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
