Measuring Python Functions Simplified: perftester Approach
In the realm of performance optimization, understanding the execution time and memory usage of your Python functions is crucial. Two popular tools for this task are the built-in module and the Python package .
While focuses on measuring the execution time of small code snippets or functions with high precision, it does not provide any memory usage information. For memory measurement, modules like or come into play. tracks memory allocations during program execution, reporting current and peak memory usage, but is more low-level. By combining and or using , developers can benchmark both runtime and memory footprint, which is essential for performance optimization, especially in memory-sensitive contexts like machine learning or big data processing.
Introducing Perftester
In this article, we'll focus on a Python package called . This lightweight tool allows for benchmarking callables, including both execution time and memory usage, providing a more comprehensive performance profile than alone.
Benchmarking Time with Perftester
To benchmark a function using , you can start by running the following command:
```python from perftester import perftest
def my_function(a, b): # Your function implementation here pass
perftest(my_function) ```
The results will be presented in terms of minimum, mean, and maximum mean execution time of running the function across all runs.
Changing Default Settings
offers a simple and natural API for changing the default settings for a particular function or all functions to be benchmarked. To change the default settings for a specific function, you can use the command:
You can also change the default settings for all functions by using:
Here, and represent the number of runs and the number of warm-up runs, respectively. Using upper-case letters for these arguments minimizes the risk of conflicts with other functions.
Benchmarking Against Another Function
To benchmark a function against another function for various combinations of arguments, you can use the following command:
You can find more information about changing the defaults and benchmarking against another function in the documentation file 'perftester/benchmarking_against_another_function.md'.
Overwriting the Empty Function Used for Relative Benchmarks
By default, uses an empty function as a baseline for relative benchmarks. However, you can overwrite this empty function with another function by using the command:
Avoiding Conflicts with Functools.wraps
To avoid conflicts between the function to be benchmarked and the function used for benchmarking, you can use as follows:
```python import functools
@functools.wraps(my_function) def benchmark_my_function(function): return perftest(function) ```
Resources for Further Reading
For more information on benchmarking and performance testing in Python, you may find these resources useful:
- Benchmarking Python code with timeit
- functools - Higher-order functions and operations on callable objects
- GitHub - nyggus/perftester: A lightweight Python package for performance testing of Python...
- GitHub - nyggus/rounder: Python package for rounding floats and complex numbers in complex Python...
- A Guide to Python Comprehensions
In conclusion, offers a simple and natural API for benchmarking callables in terms of both execution time and memory usage, providing a more comprehensive performance assessment than alone. By understanding the performance characteristics of your functions, you can optimize them for better efficiency and performance.
Whereas both built-in Python tools and aid in measuring the execution time and memory usage of Python functions, Perftester provides a lightweight, more comprehensive solution for benchmarking callables.
By using Perftester, developers can not only assess the minimum, mean, and maximum execution times of their functions but also gain insight into their memory usage.
