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1.nn.parallel.scatter 向多个设备分发参数
这是实现方法,在模型中可以值调用函数,其原理就是通过for循环 然后copy到不同的设备上
2.allreduce 函数 将所有向量相加,并将结果广播给所有的gpu
3.将一个小批量的数据均匀地分布在多个GPU上
使用多机多卡的形式
在使用多机多卡训练数据是,通常分为两种形式:(1)数据并行,模型复制为n份,然后每一份模型中传入不同bacth数据用进行训练。(2)模型并行,用于解决一张卡上容不下一个模型的参数量问题。
1.数据并行的方式
Data Parallel - Data distributed across devices
pytorch中主要有两种方式用于实现数据并行:DataParallel 和DistributedDataParallel
,这两个函数可以保证复制(replicate)出来的模型参数相同,主要区别在于DataParallel 用于线程,而
DistributedDataparallel 是用于多进程。
1.1 single machine data parallel
1.2 Distributed Data Parallel
Distributed Data Parallel 的方式 是通过 多进程实现的,每个进程读取一个小批量的数据然后传递给自己负责的一个gpu
进行计算
2.模型并行的方式
Model Parallel -Model distributed across devices
2.1 single machine Model Parallel
2.2 Distributed Data Parallel with Model Parallel