在所有的节点上都需要进行所有group的初始化,而不是只初始化当前rank所属的group,如使用12卡,group size设置为4,则12/4=3个group对应的rank分别为[0,1,2,3][4,5,6,7][8,9,10,11],这12个节点都需要初始化三个group,而不是rank0,1,2,3只用初始化group0: rank = dist.get_rank() group_ranks =...
get_rank() == 0: torch.save(model.module.state_dict(), 'results/%s/model.pth' % args.save_dir) 2.2 原理 区别 多进程和DP 不同, DDP 采用多进程,最推荐的做法是每张卡一个进程从而避免上一节所说单进程带来的影响。前文也提到了 DP 和 DDP 共用一个 parallel_apply 函数,所以 DDP 同样支持...
importtorchfrom torch.distributed.distributed_c10dimport_get_default_group defget_group(group_size,*args,**kwargs):rank=dist.get_rank()world_size=dist.get_world_size()ifgroup_size==1:# 后续不会涉及到分布式的操作returnNone elif group_size==world_size:v=float(torch.__version__.rsplit('.'...
在所有的节点上都需要进行所有group的初始化,而不是只初始化当前rank所属的group,如使用12卡,group size设置为4,则12/4=3个group对应的rank分别为[0,1,2,3][4,5,6,7][8,9,10,11],这12个节点都需要初始化三个group,而不是rank0,1,2,3只用初始化group0: rank = dist.get_rank() group_ranks =...
size = dist.get_world_size() bsz = 128 / float(size) partition_sizes = [1.0 / size for _ in range(size)] partition = DataPartitioner(dataset, partition_sizes) partition = partition.use(dist.get_rank()) train_set = torch.utils.data.DataLoader(partition, ...
ifdist.get_rank()==0:torch.save(model.module,"model.pkl")即保存rank为0的模型。其次：model =...
rank = dist.get_rank() world_size = dist.get_world_size() # prepare the dataset dataset =RandomDataset(input_size, data_size) train_sampler = torch.utils.data.distributed.DistributedSampler(dataset) rand_loader =DataLoader(dataset, batch_size=batch_size//world_size, ...
worker_group.group_world_size = group_world_sizeifgroup_rank ==0: self._set_master_addr_port(store, spec.master_addr, spec.master_port) master_addr, master_port = self._get_master_addr_port(store) restart_count = spec.max_restarts - self._remaining_restarts ...
rank = dist.get_rank() ==0else: rank =Trueloss = model(row)ifargs.distributed:# does average gradients automatically thanks to model wrapper into# `DistributedDataParallel`loss.backward()else:# scale loss according to accumulation stepsloss = loss/ACC_STEPS ...
def is_main_process(): return get_rank() == 0 def train_one_epoch(model, optimizer...