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分析 Python 基线性能

找出值得移植的慢路径

分析 Python 基线性能 是 CoddyKit 上的免费 Mojo 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 Mojo Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Mojo 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。

Start With a Baseline

Before you port anything, measure the Python app as it is today. This baseline is the number every later speedup is compared against. 📊

Why Profile First

Most of a program's time hides in a tiny slice of code. Profiling shows you exactly where, so you port the slow part and skip the rest.

Time the Whole Run

First, time the full task end to end. This wall-clock total is your headline number and the easiest thing to track over time.

import time
start = time.perf_counter()
run_app()
print(time.perf_counter() - start)

Profile by Function

Python's cProfile breaks the run down per function, so you can see which calls eat the most time across the whole program.

python -m cProfile -s cumtime app.py

Read the Hot Functions

Sort the report by total time and look at the top rows. The hot path is usually one or two functions doing the real heavy lifting.

Spot the Tight Loop

Inside the hot function, hunt for a loop over many elements doing math. That inner loop is almost always the part worth porting to Mojo.

def compute(data):
    total = 0.0
    for x in data:
        total += x * x
    return total

Check the Input Size

A loop is only worth porting if it runs a lot. Note the data size, since a million iterations matters far more than ten.

Save the Numbers

Write down the baseline time and the hot function's share. Later you will compare against these numbers to prove the port paid off.

Watch for I/O Time

If most time is spent reading files or the network, that is I/O, not compute. Mojo speeds up math, so be sure the bottleneck is CPU work.

Confirm It Is Compute-Bound

A good port target is compute-bound: lots of arithmetic over many elements with little waiting. That is exactly where Mojo shines.

Pick One Target

Choose the single slowest compute hot path to rewrite first. One clear target keeps your capstone focused and easy to measure.

Quick Check

Pick the best first move when accelerating a Python app.

Recap

Measure the Python baseline, profile to find the compute-bound hot loop, and pick one clear target. Now you know exactly what to port. 🎯

常见问题解答

「分析 Python 基线性能」课时是免费的吗？

是的 — 「分析 Python 基线性能」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 Mojo Academy 课程的其余内容，请升级到 CoddyKit PRO。 Mojo Academy 课程共包含 4 节课。

「分析 Python 基线性能」这节课中我会学到什么？

找出值得移植的慢路径你通过在浏览器中直接运行的动手代码来练习 Mojo 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 Mojo Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Mojo 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。

「分析 Python 基线性能」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 Mojo Academy 课中编写并运行代码吗？

能。每节 Mojo 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。

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