation
• Add unit tests for training components
• Follow project's existing code style

Success Criteria

• [ ] Training can run continuously without manual intervention
• [ ] Training state can be saved and restored
• [ ] All metrics are properly logged and accessible
• [ ] Configuration is reproducible
• [ ] Basic error conditions are handled gracefully
• [ ] Unit tests cover core functionality

Out of Scope (Future Issues)

• Multi-GPU support
• Distributed training
• Advanced visualization features
• Cloud integration
• Hyperparameter optimization
• Advanced data augmentation

Related Issues/PRs

• XX (Original training implementation)

• YY (Metrics implementation)