通过Python函数跟踪*最大*内存使用情况

这个问题似乎很有趣，它使我有理由研究Guppy / Heapy，对此我表示感谢。

我尝试了大约2个小时，让Heapy监视函数调用/进程，而又没有 零 运气地修改其源代码。

我确实找到了一种使用内置Python库完成任务的方法resource。请注意，文档没有指出RU_MAXRSS值返回的内容。另一个SO用户注意到它在kB中。运行Mac
OSX 7.3并看着我的系统资源在下面的测试代码中攀升，我相信返回的值以 ，而不是千 。

我如何使用resource库来监视库调用的10000英尺视图是在一个单独的（可监视的）线程中启动该函数，并在主线程中跟踪该进程的系统资源。下面是您需要运行以对其进行测试的两个文件。

-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.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