multiprocessing

问题 How to make sure that all the pool.apply_async() calls are executed and their results are accumulated through callback before a premature call to pool.close() and pool.join() ? numofProcesses = multiprocessing.cpu_count() pool = multiprocessing.Pool(processes=numofProcesses) jobs=[] for arg1, arg2 in arg1_arg2_tuples: jobs.append(pool.apply_async(function1, args=(arg1, arg2, arg3,), callback=accumulate_apply_async_result)) pool.close() pool.join() 回答1: You need to wait on the appended

问题 Below is the complete code simplified for the question. ids_to_check returns a list of ids. For my testing, I used a list of 13 random strings. #!/usr/bin/env python3 import time from multiprocessing.dummy import Pool as ThreadPool, current_process as threadpool_process import requests def ids_to_check(): some_calls() return(id_list) def execute_task(id): url = f"https://myserver.com/todos/{ id }" json_op = s.get(url,verify=False).json() value = json_op['id'] print(str(value) + '-' + str

问题 I am trouble figuring out how to make a synchronized Python object. I have a class called Observation and a class called Variable that basically looks like (code is simplified to show the essence): class Observation: def __init__(self, date, time_unit, id, meta): self.date = date self.time_unit = time_unit self.id = id self.count = 0 self.data = 0 def add(self, value): if isinstance(value, list): if self.count == 0: self.data = [] self.data.append(value) else: self.data += value self.count +=

问题 I am trouble figuring out how to make a synchronized Python object. I have a class called Observation and a class called Variable that basically looks like (code is simplified to show the essence): class Observation: def __init__(self, date, time_unit, id, meta): self.date = date self.time_unit = time_unit self.id = id self.count = 0 self.data = 0 def add(self, value): if isinstance(value, list): if self.count == 0: self.data = [] self.data.append(value) else: self.data += value self.count +=

问题 I'm trying to perform model predictions in parallel using the model.predict command provided by keras in python2. I use tensorflow 1.14.0 for python2. I have 5 model (.h5) files and would like the predict command to run in parallel.This is being run in python 2.7. I'm using multiprocessing pool for mapping the model filenames with the prediction function on multiple processes as shown below, import matplotlib as plt import numpy as np import cv2 from multiprocessing import Pool pool=Pool()

问题 I'm trying to perform model predictions in parallel using the model.predict command provided by keras in python2. I use tensorflow 1.14.0 for python2. I have 5 model (.h5) files and would like the predict command to run in parallel.This is being run in python 2.7. I'm using multiprocessing pool for mapping the model filenames with the prediction function on multiple processes as shown below, import matplotlib as plt import numpy as np import cv2 from multiprocessing import Pool pool=Pool()

问题 I'm having a hard time understanding hyper-threading. If the logical core doesn't actually exist, what's the point of using hyper-threading?. The wikipedia article states that: For each processor core that is physically present, the operating system addresses two virtual (logical) cores and shares the workload between them when possible. If the two logical cores share the same execution unit, that means one of the threads will have to be put on hold while the other executes, that being said,

问题 I'm having a hard time understanding hyper-threading. If the logical core doesn't actually exist, what's the point of using hyper-threading?. The wikipedia article states that: For each processor core that is physically present, the operating system addresses two virtual (logical) cores and shares the workload between them when possible. If the two logical cores share the same execution unit, that means one of the threads will have to be put on hold while the other executes, that being said,

问题 I'm having a hard time understanding hyper-threading. If the logical core doesn't actually exist, what's the point of using hyper-threading?. The wikipedia article states that: For each processor core that is physically present, the operating system addresses two virtual (logical) cores and shares the workload between them when possible. If the two logical cores share the same execution unit, that means one of the threads will have to be put on hold while the other executes, that being said,

问题 I am testing Python's multiprocessing module on a cluster with SLURM. I want to make absolutely sure that each of my tasks are actually running on separate cpu cores as I intend. Due to the many possibilities of configuring SLURM, this is not at all obvious. Therefore, I was wondering if there is a way to get core specific information from the running Python task. I need my Python script to get information about the core it's running on which allows to distinguish between the various cores.