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Benchmark utils

Utils for benchmark - wrapper over python timeit.

Tested on python 3.8 - 3.12

Install

Install from pypi:

pip install benchmark_utils

Or install from github repo:

pip install git+https://github.com/ayasyrev/benchmark_utils.git

Basic use.

Lets benchmark some (dummy) functions.

output

from time import sleep


def func_to_test_1(sleep_time: float = 0.1, mult: int = 1) -> None:
    """simple 'sleep' func for test"""
    sleep(sleep_time * mult)


def func_to_test_2(sleep_time: float = 0.11, mult: int = 1) -> None:
    """simple 'sleep' func for test"""
    sleep(sleep_time * mult)</details>

<!-- cell -->
<details open> <summary>output</summary>

Let's create benchmark.</details>

<!-- cell -->
<details open> <summary>output</summary>
```python
from benchmark_utils import Benchmark
```</details>

<!-- cell -->
<details open> <summary>output</summary>
```python
bench = Benchmark(
    [func_to_test_1, func_to_test_2],
)
```</details>

<!-- cell -->
```python
bench

output

Benchmark(func_to_test_1, func_to_test_2)

Now we can benchmark that functions.

# we can run bench.run() or just:
bench()

output

 Func name  | Sec / run

func_to_test_1:   0.10 0.0%

func_to_test_2:   0.11 -9.1%

We can run it again, all functions, some of it, exclude some and change number of repeats.

bench.run(num_repeats=10)

output

 Func name  | Sec / run

func_to_test_1:   0.10 0.0%

func_to_test_2:   0.11 -9.1%

After run, we can print results - sorted or not, reversed, compare results with best or not.

bench.print_results(reverse=True)

output

 Func name  | Sec / run

func_to_test_2:   0.11 0.0%

func_to_test_1:   0.10 10.0%

We can add functions to benchmark as list of functions (or partial) or as dictionary: `{"name": function}`.

output

bench = Benchmark(
    [
        func_to_test_1,
        partial(func_to_test_1, 0.12),
        partial(func_to_test_1, sleep_time=0.11),
    ]
)
```</details>

<!-- cell -->
```python
bench

output

Benchmark(func_to_test_1, func_to_test_1(0.12), func_to_test_1(sleep_time=0.11))

bench.run()

output

 Func name  | Sec / run

func_to_test_1:   0.10 0.0%

func_to_test_1(sleep_time=0.11):   0.11 -9.1%

func_to_test_1(0.12):   0.12 -16.7%

output

bench = Benchmark(
    {
        "func_1": func_to_test_1,
        "func_2": func_to_test_2,
    }
)
```</details>

<!-- cell -->
```python
bench

output

Benchmark(func_1, func_2)

When we run benchmark script in terminal, we got pretty progress thanks to rich. Lets run example_1.py from example folder:  ## BenchmarkIter With BenchmarkIter we can benchmark functions over iterables, for example read list of files or run functions with different arguments.

output

def func_to_test_1(x: int) -> None:
    """simple 'sleep' func for test"""
    sleep(0.01)


def func_to_test_2(x: int) -> None:
    """simple 'sleep' func for test"""
    sleep(0.015)


dummy_params = list(range(10))
```</details>

<!-- cell -->
<details open> <summary>output</summary>
```python
from benchmark_utils import BenchmarkIter

bench = BenchmarkIter(
    func=[func_to_test_1, func_to_test_2],
    item_list=dummy_params,
)
```</details>

<!-- cell -->
```python
bench()

output

 Func name  | Items/sec

func_to_test_1:  97.93

func_to_test_2:  65.25

We can run it again, all functions, some of it, exclude some and change number of repeats. And we can limit number of items with `num_samples` argument: `bench.run(num_samples=5)` ## Multiprocessing By default we tun functions in one thread. But we can use multiprocessing with `multiprocessing=True` argument: `bench.run(multiprocessing=True)` It will use all available cpu cores. And we can use `num_workers` argument to limit used cpu cores: `bench.run(multiprocessing=True, num_workers=2)`

bench.run(multiprocessing=True, num_workers=2)

output

 Func name  | Items/sec

func_to_test_1: 173.20

func_to_test_2: 120.80