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 is helpful for context, including an overview of the HTTP proxy actor and replica actors. :::

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 to learn more about the maximum number of nodes and other limitations.

You can check out our microbenchmark instructions 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 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 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 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.

Scaling HTTP servers

Sometimes it’s not about your code: Serve’s HTTP server can become the bottleneck. If you observe that the CPU utilization for HTTPProxyActor spikes up to 100%, the HTTP server is the bottleneck. Serve only starts a single HTTP server on the Ray head node by default. This server can handle about 3k queries per second. If your workload exceeds this number, you might want to consider starting one HTTP server per Ray node to spread the load via the location field of http_options; e.g. http_options={“location”: “EveryNode”}). This configuration tells Serve to spawn one HTTP server per node. You should put an external load balancer in front of it.