Python函数跟踪*最大*内存使用量

Question

我想知道在调用函数的过程中分配的最大RAM量是多少（在Python中）。还有其他与跟踪RAM使用相关的问题：

Which Python memory profiler is recommended?

How do I profile memory usage in Python?

但这些似乎允许您在调用heap()方法（在guppy的情况下）时跟踪更多的内存使用情况。但是，我想跟踪的是外部库中的一个函数，我不能修改它，它会使用大量的RAM，但在函数执行完成后会释放它。有什么方法可以找出函数调用期间使用的RAM总量是多少？

Answer 1

这似乎在Windows下有效。不知道其他操作系统。

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

Answer 2

使用memory_profiler可以做到这一点。函数memory_usage返回一个值列表，这些值表示一段时间内的内存使用情况（默认情况下为.1秒）。如果你需要最大值，就取列表中的最大值。小例子：

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))

在我的情况下（memory_profiler 0.25），如果打印以下输出：

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

Answer 3

这个问题似乎很有趣，它给了我一个研究古比/希比的理由，为此我感谢你。

我试了大约两个小时让Heapy监视一个函数调用/进程，但没有用zeroluck修改它的源代码。

我确实找到了使用内置Python库^{}完成任务的方法。注意，文档并没有指明RU_MAXRSS值返回的内容。另一个用户noted是以kB为单位的。运行Mac OSX 7.3并在下面的测试代码中观察我的系统资源爬升，我相信返回的值是字节而不是千字节。

关于我如何使用resource库监视库调用的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。下面我有两个文件，你需要运行来测试它。

库资源监视器-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))

可停止线程-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