Deep Learning Academy · 课时

TorchScript 与 torch.compile

冻结并加速用于服务的模型

TorchScript 与 torch.compile 是 CoddyKit 上的免费 Deep Learning Academy 课时。这是第 1 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 Deep Learning Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Deep Learning 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。

Why Production Needs a Frozen Model

Your training code is flexible Python, but servers want something fast and stable. TorchScript freezes your model into a portable, optimized form. 🚀

What TorchScript Actually Is

TorchScript is a serializable, optimizable subset of Python that runs without a Python interpreter, so your model can ship to C++ servers and mobile.

Tracing: Record One Real Run

Tracing runs your model on an example input and records every operation it performs, capturing the path the data actually took.

import torch
traced = torch.jit.trace(model, example_input)

Tracing Misses Control Flow

Because tracing only follows one run, it silently ignores if-statements and loops that depend on the data. Those branches just vanish.

Scripting: Read the Logic

Scripting compiles your code directly, preserving every loop and condition. Use it when control flow depends on the input values.

scripted = torch.jit.script(model)

Save and Load Anywhere

A scripted or traced model saves to a single self-contained file you can load on any machine, no original class definition required.

scripted.save('model.pt')
loaded = torch.jit.load('model.pt')

Meet torch.compile

The modern alternative is torch.compile, which speeds up your model with one line while keeping your code plain Python.

model = torch.compile(model)

How torch.compile Speeds Things Up

Under the hood torch.compile fuses many small operations into fewer optimized kernels, cutting overhead and boosting throughput on GPU.

The First Call Is Slow

The first forward pass after torch.compile is slow because it compiles in the background. Every call after that runs much faster. ⏳

Always Set Eval Mode First

Before you trace, script, or compile for serving, call model.eval() so dropout and batch norm behave correctly for inference.

model.eval()
traced = torch.jit.trace(model, x)

Which One Should You Reach For?

Choose TorchScript when you need a Python-free deployment file, and torch.compile when you want a quick speedup inside Python.

Quick Check

One method captures only the path one example takes. Which is it?

Recap: Freeze and Accelerate

You learned to freeze a model with TorchScript via tracing or scripting, save it anywhere, and accelerate inference in Python with torch.compile. 🎉

常见问题解答

「TorchScript 与 torch.compile」课时是免费的吗？

是的 — 「TorchScript 与 torch.compile」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 Deep Learning Academy 课程的其余内容，请升级到 CoddyKit PRO。 Deep Learning Academy 课程共包含 4 节课。

「TorchScript 与 torch.compile」这节课中我会学到什么？

冻结并加速用于服务的模型你通过在浏览器中直接运行的动手代码来练习 Deep Learning 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 Deep Learning Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Deep Learning 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 1 节课，共 4 节。

「TorchScript 与 torch.compile」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 Deep Learning Academy 课中编写并运行代码吗？

能。每节 Deep Learning 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

← 返回 Deep Learning Academy