python - multiprocessing.Process导致: OSError: [Errno 12] Cannot allocate memory even when I run only 1 process

Question

我正在尝试在远程服务器 (AWS) 中处理非常大的文本文件 (~11 GB)。需要对文件进行的处理非常复杂，使用常规 python 程序，总运行时间约为 1 个月。为了减少运行时间，我尝试在一些进程之间划分文件工作。电脑规范: Computer specs

代码:

def initiate_workers(works, num_workers, output_path):
    """
    :param works: Iterable of lists of strings (The work to be processed divided in num_workers pieces)
    :param num_workers: Number of workers
    :return: A list of Process objects where each object is ready to process its share.
    """
    res = []
    for i in range(num_workers):
        # process_batch is the processing function
        res.append(multiprocessing.Process(target=process_batch, args=(output_path + str(i), works[i])))
    return res



def run_workers(workers):
    """
    Run the workers and wait for them to finish
    :param workers: Iterable of Process objects
    """
    logging.info("Starting multiprocessing..")
    for i in range(len(workers)):
        workers[i].start()
        logging.info("Started worker " + str(i))
    for j in range(len(workers)):
        workers[j].join()

我得到以下回溯:

Traceback (most recent call last):
  File "w2v_process.py", line 93, in <module>
    run_workers(workers)
  File "w2v_process.py", line 58, in run_workers
    workers[i].start()
  File "/usr/lib/python3.6/multiprocessing/process.py", line 105, in start
    self._popen = self._Popen(self)
  File "/usr/lib/python3.6/multiprocessing/context.py", line 223, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "/usr/lib/python3.6/multiprocessing/context.py", line 277, in _Popen
    return Popen(process_obj)
  File "/usr/lib/python3.6/multiprocessing/popen_fork.py", line 19, in __init__
    self._launch(process_obj)
  File "/usr/lib/python3.6/multiprocessing/popen_fork.py", line 66, in _launch
    self.pid = os.fork()
OSError: [Errno 12] Cannot allocate memory

无论num_workers=1、6还是14，它总是会崩溃。

我做错了什么？

谢谢!

编辑

发现问题了。我在 SO 的某个地方看到 fork(回溯的最后一行)实际上使 RAM 增加了一倍。在处理文件时，我将其加载到内存中，内存大约填充了 18GB，并且考虑到 RAM 的总容量为 30GB，确实存在内存分配错误。我将大文件分成较小的文件(工作人员的数量)，并为每个 Process 对象提供该文件的路径。这样，每个进程都以懒惰的方式读取数据，一切都很好!

Answer 1

发现问题了。我在 SO 的某个地方看到 fork(回溯的最后一行)实际上使 RAM 增加了一倍。在处理文件时，我将其加载到内存中，内存大约填充了 18GB，并且考虑到 RAM 的总容量为 30GB，确实存在内存分配错误。我将大文件分成较小的文件(工作人员的数量)，并为每个 Process 对象提供该文件的路径。这样，每个进程都以懒惰的方式读取数据，一切都很好!