Python 实用宝典

Question 1

I’ve been using cProfile to profile my code, and it’s been working great. I also use gprof2dot.py to visualize the results (makes it a little clearer).

However, cProfile (and most other Python profilers I’ve seen so far) seem to only profile at the function-call level. This causes confusion when certain functions are called from different places – I have no idea if call #1 or call #2 is taking up the majority of the time. This gets even worse when the function in question is six levels deep, called from seven other places.

How do I get a line-by-line profiling?

Instead of this:

function #12, total time: 2.0s

I’d like to see something like this:

function #12 (called from somefile.py:102) 0.5s
function #12 (called from main.py:12) 1.5s

cProfile does show how much of the total time “transfers” to the parent, but again this connection is lost when you have a bunch of layers and interconnected calls.

Ideally, I’d love to have a GUI that would parse through the data, then show me my source file with a total time given to each line. Something like this:

main.py:

a = 1 # 0.0s
result = func(a) # 0.4s
c = 1000 # 0.0s
result = func(c) # 5.0s

Then I’d be able to click on the second “func(c)” call to see what’s taking up time in that call, separate from the “func(a)” call.

Does that make sense? Is there any profiling library that collects this type of information? Is there some awesome tool I’ve missed?

Question 2

I believe that’s what Robert Kern’s line_profiler is intended for. From the link:

File: pystone.py
Function: Proc2 at line 149
Total time: 0.606656 s

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
   149                                           @profile
   150                                           def Proc2(IntParIO):
   151     50000        82003      1.6     13.5      IntLoc = IntParIO + 10
   152     50000        63162      1.3     10.4      while 1:
   153     50000        69065      1.4     11.4          if Char1Glob == 'A':
   154     50000        66354      1.3     10.9              IntLoc = IntLoc - 1
   155     50000        67263      1.3     11.1              IntParIO = IntLoc - IntGlob
   156     50000        65494      1.3     10.8              EnumLoc = Ident1
   157     50000        68001      1.4     11.2          if EnumLoc == Ident1:
   158     50000        63739      1.3     10.5              break
   159     50000        61575      1.2     10.1      return IntParIO

Hope that helps!

Question 3

You could also use pprofile(pypi). If you want to profile the entire execution, it does not require source code modification. You can also profile a subset of a larger program in two ways:

toggle profiling when reaching a specific point in the code, such as:

import pprofile
profiler = pprofile.Profile()
with profiler:
    some_code
# Process profile content: generate a cachegrind file and send it to user.

# You can also write the result to the console:
profiler.print_stats()

# Or to a file:
profiler.dump_stats("/tmp/profiler_stats.txt")

toggle profiling asynchronously from call stack (requires a way to trigger this code in considered application, for example a signal handler or an available worker thread) by using statistical profiling:

import pprofile
profiler = pprofile.StatisticalProfile()
statistical_profiler_thread = pprofile.StatisticalThread(
    profiler=profiler,
)
with statistical_profiler_thread:
    sleep(n)
# Likewise, process profile content

Code annotation output format is much like line profiler:

$ pprofile --threads 0 demo/threads.py
Command line: ['demo/threads.py']
Total duration: 1.00573s
File: demo/threads.py
File duration: 1.00168s (99.60%)
Line #|      Hits|         Time| Time per hit|      %|Source code
------+----------+-------------+-------------+-------+-----------
     1|         2|  3.21865e-05|  1.60933e-05|  0.00%|import threading
     2|         1|  5.96046e-06|  5.96046e-06|  0.00%|import time
     3|         0|            0|            0|  0.00%|
     4|         2|   1.5974e-05|  7.98702e-06|  0.00%|def func():
     5|         1|      1.00111|      1.00111| 99.54%|  time.sleep(1)
     6|         0|            0|            0|  0.00%|
     7|         2|  2.00272e-05|  1.00136e-05|  0.00%|def func2():
     8|         1|  1.69277e-05|  1.69277e-05|  0.00%|  pass
     9|         0|            0|            0|  0.00%|
    10|         1|  1.81198e-05|  1.81198e-05|  0.00%|t1 = threading.Thread(target=func)
(call)|         1|  0.000610828|  0.000610828|  0.06%|# /usr/lib/python2.7/threading.py:436 __init__
    11|         1|  1.52588e-05|  1.52588e-05|  0.00%|t2 = threading.Thread(target=func)
(call)|         1|  0.000438929|  0.000438929|  0.04%|# /usr/lib/python2.7/threading.py:436 __init__
    12|         1|  4.79221e-05|  4.79221e-05|  0.00%|t1.start()
(call)|         1|  0.000843048|  0.000843048|  0.08%|# /usr/lib/python2.7/threading.py:485 start
    13|         1|  6.48499e-05|  6.48499e-05|  0.01%|t2.start()
(call)|         1|   0.00115609|   0.00115609|  0.11%|# /usr/lib/python2.7/threading.py:485 start
    14|         1|  0.000205994|  0.000205994|  0.02%|(func(), func2())
(call)|         1|      1.00112|      1.00112| 99.54%|# demo/threads.py:4 func
(call)|         1|  3.09944e-05|  3.09944e-05|  0.00%|# demo/threads.py:7 func2
    15|         1|  7.62939e-05|  7.62939e-05|  0.01%|t1.join()
(call)|         1|  0.000423908|  0.000423908|  0.04%|# /usr/lib/python2.7/threading.py:653 join
    16|         1|  5.26905e-05|  5.26905e-05|  0.01%|t2.join()
(call)|         1|  0.000320196|  0.000320196|  0.03%|# /usr/lib/python2.7/threading.py:653 join

Note that because pprofile does not rely on code modification it can profile top-level module statements, allowing to profile program startup time (how long it takes to import modules, initialise globals, …).

It can generate cachegrind-formatted output, so you can use kcachegrind to browse large results easily.

Disclosure: I am pprofile author.

Question 4

You can take help of line_profiler package for this

1. 1st install the package:

    pip install line_profiler

2. Use magic command to load the package to your python/notebook environment

    %load_ext line_profiler

3. If you want to profile the codes for a function then
do as follows:

    %lprun -f demo_func demo_func(arg1, arg2)

you will get a nice formatted output with all the details if you follow these steps :)

Line #      Hits      Time    Per Hit   % Time  Line Contents
 1                                           def demo_func(a,b):
 2         1        248.0    248.0     64.8      print(a+b)
 3         1         40.0     40.0     10.4      print(a)
 4         1         94.0     94.0     24.5      print(a*b)
 5         1          1.0      1.0      0.3      return a/b

Question 5

Just to improve @Joe Kington ‘s above-mentioned answer.

For Python 3.x, use line_profiler:

Installation:

pip install line_profiler

Usage:

Suppose you have the program main.py and within it, functions fun_a() and fun_b() that you want to profile with respect to time; you will need to use the decorator @profile just before the function definitions. For e.g.,

@profile
def fun_a():
    #do something

@profile
def fun_b():
    #do something more

if __name__ == '__main__':
    fun_a()
    fun_b()

The program can be profiled by executing the shell command:

$ kernprof -l -v main.py

The arguments can be fetched using $ kernprof -h

Usage: kernprof [-s setupfile] [-o output_file_path] scriptfile [arg] ...

Options:
  --version             show program's version number and exit
  -h, --help            show this help message and exit
  -l, --line-by-line    Use the line-by-line profiler from the line_profiler
                        module instead of Profile. Implies --builtin.
  -b, --builtin         Put 'profile' in the builtins. Use 'profile.enable()'
                        and 'profile.disable()' in your code to turn it on and
                        off, or '@profile' to decorate a single function, or
                        'with profile:' to profile a single section of code.
  -o OUTFILE, --outfile=OUTFILE
                        Save stats to <outfile>
  -s SETUP, --setup=SETUP
                        Code to execute before the code to profile
  -v, --view            View the results of the profile in addition to saving
                        it.

The results will be printed on the console as:

Total time: 17.6699 s
File: main.py
Function: fun_a at line 5

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
    5                                           @profile
    6                                           def fun_a():
...

EDIT: The results from the profilers can be parsed using the TAMPPA package. Using it, we can get line-by-line desired plots as

Question 6

PyVmMonitor has a live-view which can help you there (you can connect to a running program and get statistics from it).

See: http://www.pyvmmonitor.com/

Python 实用宝典

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