尝试访问 multiprocessing.Pool 工作进程中的持久数据时出现不稳定的运行时异常

Question

受此解决方案的启发，我尝试在 Python 中设置一个多处理工作进程池。这个想法是在工作进程实际开始工作之前将一些数据传递给工作进程并最终重用它。它旨在最小化每次调用到工作进程时需要打包/解包的数据量（即减少进程间通信开销）。我的MCVE看起来像这样：import multiprocessing as mpimport numpy as npdef create_worker_context():    global context # create "global" context in worker process    context = {}def init_worker_context(worker_id, some_const_array, DIMS, DTYPE):    context.update({        'worker_id': worker_id,        'some_const_array': some_const_array,        'tmp': np.zeros((DIMS, DIMS), dtype = DTYPE),        }) # store context information in global namespace of worker    return True # return True, verifying that the worker process received its dataclass data_analysis:    def __init__(self):        self.DTYPE = 'float32'        self.CPU_LEN = mp.cpu_count()        self.DIMS = 100        self.some_const_array = np.zeros((self.DIMS, self.DIMS), dtype = self.DTYPE)        # Init multiprocessing pool        self.cpu_pool = mp.Pool(processes = self.CPU_LEN, initializer = create_worker_context) # create pool and context in workers        pool_results = [            self.cpu_pool.apply_async(                init_worker_context,                args = (core_id, self.some_const_array, self.DIMS, self.DTYPE)            ) for core_id in range(self.CPU_LEN)            ] # pass information to workers' context        result_batches = [result.get() for result in pool_results] # check if they got the information        if not all(result_batches): # raise an error if things did not work            raise SyntaxError('Workers could not be initialized ...')    @staticmethod    def process_batch(batch_data):        context['tmp'][:,:] = context['some_const_array'] + batch_data # some fancy computation in worker        return context['tmp'] # return result我正在使用 CPython 3.6.6 运行上述内容。我很困惑。这里发生了什么？