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vmprof - a statistical program profiler
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Head over tohttps://vmprof.readthedocs.org for more info!
pip install vmprofpython -m vmprof <your program> <your program args>
Our build system ships wheels to PyPI (Linux, Mac OS X). If you build from source you needto install CPython development headers and libunwind headers (on Linux only).On Windows this means you need Microsoft Visual C++ Compiler for your Python version.
Setting up development can be done using the following commands:
$ virtualenv -p /usr/bin/python3 vmprof3$ source vmprof3/bin/activate$ python setup.py developYou need to install python development packages. In case of e.g. Debian or Ubuntu the package you need ispython3-dev andlibunwind-dev.Now it is time to write a test and implement your feature. If you wantyour changes to affect vmprof.com, head over tohttps://github.com/vmprof/vmprof-server and follow the setup instructions.
Consult our section for development athttps://vmprof.readthedocs.org for moreinformation.
vmprofshow is a command line tool that comes withVMProf. It can read profile filesand produce a formatted output.
Here is an example of how to usevmprofshow:
Run that smallish program which burns CPU cycles (with vmprof enabled):
$pypy vmprof/test/cpuburn.py# you can find cpuburn.py in the vmprof-python repoThis will produce a profile filevmprof_cpuburn.dat.Now display the profile usingvmprofshow.vmprofshow has multiple modesof showing data. We'll start with the tree-based mode.
$vmprofshow vmprof_cpuburn.dat treeYou will see a (colored) output:
$vmprofshow vmprof_cpuburn.dat tree100.0% <module> 100.0% tests/cpuburn.py:1100.0% .. test 100.0% tests/cpuburn.py:35100.0% .... burn 100.0% tests/cpuburn.py:26 99.2% ...... _iterate 99.2% tests/cpuburn.py:19 97.7% ........ _iterate 98.5% tests/cpuburn.py:19 22.9% .......... _next_rand 23.5% tests/cpuburn.py:14 22.9% ............ JIT code 100.0% 0x7fa7dba57a10 74.7% .......... JIT code 76.4% 0x7fa7dba57a10 0.1% .......... JIT code 0.1% 0x7fa7dba583b0 0.5% ........ _next_rand 0.5% tests/cpuburn.py:14 0.0% ........ JIT code 0.0% 0x7fa7dba583b0
There is also an option--html to emit the same information as HTML to viewin a browser. In this case, the tree branches can be interactively expanded andcollapsed.
vmprof supports line profiling mode, which enables collecting and showing the statistics for separate linesinside functions.
To enable collection of lines statistics add--lines argument to vmprof:
$python -m vmprof --lines -o<output-file><your program><your program args>Or passlines=True argument tovmprof.enable function, when calling vmprof from code.
To see line statistics for all functions use thelines mode ofvmprofshow:
$vmprofshow<output-file> linesTo see line statistics for a specific function use the--filter argument with the function name:
$vmprofshow<output-file> lines --filter<function-name>You will see the result:
$vmprofshow vmprof_cpuburn.dat lines --filter _next_randTotal hits: 1170 sFile: tests/cpuburn.pyFunction: _next_rand at line 14Line # Hits % Hits Line Contents======================================= 14 38 3.2 def _next_rand(self): 15 # http://rosettacode.org/wiki/Linear_congruential_generator 16 835 71.4 self._rand = (1103515245 * self._rand + 12345) & 0x7fffffff 17 297 25.4 return self._rand
vmprofshow also has aflat mode.
While the tree-based and line-based output styles forvmprofshow give a goodview of where time is spent when viewed from the 'root' of the call graph,sometimes it is desirable to get a view from 'leaves' instead. This is particularlyhelpful when functions exist that get called from multiple places, where eachinvocation does not consume much time, but all invocations taken together doamount to a substantial cost.
$vmprofshow vmprof_cpuburn.dat flat andreask_work@dunkel 15:24 28.895% - _PyFunction_Vectorcall:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/call.c:389 18.076% - _iterate:cpuburn.py:20 17.298% - _next_rand:cpuburn.py:15 5.863% - <native symbol 0x563a5f4eea51>:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/longobject.c:3707 5.831% - PyObject_SetAttr:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/object.c:1031 4.924% - <native symbol 0x563a5f43fc01>:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/abstract.c:787 4.762% - PyObject_GetAttr:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/object.c:931 4.373% - <native symbol 0x563a5f457eb1>:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/abstract.c:1071 3.758% - PyNumber_Add:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/abstract.c:957 3.110% - <native symbol 0x563a5f47c291>:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/longobject.c:4848 1.587% - PyNumber_Multiply:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/abstract.c:988 1.166% - _PyObject_GetMethod:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/object.c:1139 0.356% - <native symbol 0x563a5f4ed8f1>:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/longobject.c:3432 0.000% - <native symbol 0x7f0dce8cca80>:-:0 0.000% - test:cpuburn.py:36 0.000% - burn:cpuburn.py:27
Sometimes it may be desirable to exclude "native" functions:
$vmprofshow vmprof_cpuburn.dat flat --no-native andreask_work@dunkel 15:27 53.191% - _next_rand:cpuburn.py:15 46.809% - _iterate:cpuburn.py:20 0.000% - test:cpuburn.py:36 0.000% - burn:cpuburn.py:27
Note that the output represents the time spent in each function,exclusive offunctions called. (In--no-native mode, native-code callees remain includedin the total.)
Sometimes it may also be desirable to get timingsinclusive of called functions:
$ vmprofshow vmprof_cpuburn.dat flat --include-callees andreask_work@dunkel 15:31 100.000% - <native symbol 0x7f0dce8cca80>:-:0 100.000% - test:cpuburn.py:36 100.000% - burn:cpuburn.py:27 100.000% - _iterate:cpuburn.py:20 53.191% - _next_rand:cpuburn.py:15 28.895% - _PyFunction_Vectorcall:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/call.c:389 7.807% - PyNumber_Multiply:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/abstract.c:988 7.483% - <native symbol 0x563a5f457eb1>:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/abstract.c:1071 6.220% - <native symbol 0x563a5f4eea51>:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/longobject.c:3707 5.831% - PyObject_SetAttr:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/object.c:1031 4.924% - <native symbol 0x563a5f43fc01>:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/abstract.c:787 4.762% - PyObject_GetAttr:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/object.c:931 3.758% - PyNumber_Add:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/abstract.c:957 3.110% - <native symbol 0x563a5f47c291>:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/longobject.c:4848 1.166% - _PyObject_GetMethod:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/object.c:1139 0.356% - <native symbol 0x563a5f4ed8f1>:/home/conda/feedstock_root/build_artifacts/python-split_1608956461873/work/Objects/longobject.c:3432This view is quite similar to the "tree" view, minus the nesting.
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vmprof - a statistical program profiler
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