72 lines
2.9 KiB
Markdown
72 lines
2.9 KiB
Markdown
# Model Saving Recommendations
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During training, several PyTorch model serialization errors were identified and fixed. Here's a summary of our findings and recommendations to ensure robust model saving:
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## Issues Found
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1. **PyTorch Serialization Errors**: Errors like `PytorchStreamWriter failed writing file data...` and `unexpected pos...` indicate issues with PyTorch's serialization mechanism.
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2. **Disk Space Issues**: Our tests showed `No space left on device` errors, which can cause model corruption.
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3. **Compatibility Issues**: Some serialization methods might not be compatible with specific PyTorch versions or environments.
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## Implemented Solutions
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1. **Robust Save Function**: We added a `robust_save` function that tries multiple saving approaches in sequence:
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- First attempt: Standard save to a backup file, then copy to the target path
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- Second attempt: Save with pickle protocol 2 (more compatible)
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- Third attempt: Save without optimizer state (reduces file size)
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- Fourth attempt: Use TorchScript's `jit.save()` (different serialization mechanism)
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2. **Memory Management**: Implemented memory cleanup before saving:
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- Clearing GPU cache with `torch.cuda.empty_cache()`
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- Running garbage collection with `gc.collect()`
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3. **Error Handling**: Added comprehensive error handling around all saving operations.
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4. **Circuit Breaker Pattern**: Added circuit breakers to prevent consecutive failures during training.
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## Recommendations
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1. **Disk Space**: Ensure sufficient disk space is available (at least 1-2GB free). Large models can use several GB of disk space.
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2. **Checkpoint Cleanup**: Periodically remove old checkpoints to free up space:
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```bash
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# Example script to keep only the most recent 5 checkpoints
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Get-ChildItem -Path .\models\trading_agent_checkpoint_*.pt |
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Sort-Object LastWriteTime -Descending |
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Select-Object -Skip 5 |
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Remove-Item
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```
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3. **File System Check**: If persistent errors occur, check the file system for errors or corruption.
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4. **Use Smaller Models**: Consider reducing model size if saving large models is problematic.
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5. **Alternative Serialization**: For very large models, consider saving key parameters separately rather than the entire model.
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6. **Training Stability**: Use our improved training functions with memory management and error handling.
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## How to Test Model Saving
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We've provided a test script `test_model_save_load.py` that can verify if model saving is working correctly. Run it with:
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```bash
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python test_model_save_load.py
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```
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Or test all robust save methods with:
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```bash
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python test_model_save_load.py --test_robust
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```
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## Future Development
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1. **Checksumming**: Add checksums to saved models to verify integrity.
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2. **Compression**: Implement model compression to reduce file size.
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3. **Distributed Saving**: For very large models, explore distributed saving mechanisms.
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4. **Format Conversion**: Add ability to save models in ONNX or other portable formats. |