Memory Profiler in Python

1. Monitor Memory Consumption Using Memory Profiler Module in Python
2. Monitor Memory Consumption Using the guppy Module in Python

The Memory profilers are the modules that monitor the memory consumption of code blocks.

When they are created, it shows the amount of memory taken by variables, objects, functions, etc. This article will see Memory Profiler and guppy to analyze Python’s memory consumption.

Monitor Memory Consumption Using Memory Profiler Module in Python

The Memory Profiler is an open-source module in Python that shows line-by-line code analysis and the amount of memory consumed at each line of code.

It should be installed first using the following command:

#Python 3.x
pip install -U memory_profiler

To analyze the code put the function decorator @profile above the function, we need to analyze.

# Python 3.x
from memory_profiler import profile


@profile
def myfunc():
    var_a = [1] * (10 ** 6)
    var_b = [2] * (2 * 10 ** 7)
    del var_b
    del var_a


myfunc()

We will pass the command line argument -m memory_profiler to the Python interpreter when we run the code to display the memory consumption.

# Python 3.x
python -m memory_profiler filename.py

Output:

As we can see, the line number, memory usage, and memory increment at each line of code.

Monitor Memory Consumption Using the guppy Module in Python

The guppy module is simple and easy for memory profiling. We will write the code for guppy at some point in our code to monitor the memory consumption.

It should be installed first using the following command:

#Python 3.x
pip install guppy3

To access Python’s heapy object, we will call hpy(). We will mark a reference point using setref() to monitor the memory consumption.

To print the table, we will call the heap() function with the heap object that we have created, and to get the total number of bytes consumed, we access the size attribute of heap status.

# Python 3.x
from guppy import hpy

heap = hpy()
heap.setref()
heap_status1 = heap.heap()
print("Heap Size before creating objects:", heap_status1.size, " bytes\n")
print(heap_status1)
a = []
for i in range(1000):
    a.append(i)
heap_status2 = heap.heap()
print("Heap Size after creating objects : ", heap_status2.size, " bytes\n")
print(heap_status2)

Output: