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<img src="https://raw.githubusercontent.com/itsmeadarsh2008/fastbench/main/fastbench.svg" width="200" height="200">
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FastBench
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<img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/fastbench">
<img alt="GitHub repo size" src="https://img.shields.io/github/repo-size/itsmeadarsh2008/fastbench">
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FastBench is a high-performance Python package for benchmarking code execution time, CPU usage, and memory usage. It's implemented in Python for simplicity and provides a simple API for measuring the performance of your Python code.
## ✨ Features
- ⏱️ Measure the execution time of a function or code block
- 📊 Track CPU usage during code execution
- 🖥️ Monitor memory usage during code execution
- ⚡ Lightweight and fast
- 🔄 Simple and easy-to-use API
## Installation
You can install FastBench via pip:
```bash
pip install fastbench
```
## Usage
Here's an example of how to use FastBench to benchmark Python code:
```python
from fastbench import mt, mc, mm
# Define a sample function for testing
def sample_function(n):
return sum(range(n))
# Test the mt function (measure execution time)
time_taken = mt(sample_function, n=1000000)
print("Time taken:", time_taken)
# Test the mc function (measure CPU usage)
cpu_usage = mc(sample_function, n=1000000)
print("CPU usage:", cpu_usage)
# Test the mm function (measure memory usage)
memory_usage = mm(sample_function, n=1000000)
print("Memory usage:", memory_usage)
```
## Contributing
Contributions are welcome! Check out the [Contribution Guidelines](https://github.com/itsmeadarsh2008/fastbench/blob/main/CONTRIBUTING.md).
## License
This project is licensed under the MIT License - see the [LICENSE](https://github.com/itsmeadarsh2008/fastbench?tab=MIT-1-ov-file) file for details.
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