是否可以在事件发生后使用 concurrent.futures 在 tkinter class 中执行 function/method？如果是，如何？
Is it possible to use concurrent.futures to execute a function/method inside a tkinter class following an event? If yes, how?
我正在尝试使用 concurrent.futures.ProcessPoolExecutor 提供的工作池来加速 tkinter class 中方法的性能。这是因为执行该方法 cpu 密集并且 "parallelizing" 它应该缩短完成它的时间。我希望将它的性能与控件进行基准测试——同一方法的串行执行。我已经编写了一个 tkinter GUI 测试代码来执行此基准测试。该方法的串行执行有效，但并发部分无效。感谢任何帮助让我的代码的并发部分工作。
更新： 我已确保我已正确实施 concurrent.futures.ProcessPoolExecutor 以在 Tk() 之外解决我的问题，即来自标准 python3 脚本.在此中进行了说明。现在我想实现该答案中描述的并发方法，以便在我的 tkinter.Tk() GUI 中使用按钮。
我的测试代码如下。当你 运行 它时，会出现一个 GUI。当你点击 'FIND' 按钮时，_findmatch 函数会以串行并发的方式执行，找出数字 5 在 0 到 1E8 的数字范围内出现了多少次。串行部分有效，但并发部分在抱怨（见下文）。 有人知道如何解决这个 Pickling 错误吗？
Traceback (most recent call last):
File "/usr/lib/python3.5/multiprocessing/queues.py", line 241, in _feed
obj = ForkingPickler.dumps(obj)
File "/usr/lib/python3.5/multiprocessing/reduction.py", line 50, in dumps
cls(buf, protocol).dump(obj)
_pickle.PicklingError: Can't pickle <class '_tkinter.tkapp'>: attribute lookup tkapp on _tkinter failed
测试代码：
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import tkinter as tk # Python 3 tkinter modules
import tkinter.ttk as ttk
import concurrent.futures as cf
from time import time, sleep
from itertools import repeat, chain
class App(ttk.Frame):
def __init__(self, parent):
# Initialise App Frame
ttk.Frame.__init__(self, parent, style='App.TFrame')
self.parent=parent
self.button = ttk.Button(self, style='start.TButton', text = 'FIND',
command=self._check)
self.label0 = ttk.Label(self, foreground='blue')
self.label1 = ttk.Label(self, foreground='red')
self.label2 = ttk.Label(self, foreground='green')
self._labels()
self.button.grid(row=0, column=1, rowspan=3, sticky='nsew')
self.label0.grid(row=0, column=0, sticky='nsew')
self.label1.grid(row=1, column=0, sticky='nsew')
self.label2.grid(row=2, column=0, sticky='nsew')
def _labels(self):
self.label0.configure(text='Click "FIND" to see how many times the number 5 appears.')
self.label1.configure(text='Serial Method:')
self.label2.configure(text='Concurrent Method:')
def _check(self):
# Initialisation
self._labels()
nmax = int(1E7)
smatch=[]
cmatch=[]
number = '5'
self.label0.configure(
text='Finding the number of times {0} appears in 0 to {1}'.format(
number, nmax))
self.parent.update_idletasks()
# Run serial code
start = time()
smatch = self._findmatch(0, nmax, number)
end = time() - start
self.label1.configure(
text='Serial: Found {0} occurances, Time to Find: {1:.6f}sec'.format(
len(smatch), end))
# Run serial code concurrently with concurrent.futures
workers = 6 # Pool of workers
chunks_vs_workers = 30 # A factor of =>14 can provide optimum performance
num_of_chunks = chunks_vs_workers * workers
start = time()
cmatch = self._concurrent_map(nmax, number, workers, num_of_chunks)
end = time() - start
self.label2.configure(
text='Concurrent: Found {0} occurances, Time to Find: {1:.6f}sec'.format(
len(cmatch), end))
def _findmatch(self, nmin, nmax, number):
'''Function to find the occurence of number in range nmin to nmax and return
the found occurences in a list.'''
start = time()
match=[]
for n in range(nmin, nmax):
if number in str(n): match.append(n)
end = time() - start
#print("\n def _findmatch {0:<10} {1:<10} {2:<3} found {3:8} in {4:.4f}sec".
# format(nmin, nmax, number, len(match),end))
return match
def _concurrent_map(self, nmax, number, workers, num_of_chunks):
'''Function that utilises concurrent.futures.ProcessPoolExecutor.map to
find the occurrences of a given number in a number range in a concurrent
manner.'''
# 1. Local variables
start = time()
chunksize = nmax // num_of_chunks
#2. Parallelization
with cf.ProcessPoolExecutor(max_workers=workers) as executor:
# 2.1. Discretise workload and submit to worker pool
cstart = (chunksize * i for i in range(num_of_chunks))
cstop = (chunksize * i if i != num_of_chunks else nmax
for i in range(1, num_of_chunks + 1))
futures = executor.map(self._findmatch, cstart, cstop, repeat(number))
end = time() - start
print('\n within statement of def _concurrent_map(nmax, number, workers, num_of_chunks):')
print("found in {0:.4f}sec".format(end))
return list(chain.from_iterable(futures))
if __name__ == '__main__':
root = tk.Tk()
root.title('App'), root.geometry('550x60')
app = App(root)
app.grid(row=0, column=0, sticky='nsew')
root.rowconfigure(0, weight=1)
root.columnconfigure(0, weight=1)
app.columnconfigure(0, weight=1)
app.mainloop()
我终于找到了一种方法来回答我的问题。
Mark Summerfields 的书，Python in Practice(2014)，提到由 concurrent.futures.ProcessPoolExecutor 调用的 multiprocessing 模块只能调用可导入的函数并使用模块数据 ( 由函数调用) 可 pickleable。因此，有必要在 tkinter GUI 模块之外的单独模块中找到 concurrent.futures.ProcessPoolExecutor 及其调用的函数（及其参数），否则它将无法工作。
因此，我创建了一个单独的 class 来托管与 concurrent.futures.ProcessPoolExecutor 相关的所有代码及其调用的函数和数据，而不是将它们放在 class 应用程序中，我的 tkinter.Tk() GUI class，就像我以前做的一样。成功了！
我还设法使用 threading.Threads 并发执行我的串行和并发任务。
我在下面分享我修改后的测试代码来演示我是如何做到的，希望这对任何试图将 concurrent.futures 与 tkinter 一起使用的人有所帮助。
看到所有 CPU 都在使用 Tk GUI 加速，真是太好了。 :)
修改后的测试代码：
#!/usr/bin/python3
# -*- coding: utf-8 -*-
''' Code to demonstrate how to use concurrent.futures.Executor object with tkinter.'''
import tkinter as tk # Python 3 tkinter modules
import tkinter.ttk as ttk
import concurrent.futures as cf
import threading
from time import time, sleep
from itertools import chain
class App(ttk.Frame):
def __init__(self, parent):
# Initialise App Frame
ttk.Frame.__init__(self, parent)
self.parent=parent
self.button = ttk.Button(self, text = 'FIND', command=self._check)
self.label0 = ttk.Label(self, foreground='blue')
self.label1 = ttk.Label(self, foreground='red')
self.label2 = ttk.Label(self, foreground='green')
self._labels()
self.button.grid(row=0, column=1, rowspan=3, sticky='nsew')
self.label0.grid(row=0, column=0, sticky='nsew')
self.label1.grid(row=1, column=0, sticky='nsew')
self.label2.grid(row=2, column=0, sticky='nsew')
def _labels(self):
self.label0.configure(text='Click "FIND" to see how many times the number 5 appears.')
self.label1.configure(text='Serial Method:')
self.label2.configure(text='Concurrent Method:')
def _check(self):
# Initialisation
self._labels()
nmax = int(1E8)
workers = 6 # Pool of workers
chunks_vs_workers = 30 # A factor of =>14 can provide optimum performance
num_of_chunks = chunks_vs_workers * workers
number = '5'
self.label0.configure(
text='Finding the number of times {0} appears in 0 to {1}'.format(
number, nmax))
self.parent.update_idletasks()
# Concurrent management of serial and concurrent tasks using threading
self.serworker = threading.Thread(target=self._serial,
args=(0, nmax, number))
self.subworker = threading.Thread(target=self._concurrent,
args=(nmax, number, workers,
num_of_chunks))
self.serworker.start()
self.subworker.start()
def _serial(self, nmin, nmax, number):
fm = Findmatch
# Run serial code
start = time()
smatch = fm._findmatch(fm, 0, nmax, number)
end = time() - start
self.label1.configure(
text='Serial Method: {0} occurrences, Compute Time: {1:.6f}sec'.format(
len(smatch), end))
self.parent.update_idletasks()
#print('smatch = ', smatch)
def _concurrent(self, nmax, number, workers, num_of_chunks):
fm = Findmatch
# Run serial code concurrently with concurrent.futures .submit()
start = time()
cmatch = fm._concurrent_submit(fm, nmax, number, workers,
num_of_chunks)
end = time() - start
self.label2.configure(
text='Concurrent Method: {0} occurrences, Compute Time: {1:.6f}sec'.format(
len(cmatch), end))
self.parent.update_idletasks()
#print('cmatch = ', cmatch)
class Findmatch:
''' A class specially created to host concurrent.futures.ProcessPoolExecutor
so that the function(s) it calls can be accessible by multiprocessing
module. Multiprocessing requirements: codes must be importable and code
data must be pickerable. ref. Python in Practice, by Mark Summerfields,
section 4.3.2, pg 173, 2014'''
def __init__(self):
self.__init__(self)
def _findmatch(self, nmin, nmax, number):
'''Function to find the occurence of number in range nmin to nmax and return
the found occurences in a list.'''
start = time()
match=[]
for n in range(nmin, nmax):
if number in str(n): match.append(n)
end = time() - start
#print("\n def _findmatch {0:<10} {1:<10} {2:<3} found {3:8} in {4:.4f}sec".
# format(nmin, nmax, number, len(match),end))
return match
def _concurrent_submit(self, nmax, number, workers, num_of_chunks):
'''Function that utilises concurrent.futures.ProcessPoolExecutor.submit to
find the occurrences of a given number in a number range in a concurrent
manner.'''
# 1. Local variables
start = time()
chunksize = nmax // num_of_chunks
self.futures = []
#2. Parallelization
with cf.ProcessPoolExecutor(max_workers=workers) as executor:
# 2.1. Discretise workload and submit to worker pool
for i in range(num_of_chunks):
cstart = chunksize * i
cstop = chunksize * (i + 1) if i != num_of_chunks - 1 else nmax
self.futures.append(executor.submit(
self._findmatch, self, cstart, cstop, number))
end = time() - start
print('\n within statement of def _concurrent_submit(nmax, number, workers, num_of_chunks):')
print("found in {0:.4f}sec".format(end))
return list(chain.from_iterable(f.result() for f in cf.as_completed(
self.futures)))
if __name__ == '__main__':
root = tk.Tk()
root.title('App'), root.geometry('550x60')
app = App(root)
app.grid(row=0, column=0, sticky='nsew')
root.rowconfigure(0, weight=1)
root.columnconfigure(0, weight=1)
app.columnconfigure(0, weight=1)
app.mainloop()
我正在尝试使用 concurrent.futures.ProcessPoolExecutor 提供的工作池来加速 tkinter class 中方法的性能。这是因为执行该方法 cpu 密集并且 "parallelizing" 它应该缩短完成它的时间。我希望将它的性能与控件进行基准测试——同一方法的串行执行。我已经编写了一个 tkinter GUI 测试代码来执行此基准测试。该方法的串行执行有效，但并发部分无效。感谢任何帮助让我的代码的并发部分工作。
更新： 我已确保我已正确实施 concurrent.futures.ProcessPoolExecutor 以在 Tk() 之外解决我的问题，即来自标准 python3 脚本.在此
我的测试代码如下。当你 运行 它时，会出现一个 GUI。当你点击 'FIND' 按钮时，_findmatch 函数会以串行并发的方式执行，找出数字 5 在 0 到 1E8 的数字范围内出现了多少次。串行部分有效，但并发部分在抱怨（见下文）。 有人知道如何解决这个 Pickling 错误吗？
Traceback (most recent call last):
File "/usr/lib/python3.5/multiprocessing/queues.py", line 241, in _feed
obj = ForkingPickler.dumps(obj)
File "/usr/lib/python3.5/multiprocessing/reduction.py", line 50, in dumps
cls(buf, protocol).dump(obj)
_pickle.PicklingError: Can't pickle <class '_tkinter.tkapp'>: attribute lookup tkapp on _tkinter failed
测试代码：
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import tkinter as tk # Python 3 tkinter modules
import tkinter.ttk as ttk
import concurrent.futures as cf
from time import time, sleep
from itertools import repeat, chain
class App(ttk.Frame):
def __init__(self, parent):
# Initialise App Frame
ttk.Frame.__init__(self, parent, style='App.TFrame')
self.parent=parent
self.button = ttk.Button(self, style='start.TButton', text = 'FIND',
command=self._check)
self.label0 = ttk.Label(self, foreground='blue')
self.label1 = ttk.Label(self, foreground='red')
self.label2 = ttk.Label(self, foreground='green')
self._labels()
self.button.grid(row=0, column=1, rowspan=3, sticky='nsew')
self.label0.grid(row=0, column=0, sticky='nsew')
self.label1.grid(row=1, column=0, sticky='nsew')
self.label2.grid(row=2, column=0, sticky='nsew')
def _labels(self):
self.label0.configure(text='Click "FIND" to see how many times the number 5 appears.')
self.label1.configure(text='Serial Method:')
self.label2.configure(text='Concurrent Method:')
def _check(self):
# Initialisation
self._labels()
nmax = int(1E7)
smatch=[]
cmatch=[]
number = '5'
self.label0.configure(
text='Finding the number of times {0} appears in 0 to {1}'.format(
number, nmax))
self.parent.update_idletasks()
# Run serial code
start = time()
smatch = self._findmatch(0, nmax, number)
end = time() - start
self.label1.configure(
text='Serial: Found {0} occurances, Time to Find: {1:.6f}sec'.format(
len(smatch), end))
# Run serial code concurrently with concurrent.futures
workers = 6 # Pool of workers
chunks_vs_workers = 30 # A factor of =>14 can provide optimum performance
num_of_chunks = chunks_vs_workers * workers
start = time()
cmatch = self._concurrent_map(nmax, number, workers, num_of_chunks)
end = time() - start
self.label2.configure(
text='Concurrent: Found {0} occurances, Time to Find: {1:.6f}sec'.format(
len(cmatch), end))
def _findmatch(self, nmin, nmax, number):
'''Function to find the occurence of number in range nmin to nmax and return
the found occurences in a list.'''
start = time()
match=[]
for n in range(nmin, nmax):
if number in str(n): match.append(n)
end = time() - start
#print("\n def _findmatch {0:<10} {1:<10} {2:<3} found {3:8} in {4:.4f}sec".
# format(nmin, nmax, number, len(match),end))
return match
def _concurrent_map(self, nmax, number, workers, num_of_chunks):
'''Function that utilises concurrent.futures.ProcessPoolExecutor.map to
find the occurrences of a given number in a number range in a concurrent
manner.'''
# 1. Local variables
start = time()
chunksize = nmax // num_of_chunks
#2. Parallelization
with cf.ProcessPoolExecutor(max_workers=workers) as executor:
# 2.1. Discretise workload and submit to worker pool
cstart = (chunksize * i for i in range(num_of_chunks))
cstop = (chunksize * i if i != num_of_chunks else nmax
for i in range(1, num_of_chunks + 1))
futures = executor.map(self._findmatch, cstart, cstop, repeat(number))
end = time() - start
print('\n within statement of def _concurrent_map(nmax, number, workers, num_of_chunks):')
print("found in {0:.4f}sec".format(end))
return list(chain.from_iterable(futures))
if __name__ == '__main__':
root = tk.Tk()
root.title('App'), root.geometry('550x60')
app = App(root)
app.grid(row=0, column=0, sticky='nsew')
root.rowconfigure(0, weight=1)
root.columnconfigure(0, weight=1)
app.columnconfigure(0, weight=1)
app.mainloop()
我终于找到了一种方法来回答我的问题。
Mark Summerfields 的书，Python in Practice(2014)，提到由 concurrent.futures.ProcessPoolExecutor 调用的 multiprocessing 模块只能调用可导入的函数并使用模块数据 ( 由函数调用) 可 pickleable。因此，有必要在 tkinter GUI 模块之外的单独模块中找到 concurrent.futures.ProcessPoolExecutor 及其调用的函数（及其参数），否则它将无法工作。
因此，我创建了一个单独的 class 来托管与 concurrent.futures.ProcessPoolExecutor 相关的所有代码及其调用的函数和数据，而不是将它们放在 class 应用程序中，我的 tkinter.Tk() GUI class，就像我以前做的一样。成功了！
我还设法使用 threading.Threads 并发执行我的串行和并发任务。
我在下面分享我修改后的测试代码来演示我是如何做到的，希望这对任何试图将 concurrent.futures 与 tkinter 一起使用的人有所帮助。
看到所有 CPU 都在使用 Tk GUI 加速，真是太好了。 :)
修改后的测试代码：
#!/usr/bin/python3
# -*- coding: utf-8 -*-
''' Code to demonstrate how to use concurrent.futures.Executor object with tkinter.'''
import tkinter as tk # Python 3 tkinter modules
import tkinter.ttk as ttk
import concurrent.futures as cf
import threading
from time import time, sleep
from itertools import chain
class App(ttk.Frame):
def __init__(self, parent):
# Initialise App Frame
ttk.Frame.__init__(self, parent)
self.parent=parent
self.button = ttk.Button(self, text = 'FIND', command=self._check)
self.label0 = ttk.Label(self, foreground='blue')
self.label1 = ttk.Label(self, foreground='red')
self.label2 = ttk.Label(self, foreground='green')
self._labels()
self.button.grid(row=0, column=1, rowspan=3, sticky='nsew')
self.label0.grid(row=0, column=0, sticky='nsew')
self.label1.grid(row=1, column=0, sticky='nsew')
self.label2.grid(row=2, column=0, sticky='nsew')
def _labels(self):
self.label0.configure(text='Click "FIND" to see how many times the number 5 appears.')
self.label1.configure(text='Serial Method:')
self.label2.configure(text='Concurrent Method:')
def _check(self):
# Initialisation
self._labels()
nmax = int(1E8)
workers = 6 # Pool of workers
chunks_vs_workers = 30 # A factor of =>14 can provide optimum performance
num_of_chunks = chunks_vs_workers * workers
number = '5'
self.label0.configure(
text='Finding the number of times {0} appears in 0 to {1}'.format(
number, nmax))
self.parent.update_idletasks()
# Concurrent management of serial and concurrent tasks using threading
self.serworker = threading.Thread(target=self._serial,
args=(0, nmax, number))
self.subworker = threading.Thread(target=self._concurrent,
args=(nmax, number, workers,
num_of_chunks))
self.serworker.start()
self.subworker.start()
def _serial(self, nmin, nmax, number):
fm = Findmatch
# Run serial code
start = time()
smatch = fm._findmatch(fm, 0, nmax, number)
end = time() - start
self.label1.configure(
text='Serial Method: {0} occurrences, Compute Time: {1:.6f}sec'.format(
len(smatch), end))
self.parent.update_idletasks()
#print('smatch = ', smatch)
def _concurrent(self, nmax, number, workers, num_of_chunks):
fm = Findmatch
# Run serial code concurrently with concurrent.futures .submit()
start = time()
cmatch = fm._concurrent_submit(fm, nmax, number, workers,
num_of_chunks)
end = time() - start
self.label2.configure(
text='Concurrent Method: {0} occurrences, Compute Time: {1:.6f}sec'.format(
len(cmatch), end))
self.parent.update_idletasks()
#print('cmatch = ', cmatch)
class Findmatch:
''' A class specially created to host concurrent.futures.ProcessPoolExecutor
so that the function(s) it calls can be accessible by multiprocessing
module. Multiprocessing requirements: codes must be importable and code
data must be pickerable. ref. Python in Practice, by Mark Summerfields,
section 4.3.2, pg 173, 2014'''
def __init__(self):
self.__init__(self)
def _findmatch(self, nmin, nmax, number):
'''Function to find the occurence of number in range nmin to nmax and return
the found occurences in a list.'''
start = time()
match=[]
for n in range(nmin, nmax):
if number in str(n): match.append(n)
end = time() - start
#print("\n def _findmatch {0:<10} {1:<10} {2:<3} found {3:8} in {4:.4f}sec".
# format(nmin, nmax, number, len(match),end))
return match
def _concurrent_submit(self, nmax, number, workers, num_of_chunks):
'''Function that utilises concurrent.futures.ProcessPoolExecutor.submit to
find the occurrences of a given number in a number range in a concurrent
manner.'''
# 1. Local variables
start = time()
chunksize = nmax // num_of_chunks
self.futures = []
#2. Parallelization
with cf.ProcessPoolExecutor(max_workers=workers) as executor:
# 2.1. Discretise workload and submit to worker pool
for i in range(num_of_chunks):
cstart = chunksize * i
cstop = chunksize * (i + 1) if i != num_of_chunks - 1 else nmax
self.futures.append(executor.submit(
self._findmatch, self, cstart, cstop, number))
end = time() - start
print('\n within statement of def _concurrent_submit(nmax, number, workers, num_of_chunks):')
print("found in {0:.4f}sec".format(end))
return list(chain.from_iterable(f.result() for f in cf.as_completed(
self.futures)))
if __name__ == '__main__':
root = tk.Tk()
root.title('App'), root.geometry('550x60')
app = App(root)
app.grid(row=0, column=0, sticky='nsew')
root.rowconfigure(0, weight=1)
root.columnconfigure(0, weight=1)
app.columnconfigure(0, weight=1)
app.mainloop()