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安装 PyTorch 并验证导入

运行 pip install torch，并进行一行代码的基本检查

安装 PyTorch 并验证导入 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 PyTorch First

Before you train anything, you need a toolkit. PyTorch is the library you will use to build and train every model in this course. 🔧

It's Just a Python Package

PyTorch installs like any other Python package, so you grab it with pip. No special compiler or magic setup is needed to get started.

The Install Command

This single pip install line pulls down PyTorch and its companion packages so your environment is ready to build models.

pip install torch torchvision torchaudio

What torchvision Adds

The torchvision package ships datasets, image transforms, and pretrained models, so you rarely start an image project from scratch.

Import It in Code

Once installed, you bring PyTorch into any script with one line. The convention is to import torch and use it directly, no alias needed.

import torch

The One-Line Sanity Check

To prove the install worked, print the version. If a number appears instead of an error, PyTorch is alive and ready.

import torch
print(torch.__version__)

Reading the Version

A version like 2.3.0 means a successful install. The first number is the major version, which signals big, sometimes breaking, changes.

When Import Fails

A ModuleNotFoundError means Python cannot find torch. Usually you installed into a different environment than the one running your code.

Use a Virtual Environment

A virtual environment keeps PyTorch and its versions isolated per project, so one upgrade never breaks another app on your machine.

python -m venv .venv

Activate, Then Install

Always activate the environment before you pip install, so the package lands where your scripts will actually look for it.

source .venv/bin/activate

CPU Build Is Enough for Now

The default CPU build runs everything in this course. You only need a GPU-specific install later, once your models grow larger.

Quick Check

Let's confirm you know the fastest way to test the install.

Recap

You installed PyTorch with pip in a virtual environment and confirmed it works by printing its version. Your workshop is open for business. 🎉

常见问题解答

「安装 PyTorch 并验证导入」课时是免费的吗？

是的 — 「安装 PyTorch 并验证导入」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。

「安装 PyTorch 并验证导入」这节课中我会学到什么？

运行 pip install torch，并进行一行代码的基本检查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。

「安装 PyTorch 并验证导入」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。

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