ai_toolkit/en/docs/onnxoptimizer

🤖 ONNX Optimizer Documentation
Optimize your AI models with ONNX Runtime tools

📚 What is ONNX Optimizer?

ONNX Optimizer is a tool within the AI Toolkit that helps improve the performance and efficiency of machine learning models. It leverages advanced techniques to simplify model graphs, quantize weights, and remove redundant operations, making your models faster and more resource-friendly.

• Model Graph Simplification: Reduces complexity by pruning unnecessary nodes.
• Quantization: Converts floating-point data to lower precision (e.g., INT8) for optimized execution.
• Optimization Passes: Applies transformations like fusing layers or constant folding.

🔧 How to Use ONNX Optimizer

1. Install the toolkit

   pip install onnxruntime-tools
   

2. Run optimization
   Use the command-line interface or integrate with Python APIs.
   Example:

   onnxoptimizer --input model.onnx --output optimized_model.onnx
   

3. Verify results
   Check the output model via ONNX Runtime Validator to ensure compatibility.

📌 Key Features

• ✅ Cross-platform support (Windows, Linux, macOS)
• ✅ Integration with ONNX Runtime for seamless execution
• ✅ Support for multiple optimization strategies

🌐 Related Resources

• Explore ONNX Runtime documentation for advanced usage
• AI Toolkit Home for more tools and tutorials