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示例:
from torch.utils.data import DataLoader train_loader =
DataLoader(dataset=train_data, batch_size=batch, shuffle=True, num_worker=4)
valid_loader = DataLoader(dataset=valid_data, batch_size=batch, num_worker=4)
1、num_workers是加载数据（batch）的线程数目
num_workers通过影响数据加载速度，从而影响训练速度
。每轮dataloader加载数据时：dataloader一次性创建num_worker个worker，worker就是普通的工作进程，并用batch_sampler将指定batch分配给指定worker，worker将它负责的batch加载进RAM。然后，dataloader从RAM中找本轮迭代要用的batch，如果找到了，就使用。如果没找到，就要num_worker个worker继续加载batch到内存，直到dataloader在RAM中找到目标batch。
num_worker个worker --> RAM --> dataloader
num_worker设置得大，好处是寻batch速度快，
因为下一轮迭代的batch很可能在上一轮迭代时已经加载好了。坏处是内存开销大，加重了CPU负担。num_workers的设置值一般是自己电脑的CPU核心数，如果CPU很强、RAM也很充足，就可以设置得更大些。
如果num_worker设为0，意味着每一轮迭代时，dataloader不再有自主加载数据到RAM这一步骤（因为没有worker了），而是在RAM中找batch，找不到时再加载相应的batch。缺点是速度更慢。
2、num_workers的选取
当加载batch的时间 < 数据训练的时间
GPU每次训练完都可以直接从CPU中取到next batch的数据
无需额外的等待，因此也不需要多余的worker，即使增加worker也不会影响训练速度
当加载batch的时间 > 数据训练的时间
GPU每次训练完都需要等待CPU完成数据的载入
若增加worker，即使worker_1还未就绪，GPU也可以取worker_2的数据来训练
3、可能出现的问题
问题一：
设置了num_workers的数量为cpu核心数，但是我的cpu利用率没有到100%，基本上在20%-30%之间，但是有的时候又能到100%。这是因为worker加载数据的瓶颈一般是在硬盘的速度而不是cpu的速度，cpu利用率不到100%很正常。
问题二：当你设置了num_workers = 8,4,2 ；有的时候会出现每一个epoch的速度都没有设置为0 快，反而设置为0的时候最快。解决方法是加上
os.environ[‘KMP_DUPLICATE_LIB_OK’]=‘True’。把 dataloader包装在 if name==“main”中。
