memory-profiler 0.29

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

Installation

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 setup.py install

Usage

The line-by-line profiler is used much in the same way of the line_profiler: first decorate the function you would like to profile with @profile and then run the script with a special script (in this case with specific arguments to the Python interpreter).

In the following example, we create a simple function my_func that allocates lists a, b and then deletes b:

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

if __name__ == '__main__':
    my_func()

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 example.py, this would result in:

$ python -m memory_profiler example.py

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.

from memory_profiler import profile

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

mprof run <executable>
mprof plot

def my_func():
    a = [1] * (10 ** 6)
    with profile.timestamp("b_computation"):
        b = [2] * (2 * 10 ** 7)
    del b
    return a

$ python -m memory_profiler --pdb-mmem=100 my_script.py

API

memory_profiler exposes a number of functions to be used in third-party code.

memory_usage(proc=-1, interval=.1, timeout=None) returns the memory usage over a time interval. The first argument, proc represents what should be monitored. This can either be the PID of a process (not necessarily a Python program), a string containing some python code to be evaluated or a tuple (f, args, kw) containing a function and its arguments to be evaluated as f(*args, **kw). For example,

>>> from memory_profiler import memory_usage
>>> mem_usage = memory_usage(-1, interval=.2, timeout=1)
>>> print(mem_usage)
    [7.296875, 7.296875, 7.296875, 7.296875, 7.296875]

Here I’ve told memory_profiler to get the memory consumption of the current process over a period of 1 second with a time interval of 0.2 seconds. As PID I’ve given it -1, which is a special number (PIDs are usually positive) that means current process, that is, I’m getting the memory usage of the current Python interpreter. Thus I’m getting around 7MB of memory usage from a plain python interpreter. If I try the same thing on IPython (console) I get 29MB, and if I try the same thing on the IPython notebook it scales up to 44MB.

If you’d like to get the memory consumption of a Python function, then you should specify the function and its arguments in the tuple (f, args, kw). For example:

>>> # define a simple function
>>> def f(a, n=100):
    ...     import time
    ...     time.sleep(2)
    ...     b = [a] * n
    ...     time.sleep(1)
    ...     return b
    ...
>>> from memory_profiler import memory_usage
>>> memory_usage((f, (1,), {'n' : int(1e6)}))

This will execute the code f(1, n=int(1e6)) and return the memory consumption during this execution.

IPython integration

After installing the module, if you use IPython, you can use the %mprun and %memit magics.

For IPython 0.11+, you can use the module directly as an extension, with %load_ext memory_profiler

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

c.InteractiveShellApp.extensions = [
    'memory_profiler',
]

(If the config file doesn’t already exist, run ipython profile create in a terminal.)

It then can be used directly from IPython to obtain a line-by-line report using the %mprun magic command. In this case, you can skip the @profile decorator and instead use the -f parameter, like this. Note however that function my_func must be defined in a file (cannot have been defined interactively in the Python interpreter):

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.

For IPython 0.10, you can install it by editing the IPython configuration file ~/.ipython/ipy_user_conf.py 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)

Frequently Asked Questions

• Q: How accurate are the results ?

• A: This module gets the memory consumption by querying the operating system kernel about the amount of memory the current process has allocated, which might be slightly different from the amount of memory that is actually used by the Python interpreter. Also, because of how the garbage collector works in Python the result might be different between platforms and even 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 memory-profiling tag. Send issues, proposals, etc. to github’s issue tracker .

If you’ve got questions regarding development, you can email me directly at fabian@fseoane.net

Development

Latest sources are available from github:

https://github.com/fabianp/memory_profiler

Projects using memory_profiler

Benchy

Authors

This module was written by Fabian Pedregosa and Philippe Gervais inspired by Robert Kern’s line profiler.

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

Victor added python3 support, bugfixes and general cleanup.

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

Thomas Kluyver added the IPython extension.

License

Simplified BSD