How to profile memory usage in Python

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One of the ways Python makes development fast (not to mention easier than languages like C and C++ ) is memory management. In Python it's simple because the language handles memory management for you. However, this doesn't mean memory should be forgotten. Good developers will want to track the memory usage of their application and look to lower memory usage. This post will explain common tools for doing this.

Profiling size of individual objects

The lowest layer of memory profiling involves looking at a single object in memory. You can do this by opening up a shell and doing something like the following:

>>> import sys >>> sys.getsizeof({}) 136 >>> sys.getsizeof([]) 32 >>> sys.getsizeof(set()) 112

The above snippet illustrates the overhead associated with a list object. A list is 32 bytes (on a 32-bit machine running Python 2.7.3). This style of profiling is useful when determining what type of data type to use.

Profiling a single function or method

The easiest way to profile a single method or function is the open source memory-profiler package. It's similar to line_profiler , which I've written about before .

You can use it by putting the @profile decorator around any function or method and running python -m memory_profiler myscript. You'll see line-by-line memory usage once your script exits.

This is extremely useful if you're wanting to profile a section of memory-intensive code, but it won't help much if you have no idea where the biggest memory usage is. In that case, a higher-level approach of profiling is needed first.

Profiling an entire application

There are a number of ways to profile an entire Python application. You can use the standard unix tools, top and ps . But a more Python-specific way is to use guppy .

To use guppy you drop something like the following in your code:

from guppy import hpy h = hpy() print h.heap()

This will print a nice table of usage grouped by object type. Here's an example of an PyQt4 application I've been working on:

Partition of a set of 235760 objects. Total size = 19909080 bytes. Index Count % Size % Cumulative % Kind (class / dict of class) 0 97264 41 8370996 42 8370996 42 str 1 47430 20 1916788 10 10287784 52 tuple 2 937 0 1106440 6 11394224 57 dict of PyQt4.QtCore.pyqtWrapperType 3 646 0 1033648 5 12427872 62 dict of module 4 11683 5 841176 4 13269048 67 types.CodeType 5 11684 5 654304 3 13923352 70 function 6 1200 1 583872 3 14507224 73 dict of type 7 782 0 566768 3 15073992 76 dict (no owner) 8 1201 1 536512 3 15610504 78 type 9 1019 0 499124 3 16109628 81 unicode

This type of profiling can be difficult if you have a large application using a relatively small number of object types.

mprof
Finally, I recently discovered memory-profiler comes with a script called mprof, which can show you memory usage over the lifetime of your application. This can be useful if you want to see if your memory is getting cleaned up and released periodically.

Even better, using mprof is easy; just run mprof run script script_args in your shell of choice. mprof will automatically create a graph of your script's memory usage over time, which you can view by running mprof plot. It’s important to note here that plotting requires matplotlib .

Of course, I'm sure there are other approaches to profiling memory usage in Python, and I’m curious to know yours. Go ahead and take a minute to let me know your recommendations in the comments.

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Contributor

Luke Lee

Luke Lee lives in Dresden, Germany and also writes at www.lukelee.me.