train_loader = DataLoader(train_set, batch_size=batch_size, sampler=train_sampler)# 对于测试集来说,可以选择使用DistributedSampler,也可以选择不使用,这里选择使用test_sampler = DistributedSampler(test_set, num_replicas=world_size, rank=rank) test_loader = DataLoader(test_set, batch_size=batch_size,...
local_rank = torch.distributed.get_rank() torch.cuda.set_device(local_rank) device = torch.device("cuda", local_rank) #设置模型并行 model=torch.nn.parallel.DistributedDataParallel(model) #设置数据并行 from torch.utils.data.distributed import DistributedSampler sampler = DistributedSampler(dataset) ...
def run(rank, size): """ Distributed Synchronous SGD Example """ torch.manual_seed(1234) train_set, bsz = partition_dataset() model = Net() model = model model = model.cuda(rank) optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5) num_batches = ceil(len(train_set.dat...
# filename 'ptdist.py'import torchimport torch.distributed as distdefmain(rank, world):if rank == 0: x = torch.tensor([1., -1.]) # Tensor of interest dist.send(x, dst=1) print('Rank-0 has sent the following tensor to Rank-1') print(x)else: z = torch.tensor([0., 0.])...
[源码解析] PyTorch 分布式(8) --- DistributedDataParallel之论文篇 0x00 摘要 0x01 原文摘要 0x02 引论 2.1 挑战 2.2 实现和评估 0x03 背景 3.1 PyTorch 3.2 数据并行 3.3 AllReduce 0x04 系统设计 4.1 API 4.2 梯度规约 4.2.1 A Naive Solution 4.2.2 ...
importtorch.distributedasdist defmain(rank, world): ifrank ==0: x = torch.tensor([1.,-1.])# Tensor of interest dist.send(x, dst=1) print('Rank-0 has sent the following tensor to Rank-1') print(x) else: z = torch.tensor([0.,0.])# A holder for recieving the tensor ...
AllReduce是一个基础通信API,其被 DistributedDataParallel 用于计算所有进程的梯度求和。 多个通信库都提供了AllReduce ,包括NCCL、Gloo和MPI。AllReduce操作要求每个参与进程都提供一个大小相等的张量,然后将给定的算术运算(如sum、prod、min、max)应用于所有进程的输入张量,并向每个参与者返回相同的结果张量。
2. 使用 torch.distributed 加速并行训练: DataParallel:单进程控制多 GPU。 DistributedDataParallel:多进程控制多 GPU,一起训练模型。 2.1 介绍 在1.0 之后,官方终于对分布式的常用方法进行了封装,支持 all-reduce,broadcast,send 和 receive 等等。通过 MPI 实现 CPU 通信,通过 NCCL 实现 GPU 通信。官方也曾经提到...
dist.reduce_op.MIN 除了dist.all_reduce(tensor, op, group) 之外,PyTorch 中目前共有 6 种组间通信方式 distributed.scatter(tensor, scatter_list=None, src=0, group=None, async_op=False):将张量 scatter_list[i] 复制第 i 个进程的过程。例如,在...
import torch.distributed as dist def main(rank, world): if rank == 0: x = torch.tensor([1., -1.]) # Tensor of interest dist.send(x, dst=1) print('Rank-0 has sent the following tensor to Rank-1') print(x) else: z = torch.tensor([0., 0.]) # A holder for recieving ...