🚀 ONNX Runtime Optimization Techniques

🔧 Key Optimization Strategies

• Model Pruning
  Reduce model size by removing redundant weights.

  

• Quantization
  Convert floating-point operations to lower precision (e.g., INT8) for faster inference.

  

• Graph Optimization
  Simplify computational graphs via fusion and elimination.

  

📦 Runtime Configuration Tips

• Enable GPU acceleration:

  sess = onnxruntime.InferenceSession("model.onnx", providers=["CUDAExecutionProvider"])
  

• Adjust thread count for parallel processing:

🧰 Performance Tuning Tools

• Use ONNX Runtime Profiler to analyze latency and memory usage.

📚 Further Reading

For advanced optimization techniques, visit our ONNX Runtime Optimization Guide.