How can I explicitly free memory in Python?

Question

I wrote a Python program that acts on a large input file to create a few million objects representing triangles. The algorithm is:

1. read an input file

Answer 1

Python is garbage-collected, so if you reduce the size of your list, it will reclaim memory. You can also use the "del" statement to get rid of a variable completely:

biglist = [blah,blah,blah]
#...
del biglist

Answer 2

As other answers already say, Python can keep from releasing memory to the OS even if it's no longer in use by Python code (so gc.collect() doesn't free anything) especially in a long-running program. Anyway if you're on Linux you can try to release memory by invoking directly the libc function malloc_trim (man page). Something like:

import ctypes
libc = ctypes.CDLL("libc.so.6")
libc.malloc_trim(0)

Answer 3

I had a similar problem in reading a graph from a file. The processing included the computation of a 200 000x200 000 float matrix (one line at a time) that did not fit into memory. Trying to free the memory between computations using gc.collect() fixed the memory-related aspect of the problem but it resulted in performance issues: I don't know why but even though the amount of used memory remained constant, each new call to gc.collect() took some more time than the previous one. So quite quickly the garbage collecting took most of the computation time.

To fix both the memory and performance issues I switched to the use of a multithreading trick I read once somewhere (I'm sorry, I cannot find the related post anymore). Before I was reading each line of the file in a big for loop, processing it, and running gc.collect() every once and a while to free memory space. Now I call a function that reads and processes a chunk of the file in a new thread. Once the thread ends, the memory is automatically freed without the strange performance issue.

Practically it works like this:

from dask import delayed  # this module wraps the multithreading
def f(storage, index, chunk_size):  # the processing function
    # read the chunk of size chunk_size starting at index in the file
    # process it using data in storage if needed
    # append data needed for further computations  to storage 
    return storage

partial_result = delayed([])  # put into the delayed() the constructor for your data structure
# I personally use "delayed(nx.Graph())" since I am creating a networkx Graph
chunk_size = 100  # ideally you want this as big as possible while still enabling the computations to fit in memory
for index in range(0, len(file), chunk_size):
    # we indicates to dask that we will want to apply f to the parameters partial_result, index, chunk_size
    partial_result = delayed(f)(partial_result, index, chunk_size)

    # no computations are done yet !
    # dask will spawn a thread to run f(partial_result, index, chunk_size) once we call partial_result.compute()
    # passing the previous "partial_result" variable in the parameters assures a chunk will only be processed after the previous one is done
    # it also allows you to use the results of the processing of the previous chunks in the file if needed

# this launches all the computations
result = partial_result.compute()

# one thread is spawned for each "delayed" one at a time to compute its result
# dask then closes the tread, which solves the memory freeing issue
# the strange performance issue with gc.collect() is also avoided

Answer 4

The del statement might be of use, but IIRC it isn't guaranteed to free the memory. The docs are here ... and a why it isn't released is here.

I have heard people on Linux and Unix-type systems forking a python process to do some work, getting results and then killing it.

This article has notes on the Python garbage collector, but I think lack of memory control is the downside to managed memory

Answer 5

Others have posted some ways that you might be able to "coax" the Python interpreter into freeing the memory (or otherwise avoid having memory problems). Chances are you should try their ideas out first. However, I feel it important to give you a direct answer to your question.

There isn't really any way to directly tell Python to free memory. The fact of that matter is that if you want that low a level of control, you're going to have to write an extension in C or C++.

That said, there are some tools to help with this:

• cython
• swig
• boost python

Answer 6

You can't explicitly free memory. What you need to do is to make sure you don't keep references to objects. They will then be garbage collected, freeing the memory.

In your case, when you need large lists, you typically need to reorganize the code, typically using generators/iterators instead. That way you don't need to have the large lists in memory at all.

http://www.prasannatech.net/2009/07/introduction-python-generators.html