importmathimportdatetimeimportmultiprocessingasmpdeftrain_on_parameter(name,param):result=0fornuminparam:result+=math.sqrt(num*math.tanh(num)/math.log2(num)/math.log10(num))return{name:result}if__name__=='__main__':start_t=datetime.datetime.now()num_cores=int(mp.cpu_count())print("本...
importmultiprocessingasmpdefsquare(n):returnn**2if__name__=="__main__":# 获取核心数量num_cores=mp.cpu_count()# 设置使用的核心数量num_processes=num_cores-1pool=mp.Pool(processes=num_processes)# 生成数据data=list(range(1,1000001))# 处理数据result=pool.map(square,data)# 获取结果print(resu...
num_cores=int(mp.cpu_count())print("本地计算机有:"+ str(num_cores) +"核心") pool=mp.Pool(num_cores) param_dict= {'task1': list(range(10, 30000000)),'task2': list(range(30000000, 60000000)),'task3': list(range(60000000, 90000000)),'task4': list(range(90000000, 120000000)),...
Total = cpu_dict[cpucore]["user"] + cpu_dict[cpucore]["nice"] + cpu_dict[cpucore]["system"] + cpu_dict[cpucore]["idle"] + cpu_dict[cpucore]["iowait"] + cpu_dict[cpucore]["irq"] + cpu_dict[cpucore]["softirq"] idle = cpu_dict[cpucore]["idle"] + cpu_dict[cpucore][...
n_workers = 2 * mp.cpu_count() print(f"{n_workers} workers are available") >>> 8 workers are available 在下一步,我们将使用pandas read_csv函数摄取大型CSV文件。然后,打印出数据框的形状、列的名称和处理时间。 注意:Jupyter的神奇函数%time可以在处理结束后显示CPU times和wall time。
# Create process pool, process count is cpu count pool = mp.Pool(processes=mp.cpu_count()) pool.map(__opFunc, dataList) # Need to wait finish. pool.close() pool.join() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 在其他地方调用opFunc函数即可。
它也提供了获取CPU数量的方法,即 multiprocessing.cpu_count()。 python import multiprocessing def get_cpu_count_mp(): cpu_count = multiprocessing.cpu_count() print(f"Number of CPUs (using multiprocessing): {cpu_count}") return cpu_count # 使用示例 get_cpu_count_mp() 3. 使用 psutil 模块 ...
run_imap_mp(func,range(100000))#num_processes = multiprocessing.cpu_count()-2 # 使用核心数#pool = multiprocessing.Pool(processes=2) # 实例化进程池#func = partial(add_value,b = 5)#print(pool.map(func,alist)) 运行结果如下所示:
cpu_count()) # print("本地计算机有: " + str(num_cores) + " 核心") pool = mp.Pool(num_cores) # # preparation processes processes = [pool.apply_async(func0, args=(arg0,)) for arg0 in argList] processes.append(pool.apply_async(func1, args=(argX,))) # start_t = datetime....
20 num_cores = int(mp.cpu_count())21print("本计算机总共有: " + str(num_cores) + " 核⼼")22 23# 进程池: Pool() 函数创建了⼀个进程池类，⽤来管理多进程的⽣命周期和资源分配。24# 这⾥进程池传⼊的参数是核⼼数量，意思是最多有多少个进程可以进⾏并⾏运算。25 p...