Overview

This repo includes scripts for profiling memory usage and performance in Python.

What is Memory Profiling?

Memory profiling is the process of analyzing a program to understand its memory consumption patterns. This involves tracking how much memory your application uses and identifying memory-intensive parts of your code.

Why Use Memory Profiling?

Optimize Memory Usage: Memory profiling helps identify memory leaks and inefficient memory usage, which can lead to improved performance and reduced memory consumption.
Enhance Performance: By understanding which parts of your code are using the most memory, you can refactor or optimize those areas to make your application more efficient.
Resource Management: For applications running on systems with limited resources, efficient memory usage is critical to maintaining stability and performance.

What is Performance Profiling?

Performance profiling is the process of analyzing a program to determine its execution time and identify bottlenecks. This involves measuring how long different parts of your code take to execute.

Why Use Performance Profiling?

Identify Bottlenecks: Performance profiling helps pinpoint parts of your code that are slow or inefficient, allowing you to focus optimization efforts where they are needed most.
Improve Efficiency: By understanding execution times, you can make informed decisions about code improvements to enhance overall performance.
Benchmarking: Profiling provides benchmarks that help you measure the impact of optimizations and compare different approaches.

Files

memory_profiler_example.py: Demonstrates how to use the memory_profiler library to track memory usage of a Python function.
performance_profiling.py: Uses the cProfile module to profile the execution time of a Python function.

Setup

To use these profiling scripts, make sure you have the necessary libraries installed:

pip install memory_profiler