Python Guppy Recent Memory Usage of a Program














































Python Guppy Recent Memory Usage of a Program




Recent Memory Usage of a Program

In this Python article, we will check the recent memory usage of the program using guppy module. guppy module is used to provide the Heapy subsystem, which supports object and heap memory sizing, profiling and debugging. It assists us in directly displaying the RAM memory usage in a well-formatted tabular manner. 

guppy is not an in-built module, meaning it does not come preinstalled with Python 3 IDLE (Integrated Development Learning Environment, available at python.org, the official website for Python). 'guppy' was initially implemented with Python v2.x, and still guppy supports Python 2. So, they've released 'guppy3' to extend their support to Python v3. It can be installed using any package manager like pip. You'll need to install "guppy" for Python 2, and "guppy3" for Python 3, from your python package manager.

For accessing heap memory, we will use guppy.hpy().heap() method. Here, guppy.hpy() method is the top level interface to Heapy. Heap is a hierarchical data structure, is a location in memory where memory may get allocated randomly.

For better understanding, check the source codes given below:

from guppy import hpy
import networkx as nx

h = hpy()
L=[1,2,3]

h.heap()

> Partition of a set of 89849 objects. Total size = 12530016 bytes.
>  Index  Count   %     Size   % Cumulative  % Kind (class / dict of class)
>     0  40337  45  3638400  29   3638400  29 str
>     1  21681  24  1874216  15   5512616  44 tuple
>     2   1435   2  1262344  10   6774960  54 dict (no owner)

If you want to plot the total usage as "time" progresses in a simulation,
you can call hpy() function along with size to get  complete details about the memory 
usage of the code.
This is the basic bit of code you can do:
x = h.heap()
x.size
returns the total size. For example:
from guppy import hpy
import networkx as nx

h = hpy()

num_nodes = 1000  
num_edges = 5000  
G = nx.gnm_random_graph(num_nodes, num_edges)

x = h.heap()
print(x.size)
#prints
#19820968
#which is consistent with the Total size reported by
print(x)
# Partition of a set of 118369 objects. Total size = 19820904 bytes.
#  Index  Count   %     Size   % Cumulative  % Kind (class / dict of class)
#      0  51057  43  6905536  35   6905536  35 str
#      1   7726   7  3683536  19  10589072  53 dict (no owner)
#      2  28416  24  2523064  13  13112136  66 tuple
#      3    516   0  1641312   8  14753448  74 dict of module
#      4   7446   6   953088   5  15706536  79 types.CodeType
#      5   6950   6   834000   4  16540536  83 function
#      6    584   0   628160   3  17168696  87 dict of type
#      7    584   0   523144   3  17691840  89 type
#      8    169   0   461696   2  18153536  92 unicode
#      9    174   0   181584   1  18335120  93 dict of class
# <235 more rows. Type e.g. '_.more' to view.>


Happy Pythoning..!!



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