Metadata-Version: 2.1
Name: pytorch-transfer-learning-debugger
Version: 0.1.0
Summary: A debugger for PyTorch transfer-learning & fine-tuning jobs
Home-page: https://github.com/yourusername/pytorch-transfer-learning-debugger
Author: Zach Colin Wolpe
Author-email: zachcolinwolpe@gmail.com
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2.0.0
Requires-Dist: torchvision>=0.15.0
Requires-Dist: numpy>=1.24.0
Requires-Dist: pillow>=9.0.0
Requires-Dist: matplotlib>=3.7.0
Requires-Dist: tqdm>=4.65.0
Provides-Extra: dev
Requires-Dist: pytest>=7.0.0; extra == "dev"
Requires-Dist: black; extra == "dev"
Requires-Dist: flake8; extra == "dev"
Requires-Dist: mypy; extra == "dev"

# PyTorch Transfer Learning Debugger




---
# Getting Started

pip install the package:

```bash
pip install torch-transfer-learning-debugger
```

or

```bash
pip install git+<URL>
```

Then, run the debugger by incorporating it in your training loop.

```python


```



----
# Potential Faults during Transfer Learning

| Category | Failure Points | Common Solutions |
|----------|---------------|------------------|
| Learning Rate | • Too high: unstable training<br>• Too low: slow learning/stuck<br>• Improper adjustment for transfer learning | • Start with 10-3 or 10-4 of original LR<br>• Use LR finder<br>• Implement LR scheduling |
| Layer Freezing | • Wrong layers frozen<br>• Too many/few layers frozen<br>• No gradual unfreezing | • Start by freezing all but final layers<br>• Gradually unfreeze from top<br>• Monitor layer gradients |
| Data Issues | • Insufficient data<br>• Poor quality/preprocessing<br>• Domain shift<br>• Class imbalance<br>• Wrong normalization | • Data augmentation<br>• Match source preprocessing<br>• Balance classes<br>• Use validation set |
| Architecture | • Bad final layer modifications<br>• Size/channel mismatches<br>• Poor layer initialization<br>• Wrong output dimensions | • Verify input/output dimensions<br>• Use proper initialization<br>• Match pretrained architecture |
| Optimization | • Wrong optimizer choice<br>• Incorrect loss function<br>• Bad batch size<br>• Poor momentum settings | • Use Adam/AdamW for fine-tuning<br>• Verify loss matches task<br>• Start with small batches |
| Implementation | • Model not in train mode<br>• Gradients not zeroed<br>• Wrong device (CPU/GPU)<br>• Memory leaks | • Use training checklist<br>• Implement proper train/eval<br>• Check device placement<br>• Monitor memory usage |
| Pretrained Model | • Wrong pretrained weights<br>• Corrupted weights<br>• Version incompatibility | • Verify model source<br>• Check model checksums<br>• Match framework versions |
| Monitoring | • Poor metric tracking<br>• No early stopping<br>• Missing validation<br>• No gradient monitoring | • Use debugging tools<br>• Implement validation loops<br>• Track multiple metrics<br>• Monitor gradient flow |



----
# For Developers: Fork

A debugger for running PyTorch transfer-learning &amp; fine-tuning jobs.



```
: zachcolinwolpe@gmail.com
: 06.12.2024
```
