8.3. Profiling¶

8.3.1. 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.

8.3.2. Profilers¶

cProfile (CPython built-in)
yappi https://github.com/sumerc/yappi
PyCharm IDE
vmprof https://vmprof.readthedocs.io/en/latest/

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

Figure 8.1. PyCharm Profiler Factorial [jetbrains]¶

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

Figure 8.2. PyCharm Profiler Fibonacci [koderdojo]¶

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

Figure 8.3. PyCharm Profiler [csdnimg]¶

8.3.3. Profiling with yappi¶

import yappi

def a():
    for _ in range(10000000):  # 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

8.3.4. 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 8.1. 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 8.2. 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

8.3.5. References¶

koderdojo: https://www.koderdojo.com/media/default/articles/profile-fibonacci-number-30-pycharm.png
jetbrains: https://resources.jetbrains.com/help/img/idea/2020.3/profiler_call_graph.png
csdnimg: https://img-blog.csdnimg.cn/20191008141801582.png