pyrofiler 0.1.11

Toolset for granular and live profiling

Pyrofiler

Toolset for granular memory and cpu live profiling

Quick start

Contextmanager that measures time of execution

# examples/simple_profile.py
import pyrofiler
import time

with pyrofiler.timing('Time elapsed'):
    time.sleep(1)

$ python simple_profile.py
Time elapsed : 1.001563310623169

Decorators for profiling functions

# examples/simple_profile_cpu.py
import pyrofiler

@pyrofiler.cpu_util(description='Cpu usage')
@pyrofiler.timed('Time elapsed')
def sum_series(x, N):
    return sum([x**i/i for i in range(1, N)])

sum_series(.3, 1000_000)

$ python simple_profile_cpu.py
Time elapsed : 0.13478374481201172
Cpu usage : 29.4

Aggregate the results in common context:

# examples/profile_with_context.py
from pyrofiler import Profiler
import time

prof = Profiler()

with prof.timing('Time 1'):
    time.sleep(1)

with prof.timing('Time 2'):
    time.sleep(1.5)

print('Profiling data recorded:')
print(prof.data)

$ python profile_with_context.py
Time 1 : 1.0011215209960938
Time 2 : 1.5020403861999512
Profiling data recorded:
{'Time 1': 1.0011215209960938, 'Time 2': 1.5020403861999512}

You can use other actions, for example appending results to some list in data. Check the documentation for more use cases

Design

There are following types of objects in pyrofiler:

1. Measures, which are run as a context manager

2. Decorators, that are based on measures

3. Profiler class that uses decorators to aggregate data

Callbacks

The decorators have an optional argument callback, to which you can pass a function that will handle the data. The function will be passed profiling results as a first argument, as well as any other arguments that you provided to original decorator.

Here, a custom spice argument is provided

def print_spicy_time(time, spice):
    print(f'Spice {spice} took {time} seconds')

@pyrofiler.timed(spice='spicy', callback=print_spicy_time)
def spicy_sleep():
    time.sleep(10)

Similar products

• Syrpy https://github.com/jeetsukumaran/Syrupy

• Scalene https://github.com/emeryberger/scalene

• … and lots of others

Problems

Either you have a cli tool that profiles memory and cpu, but no code api for granular data

or you have stuff like decorators and no memory profiling

Having a live dashboard would help also, use https://github.com/libvis for that

Features

• TODO

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.1.0 (2020-03-04)

• First release on PyPI.