mbench

---

Simple benchmarking tool for a module, function, or block of code.

Installation

pip install mbench

Usage

from mbench import profileme
profileme()

def some_function():
    print("Hello")

some_function()

        Profile Information for         
     __main__.test_get_object_speed     
┏━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃           Metric ┃ Value             ┃
┡━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│         Duration │ 1.461318 seconds  │
│         CPU time │ 24.613764 seconds │
│     Memory usage │ 250.56 MB         │
│        GPU usage │ 0.00 B            │
│       GPU usages │                   │
│        I/O usage │ 0.00 B            │
│     Avg Duration │ 0.292264 seconds  │
│     Avg CPU time │ 4.922753 seconds  │
│ Avg Memory usage │ 50.11 MB          │
│    Avg GPU usage │ 0.00 B            │
│   Avg GPU usages │                   │
│    Avg I/O usage │ 0.00 B            │
│      Total calls │ 5                 │
└──────────────────┴───────────────────┘

As a Decorator

from mbench import profile
@profile
def some_function():
    print("Hello")

As a Context Manager

from mbench import profiling
with profiling:
  run_anything()

when calling

Functions you want to profile must

1. Be defined in the same module that the profileme function is being called.
2. Be called after profileme(when="calling") is called.

when called

Functions you want to profile must

1. Be called in the same module that the profileme function is being called.
2. Be called after profileme(when="called") is called.

Docs

profileme(when: Literal['called', 'calling'] = 'called')
    Profile all functions in a module. Set `when` to 'calling' to profile only the functions called by the target module.

License

mbench is distributed under the terms of the MIT License.