Profiling for Ray Developers¶

This document details, for Ray developers, how to use pprof to profile Ray binaries.

Installation¶

These instructions are for Ubuntu only. Attempts to get pprof to correctly symbolize on Mac OS have failed.

sudo apt-get install google-perftools libgoogle-perftools-dev

Launching the to-profile binary¶

If you want to launch Ray in profiling mode, define the following variables:

export RAYLET_PERFTOOLS_PATH=/usr/lib/x86_64-linux-gnu/libprofiler.so
export RAYLET_PERFTOOLS_LOGFILE=/tmp/pprof.out

The file /tmp/pprof.out will be empty until you let the binary run the target workload for a while and then kill it via ray stop or by letting the driver exit.

Visualizing the CPU profile¶

The output of pprof can be visualized in many ways. Here we output it as a zoomable .svg image displaying the call graph annotated with hot paths.

# Use the appropriate path.
RAYLET=ray/python/ray/core/src/ray/raylet/raylet

google-pprof -svg $RAYLET /tmp/pprof.out > /tmp/pprof.svg
# Then open the .svg file with Chrome.

# If you realize the call graph is too large, use -focus=<some function> to zoom
# into subtrees.
google-pprof -focus=epoll_wait -svg $RAYLET /tmp/pprof.out > /tmp/pprof.svg

Here’s a snapshot of an example svg output, taken from the official documentation:

Running Microbenchmarks¶

To run a set of single-node Ray microbenchmarks, use:

ray microbenchmark

You can find the microbenchmark results for Ray releases in the GitHub release logs.

References¶

The pprof documentation.
A Go version of pprof.
The gperftools, including libprofiler, tcmalloc, and other goodies.