How can you use cProfile to find memory usage and memory leaks in Python code?

Detecting Memory Usage and Leaks with cProfile in Python

'cProfile' is primarily focused on profiling code execution time and identifying performance bottlenecks related to CPU usage. The tool does not provide direct insight into memory usage or memory leaks. You can use 'memory_profiler' to detect memory-related problems and optimize memory usage in Python code.

Using 'memory_profiler' to detect memory usage and leaks:

Install memory_profiler:

Install the 'memory_profiler' package using pip0:

pip install memory-profiler

Decorate Functions for Memory Profiling:

To profile memory usage, you need to decorate the functions you want to monitor with the '@profile ' decorator provided by 'memory_profiler'.

Code:

# test.py
from memory_profiler import profile
@profile
def my_func():
    a = [1] * (10 ** 6)
    b = [2] * (2 * 10 ** 7)
    del b
    return a
if __name__ == '__main__':
    my_func()

Run the Python Script with mprof run:

Use the mprof run command to profile the memory usage of your Python script. For example:

mprof run my_script.py

This command will execute your script and collect memory usage data.

Example:

(base) C:\Users\ME>mprof run test.py
mprof: Sampling memory every 0.1s
running new process
running as a Python program...
Filename: test.py
Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
     3     43.4 MiB     43.4 MiB           1   @profile
     4                                         def my_func():
     5     51.0 MiB      7.6 MiB           1       a = [1] * (10 ** 6)
     6    203.6 MiB    152.6 MiB           1       b = [2] * (2 * 10 ** 7)
     7     51.0 MiB   -152.6 MiB           1       del b
     8     51.0 MiB      0.0 MiB           1       return a

Visualize Memory Usage Information:

After the script finishes execution, use the 'mprof' plot command to visualize the memory usage report:

mprof plot

Visualize Memory Usage Information:

After the script finishes execution, use the 'mprof plot' command to visualize the memory usage report:

Detecting Memory Leaks:

Memory leaks occur when a function continuously consumes more memory without releasing it. If you suspect a memory leak, you can look for increasing memory usage over time in the profiling results.

Optimizing memory usage:

• To optimize memory usage, carefully analyze memory_profiler's memory usage patterns. Pay attention to the following:
• Avoid creating large data structures unnecessary.
• Release memory by explicitly setting variables to None when no longer needed.
• When dealing with large datasets, use generators and iterators instead of lists.
• Use memory-efficient data structures, such as arrays or numpy arrays.
• Analyze your code with different inputs to identify memory usage patterns.

'myprof' other commands:

Usage:

mprof <command> <options> <arguments>

Available commands:

• run : run a given command or python file
• attach : alias for 'run --attach': attach to an existing process by pid or name
• rm : remove a given file generated by mprof
• clean : clean the current directory from files created by mprof
• list : display existing profiles, with indices
• plot : plot memory consumption generated by mprof run
• peak : print the maximum memory used by an mprof run

Type mprof <command> --help for usage help on a specific command.

For example, mprof plot --help will list all plotting options.