ray/doc/source/profiling.rst

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Documentation- Basic Profiling for Ray Users (#2326) * Ray documentation - created new section 'Profiling for Ray Users', opposed to current Profiling section for Ray developers. Completed three sections 'A Basic Profiling Example', 'Timing Performance Using Python's Timestamps', and 'Profiling Using An External Profiler (Line_Profiler).' Left to-do two sections on CProfile and Ray Timeline Visualization.' * Ray documentation - Fixed rst codeblock linebreaks in 'User Profiling' * Ray documentation - For User Profiling, added section on cProfile * Ray documentation - For User Profiling, completed Ray Timeline Visualization section, including graphical images * Ray documentation - made User Profiling timeline image larger, minor wording edits * Ray documentation - minor wording edits to User Profiling * Ray documentation - User Profiling- fixed broken link * Minor wording changes requested by Philipp Moritz addressed. Still need to address (1) compressing the image files, (2) correcting ex 3 to not be remote, and (3) using cProfile on an actor * Ray documentation - For user-profiling.rst, revised example 3 to show a semi-parallelized example. Compressed timeline example image to be under 50 KB, removed view timeline GUI image. Updated timeline example image to reflect revised example 3. cProfile actor example left * Ray documentation - in user-profiling.rst, added a new example including actors in the cProfile section * Ray documentation - For user-profiling.rst, added section header for the Ray actor cProfile example * Update user-profiling.rst * Update user-profiling.rst * 4 space indentation * Update user-profiling.rst * Update user-profiling.rst * Update user-profiling.rst * corrections
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Profiling for Ray Developers
============================
This document details, for Ray developers, how to analyze Ray performance.
Getting a stack trace of Ray C++ processes
------------------------------------------
You can use the following GDB command to view the current stack trace of any
running Ray process (e.g., raylet). This can be useful for debugging 100% CPU
utilization or infinite loops (simply run the command a few times to see what
the process is stuck on).
.. code-block:: shell
sudo gdb -batch -ex "thread apply all bt" -p <pid>
Note that you can find the pid of the raylet with ``pgrep raylet``.
Installation
------------
These instructions are for Ubuntu only. Attempts to get ``pprof`` to correctly
symbolize on Mac OS have failed.
.. code-block:: bash
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:
.. code-block:: bash
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.
.. code-block:: bash
# 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:
.. image:: http://goog-perftools.sourceforge.net/doc/pprof-test-big.gif
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Running Microbenchmarks
-----------------------
To run a set of single-node Ray microbenchmarks, use:
.. code-block:: bash
ray microbenchmark
You can find the microbenchmark results for Ray releases in the `GitHub release logs <https://github.com/ray-project/ray/tree/master/release/release_logs>`__.
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References
----------
- The `pprof documentation <http://goog-perftools.sourceforge.net/doc/cpu_profiler.html>`_.
- A `Go version of pprof <https://github.com/google/pprof>`_.
- The `gperftools <https://github.com/gperftools/gperftools>`_, including libprofiler, tcmalloc, and other goodies.