auto-profiler 2.0

A realtime timer for profiling a Python function or snippet.

auto_profiler

A realtime timer for profiling a Python function or snippet.

Features

• Filtering external libraries profiling.
• Filtering very short time functions-> threshold
• Allow depth: you can easily find the time consuming function
• Allow loop or multiple function call
• Allow recursive function call
• Disable it globaly by Profiler.GlobalDisable=True to save time :)

Installation

Release version:

$ pip install auto_profiler

Development version:

$ pip install -e git+https://github.com/modaresimr/auto_profiler.git#egg=auto_profiler

Install in Jupyter

$ pip install ipytree
$ jupyter nbextension enable --py --sys-prefix ipytree

Quick start

Jupyter Notebook

Auto profiling

More commonly, chances are that we want to measure the execution time of an entry function and all its subfunctions. In this case, it's too tedious to do it manually, and we can leverage Profiler to inject all the timing points for us automatically:

import time # line number 1
import random

from auto_profiler import Profiler, Tree

def f1():
    mysleep(.6+random.random())

def mysleep(t):
    time.sleep(t)

def fact(i):
    f1()
    if(i==1):
        return 1
    return i*fact(i-1)


@Profiler()
def main():
    for i in range(5):
        f1()

    fact(3)


if __name__ == '__main__':
    main()

Example Output

In Jupyter

Time   [Hits * PerHit] Function name [Called from] [function location]
-----------------------------------------------------------------------
8.974s [1 * 8.974]  main  [auto-profiler/profiler.py:267]  [/test/t2.py:30]
├── 5.954s [5 * 1.191]  f1  [/test/t2.py:34]  [/test/t2.py:14]
│   └── 5.954s [5 * 1.191]  mysleep  [/test/t2.py:15]  [/test/t2.py:17]
│       └── 5.954s [5 * 1.191]  <time.sleep>
|
|
|   # The rest is for the example recursive function call fact
└── 3.020s [1 * 3.020]  fact  [/test/t2.py:36]  [/test/t2.py:20]
    ├── 0.849s [1 * 0.849]  f1  [/test/t2.py:21]  [/test/t2.py:14]
    │   └── 0.849s [1 * 0.849]  mysleep  [/test/t2.py:15]  [/test/t2.py:17]
    │       └── 0.849s [1 * 0.849]  <time.sleep>
    └── 2.171s [1 * 2.171]  fact  [/test/t2.py:24]  [/test/t2.py:20]
        ├── 1.552s [1 * 1.552]  f1  [/test/t2.py:21]  [/test/t2.py:14]
        │   └── 1.552s [1 * 1.552]  mysleep  [/test/t2.py:15]  [/test/t2.py:17]
        └── 0.619s [1 * 0.619]  fact  [/test/t2.py:24]  [/test/t2.py:20]
            └── 0.619s [1 * 0.619]  f1  [/test/t2.py:21]  [/test/t2.py:14]

Manual profiling

Sometimes, we only want to measure the execution time of partial snippets or a few functions, then we can inject all timing points into our code manually by leveraging Timer:

# manual_example.py

import time

from auto_profiler import Timer, Tree


def main():
    t = Timer('sleep1', parent_name='main').start()
    time.sleep(1)
    t.stop()

    t = Timer('sleep2', parent_name='main').start()
    time.sleep(1.5)
    t.stop()

    print(Tree(Timer.root))


if __name__ == '__main__':
    main()

Run the example code:

$ python manual_example.py

and it will show you the profiling result:

2.503s  main
├── 1.001s  sleep1
└── 1.501s  sleep2

advanced setup

def show(p):
    print('Time   [Hits * PerHit] Function name [Called from] [Function Location]\n'+\
          '-----------------------------------------------------------------------')
    print(Tree(p.root, threshold=0.5))
    
@Profiler(depth=4, on_disable=show)
def main():
    for i in range(5):
        f1()

    fact(3)

Supported frameworks

While you can do profiling on normal Python code, as a web developer, chances are that you will usually do profiling on web service code.

Currently supported web frameworks:

• Flask

Examples

For profiling web service code (involving web requests), check out examples.

License

MIT