pyrofiler 0.1.4

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

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.