相关疑难解决方法(0)

我试图使用部分函数,​​以便pool.map()可以定位具有多个参数的函数(在本例中为Lock()对象).

这是示例代码(取自我之前的一个问题的答案):

from functools import partial

def target(lock, iterable_item):
    for item in items:
        # Do cool stuff
        if (... some condition here ...):
            lock.acquire()
            # Write to stdout or logfile, etc.
            lock.release()

def main():
    iterable = [1, 2, 3, 4, 5]
    pool = multiprocessing.Pool()
    l = multiprocessing.Lock()
    func = partial(target, l)
    pool.map(func, iterable)
    pool.close()
    pool.join()

但是,当我运行此代码时,我收到错误:

Runtime Error: Lock objects should only be shared between processes through inheritance.

我在这里错过了什么？如何在子进程之间共享锁？

I am currently running some endless tasks using asyncio.wait

I need a special function to run when all the others are on await

import asyncio 

async def special_function():
    while True:
        # does some work, 
        # Passes control back to controller to run main_tasks
        # if they are no longer waiting.
        await asyncio.sleep(0)

async def handler():
    tasks = [task() for task in main_tasks]

    # Adding the task that I want to run when all main_tasks are awaiting:
    tasks.append(special_function())

    await asyncio.wait(tasks)

asyncio.get_event_loop().run_until_complete(handler()) …

我multiprocessing.Pool()用来并行化一些重的Pandas处理,但发现它有点太成功了.我的CPU使用率达到100%,我的整台计算机变得非常无响应.甚至鼠标也变得难以使用.

我可以使用此代码更改进程的进程优先级.

import psutil
p = psutil.Process(os.getpid())
p.nice = psutil.BELOW_NORMAL_PRIORITY_CLASS

但是,当我查看Windows任务管理器时,我发现只有主python.exe进程已更改为低于正常优先级.

有没有一种降低池进程优先级的好方法？