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批处理、打乱与 num_workers

配置 DataLoader 以提升速度

批处理、打乱与 num_workers 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。

Meet the DataLoader

A dataset hands over one sample at a time, but training wants groups. The DataLoader wraps your dataset and serves it in convenient batches. 📦

from torch.utils.data import DataLoader
loader = DataLoader(ds)

Batching Saves Time

Set batch_size and the loader stacks that many samples into one tensor. Bigger batches use your hardware better and smooth out noisy updates.

loader = DataLoader(ds, batch_size=32)

One Batch, Stacked Together

Each batch adds a new first dimension. Thirty-two samples of shape 784 become a single tensor shaped 32 by 784, ready for the model.

Loop Over Batches

You iterate the loader like any Python sequence. Each turn of the loop yields one batch of inputs and labels for your training step.

for xb, yb in loader:
    pred = model(xb)

Shuffle Every Epoch

Setting shuffle to True reorders samples each epoch. This breaks accidental ordering so the model cannot memorize the sequence of your data.

loader = DataLoader(ds, batch_size=32, shuffle=True)

Shuffle Train, Not Test

Turn shuffling on for the training set but off for validation and test. Evaluation just measures performance, so a stable order is fine there.

num_workers Loads in Parallel

Reading and decoding data can stall the GPU. Setting num_workers above zero spawns helper processes that prepare the next batch while the model trains.

loader = DataLoader(ds, batch_size=32, num_workers=4)

Pick a Sensible Worker Count

A common start for num_workers is the number of CPU cores you have. Too many can thrash memory, so measure rather than guess blindly.

pin_memory Speeds GPU Copies

When training on a GPU, set pin_memory to True. It places batches in page-locked memory so transfers to the device run noticeably faster.

loader = DataLoader(ds, batch_size=32, pin_memory=True)

Handle the Last Batch

The final batch is often smaller than the rest. Use drop_last True to discard it when your model needs every batch the same size.

loader = DataLoader(ds, batch_size=32, drop_last=True)

One Loader Per Split

In practice you build a separate loader for train, validation, and test. Each gets its own settings, like shuffle on only for training.

Quick Check

What does setting num_workers above zero actually do?

Recap

A DataLoader batches your dataset, shuffles training data, and uses num_workers to load batches in parallel. It keeps your model fed and fast. 🎉

常见问题解答

「批处理、打乱与 num_workers」课时是免费的吗？

是的 — 「批处理、打乱与 num_workers」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。

「批处理、打乱与 num_workers」这节课中我会学到什么？

配置 DataLoader 以提升速度 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。

「批处理、打乱与 num_workers」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. 编写自定义数据集类
  2. 批处理、打乱与 num_workers
  3. 使用 collate_fn 处理可变长度输入
  4. 归一化与标准化输入
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