Monitor Memory usage of Python code
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Memory Profiler

This is a python module for monitoring memory consumption of a process as well as line-by-line analysis of memory consumption for python programs.

It's a pure python module and has the psutil module as optional (but highly recommended) dependencies.


To install through easy_install or pip:

$ easy_install -U memory_profiler # pip install -U memory_profiler

To install from source, download the package, extract and type:

$ python install

After installing the module, if you use IPython, you can set up the %mprun and %memit magics by following these steps.

For IPython 0.10, you can install it by editing the IPython configuration file ~/.ipython/ to add the following lines:

# These two lines are standard and probably already there.
import IPython.ipapi
ip = IPython.ipapi.get()

# These two are the important ones.
import memory_profiler
ip.expose_magic('mprun', memory_profiler.magic_mprun)
ip.expose_magic('memit', memory_profiler.magic_memit)prun)

For IPython 0.11+, you have to create a file named ~/.ipython/extensions/ with the following content:

import memory_profiler

def load_ipython_extension(ip):
    ip.define_magic('mprun', memory_profiler.magic_mprun)
    ip.define_magic('memit', memory_profiler.magic_memit)

If you don't have an IPython profile already set up, create one using the following command:

$ ipython profile create

Then, edit the configuration file for your IPython profile, ~/.ipython/profile_default/, to register the extension like this (If you already have other extensions, just add this one to the list):

c.TerminalIPythonApp.extensions = [
c.InteractiveShellApp.extensions = [


The line-by-line profiler is used much in the same way of the line_profiler: you must first decorate the function you would like to profile with @profile. In this example, we create a simple function my_func that allocates lists a, b and then deletes b:

def my_func():
    a = [1] * (10 ** 6)
    b = [2] * (2 * 10 ** 7)
    del b
    return a

if __name__ == '__main__':

Execute the code passing the option -m memory_profiler to the python interpreter to load the memory_profiler module and print to stdout the line-by-line analysis. If the file name was, this would result in:

$ python -m memory_profiler

Output will follow:

Line #    Mem usage  Increment   Line Contents
     3                           @profile
     4      5.97 MB    0.00 MB   def my_func():
     5     13.61 MB    7.64 MB       a = [1] * (10 ** 6)
     6    166.20 MB  152.59 MB       b = [2] * (2 * 10 ** 7)
     7     13.61 MB -152.59 MB       del b
     8     13.61 MB    0.00 MB       return a

The first column represents the line number of the code that has been profiled, the second column (Mem usage) the memory usage of the Python interpreter after that line has been executed. The third column (Increment) represents the difference in memory of the current line with respect to the last one. The last column (Line Contents) prints the code that has been profiled.

The same output can be obtained in IPython by using the %mprun magic command. In this case, you can skip the @profile decorator and instead use the -f parameter, like this:

In [1] from example import my_func

In [2] %mprun -f my_func my_func()

Another useful magic that we define is %memit, which is analogous to %timeit. It can be used as follows:

In [1]: import numpy as np

In [2]: %memit np.zeros(1e7)
maximum of 3: 76.402344 MB per loop

For more details, see the docstrings of the magics.

Frequently Asked Questions

  • Q: How accurate are the results ?
  • A: This module gets the memory consumption by querying the operating system kernel about the ammount of memory the current process has allocated, which might be slightly different from the ammount of memory that is actually used by the Python interpreter. For this reason, the output is only an approximation, and might vary between runs.
  • Q: Does it work under windows ?
  • A: Yes, but you will need the psutil module.

Support, bugs & wish list

For support, please ask your question on stack overflow and add the profiling tag. Send issues, proposals, etc. to github's issue tracker .

If you've got questions regarding development, you can email me directly at


Latest sources are available from github:


This module was written by Fabian Pedregosa inspired by Robert Kern's line profiler.

Tom added windows support and speed improvements via the psutil module.

Victor added python3, bugfixes and general cleanup.

Vlad Niculae added the %mprun and %memit IPython magics.


Simplified BSD