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使用 torch.no_grad() 进行推理

跳过计算图跟踪以节省内存

使用 torch.no_grad() 进行推理 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。

Not Every Pass Needs Grads

You only need gradients while training. When you just want predictions, tracking the graph is wasted work, so PyTorch lets you turn it off. 🛑

Meet torch.no_grad

Wrap code in a torch.no_grad() block and autograd stops recording inside it. No graph is built, and no gradients are stored.

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

Why Skip the Graph

Building the graph costs memory to remember every step for a backward pass that will never come. During inference that overhead is pure waste.

Faster and Lighter

Inside no_grad your forward pass uses less memory and runs a little faster. On big models this lets you fit larger batches when only predicting.

Outputs Are Detached

Tensors created inside the block have requires_grad false. They are plain results you can print, save, or turn into NumPy without complaint.

Use It for Evaluation

Validation and test loops should always run under no_grad. You are scoring the model, not training it, so there is no reason to track gradients.

with torch.no_grad():
    for x, y in val_loader:
        out = model(x)

Pair It With eval Mode

For inference, set model.eval() and wrap calls in no_grad together. eval fixes layers like dropout; no_grad stops the graph. They solve different problems.

It Is a Context Manager

no_grad only affects code inside the with block. Once you leave it, autograd switches tracking back on automatically for your next training step.

The decorator form

You can also tag a whole function with @torch.no_grad(). Every tensor op inside that function then runs without building a graph.

@torch.no_grad()
def predict(x):
    return model(x)

Detach for a Single Tensor

Need to free just one tensor from the graph instead of a whole block? Call .detach() on it to get a copy that carries no gradient history.

frozen = output.detach()

A Safe, Common Habit

Reach for no_grad any time you are not learning: inference, metrics, or saving outputs. It is a tiny change that quietly saves memory and speed.

Quick Check

Confirm when to use no_grad.

Recap

Wrap inference in torch.no_grad() to skip graph building, saving memory and time. Pair it with model.eval() whenever you predict instead of train. 🚀

常见问题解答

「使用 torch.no_grad() 进行推理」课时是免费的吗？

是的 — 「使用 torch.no_grad() 进行推理」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 torch.no_grad() 进行推理」这节课中我会学到什么？

跳过计算图跟踪以节省内存 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 torch.no_grad() 进行推理」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. requires_grad 与计算图
  2. 调用 backward() 获取梯度
  3. 读取并清零 .grad
  4. 使用 torch.no_grad() 进行推理
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