Here's whytorch_distributed_zero_firstis used to download the model on a single process: Prevent Redundant Downloads:In a distributed setup, if every process tries to download the model simultaneously, it can l
deftorch_distributed_zero_first(local_rank:int): """ Decorator to make all processes in distributed training wait for each local_master to do something. """ iflocal_ranknotin[-1,0]: torch.distributed.barrier() yield#中断后执行上下文代码,然后返回到此处继续往下执行 iflocal_rank==0: torch.d...
def torch_distributed_zero_first(local_rank: int): """ Decorator to make all processes in distributed training wait for each local_master to do something. """ if local_rank not in [-1, 0]: torch.distributed.barrier() yield #中断后执行上下文代码,然后返回到此处继续往下执行 if local_rank ...
在多个进程和多个 GPU 上进行数据并行训练,通常用于大规模分布式训练。 import os import torch import torch.distributed as dist import torch.multiprocessing as mp import torch.nn as nn import torch.optim as optim from torch.nn.parallel import DistributedDataParallel as DDP # 定义一个简单的模型 class ...
distributed.optim.ZeroRedundancyOptimizer(params, optimizer_class, process_group=None, parameters_as_bucket_view=False, overlap_with_ddp=False, **defaults) 参数: params(Iterable) -torch.Tensor 的Iterable 给出所有参数,这些参数将跨等级分片。 关键字参数: optimizer_class(torch.nn.Optimizer) -局部优化...
Modified the initialization check in torch_distributed_zero_first from is_initialized to a combination of is_available and is_initialized. 🎯 Purpose & Impact Purpose: To prevent the error 'Default process group has not been initialized' during distributed training setups. Impact: Ensures a more...
from torch.nn.parallel import DistributedDataParallel as DDP os.environ["MASTER_ADDR"] = "127.0.0.1" os.environ["MASTER_PORT"] = "29500" dist.init_process_group("nccl", rank=0, world_size=1) torch.cuda.set_device(0) model = DDP(torchvision.models.resnet18().to(device)) ...
importtorch.distributedasdist fromtorch.nn.parallelimportDistributedDataParallelasDDP os.environ["MASTER_ADDR"] ="127.0.0.1" os.environ["MASTER_PORT"] ="29500" dist.init_process_group("nccl", rank=0, world_size=1) torch.cuda.set_device(0) ...
a tuple of two ints – in which case, the first int is used for the height dimension, and the second int for the width dimension Note If the sum to the power of p is zero, the gradient of this function is not defined. This implementation will set the gradient to zero in this ca...
optimizer.zero_grad()output=model(data)loss=criterion(output, target) loss.backward() optimizer.step() ifcapture_metrics: # update metrics # 更新指标 metrics["avg_loss"].update(loss) forname, metricinmetrics.items(): ifname!="avg_loss": ...