QuickBench 0.0.12

A lightweight utility to time python functions and blocks.

QuickBench

A lightweight, zero-dependency Python utility for timing code execution. QuickBench provides both a decorator and a context manager which can easily measure how long functions or code blocks take to run.

QuickBench is the all-in-one performance toolkit for Python developers. Whether you are optimizing a for loop, comparing Machine Learning models, or evaluating LLM latency, QuickBench handles the metrics so you can focus on the code.

Why QuickBench?

• Universal Timer: Benchmark functions or blocks of code with a simple decorator.
• ML Benchmarking: Automatically compare sklearn, xgboost, etc. (Accuracy, F1, RMSE).
• LLM Benchmarking: Measure Token/sec and Latency for GPT, Llama, or Claude wrappers.
• pandas Output: All results return clean DataFrames ready for analysis.

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Features

• Decorator Support: Time entire functions with a single line (@monitor).
• Context Manager: Time specific blocks of code inside a function (with monitor():).
• Human Readable: Automatically formats output to ns, µs, ms, or s.
• Zero Dependencies: Pure Python, no heavy libraries required.

Installation

pip install QuickBench

Usage

1. As a Decorator Use @monitor to time a specific function.

from QuickBench import monitor
import time

@monitor
def heavy_computation():
    # Simulating work
    time.sleep(1.5)
    return "Done"

heavy_computation()

# Output: [heavy_computation] finished in 1.50 s

2. As a Context Manager Use with monitor(): to time a specific chunk of logic.

from QuickBench import monitor
import time

def process_data():
    print("Preparing data...")
    
    with monitor(label="Database Query"):
        # Only time this specific part
        time.sleep(0.25)
    
    print("Finished.")

process_data()

# Output: [Database Query] finished in 250.00 ms

Author: Ankit Dutta