Performance Tuning#

This section should help you:

  • understand Ray Serve’s performance characteristics

  • find ways to debug and tune your Serve application’s performance

Note

This section offers some tips and tricks to improve your Ray Serve application’s performance. Check out the architecture page for helpful context, including an overview of the HTTP proxy actor and deployment replica actors.

Performance and benchmarks#

Ray Serve is built on top of Ray, so its scalability is bounded by Ray’s scalability. See Ray’s scalability envelope to learn more about the maximum number of nodes and other limitations.

Debugging performance issues#

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

Once you set up monitoring with Ray and Ray Serve, these issues may appear as:

  • serve_num_router_requests_total staying constant while your load increases

  • serve_deployment_processing_latency_ms spiking up as queries queue up in the background

The following are ways to address these issues:

  1. Make sure you are using the right hardware and resources:

    • Are you reserving GPUs for your deployment replicas using ray_actor_options (e.g., ray_actor_options={“num_gpus”: 1})?

    • Are you reserving one or more cores for your deployment replicas using ray_actor_options (e.g., ray_actor_options={“num_cpus”: 2})?

    • Are you setting OMP_NUM_THREADS to increase the performance of your deep learning framework?

  2. Try batching your requests. See Dynamic Request Batching.

  3. Consider using async methods in your callable. See the section below.

  4. Set an end-to-end timeout for your HTTP requests. See the section below.

Using async methods#

Note

According to the FastAPI documentation, def endpoint functions are called in a separate threadpool, so you might observe many requests running at the same time inside one replica, and this scenario might cause OOM or resource starvation. In this case, you can try to use async def to control the workload performance.

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_ongoing_requests. 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_ongoing_requests=1000).

Set an end-to-end request timeout#

By default, Serve lets client HTTP requests run to completion no matter how long they take. However, slow requests could bottleneck the replica processing, blocking other requests that are waiting. Set an end-to-end timeout, so slow requests can be terminated and retried.

You can set an end-to-end timeout for HTTP requests by setting the request_timeout_s in the http_options field of the Serve config. HTTP Proxies wait for that many seconds before terminating an HTTP request. This config is global to your Ray cluster, and it can’t be updated during runtime. Use client-side retries to retry requests that time out due to transient failures.

Give the Serve Controller more time to process requests#

The Serve Controller runs on the Ray head node and is responsible for a variety of tasks, including receiving autoscaling metrics from other Ray Serve components. If the Serve Controller becomes overloaded (symptoms might include high CPU usage and a large number of pending ServeController.record_handle_metrics tasks), you can increase the interval between cycles of the control loop by setting the RAY_SERVE_CONTROL_LOOP_INTERVAL_S environment variable (defaults to 0.1 seconds). This setting gives the Controller more time to process requests and may help alleviate the overload.