Deep Learning Academy · 课时

使用 FastAPI 提供服务

将推理封装为 REST 端点

使用 FastAPI 提供服务 是 CoddyKit 上的免费 Deep Learning Academy 课时。这是第 4 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。

From Saved Model to Live API

A trained model only helps users when it is reachable. FastAPI wraps your model in a web endpoint they can call over HTTP. 🌍

Why FastAPI Fits Inference

FastAPI is fast, async-friendly, and auto-generates docs. That makes it a clean way to serve model predictions as a REST service.

Load the Model Once at Startup

Load your model a single time when the server boots, not on every request. Loading per call would make each prediction painfully slow.

model = torch.jit.load('model.pt')
model.eval()

Create the App

You start by creating a FastAPI instance. This object holds your routes and becomes the server you run.

from fastapi import FastAPI
app = FastAPI()

Validate Input with Pydantic

Define a Pydantic model for the request body so FastAPI checks the shape and types of incoming data automatically.

from pydantic import BaseModel
class Item(BaseModel):
    features: list[float]

Define a Predict Route

A POST route receives the validated data, runs the model, and returns the result as JSON the client can read.

@app.post('/predict')
def predict(item: Item):
    ...

Run Inference Without Gradients

Wrap the forward pass in torch.no_grad() so the server skips gradient tracking and saves time and memory on every call.

with torch.no_grad():
    output = model(x)

Return a Clean JSON Response

Convert the tensor output to plain Python numbers before returning, since raw tensors are not directly JSON serializable.

return {'prediction': output.argmax().item()}

Serve It with Uvicorn

Uvicorn is the server that runs your FastAPI app. One command brings your prediction endpoint online. 🚀

uvicorn main:app --host 0.0.0.0 --port 8000

Explore the Auto Docs

FastAPI builds interactive docs at the /docs path, so you and your clients can test the endpoint right in the browser.

Add a Health Check Route

A tiny health endpoint lets load balancers confirm the server is alive, a small touch that makes deployment far more reliable.

@app.get('/health')
def health():
    return {'status': 'ok'}

Quick Check

You want each request validated for correct fields and types automatically. What does that?

Recap: Your Model Is Live

You served a model with FastAPI: load once at startup, validate with Pydantic, predict under no_grad, and run it on Uvicorn. 🎉

常见问题解答

「使用 FastAPI 提供服务」课时是免费的吗？

是的 — 「使用 FastAPI 提供服务」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。

「使用 FastAPI 提供服务」这节课中我会学到什么？

将推理封装为 REST 端点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 4 节课，共 4 节。

「使用 FastAPI 提供服务」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