unix - Is there any simple way to benchmark Python script?

Question

Usually I use shell command time. My purpose is to test if data is small, medium, large or very large set, how much time and memory usage will be.

Any tools for Linux or just Python to do this?

Answer 1

Have a look at timeit, the python profiler and pycallgraph. Also make sure to have a look at the comment below by nikicc mentioning "SnakeViz". It gives you yet another visualisation of profiling data which can be helpful.

timeit

def test():
    """Stupid test function"""
    lst = []
    for i in range(100):
        lst.append(i)

if __name__ == '__main__':
    import timeit
    print(timeit.timeit("test()", setup="from __main__ import test"))

    # For Python>=3.5 one can also write:
    print(timeit.timeit("test()", globals=locals()))

Essentially, you can pass it python code as a string parameter, and it will run in the specified amount of times and prints the execution time. The important bits from the docs:

timeit.timeit(stmt='pass', setup='pass', timer=<default timer>, number=1000000, globals=None) Create a Timer instance with the given statement, setup code and timer function and run its timeit method with number executions. The optional globals argument specifies a namespace in which to execute the code.

... and:

Timer.timeit(number=1000000) Time number executions of the main statement. This executes the setup statement once, and then returns the time it takes to execute the main statement a number of times, measured in seconds as a float. The argument is the number of times through the loop, defaulting to one million. The main statement, the setup statement and the timer function to be used are passed to the constructor.

Note: By default, timeit temporarily turns off garbage collection during the timing. The advantage of this approach is that it makes independent timings more comparable. This disadvantage is that GC may be an important component of the performance of the function being measured. If so, GC can be re-enabled as the first statement in the setup string. For example:

timeit.Timer('for i in xrange(10): oct(i)', 'gc.enable()').timeit()

Profiling

Profiling will give you a much more detailed idea about what's going on. Here's the "instant example" from the official docs:

import cProfile
import re
cProfile.run('re.compile("foo|bar")')

Which will give you:

      197 function calls (192 primitive calls) in 0.002 seconds

Ordered by: standard name

ncalls  tottime  percall  cumtime  percall filename:lineno(function)
     1    0.000    0.000    0.001    0.001 <string>:1(<module>)
     1    0.000    0.000    0.001    0.001 re.py:212(compile)
     1    0.000    0.000    0.001    0.001 re.py:268(_compile)
     1    0.000    0.000    0.000    0.000 sre_compile.py:172(_compile_charset)
     1    0.000    0.000    0.000    0.000 sre_compile.py:201(_optimize_charset)
     4    0.000    0.000    0.000    0.000 sre_compile.py:25(_identityfunction)
   3/1    0.000    0.000    0.000    0.000 sre_compile.py:33(_compile)

Both of these modules should give you an idea about where to look for bottlenecks.

Also, to get to grips with the output of profile, have a look at this post

pycallgraph

NOTE pycallgraph has been officially abandoned since Feb. 2018. As of Dec. 2020 it was still working on Python 3.6 though. As long as there are no core changes in how python exposes the profiling API it should remain a helpful tool though.

This module uses graphviz to create callgraphs like the following:

You can easily see which paths used up the most time by colour. You can either create them using the pycallgraph API, or using a packaged script:

pycallgraph graphviz -- ./mypythonscript.py

The overhead is quite considerable though. So for already long-running processes, creating the graph can take some time.

Answer 2

I use a simple decorator to time the func

import time

def st_time(func):
    """
        st decorator to calculate the total time of a func
    """

    def st_func(*args, **keyArgs):
        t1 = time.time()
        r = func(*args, **keyArgs)
        t2 = time.time()
        print("Function=%s, Time=%s" % (func.__name__, t2 - t1))
        return r

    return st_func

Answer 3

The timeit module was slow and weird, so I wrote this:

def timereps(reps, func):
    from time import time
    start = time()
    for i in range(0, reps):
        func()
    end = time()
    return (end - start) / reps

Example:

import os
listdir_time = timereps(10000, lambda: os.listdir('/'))
print "python can do %d os.listdir('/') per second" % (1 / listdir_time)

For me, it says:

python can do 40925 os.listdir('/') per second

This is a primitive sort of benchmarking, but it's good enough.

Answer 4

I usually do a quick time ./script.py to see how long it takes. That does not show you the memory though, at least not as a default. You can use /usr/bin/time -v ./script.py to get a lot of information, including memory usage.

Answer 5

Memory Profiler for all your memory needs.

https://pypi.python.org/pypi/memory_profiler

Run a pip install:

pip install memory_profiler

Import the library:

import memory_profiler

Add a decorator to the item you wish to profile:

@profile
def my_func():
    a = [1] * (10 ** 6)
    b = [2] * (2 * 10 ** 7)
    del b
    return a

if __name__ == '__main__':
    my_func()

Execute the code:

python -m memory_profiler example.py

Recieve the output:

 Line #    Mem usage  Increment   Line Contents
 ==============================================
 3                           @profile
 4      5.97 MB    0.00 MB   def my_func():
 5     13.61 MB    7.64 MB       a = [1] * (10 ** 6)
 6    166.20 MB  152.59 MB       b = [2] * (2 * 10 ** 7)
 7     13.61 MB -152.59 MB       del b
 8     13.61 MB    0.00 MB       return a

Examples are from the docs, linked above.

Answer 6

Have a look at nose and at one of its plugins, this one in particular.

Once installed, nose is a script in your path, and that you can call in a directory which contains some python scripts:

$: nosetests

This will look in all the python files in the current directory and will execute any function that it recognizes as a test: for example, it recognizes any function with the word test_ in its name as a test.

So you can just create a python script called test_yourfunction.py and write something like this in it:

$: cat > test_yourfunction.py

def test_smallinput():
    yourfunction(smallinput)

def test_mediuminput():
    yourfunction(mediuminput)

def test_largeinput():
    yourfunction(largeinput)

Then you have to run

$: nosetest --with-profile --profile-stats-file yourstatsprofile.prof testyourfunction.py

and to read the profile file, use this python line:

python -c "import hotshot.stats ; stats = hotshot.stats.load('yourstatsprofile.prof') ; stats.sort_stats('time', 'calls') ; stats.print_stats(200)"

Answer 7

snakeviz interactive viewer for cProfile

https://github.com/jiffyclub/snakeviz/

cProfile was mentioned at https://stackoverflow.com/a/1593034/895245 and snakeviz was mentioned in a comment, but I wanted to highlight it further.

It is very hard to debug program performance just by looking at cprofile / pstats output, because they can only total times per function out of the box.

However, what we really need in general is to see a nested view containing the stack traces of each call to actually find the main bottlenecks easily.

And this is exactly what snakeviz provides via its default "icicle" view.

First you have to dump the cProfile data to a binary file, and then you can snakeviz on that

pip install -u snakeviz
python -m cProfile -o results.prof myscript.py
snakeviz results.prof

This prints an URL to stdout which you can open on your browser, which contains the desired output that looks like this:

and you can then:

• hover each box to see the full path to the file that contains the function
• click on a box to make that box show up on the top as a way to zoom in

More profile oriented question: How can you profile a Python script?

Answer 8

Be carefull timeit is very slow, it take 12 second on my medium processor to just initialize (or maybe run the function). you can test this accepted answer

def test():
    lst = []
    for i in range(100):
        lst.append(i)

if __name__ == '__main__':
    import timeit
    print(timeit.timeit("test()", setup="from __main__ import test")) # 12 second

for simple thing I will use time instead, on my PC it return the result 0.0

import time

def test():
    lst = []
    for i in range(100):
        lst.append(i)

t1 = time.time()

test()

result = time.time() - t1
print(result) # 0.000000xxxx

Answer 9

The easy way to quickly test any function is to use this syntax : %timeit my_code

For instance :

%timeit a = 1

13.4 ns ± 0.781 ns per loop (mean ± std. dev. of 7 runs, 100000000 loops each)

Answer 10

If you don't want to write boilerplate code for timeit and get easy to analyze results, take a look at benchmarkit. Also it saves history of previous runs, so it is easy to compare the same function over the course of development.

# pip install benchmarkit

from benchmarkit import benchmark, benchmark_run

N = 10000
seq_list = list(range(N))
seq_set = set(range(N))

SAVE_PATH = '/tmp/benchmark_time.jsonl'

@benchmark(num_iters=100, save_params=True)
def search_in_list(num_items=N):
    return num_items - 1 in seq_list

@benchmark(num_iters=100, save_params=True)
def search_in_set(num_items=N):
    return num_items - 1 in seq_set

benchmark_results = benchmark_run(
   [search_in_list, search_in_set],
   SAVE_PATH,
   comment='initial benchmark search',
)  

Prints to terminal and returns list of dictionaries with data for the last run. Command line entrypoints also available.

If you change N=1000000 and rerun