Python进程池Pool初始化详解

from multiprocessing import Pool

# 初始化进程池
pool = Pool(processes=4)  # 创建包含4个工作进程的进程池
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import multiprocessing
import os

# 获取CPU核心数
cpu_count = os.cpu_count()
print(f"系统CPU核心数: {cpu_count}")

# 初始化进程池，使用一半的CPU核心
pool = multiprocessing.Pool(processes=cpu_count // 2)
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def init_worker():
    # 每个工作进程初始化时执行
    print(f"Worker PID: {os.getpid()} 初始化")

# 初始化带initializer的进程池
pool = multiprocessing.Pool(
    processes=4,
    initializer=init_worker
)
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def setup_worker(config):
    # 带参数的初始化函数
    print(f"Worker {os.getpid()} 初始化，配置: {config}")

# 初始化参数
worker_config = {"mode": "production", "log_level": "info"}

# 初始化进程池
pool = multiprocessing.Pool(
    processes=4,
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    initializer=setup_worker,
    initargs=(worker_config,),
    maxtasksperchild=100  # 每个工作进程处理100个任务后重启
)

with Pool(4) as pool:
    results = pool.map(process_data, data_list)
# 退出with块后自动关闭进程池

try:
    pool = Pool(4)
    # 执行任务
except Exception as e:
    print(f"发生错误: {e}")
finally:
    pool.close()  # 停止接受新任务
    pool.join()   # 等待工作进程退出
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import multiprocessing
import os
import time

# 初始化函数
def init_worker():
    print(f"Worker {os.getpid()} 初始化完成")

# 任务处理函数
def process_task(data):
    print(f"进程 {os.getpid()} 处理数据: {data}")
    time.sleep(1)  # 模拟处理时间
    return data * 2

if __name__ == "__main__":
    # 创建进程池
    with multiprocessing.Pool(
        processes=4, 
        initializer=init_worker
    ) as pool:
        
        # 准备任务数据
        data = [1, 2, 3, 4, 5, 6, 7, 8]
        
        # 使用map方法并行处理
        results = pool.map(process_task, data)
        
        print("处理结果:", results)
    
    print("所有任务已完成")