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https://github.com/dewitt4/edge-ai-optimization
Some practical Python scripts for AI model optimization
https://github.com/dewitt4/edge-ai-optimization
ai optimization python
Last synced: 16 days ago
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Some practical Python scripts for AI model optimization
- Host: GitHub
- URL: https://github.com/dewitt4/edge-ai-optimization
- Owner: dewitt4
- License: mit
- Created: 2024-12-23T18:48:56.000Z (24 days ago)
- Default Branch: main
- Last Pushed: 2024-12-23T18:57:11.000Z (24 days ago)
- Last Synced: 2024-12-23T19:39:24.528Z (24 days ago)
- Topics: ai, optimization, python
- Language: Python
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Edge AI Optimization Tools
A collection of Python utilities for optimizing and deploying AI models on edge devices.
## Features
- Model quantization using PyTorch
- Weight pruning for model compression
- Knowledge distillation framework
- ONNX export with optimization
- Performance benchmarking tools
## Requirements
```
torch>=1.9.0
onnx>=1.10.0
onnxruntime>=1.8.0
numpy>=1.19.0
```
## Installation
```bash
git clone https://github.com/yourusername/edge-ai-optimization
cd edge-ai-optimization
pip install -r requirements.txt
```
## Usage
### Quantization
Reduces model precision to decrease size while maintaining accuracy:
```python
from edge_optimization import quantize_model
quantized_model = quantize_model(your_model, calibration_data)
```
### Pruning
Removes unnecessary weights to reduce model size:
```python
from edge_optimization import prune_model
pruned_model = prune_model(your_model, amount=0.3) # Removes 30% of weights
```
### Knowledge Distillation
Trains a smaller student model using a larger teacher model:
```python
distillation_loss = DistillationLoss(temperature=3.0)
loss = distillation_loss(student_logits, teacher_logits)
```
### ONNX Export
Exports and optimizes models for edge deployment:
```python
from edge_optimization import optimize_for_edge
optimize_for_edge(model, sample_input, "model.onnx")
```
### Benchmarking
Measures model performance metrics:
```python
metrics = benchmark_model(model, test_data, device='cuda')
print(f"FPS: {metrics['fps']}")
```
## Performance Considerations
- Quantization typically reduces model size by 75% with minimal accuracy loss
- Pruning can reduce model size by 30-50% depending on architecture
- ONNX optimization can improve inference speed by 20-40%
- Consider batch size and input dimensions for optimal performance
## Known Limitations
- Quantization requires calibration data for best results
- Pruning may affect model accuracy on complex tasks
- ONNX optimization is model-architecture dependent
- GPU required for maximum performance benefits
## Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## Citation
If you use this code in your research, please cite:
```bibtex
@software{edge_ai_optimization,
author = {dewitt4},
title = {Edge AI Optimization Tools},
year = {2024},
url = {https://github.com/dewitt4/edge-ai-optimization}
}
```