benchmark-utils 0.2.5

Utils for benchmark.

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Benchmark utils

Utils for benchmark - wrapper over python timeit.

Tested on python 3.10 - 3.14

Install

Install from pypi:

pip install benchmark_utils

Or with uv:

uv pip install benchmark_utils

Or install from github repo:

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

For development, use uv:

uv pip install -e .

Basic use.

Lets benchmark some (dummy) functions.

output

```python 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)

output

Let's create benchmark.

output

```python from benchmark_utils import Benchmark ```

output

```python bench = Benchmark( [func_to_test_1, func_to_test_2], ) ```

bench

output

Benchmark(func_to_test_1, func_to_test_2)</details>

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

```python bench = Benchmark( [ func_to_test_1, partial(func_to_test_1, 0.12), partial(func_to_test_1, sleep_time=0.11), ] ) ```

bench

output

Benchmark(func_to_test_1, func_to_test_1(0.12), func_to_test_1(sleep_time=0.11))</details>

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

```python bench = Benchmark( { "func_1": func_to_test_1, "func_2": func_to_test_2, } ) ```

bench

output

Benchmark(func_1, func_2)</details>

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

```python 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))

<!-- 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