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Document how to profile Ray using pprof. (#1464)
* Document how to profile Ray using pprof. * Link to profiling.rst via main doc site; fix lint * Some changes and clarifications.
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@ -106,4 +106,5 @@ Example Program
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troubleshooting.rst
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development.rst
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profiling.rst
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contact.rst
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94
doc/source/profiling.rst
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94
doc/source/profiling.rst
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Profiling Ray
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=============
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This document details, for Ray developers, how to use ``pprof`` to profile Ray
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binaries.
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Installation
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------------
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These instructions are for Ubuntu only. Attempts to get ``pprof`` to correctly
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symbolize on Mac OS have failed.
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.. code-block:: bash
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sudo apt-get install google-perftools libgoogle-perftools-dev
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Changes to compilation and linking
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----------------------------------
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Let's say we want to profile the ``plasma_manager``. Change the link
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instruction in ``src/plasma/CMakeLists.txt`` from
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.. code-block:: cmake
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target_link_libraries(plasma_manager common ${PLASMA_STATIC_LIB} ray_static ${ARROW_STATIC_LIB} -lpthread)
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to additionally include ``-lprofiler``:
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.. code-block:: cmake
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target_link_libraries(plasma_manager common ${PLASMA_STATIC_LIB} ray_static ${ARROW_STATIC_LIB} -lpthread -lprofiler)
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Additionally, add ``-g -ggdb`` to ``CMAKE_C_FLAGS`` and ``CMAKE_CXX_FLAGS`` to
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enable the debug symbols. (Keeping ``-O3`` seems okay.)
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Recompile.
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Launching the to-profile binary
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-------------------------------
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In various places, instead of launching the target binary via
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``plasma_manager <args>``, it must be launched with
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.. code-block:: bash
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LD_PRELOAD=/usr/lib/libprofiler.so CPUPROFILE=/tmp/pprof.out plasma_manager <args>
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In practice, this means modifying ``python/ray/plasma/plasma.py`` so that the
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manager is launched with a command that passes a ``modified_env`` into
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``Popen``.
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.. code-block:: python
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modified_env = os.environ.copy()
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modified_env["LD_PRELOAD"] = "/usr/lib/libprofiler.so"
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modified_env["CPUPROFILE"] = "/tmp/pprof.out"
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process = subprocess.Popen(command,
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stdout=stdout_file,
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stderr=stderr_file,
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env=modified_env)
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The file ``/tmp/pprof.out`` will be empty until you let the binary run the
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target workload for a while and then ``kill`` it.
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Visualizing the CPU profile
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---------------------------
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The output of ``pprof`` can be visualized in many ways. Here we output it as a
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zoomable ``.svg`` image displaying the call graph annotated with hot paths.
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.. code-block:: bash
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# Use the appropriate path.
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PLASMA_MANAGER=ray/python/ray/core/src/plasma/plasma_manager
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google-pprof -svg $PLASMA_MANAGER /tmp/pprof.out > /tmp/pprof.svg
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# Then open the .svg file with Chrome.
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# If you realize the call graph is too large, use -focus=<some function> to zoom
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# into subtrees.
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google-pprof -focus=epoll_wait -svg $PLASMA_MANAGER /tmp/pprof.out > /tmp/pprof.svg
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Here's a snapshot of an example svg output, taken from the official
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documentation:
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.. image:: http://goog-perftools.sourceforge.net/doc/pprof-test-big.gif
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References
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----------
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- The `pprof documentation <http://goog-perftools.sourceforge.net/doc/cpu_profiler.html>`_.
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- A `Go version of pprof <https://github.com/google/pprof>`_.
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- The `gperftools <https://github.com/gperftools/gperftools>`_, including libprofiler, tcmalloc, and other goodies.
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