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启用自适应微批处理

自动组合请求，提高吞吐量

启用自适应微批处理 是 CoddyKit 上的免费 MLOps Academy 课时。这是第 3 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 MLOps Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 MLOps 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。

The Throughput Problem

Calling a model once per request wastes hardware. Models run far faster on a batch of inputs than on the same inputs one at a time. ⚡

What Micro-Batching Does

Adaptive batching collects incoming requests for a brief window, runs them together, then splits results back to each caller automatically.

Why Adaptive

The window size is not fixed. BentoML watches live latency and adapts the batch size so it stays fast under both light and heavy load.

It Lives on the Runner

Batching is configured per model, not per request. You enable it on the runnable by marking which methods support batched inputs.

Turn It On

For a custom runnable you set batchable to true on the method. BentoML then groups calls to that method behind the scenes.

@bentoml.Runnable.method(batchable=True)
def predict(self, inputs):
    ...

Pick the Batch Axis

BentoML needs to know how to stack inputs. The batch_dim argument tells it which axis to concatenate along, usually axis 0.

@bentoml.Runnable.method(batchable=True, batch_dim=0)

Cap the Batch Size

You bound how big a batch can grow. max_batch_size caps the number of requests merged so one giant batch never stalls others.

Cap the Wait Time

You also bound how long to wait. max_latency_ms sets the longest a request may sit in the queue before the batch fires.

Tune It in Config

You set these limits without touching code. A bentoml_configuration file lets you adjust batching per runner for each environment.

runners:
  predict:
    batching:
      max_batch_size: 32

The Trade-off

Bigger batches lift throughput but add a little latency per request. Tuning means finding the sweet spot for your traffic.

Watch It Work

You do not change your client at all. Callers still send single requests while BentoML merges them under the hood transparently.

Quick Check

You raise max_batch_size to a large value. What is the likely effect on a single request?

Recap

You learned that adaptive batching groups requests on a batchable runner, tuned by max_batch_size and max_latency_ms, trading a little latency for big throughput. 🙌

常见问题解答

「启用自适应微批处理」课时是免费的吗？

是的 — 「启用自适应微批处理」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 MLOps Academy 课程的其余内容，请升级到 CoddyKit PRO。 MLOps Academy 课程共包含 4 节课。

「启用自适应微批处理」这节课中我会学到什么？

自动组合请求，提高吞吐量你通过在浏览器中直接运行的动手代码来练习 MLOps 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 MLOps Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 MLOps 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 3 节课，共 4 节。

「启用自适应微批处理」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 MLOps Academy 课中编写并运行代码吗？

能。每节 MLOps 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

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