(serve-performance-batching-requests)=
# Dynamic Request Batching

Serve offers a request batching feature that can improve your service throughput without sacrificing latency. This is possible because ML models can utilize efficient vectorized computation to process a batch of request at a time. Batching is also necessary when your model is expensive to use and you want to maximize the utilization of hardware.

Machine Learning (ML) frameworks such as Tensorflow, PyTorch, and Scikit-Learn support evaluating multiple samples at the same time.
Ray Serve allows you to take advantage of this feature via dynamic request batching.
When a request arrives, Serve puts the request in a queue. This queue buffers the requests to form a batch. The deployment picks up the batch and evaluates it. After the evaluation, the resulting batch will be split up, and each response is returned individually.

## Enable batching for your deployment
You can enable batching by using the {mod}`ray.serve.batch` decorator. Let's take a look at a simple example by modifying the `Model` class to accept a batch.
```{literalinclude} ../doc_code/batching_guide.py
---
start-after: __single_sample_begin__
end-before: __single_sample_end__
---
```

The batching decorators expect you to make the following changes in your method signature:
- The method is declared as an async method because the decorator batches in asyncio event loop.
- The method accepts a list of its original input types as input. For example, `arg1: int, arg2: str` should be changed to `arg1: List[int], arg2: List[str]`.
- The method returns a list. The length of the return list and the input list must be of equal lengths for the decorator to split the output evenly and return a corresponding response back to its respective request.

```{literalinclude} ../doc_code/batching_guide.py
---
start-after: __batch_begin__
end-before: __batch_end__
emphasize-lines: 6-9
---
```

You can supply two optional parameters to the decorators.
- `batch_wait_timeout_s` controls how long Serve should wait for a batch once the first request arrives.
- `max_batch_size` controls the size of the batch.
Once the first request arrives, the batching decorator will wait for a full batch (up to `max_batch_size`) until `batch_wait_timeout_s` is reached. If the timeout is reached, the batch will be sent to the model regardless the batch size.

:::{tip}
You can reconfigure your `batch_wait_timeout_s` and `max_batch_size` parameters using the `set_batch_wait_timeout_s` and `set_max_batch_size` methods:

```{literalinclude} ../doc_code/batching_guide.py
---
start-after: __batch_params_update_begin__
end-before: __batch_params_update_end__
---
```

Use these methods in the `reconfigure` [method](serve-user-config) to control the `@serve.batch` parameters through your Serve configuration file.
:::

(serve-streaming-batched-requests-guide)=

## Streaming batched requests

Use an async generator to stream the outputs from your batched requests. Let's convert the `StreamingResponder` class to accept a batch.

```{literalinclude} ../doc_code/batching_guide.py
---
start-after: __single_stream_begin__
end-before: __single_stream_end__
---
```

Decorate async generator functions with the {mod}`ray.serve.batch` decorator. Similar to non-streaming methods, the function takes in a `List` of inputs and in each iteration it `yield`s an iterable of outputs with the same length as the input batch size.

```{literalinclude} ../doc_code/batching_guide.py
---
start-after: __batch_stream_begin__
end-before: __batch_stream_end__
---
```

Calling the `serve.batch`-decorated function returns an async generator that can be awaited to receive results.

Some inputs within a batch may generate fewer outputs than others. When a particular input has nothing left to yield, pass a `StopIteration` object into the output iterable. This terminates the generator that was returned when the `serve.batch` function was called with that input. When streaming generators returned by `serve.batch`-decorated functions over HTTP, this allows the end client's connection to terminate once its call is done, instead of waiting until the entire batch is done.

## Tips for fine-tuning batching parameters

`max_batch_size` ideally should be a power of 2 (2, 4, 8, 16, ...) because CPUs and GPUs are both optimized for data of these shapes. Large batch sizes incur a high memory cost as well as latency penalty for the first few requests.

`batch_wait_timeout_s` should be set considering the end to end latency SLO (Service Level Objective). For example, if your latency target is 150ms, and the model takes 100ms to evaluate the batch, the `batch_wait_timeout_s` should be set to a value much lower than 150ms - 100ms = 50ms.

When using batching in a Serve Deployment Graph, the relationship between an upstream node and a downstream node might affect the performance as well. Consider a chain of two models where first model sets `max_batch_size=8` and second model sets `max_batch_size=6`. In this scenario, when the first model finishes a full batch of 8, the second model will finish one batch of 6 and then to fill the next batch, which will initially only be partially filled with 8 - 6 = 2 requests, incurring latency costs. The batch size of downstream models should ideally be multiples or divisors of the upstream models to ensure the batches play well together.