Python memory leaks

Have a look at this article: Tracing python memory leaks

Also, note that the garbage collection module actually can have debug flags set. Look at the set_debug function. Additionally, look at this code by Gnibbler for determining the types of objects that have been created after a call.

I tried out most options mentioned previously but found this small and intuitive package to be the best: pympler

It's quite straight forward to trace objects that were not garbage-collected, check this small example:

install package via pip install pympler

from pympler.tracker import SummaryTracker
tracker = SummaryTracker()

# ... some code you want to investigate ...

tracker.print_diff()

The output shows you all the objects that have been added, plus the memory they consumed.

Sample output:

                                 types |   # objects |   total size
====================================== | =========== | ============
                                  list |        1095 |    160.78 KB
                                   str |        1093 |     66.33 KB
                                   int |         120 |      2.81 KB
                                  dict |           3 |       840 B
      frame (codename: create_summary) |           1 |       560 B
          frame (codename: print_diff) |           1 |       480 B

This package provides a number of more features. Check pympler's documentation, in particular the section Identifying memory leaks.

Let me recommend mem_top tool I created

It helped me to solve a similar issue

It just instantly shows top suspects for memory leaks in a Python program

You should specially have a look on your global or static data (long living data).

When this data grows without restriction, you can also get troubles in Python.

The garbage collector can only collect data, that is not referenced any more. But your static data can hookup data elements that should be freed.

Another problem can be memory cycles, but at least in theory the Garbage collector should find and eliminate cycles -- at least as long as they are not hooked on some long living data.

What kinds of long living data are specially troublesome? Have a good look on any lists and dictionaries -- they can grow without any limit. In dictionaries you might even don't see the trouble coming since when you access dicts, the number of keys in the dictionary might not be of big visibility to you ...