0Pricing
Deep Learning Academy · 课时

CPU、GPU 与 MPS：选择设备

检测 CUDA、Apple MPS，或退回使用 CPU

CPU、GPU 与 MPS：选择设备 是 CoddyKit 上的免费 Deep Learning Academy 课时。 这是第 2 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。

Where Math Happens

Every tensor lives on a device: the CPU, a GPU, or Apple's MPS. The device decides how fast your model trains. ⚡

The CPU Always Works

Your CPU is the safe default. It runs every PyTorch operation, just more slowly on the heavy matrix math that big networks demand.

The GPU Goes Fast

A GPU does thousands of multiplications in parallel, making it far faster for deep learning. PyTorch reaches NVIDIA GPUs through CUDA.

Apple Silicon Has MPS

On a Mac with Apple Silicon, MPS taps the built-in GPU. It is the fast path for M-series chips without any NVIDIA hardware.

Detect CUDA

Ask PyTorch whether an NVIDIA GPU is available before you use one. cuda.is_available returns True or False so you never assume.

torch.cuda.is_available()

Detect MPS

On a Mac, check the Apple backend the same way. mps.is_available tells you if the M-series GPU is ready to use.

torch.backends.mps.is_available()

Pick the Best Device

A clean pattern tries CUDA first, then MPS, then falls back to CPU. This device string adapts to whatever machine runs your code.

device = 'cuda' if torch.cuda.is_available() else 'cpu'

Move a Tensor

Send data to the chosen device with .to(device). The tensor's numbers are unchanged, but its math now runs there.

x = torch.tensor([1.0, 2.0]).to(device)

Keep Everything Together

Your model and your data must share one device. Mixing CPU and GPU tensors throws a device mismatch error during the forward pass.

Check Where a Tensor Lives

Not sure where data sits? Read the .device attribute. It prints cpu, cuda, or mps so you can confirm placement at a glance.

print(x.device)

When to Use Which

Use the GPU for real training, and the CPU for quick tests or tiny data. The fastest device is the one your hardware actually has.

Quick Check

Pick the right call for a Mac with Apple Silicon.

Recap

You learned to detect CUDA, MPS, or CPU, pick the best one, and move tensors there. Same code, fastest hardware available. 🚀

常见问题解答

「CPU、GPU 与 MPS：选择设备」课时是免费的吗？

是的 — 「CPU、GPU 与 MPS：选择设备」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。

「CPU、GPU 与 MPS：选择设备」这节课中我会学到什么？

检测 CUDA、Apple MPS，或退回使用 CPU 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 2 节课，共 4 节。

「CPU、GPU 与 MPS：选择设备」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

  1. 安装 PyTorch 并验证导入
  2. CPU、GPU 与 MPS：选择设备
  3. 笔记本、脚本与可复现的随机种子
  4. 您的第一个 torch.tensor
← 返回 Deep Learning Academy