ส่งออกเป็น ONNX เพื่อการใช้งานข้ามระบบ

You trained in PyTorch but want to serve in C++ or the browser. A framework-locked file makes that painful. ONNX fixes it. 🌍

ONNX is an open, shared format for models. It describes the math as a graph that many frameworks and runtimes can read.

Under the hood an ONNX model is a graph of standard operators like MatMul and Relu, plus the learned weights.

Export once and the same file runs on servers, mobile, and edge devices through different runtimes, no retraining needed.

PyTorch ships an exporter. You pass the model and a sample input to torch.onnx.export so it can trace the graph.

The exporter runs your model on the sample to record which ops fire. That traced run becomes the graph it saves.

For scikit-learn use the skl2onnx library. You give it the model and the input shape to produce an ONNX graph.

ONNX Runtime is a fast, cross-platform engine. You load the .onnx file into a session and call run to get predictions.

ONNX Runtime can use execution providers like CUDA or TensorRT to accelerate the same model on different hardware. ⚡

Each export targets an opset version that defines available operators. Match it to what your runtime supports, or loading fails.

After export, run both models on the same input and compare. Confirm the ONNX result matches the original before you ship it. ✅

Let's see how well you understand ONNX.

You can export models to ONNX, run them with ONNX Runtime on any hardware, mind the opset, and always verify outputs match. 🎉