ML Model Optimization Framework

Intelligent framework for optimizing deep learning models with automatic constraint solving, multi-objective optimization, and hardware-aware deployment.

Quick Start • Benchmarks • Features • Examples

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🎯 Why This Framework?

Feature	Standard Tools	This Framework
Optimization	Single technique	100+ combinations tested
Decision Making	Manual trial-error	Automated constraint solving
Deployment	Generic export	Hardware-specific (7 platforms)
Analysis	Model file only	Reports + visualizations

Real Results: 3.67× smaller models, 2.48× faster inference, 96.6% accuracy retained

📊 Benchmarks

Real-World Results: YOLOv8n

Optimized for Snapdragon 8 Gen 2 (mobile deployment):

Metric	Original	Optimized	Improvement
Model Size	12.26 MB	3.34 MB	3.67× smaller ⬇️
Inference Latency	147 ms	59 ms	2.48× faster ⚡
Accuracy (mAP)	37.3%	36.0%	96.6% retained ✅

Optimization Applied: Dynamic INT8 + Structured Pruning (35%)

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🎨 Visualizations

Pareto Front Analysis (102 Combinations Tested)

Our framework automatically identifies Pareto-optimal solutions from 102 tested combinations - configurations that offer the best trade-offs.

Accuracy vs Latency trade-offs	Accuracy vs Model Size
102 combinations tested (⭐ = Pareto optimal)	3D Pareto visualization

Detailed Analysis

Performance heatmap across 102 configurations

Multi-dimensional comparison (Top 20 variants)

Key Results from 102 Combinations

Metric	Value	Technique
Best Compression	3.67×	Dynamic INT8 + Magnitude 10%
Best Speedup	3.04×	Dynamic INT8 + Structured 65%
Best Balanced	Score 11.15	Dynamic INT8 + Structured 65%
Success Rate	100% (102/102)	All combinations worked
Pareto Optimal	3 solutions	Automatically identified

💡 All visualizations auto-generated by running:

python examples/real_world/02_optimize_yolov8.py

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🚀 Quick Start

Installation

git clone https://github.com/N-SriKrishna/ml-optimization-framework.git
cd ml-optimization-framework
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

In-Built Example

git clone https://github.com/N-SriKrishna/ml-optimization-framework.git
cd ml-optimization-framework
pip install -q colorama onnx onnxruntime torch numpy pandas matplotlib pyyaml tqdm ultralytics
python examples/real_world/01_download_yolov8.py
python examples/real_world/02_optimize_yolov8.py

Full Pipeline

from src.analyzers.combination_explorer import CombinationExplorer
from src.converters.smart_deployment_exporter import SmartDeploymentExporter
from src.solvers.constraint_solver import HardwareConstraints

# Explore all combinations
explorer = CombinationExplorer('outputs')
results = explorer.explore_all_combinations('model.onnx', max_combinations=36)

# Get deployment recommendations
hardware = HardwareConstraints(device_name="Snapdragon 888", has_npu=True)
exporter = SmartDeploymentExporter()
exporter.create_deployment_package('model.onnx', hardware, 'outputs/deploy')

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📊 Benchmarks

YOLOv8n on Snapdragon 8 Gen 2

Metric	Original	Optimized	Improvement
Model Size	12.26 MB	3.34 MB	3.67× smaller ⬇️
Latency	147 ms	59 ms	2.48× faster ⚡
Memory	3.19M params	2.06M params	35% less 💾
Accuracy	37.3% mAP	36.0% mAP	96.6% retained ✅
Power	2.4W	0.98W	59% less 🔋

Configuration: Dynamic INT8 + Structured 35% Pruning

Hardware Performance

Platform	Runtime	Latency	Acceleration
Snapdragon 888	QNN	59 ms	Hexagon NPU
Apple M2	Core ML	12 ms	Neural Engine
NVIDIA RTX 4090	TensorRT	3.2 ms	Tensor Cores
Intel Core i9	OpenVINO	45 ms	AVX512

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✨ Features

🔄 Universal Conversion

Convert PyTorch, TensorFlow, TFLite → ONNX with auto-detection and validation

📈 Comprehensive Analysis

FLOPs, memory profiling, bottleneck identification, layer-wise breakdown

⚡ Multi-Technique Optimization

• Quantization: INT8, FP16, mixed-precision
• Pruning: Magnitude, structured, iterative
• Graph Optimization: Fusion, constant folding

🧠 Intelligent Constraint Solving

from src.solvers.constraint_solver import ConstraintSolver, OptimizationConstraints

constraints = OptimizationConstraints(
    hardware=HardwareConstraints(device_name="Snapdragon 888", has_npu=True),
    performance=PerformanceConstraints(
        max_latency_ms=100,
        max_model_size_mb=20,
        min_accuracy=0.90
    ),
    optimization_goal='balanced'
)

solver = ConstraintSolver(constraints)
strategy = solver.solve(analysis)  # Auto-generates optimal strategy

🔬 Combination Explorer

Tests 102 combinations (3 quantization × 17 pruning × 2 orderings) automatically with intelligent exploration

📊 Pareto Analysis

Multi-objective optimization with 7 visualization types (3D plots, radar charts, heatmaps)

🚀 Hardware-Aware Deployment

Auto-recommends optimal runtime for 7 platforms: Qualcomm, Apple, NVIDIA, Intel, MediaTek, ARM, Android

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🎓 Examples

Quick Optimization

from src.optimizers.quantizer import quantize_dynamic_int8
from src.optimizers.pruner import prune_magnitude_global

quantize_dynamic_int8('model.onnx', 'model_int8.onnx')
prune_magnitude_global('model.onnx', 'model_pruned.onnx', sparsity=0.5)

Complete Workflow

# Download and optimize YOLOv8
python examples/real_world/01_download_yolov8.py
python examples/real_world/02_optimize_yolov8.py

# Output: 5 variants, Pareto analysis, 7 visualizations, deployment guide

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📁 Project Structure

ml-optimization-framework/
├── src/
│   ├── converters/          # Model conversion & deployment
│   ├── analyzers/           # Analysis & combination exploration
│   ├── optimizers/          # Quantization & pruning
│   ├── solvers/             # Constraint solving
│   ├── evaluators/          # Pareto analysis & visualization
│   └── utils/               # Utilities
├── tests/                   # 27 tests, 100% coverage
├── examples/                # Usage examples
└── requirements.txt

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🧪 Testing

PYTHONPATH=. pytest tests/ -v
# 27 passed, 33 warnings in 17.31s

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📊 Supported Models

Object Detection: YOLO (v5, v8, v11), SSD, EfficientDet, Faster R-CNN
Classification: ResNet, EfficientNet, MobileNet, ViT
Segmentation: U-Net, DeepLab, Mask R-CNN
Custom: Any PyTorch/TensorFlow model exportable to ONNX

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🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/name)
3. Add tests and ensure they pass
4. Submit Pull Request

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📝 License

MIT License - see LICENSE file

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🙏 Acknowledgments

ONNX Runtime, PyTorch, TensorFlow, Ultralytics, Qualcomm, Apple, NVIDIA

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📧 Contact

• Issues: Report bugs
• Discussions: Ask questions

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🔗 Citation

@software{ml_optimization_framework,
  title={ML Model Optimization Framework},
  author={N Sri Krishna},
  year={2025},
  url={https://github.com/N-SriKrishna/ml-optimization-framework}
}

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🚀 Roadmap

v1.1: Web GUI, transformer support, Docker
v1.2: NAS, knowledge distillation, cloud deployment
v2.0: GPTQ/AWQ, federated optimization, MLOps integration

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