memory_performance.py

from typing import List
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from memory_profiler import memory_usage
from multiprocessing import Pool, cpu_count
import gc

def mem_inline(function, *arg) -> float:
    return memory_usage(proc=(function, arg), max_usage=True)#[0]

def mem_it(functions: List[str], args):
    gc.collect()
    results = []
    for function in functions:
        for arg in args:
            results.append(mem_inline(function, arg))
            gc.collect()

    data = np.reshape(results, (len(functions), len(args)))
    result = pd.DataFrame(index=[f.__self__.__class__.__name__ for f in functions], columns=args, data=data)

    ax = result.T.plot()
    ax.set_xlabel('input size')
    ax.set_ylabel('time (MiB)');
    
functions = [function1,
             function2,
             function3]
args = [10, 50, 100, 500, 1_000, 5_000, 10_000]
mem_it(functions, args)