How do I profile memory usage in Python?


Question

I've recently become interested in algorithms and have begun exploring them by writing a naive implementation and then optimizing it in various ways.

I'm already familiar with the standard Python module for profiling runtime (for most things I've found the timeit magic function in IPython to be sufficient), but I'm also interested in memory usage so I can explore those tradeoffs as well (e.g. the cost of caching a table of previously computed values versus recomputing them as needed). Is there a module that will profile the memory usage of a given function for me?

1
187
2/16/2009 9:34:43 AM

Accepted Answer

This one has been answered already here: Python memory profiler

Basically you do something like that (cited from Guppy-PE):

>>> from guppy import hpy; h=hpy()
>>> h.heap()
Partition of a set of 48477 objects. Total size = 3265516 bytes.
 Index  Count   %     Size   % Cumulative  % Kind (class / dict of class)
     0  25773  53  1612820  49   1612820  49 str
     1  11699  24   483960  15   2096780  64 tuple
     2    174   0   241584   7   2338364  72 dict of module
     3   3478   7   222592   7   2560956  78 types.CodeType
     4   3296   7   184576   6   2745532  84 function
     5    401   1   175112   5   2920644  89 dict of class
     6    108   0    81888   3   3002532  92 dict (no owner)
     7    114   0    79632   2   3082164  94 dict of type
     8    117   0    51336   2   3133500  96 type
     9    667   1    24012   1   3157512  97 __builtin__.wrapper_descriptor
<76 more rows. Type e.g. '_.more' to view.>
>>> h.iso(1,[],{})
Partition of a set of 3 objects. Total size = 176 bytes.
 Index  Count   %     Size   % Cumulative  % Kind (class / dict of class)
     0      1  33      136  77       136  77 dict (no owner)
     1      1  33       28  16       164  93 list
     2      1  33       12   7       176 100 int
>>> x=[]
>>> h.iso(x).sp
 0: h.Root.i0_modules['__main__'].__dict__['x']
>>> 
106
5/23/2017 12:26:32 PM

Python 3.4 includes a new module: tracemalloc. It provides detailed statistics about which code is allocating the most memory. Here's an example that displays the top three lines allocating memory.

from collections import Counter
import linecache
import os
import tracemalloc

def display_top(snapshot, key_type='lineno', limit=3):
    snapshot = snapshot.filter_traces((
        tracemalloc.Filter(False, "<frozen importlib._bootstrap>"),
        tracemalloc.Filter(False, "<unknown>"),
    ))
    top_stats = snapshot.statistics(key_type)

    print("Top %s lines" % limit)
    for index, stat in enumerate(top_stats[:limit], 1):
        frame = stat.traceback[0]
        # replace "/path/to/module/file.py" with "module/file.py"
        filename = os.sep.join(frame.filename.split(os.sep)[-2:])
        print("#%s: %s:%s: %.1f KiB"
              % (index, filename, frame.lineno, stat.size / 1024))
        line = linecache.getline(frame.filename, frame.lineno).strip()
        if line:
            print('    %s' % line)

    other = top_stats[limit:]
    if other:
        size = sum(stat.size for stat in other)
        print("%s other: %.1f KiB" % (len(other), size / 1024))
    total = sum(stat.size for stat in top_stats)
    print("Total allocated size: %.1f KiB" % (total / 1024))


tracemalloc.start()

counts = Counter()
fname = '/usr/share/dict/american-english'
with open(fname) as words:
    words = list(words)
    for word in words:
        prefix = word[:3]
        counts[prefix] += 1
print('Top prefixes:', counts.most_common(3))

snapshot = tracemalloc.take_snapshot()
display_top(snapshot)

And here are the results:

Top prefixes: [('con', 1220), ('dis', 1002), ('pro', 809)]
Top 3 lines
#1: scratches/memory_test.py:37: 6527.1 KiB
    words = list(words)
#2: scratches/memory_test.py:39: 247.7 KiB
    prefix = word[:3]
#3: scratches/memory_test.py:40: 193.0 KiB
    counts[prefix] += 1
4 other: 4.3 KiB
Total allocated size: 6972.1 KiB

When is a memory leak not a leak?

That example is great when the memory is still being held at the end of the calculation, but sometimes you have code that allocates a lot of memory and then releases it all. It's not technically a memory leak, but it's using more memory than you think it should. How can you track memory usage when it all gets released? If it's your code, you can probably add some debugging code to take snapshots while it's running. If not, you can start a background thread to monitor memory usage while the main thread runs.

Here's the previous example where the code has all been moved into the count_prefixes() function. When that function returns, all the memory is released. I also added some sleep() calls to simulate a long-running calculation.

from collections import Counter
import linecache
import os
import tracemalloc
from time import sleep


def count_prefixes():
    sleep(2)  # Start up time.
    counts = Counter()
    fname = '/usr/share/dict/american-english'
    with open(fname) as words:
        words = list(words)
        for word in words:
            prefix = word[:3]
            counts[prefix] += 1
            sleep(0.0001)
    most_common = counts.most_common(3)
    sleep(3)  # Shut down time.
    return most_common


def main():
    tracemalloc.start()

    most_common = count_prefixes()
    print('Top prefixes:', most_common)

    snapshot = tracemalloc.take_snapshot()
    display_top(snapshot)


def display_top(snapshot, key_type='lineno', limit=3):
    snapshot = snapshot.filter_traces((
        tracemalloc.Filter(False, "<frozen importlib._bootstrap>"),
        tracemalloc.Filter(False, "<unknown>"),
    ))
    top_stats = snapshot.statistics(key_type)

    print("Top %s lines" % limit)
    for index, stat in enumerate(top_stats[:limit], 1):
        frame = stat.traceback[0]
        # replace "/path/to/module/file.py" with "module/file.py"
        filename = os.sep.join(frame.filename.split(os.sep)[-2:])
        print("#%s: %s:%s: %.1f KiB"
              % (index, filename, frame.lineno, stat.size / 1024))
        line = linecache.getline(frame.filename, frame.lineno).strip()
        if line:
            print('    %s' % line)

    other = top_stats[limit:]
    if other:
        size = sum(stat.size for stat in other)
        print("%s other: %.1f KiB" % (len(other), size / 1024))
    total = sum(stat.size for stat in top_stats)
    print("Total allocated size: %.1f KiB" % (total / 1024))


main()

When I run that version, the memory usage has gone from 6MB down to 4KB, because the function released all its memory when it finished.

Top prefixes: [('con', 1220), ('dis', 1002), ('pro', 809)]
Top 3 lines
#1: collections/__init__.py:537: 0.7 KiB
    self.update(*args, **kwds)
#2: collections/__init__.py:555: 0.6 KiB
    return _heapq.nlargest(n, self.items(), key=_itemgetter(1))
#3: python3.6/heapq.py:569: 0.5 KiB
    result = [(key(elem), i, elem) for i, elem in zip(range(0, -n, -1), it)]
10 other: 2.2 KiB
Total allocated size: 4.0 KiB

Now here's a version inspired by another answer that starts a second thread to monitor memory usage.

from collections import Counter
import linecache
import os
import tracemalloc
from datetime import datetime
from queue import Queue, Empty
from resource import getrusage, RUSAGE_SELF
from threading import Thread
from time import sleep

def memory_monitor(command_queue: Queue, poll_interval=1):
    tracemalloc.start()
    old_max = 0
    snapshot = None
    while True:
        try:
            command_queue.get(timeout=poll_interval)
            if snapshot is not None:
                print(datetime.now())
                display_top(snapshot)

            return
        except Empty:
            max_rss = getrusage(RUSAGE_SELF).ru_maxrss
            if max_rss > old_max:
                old_max = max_rss
                snapshot = tracemalloc.take_snapshot()
                print(datetime.now(), 'max RSS', max_rss)


def count_prefixes():
    sleep(2)  # Start up time.
    counts = Counter()
    fname = '/usr/share/dict/american-english'
    with open(fname) as words:
        words = list(words)
        for word in words:
            prefix = word[:3]
            counts[prefix] += 1
            sleep(0.0001)
    most_common = counts.most_common(3)
    sleep(3)  # Shut down time.
    return most_common


def main():
    queue = Queue()
    poll_interval = 0.1
    monitor_thread = Thread(target=memory_monitor, args=(queue, poll_interval))
    monitor_thread.start()
    try:
        most_common = count_prefixes()
        print('Top prefixes:', most_common)
    finally:
        queue.put('stop')
        monitor_thread.join()


def display_top(snapshot, key_type='lineno', limit=3):
    snapshot = snapshot.filter_traces((
        tracemalloc.Filter(False, "<frozen importlib._bootstrap>"),
        tracemalloc.Filter(False, "<unknown>"),
    ))
    top_stats = snapshot.statistics(key_type)

    print("Top %s lines" % limit)
    for index, stat in enumerate(top_stats[:limit], 1):
        frame = stat.traceback[0]
        # replace "/path/to/module/file.py" with "module/file.py"
        filename = os.sep.join(frame.filename.split(os.sep)[-2:])
        print("#%s: %s:%s: %.1f KiB"
              % (index, filename, frame.lineno, stat.size / 1024))
        line = linecache.getline(frame.filename, frame.lineno).strip()
        if line:
            print('    %s' % line)

    other = top_stats[limit:]
    if other:
        size = sum(stat.size for stat in other)
        print("%s other: %.1f KiB" % (len(other), size / 1024))
    total = sum(stat.size for stat in top_stats)
    print("Total allocated size: %.1f KiB" % (total / 1024))


main()

The resource module lets you check the current memory usage, and save the snapshot from the peak memory usage. The queue lets the main thread tell the memory monitor thread when to print its report and shut down. When it runs, it shows the memory being used by the list() call:

2018-05-29 10:34:34.441334 max RSS 10188
2018-05-29 10:34:36.475707 max RSS 23588
2018-05-29 10:34:36.616524 max RSS 38104
2018-05-29 10:34:36.772978 max RSS 45924
2018-05-29 10:34:36.929688 max RSS 46824
2018-05-29 10:34:37.087554 max RSS 46852
Top prefixes: [('con', 1220), ('dis', 1002), ('pro', 809)]
2018-05-29 10:34:56.281262
Top 3 lines
#1: scratches/scratch.py:36: 6527.0 KiB
    words = list(words)
#2: scratches/scratch.py:38: 16.4 KiB
    prefix = word[:3]
#3: scratches/scratch.py:39: 10.1 KiB
    counts[prefix] += 1
19 other: 10.8 KiB
Total allocated size: 6564.3 KiB

If you're on Linux, you may find /proc/self/statm more useful than the resource module.


Licensed under: CC-BY-SA with attribution
Not affiliated with: Stack Overflow
Icon