6.7. Profiling

6.7.1. Rationale

  • A profile is a set of statistics that describes how often and for how long various parts of the program executed

  • The profiler modules are designed to provide an execution profile for a given program, not for benchmarking purposes (for that, there is timeit for reasonably accurate results). This particularly applies to benchmarking Python code against C code: the profilers introduce overhead for Python code, but not for C-level functions, and so the C code would seem faster than any Python one.

6.7.2. Profilers

../../_images/performance-profiler-factorial.png

Figure 6.1. PyCharm Profiler Factorial 2

../../_images/performance-profiler-fibonacci.png

Figure 6.2. PyCharm Profiler Fibonacci 1

../../_images/performance-profiler-pycharm.png

Figure 6.3. PyCharm Profiler 3

6.7.3. Profiling with yappi

>>> 
... import yappi
...
... def a():
...     for _ in range(1_000):  # do something CPU heavy
...         pass
...
... yappi.set_clock_type("cpu") # Use set_clock_type("wall") for wall time
... yappi.start()
... a()
...
... yappi.get_func_stats().print_all()
... yappi.get_thread_stats().print_all() 
Clock type: CPU
Ordered by: totaltime, desc

name ncall tsub ttot tavg doc.py:5 a 1 0.117907 0.117907 0.117907

name id tid ttot scnt _MainThread 0 139867147315008 0.118297 1

>>> 
... import yappi
... import time
... import threading
...
... _NTHREAD = 3
...
...
... def _work(n):
...     time.sleep(n * 0.1)
...
...
... yappi.start()
...
... threads = []
... # generate _NTHREAD threads
... for i in range(_NTHREAD):
...     t = threading.Thread(target=_work, args=(i + 1, ))
...     t.start()
...     threads.append(t)
... # wait all threads to finish
... for t in threads:
...     t.join()
...
... yappi.stop()
...
... # retrieve thread stats by their thread id (given by yappi)
... threads = yappi.get_thread_stats()
... for thread in threads:
...     print("Function stats for (%s) (%d)" % (thread.name, thread.id)
...     )  # it is the Thread.__class__.__name__
...     yappi.get_func_stats(ctx_id=thread.id).print_all()  
Function stats for (Thread) (3)

name ncall tsub ttot tavg ..hon3.7/threading.py:859 Thread.run 1 0.000017 0.000062 0.000062 doc3.py:8 _work 1 0.000012 0.000045 0.000045

Function stats for (Thread) (2)

name ncall tsub ttot tavg ..hon3.7/threading.py:859 Thread.run 1 0.000017 0.000065 0.000065 doc3.py:8 _work 1 0.000010 0.000048 0.000048

Function stats for (Thread) (1)

name ncall tsub ttot tavg ..hon3.7/threading.py:859 Thread.run 1 0.000010 0.000043 0.000043 doc3.py:8 _work 1 0.000006 0.000033 0.000033

Async application:

>>> 
... import asyncio
... import yappi
...
... async def foo():
...     await asyncio.sleep(1.0)
...     await baz()
...     await asyncio.sleep(0.5)
...
... async def bar():
...     await asyncio.sleep(2.0)
...
... async def baz():
...     await asyncio.sleep(1.0)
...
...
... yappi.set_clock_type("WALL")
...
... with yappi.run():
...     asyncio.run(foo())
...     asyncio.run(bar())
...
... yappi.get_func_stats().print_all()  
Clock type: WALL
Ordered by: totaltime, desc

name ncall tsub ttot tavg doc4.py:5 foo 1 0.000030 2.503808 2.503808 doc4.py:11 bar 1 0.000012 2.002492 2.002492 doc4.py:15 baz 1 0.000013 1.001397 1.001397

6.7.4. Profiling with cProfile

>>> 
... import cProfile
...
... cProfile.run('import re; re.compile("foo|bar")')  
       216 function calls (209 primitive calls) in 0.000 seconds
Ordered by: standard name
ncalls  tottime  percall  cumtime  percall filename:lineno(function)
     1    0.000    0.000    0.000    0.000 <string>:1(<module>)
     2    0.000    0.000    0.000    0.000 enum.py:284(__call__)
     2    0.000    0.000    0.000    0.000 enum.py:526(__new__)
     1    0.000    0.000    0.000    0.000 enum.py:836(__and__)
     1    0.000    0.000    0.000    0.000 pydev_import_hook.py:16(do_import)
     1    0.000    0.000    0.000    0.000 re.py:232(compile)
     1    0.000    0.000    0.000    0.000 re.py:271(_compile)
     1    0.000    0.000    0.000    0.000 sre_compile.py:249(_compile_charset)
     1    0.000    0.000    0.000    0.000 sre_compile.py:276(_optimize_charset)
     2    0.000    0.000    0.000    0.000 sre_compile.py:453(_get_iscased)
     1    0.000    0.000    0.000    0.000 sre_compile.py:461(_get_literal_prefix)
     1    0.000    0.000    0.000    0.000 sre_compile.py:492(_get_charset_prefix)
     1    0.000    0.000    0.000    0.000 sre_compile.py:536(_compile_info)
     2    0.000    0.000    0.000    0.000 sre_compile.py:595(isstring)
     1    0.000    0.000    0.000    0.000 sre_compile.py:598(_code)
   3/1    0.000    0.000    0.000    0.000 sre_compile.py:71(_compile)
     1    0.000    0.000    0.000    0.000 sre_compile.py:759(compile)
     3    0.000    0.000    0.000    0.000 sre_parse.py:111(__init__)
     7    0.000    0.000    0.000    0.000 sre_parse.py:160(__len__)
    18    0.000    0.000    0.000    0.000 sre_parse.py:164(__getitem__)
     7    0.000    0.000    0.000    0.000 sre_parse.py:172(append)
   3/1    0.000    0.000    0.000    0.000 sre_parse.py:174(getwidth)
     1    0.000    0.000    0.000    0.000 sre_parse.py:224(__init__)
     8    0.000    0.000    0.000    0.000 sre_parse.py:233(__next)
     2    0.000    0.000    0.000    0.000 sre_parse.py:249(match)
     6    0.000    0.000    0.000    0.000 sre_parse.py:254(get)
     1    0.000    0.000    0.000    0.000 sre_parse.py:286(tell)
     1    0.000    0.000    0.000    0.000 sre_parse.py:417(_parse_sub)
     2    0.000    0.000    0.000    0.000 sre_parse.py:475(_parse)
     1    0.000    0.000    0.000    0.000 sre_parse.py:76(__init__)
     2    0.000    0.000    0.000    0.000 sre_parse.py:81(groups)
     1    0.000    0.000    0.000    0.000 sre_parse.py:903(fix_flags)
     1    0.000    0.000    0.000    0.000 sre_parse.py:919(parse)
     1    0.000    0.000    0.000    0.000 {built-in method _sre.compile}
     1    0.000    0.000    0.000    0.000 {built-in method builtins.__import__}
     1    0.000    0.000    0.000    0.000 {built-in method builtins.exec}
     25    0.000    0.000    0.000    0.000 {built-in method builtins.isinstance}
     29/26    0.000    0.000    0.000    0.000 {built-in method builtins.len}
     2    0.000    0.000    0.000    0.000 {built-in method builtins.max}
     9    0.000    0.000    0.000    0.000 {built-in method builtins.min}
     6    0.000    0.000    0.000    0.000 {built-in method builtins.ord}
     48    0.000    0.000    0.000    0.000 {method 'append' of 'list' objects}
     1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}
     5    0.000    0.000    0.000    0.000 {method 'find' of 'bytearray' objects}
     1    0.000    0.000    0.000    0.000 {method 'items' of 'dict' objects}
Table 6.2. cProfile

Name

Description

ncalls

for the number of calls

tottime

for the total time spent in the given function (and excluding time made in calls to sub-functions)

percall

is the quotient of tottime divided by ncalls

cumtime

is the cumulative time spent in this and all subfunctions (from invocation till exit)

percall

is the quotient of cumtime divided by primitive calls

filename:lineno(function)

provides the respective data of each function

Table 6.3. cProfile

Name

Description

calls

call count

cumulative

cumulative time

cumtime

cumulative time

file

file name

filename

file name

module

file name

ncalls

call count

pcalls

primitive call count

line

line number

name

function name

nfl

name/file/line

stdname

standard name

time

internal time

tottime

internal time

$ python -m cProfile [-o output_file] [-s sort_order] FILE.py

6.7.5. References

1

https://www.koderdojo.com/media/default/articles/profile-fibonacci-number-30-pycharm.png

2

https://resources.jetbrains.com/help/img/idea/2020.3/profiler_call_graph.png

3

https://img-blog.csdnimg.cn/20191008141801582.png