python 多进程multiprocessing 内存占用 物理内存 虚拟内存 windows 分析

只有

import time
time.sleep(10)

前边加上

from torch.utils.data import Dataset

import torch

# 检查是否支持 CUDA
if torch.cuda.is_available():
    print("CUDA is available!")
    print(f"Device count: {torch.cuda.device_count()}")
    print(f"Current device: {torch.cuda.current_device()}")
    print(f"Device name: {torch.cuda.get_device_name(torch.cuda.current_device())}")
else:
    print("CUDA is not available.")


import time
time.sleep(10)

这样看来

import torch和from torch.utils.data import Dataset占用的内存是一样的

from transformers import AutoModelForZeroShotImageClassification, AutoProcessor

# import torch
#
# # 检查是否支持 CUDA
# if torch.cuda.is_available():
#     print("CUDA is available!")
#     print(f"Device count: {torch.cuda.device_count()}")
#     print(f"Current device: {torch.cuda.current_device()}")
#     print(f"Device name: {torch.cuda.get_device_name(torch.cuda.current_device())}")
# else:
#     print("CUDA is not available.")
import time
time.sleep(10)

中间的torch取消注释后几乎不变

多进程测试

from transformers import AutoModelForZeroShotImageClassification, AutoProcessor
import time

def apply_test(s):
    time.sleep(3)
    print(s)

if __name__ == '__main__':
    import multiprocessing
    pool = multiprocessing.Pool(3)
    for i in range(3):
        res = pool.apply_async(apply_test, [i])


    time.sleep(20)

import time

def apply_test(s):
    time.sleep(3)
    print(s)

if __name__ == '__main__':
    from transformers import AutoModelForZeroShotImageClassification, AutoProcessor
    import multiprocessing
    pool = multiprocessing.Pool(3)
    for i in range(3):
        res = pool.apply_async(apply_test, [i])


    time.sleep(20)