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导出为 ONNX，提升可移植性

在不同框架和运行时之间迁移模型

导出为 ONNX，提升可移植性 是 CoddyKit 上的免费 MLOps Academy 课时。这是第 3 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 MLOps Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 MLOps Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

The Portability Problem

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

What ONNX Is

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

A Graph of Operators

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

Train Once, Run Anywhere

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

Export from PyTorch

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

import torch
torch.onnx.export(model, sample, 'model.onnx')

Why a Sample Input?

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

Convert from sklearn

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

from skl2onnx import to_onnx
onx = to_onnx(model, X[:1])

Run with ONNX Runtime

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

import onnxruntime as ort
sess = ort.InferenceSession('model.onnx')

Speed and Hardware

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

Opset Versions

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

Always Verify Outputs

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

Quick Check

Let's see how well you understand ONNX.

Recap

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

常见问题解答

「导出为 ONNX，提升可移植性」课时是免费的吗？

是的 — 「导出为 ONNX，提升可移植性」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 MLOps Academy 课程的其余内容，请升级到 CoddyKit PRO。 MLOps Academy 课程共包含 4 节课。

「导出为 ONNX，提升可移植性」这节课中我会学到什么？

在不同框架和运行时之间迁移模型你通过在浏览器中直接运行的动手代码来练习 MLOps Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 MLOps Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 MLOps Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 3 节课，共 4 节。

「导出为 ONNX，提升可移植性」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 MLOps Academy 课中编写并运行代码吗？

能。每节 MLOps Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

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