ray/doc/source/serve/performance.md

# Performance Tuning

This section should help you:

- understand the performance characteristics of Ray Serve
- find ways to debug and tune the performance of your Serve deployment

:::{note}
While this section offers some tips and tricks to improve the performance of your Serve deployment,
the [architecture doc](serve-architecture) is helpful for context, including an overview of the HTTP proxy actor and replica actors.
:::

```{contents}
```

## Performance and known benchmarks

We are continuously benchmarking Ray Serve. The metrics we care about are latency, throughput, and scalability. We can confidently say:

- Ray Serve’s latency overhead is single digit milliseconds, around 1-2 milliseconds on average.
- For throughput, Serve achieves about 3-4k queries per second on a single machine (8 cores) using 1 HTTP proxy actor and 8 replicas performing noop requests.
- It is horizontally scalable so you can add more machines to increase the overall throughput. Ray Serve is built on top of Ray,
  so its scalability is bounded by Ray’s scalability. Please check out Ray’s [scalability envelope](https://github.com/ray-project/ray/blob/master/release/benchmarks/README.md)
  to learn more about the maximum number of nodes and other limitations.

You can check out our [microbenchmark instructions](https://github.com/ray-project/ray/blob/master/python/ray/serve/benchmarks/README.md)
to benchmark on your hardware.

## Debugging performance issues

The performance issue you're most likely to encounter is high latency and/or low throughput for requests.

If you have set up [monitoring](serve-monitoring) with Ray and Ray Serve, you will likely observe the following:

- `serve_num_router_requests` is constant while your load increases
- `serve_deployment_queuing_latency_ms` is spiking up as queries queue up in the background

Given these symptoms, there are several ways to fix the issue.

### Choosing the right hardware

Make sure you are using the right hardware and resources.
Are you using GPUs (`ray_actor_options={“num_gpus”: 1}`)? Are you using one or more cores (`ray_actor_options={“num_cpus”: 2}`) and setting [`OMP_NUM_THREADS`](serve-omp-num-threads) to increase the performance of your deep learning framework?

### `async` methods

Are you using `async def` in your callable? If you are using `asyncio` and
hitting the same queuing issue mentioned above, you might want to increase
`max_concurrent_queries`. Serve sets a low number (100) by default so the client gets
proper backpressure. You can increase the value in the Deployment decorator; e.g.
`@serve.deployment(max_concurrent_queries=1000)`.

### Batching

If your deployment can process a batch at a time at a sublinear latency
(for example, if it takes 1ms to process 1 query and 5ms to process 10 of them)
then batching is your best approach. Check out the [batching guide](serve-batching) to
make your deployment accept batches (especially for GPU-based ML inference). You might want to tune `max_batch_size` and `batch_wait_timeout` in the `@serve.batch` decorator to maximize the benefits:

- `max_batch_size` specifies how big the batch should be. Generally,
  we recommend choosing the largest batch size your function can handle
  AND the performance improvement is no longer sublinear. Take a dummy
  example: suppose it takes 1ms to process 1 query, 5ms to process 10 queries,
  and 6ms to process 11 queries. Here you should set the batch size to to 10
  because adding more queries won’t improve the performance.
- `batch_wait_timeout` specifies the maximum amount of time to wait before
  a batch should be processed, even if it’s not full.  It should be set according
  to the equation `batch_wait_timeout + full batch processing time ~= expected latency`.
  The larger `batch_wait_timeout` is, the more full the typical batch will be.
  To maximize throughput, you should set `batch_wait_timeout` as large as possible without exceeding your desired expected latency in the equation above.