Python performance profiling¶
It is important to generate a profile of code performance to understand where to focus optimization efforts. A useful guide to optimization of python code in general, and SciPy/NumPy in particular, is: http://scipy-lectures.github.io/advanced/optimizing/.
Function-level profiling¶
Consider the code in mosaic.py.
To profile it:
python -m cProfile -o cprofile-mosaic.dat `which mosaic.py` \
/data3a/work/price/SUPA-MIT/rerun/cosmos --id field=COSMOS \
filter=W-S-I+ expTime=120.0 --clobber-config
Then, in a Python session:
import pstats
p = pstats.Stats("cprofile-mosaic.dat")
p.sort_stats("cumulative").print_stats(30) # Print top 30 cumulative
The results are:
n calls (6698794 primitive calls) in 36.536 seconds
Ordered by: cumulative time
List reduced from 4671 to 30 due to restriction <30>
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.004 0.004 36.538 36.538 /home/price/hsc/meas_mosaic/bin/mosaic.py:3(<module>)
1 0.000 0.000 34.707 34.707 /data1a/ana/products2.1/Linux64/pipe_base/HSC-2.4.1a_hsc/python/lsst/pipe/base/cmdLineTask.py:243(parseAndRun)
1 0.000 0.000 34.324 34.324 /data1a/ana/products2.1/Linux64/pipe_base/HSC-2.4.1a_hsc/python/lsst/pipe/base/cmdLineTask.py:87(run)
30 0.000 0.000 34.317 1.144 {map}
1 0.000 0.000 34.303 34.303 /home/price/hsc/meas_mosaic/python/lsst/meas/mosaic/mosaicTask.py:45(__call__)
1 0.073 0.073 34.303 34.303 /home/price/hsc/meas_mosaic/python/lsst/meas/mosaic/mosaicTask.py:1112(run)
1 0.176 0.176 34.230 34.230 /home/price/hsc/meas_mosaic/python/lsst/meas/mosaic/mosaicTask.py:950(mosaic)
1 2.404 2.404 25.686 25.686 /home/price/hsc/meas_mosaic/python/lsst/meas/mosaic/mosaicTask.py:268(readCatalog)
360 0.740 0.002 20.289 0.056 /home/price/hsc/meas_mosaic/python/lsst/meas/mosaic/mosaicTask.py:205(getAllForCcd)
1008 0.012 0.000 13.592 0.013 /data1a/ana/products2.1/Linux64/daf_persistence/HSC-2.1.2a_hsc/python/lsst/daf/persistence/butlerSubset.py:171(get)
1008 0.025 0.000 13.579 0.013 /data1a/ana/products2.1/Linux64/daf_persistence/HSC-2.1.2a_hsc/python/lsst/daf/persistence/butler.py:209(get)
648 0.007 0.000 12.235 0.019 /data1a/ana/products2.1/Linux64/daf_persistence/HSC-2.1.2a_hsc/python/lsst/daf/persistence/butler.py:239(<lambda>)
648 0.014 0.000 12.228 0.019 /data1a/ana/products2.1/Linux64/daf_persistence/HSC-2.1.2a_hsc/python/lsst/daf/persistence/butler.py:386(_read)
324 0.001 0.000 10.380 0.032 /home/price/hsc/afw/python/lsst/afw/table/tableLib.py:7836(readFits)
324 10.379 0.032 10.379 0.032 {_tableLib.SourceCatalog_readFits}
1 0.121 0.121 7.344 7.344 /home/price/hsc/meas_mosaic/python/lsst/meas/mosaic/mosaicTask.py:318(mergeCatalog)
1 0.000 0.000 6.680 6.680 /home/price/hsc/meas_mosaic/python/lsst/meas/mosaic/mosaicLib.py:1400(kdtreeSource)
1 6.680 6.680 6.680 6.680 {_mosaicLib.kdtreeSource}
360 0.001 0.000 2.248 0.006 /home/price/hsc/afw/python/lsst/afw/image/imageLib.py:8635(makeWcs)
360 2.137 0.006 2.248 0.006 {_imageLib.makeWcs}
648 0.331 0.001 2.148 0.003 /home/price/hsc/meas_mosaic/python/lsst/meas/mosaic/mosaicTask.py:173(selectStars)
153718 0.679 0.000 1.899 0.000 /home/price/hsc/meas_mosaic/python/lsst/meas/mosaic/mosaicLib.py:776(__init__)
324 0.001 0.000 1.779 0.005 /home/price/hsc/afw/python/lsst/afw/table/tableLib.py:6266(readFits)
324 1.779 0.005 1.779 0.005 {_tableLib.BaseCatalog_readFits}
2916 0.178 0.000 1.450 0.000 /home/price/hsc/afw/python/lsst/afw/table/tableLib.py:726(find)
360 0.000 0.000 1.128 0.003 /data1a/ana/products2.1/Linux64/daf_persistence/HSC-2.1.2a_hsc/python/lsst/daf/persistence/butler.py:236(<lambda>)
360 0.001 0.000 1.127 0.003 /data1a/ana/products2.1/Linux64/daf_butlerUtils/HSC-2.2.0c_hsc/python/lsst/daf/butlerUtils/cameraMapper.py:315(<lambda>)
360 0.001 0.000 1.126 0.003 /home/price/hsc/afw/python/lsst/afw/image/imageLib.py:1159(readMetadata)
360 1.126 0.003 1.126 0.003 {_imageLib.readMetadata}
1 0.004 0.004 1.036 1.036 /home/price/hsc/meas_mosaic/python/lsst/meas/mosaic/mosaicTask.py:3(<module>)
It is often most useful to look at the cumtime column, which is the time spent in that function and what it calls.
The results here show that 10/36 = 28% is being spent in readFits, but 26/36 = 72% is devoted to I/O (readCatalog).
That might suggest that some Python code should get pushed down to C++.
If you do:
p.print_callees("readCatalog")
p.print_callees("getAllForCcd")
you can see that the cumtime column doesn’t add up to the cumtime values in the above, so the remaining time is time spent within those functions doing work.
For more details on pstats and python profiling in general see http://docs.python.org/library/profile.html.
snakeviz is a web interface for visualizing the python profile output files, installable via pip: pass it the profile output file you wish to visualize to open an interactive browser window.
If you run a pipeline with the --profile somefile.prof option documented below, you can run snakeviz somefile.prof on your local computer to get an interactive view of the entire profile in a web browser.
Another useful tool for visualising the call graph is gprof2dot:
gprof2dot -f pstats -e 0.01 cprofile-mosaic.dat | dot -Tpng -o cprofile-mosaic.png
Stack profiling¶
The LSST stack contains some support for obtaining a python profile easily:
pipetasksupports a--profile somefile.profcommand-line argument to write the full run profile to the specified filename (somefile.profin this case).BatchCmdLineTask(the front-end to scripts such assingleFrameDriver.pyin pipe_drivers) supports a--batch-profilecommand-line argument switch. The profile is written toprofile-<job>-<hostname>-<pid>.dat.ci_hsc (an integration test package, driven by SCons) supports a
--enable-profilecommand-line argument specifying a base filename for the profiles (default isprofile). The profiles are written to<base>-<sequenceNumber>-<script>.pstats. This is useful for profiling the entire stack.
All of the above profile outputs can be read using pstats, snakeviz, or other tools that support the cProfile output format.
Note that our timing decorator–lsst/utils/timer.py:timeMethod, used to generate timing metrics metdata for Tasks–will result in a call graph that is difficult to interpret, due to every run() method being called by timeMethod_wrapper().
DM-34978 gives a plan for a workaround of this; until that is merged, you can try the branch of utils described on DM-34881 to disable the timer during your profiling run.
Line profiling¶
Having found the particular function that’s consuming all the time, you may want finer granularity. For this, use line profiler. Installation is a simple matter of:
pip install line_profiler
Put an @profile decorator on the function of interest, and run:
kernprof.py -l -v /path/to/script.py <arguments>
Statistical Profiling¶
A different method of profiling is “statistical profiling”, which repeatedly pauses the python interpreter and samples the call stack each time.
This call stack sampling can help to work around circular call graphs that result from our timing decorator and how the standard profiler interprets each function call.
pyinstrument is an example of a statistical profiler that can be used with the Science Pipelines pipetask command.
You have to run pipetask via pyinstrument:
PIPETASKCMD=`which pipetask`
pyinstrument -r speedscope -o report.speedscope ${PIPETASKCMD} run ...
The -r speedscope option produces a file that can be dropped into the web-based speedscope flamegraph visualizer to explore your profile results.
You can also run with -r html to get a single html web page of the results in a mostly-text format, but it is generally not as useful as the more interactive speedscope option.
Note that the -t option for rendering as a single timeline is compatible with -r html but not with -r speedscope.