Tracking *maximum* memory usage by a Python function

It is possible to do this with memory_profiler. The function memory_usage returns a list of values, these represent the memory usage over time (by default over chunks of .1 second). If you need the maximum, just take the max of that list. Little example:

from memory_profiler import memory_usage
from time import sleep

def f():
    # a function that with growing
    # memory consumption
    a = [0] * 1000
    sleep(.1)
    b = a * 100
    sleep(.1)
    c = b * 100
    return a

mem_usage = memory_usage(f)
print('Memory usage (in chunks of .1 seconds): %s' % mem_usage)
print('Maximum memory usage: %s' % max(mem_usage))

In my case (memory_profiler 0.25) if prints the following output:

Memory usage (in chunks of .1 seconds): [45.65625, 45.734375, 46.41015625, 53.734375]
Maximum memory usage: 53.734375

This question seemed rather interesting and it gave me a reason to look into Guppy / Heapy, for that I thank you.

I tried for about 2 hours to get Heapy to do monitor a function call / process without modifying its source with zero luck.

I did find a way to accomplish your task using the built in Python library resource. Note that the documentation does not indicate what the RU_MAXRSS value returns. Another SO user noted that it was in kB. Running Mac OSX 7.3 and watching my system resources climb up during the test code below, I believe the returned values to be in Bytes, not kBytes.

A 10000ft view on how I used the resource library to monitor the library call was to launch the function in a separate (monitor-able) thread and track the system resources for that process in the main thread. Below I have the two files that you'd need to run to test it out.

Library Resource Monitor - whatever_you_want.py

import resource
import time

from stoppable_thread import StoppableThread


class MyLibrarySniffingClass(StoppableThread):
    def __init__(self, target_lib_call, arg1, arg2):
        super(MyLibrarySniffingClass, self).__init__()
        self.target_function = target_lib_call
        self.arg1 = arg1
        self.arg2 = arg2
        self.results = None

    def startup(self):
        # Overload the startup function
        print "Calling the Target Library Function..."

    def cleanup(self):
        # Overload the cleanup function
        print "Library Call Complete"

    def mainloop(self):
        # Start the library Call
        self.results = self.target_function(self.arg1, self.arg2)

        # Kill the thread when complete
        self.stop()

def SomeLongRunningLibraryCall(arg1, arg2):
    max_dict_entries = 2500
    delay_per_entry = .005

    some_large_dictionary = {}
    dict_entry_count = 0

    while(1):
        time.sleep(delay_per_entry)
        dict_entry_count += 1
        some_large_dictionary[dict_entry_count]=range(10000)

        if len(some_large_dictionary) > max_dict_entries:
            break

    print arg1 + " " +  arg2
    return "Good Bye World"

if __name__ == "__main__":
    # Lib Testing Code
    mythread = MyLibrarySniffingClass(SomeLongRunningLibraryCall, "Hello", "World")
    mythread.start()

    start_mem = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
    delta_mem = 0
    max_memory = 0
    memory_usage_refresh = .005 # Seconds

    while(1):
        time.sleep(memory_usage_refresh)
        delta_mem = (resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) - start_mem
        if delta_mem > max_memory:
            max_memory = delta_mem

        # Uncomment this line to see the memory usuage during run-time 
        # print "Memory Usage During Call: %d MB" % (delta_mem / 1000000.0)

        # Check to see if the library call is complete
        if mythread.isShutdown():
            print mythread.results
            break;

    print "\nMAX Memory Usage in MB: " + str(round(max_memory / 1000.0, 3))

Stoppable Thread - stoppable_thread.py

import threading
import time

class StoppableThread(threading.Thread):
    def __init__(self):
        super(StoppableThread, self).__init__()
        self.daemon = True
        self.__monitor = threading.Event()
        self.__monitor.set()
        self.__has_shutdown = False

    def run(self):
        '''Overloads the threading.Thread.run'''
        # Call the User's Startup functions
        self.startup()

        # Loop until the thread is stopped
        while self.isRunning():
            self.mainloop()

        # Clean up
        self.cleanup()

        # Flag to the outside world that the thread has exited
        # AND that the cleanup is complete
        self.__has_shutdown = True

    def stop(self):
        self.__monitor.clear()

    def isRunning(self):
        return self.__monitor.isSet()

    def isShutdown(self):
        return self.__has_shutdown


    ###############################
    ### User Defined Functions ####
    ###############################

    def mainloop(self):
        '''
        Expected to be overwritten in a subclass!!
        Note that Stoppable while(1) is handled in the built in "run".
        '''
        pass

    def startup(self):
        '''Expected to be overwritten in a subclass!!'''
        pass

    def cleanup(self):
        '''Expected to be overwritten in a subclass!!'''
        pass

You can use python library resource to get memory usage.

import resource
resource.getrusage(resource.RUSAGE_SELF).ru_maxrss

It will give memory usage in kilobytes, to convert in MB divide by 1000.

This appears to work under Windows. Don't know about other operating systems.

In [50]: import os

In [51]: import psutil

In [52]: process = psutil.Process(os.getpid())

In [53]: process.get_ext_memory_info().peak_wset
Out[53]: 41934848