0.01) criterion = nn.MSELoss() for epoch in range(epochs): optimizer.zero_grad() output = ddp_model(data.to(rank)) loss = criterion(output, target.to(rank)) loss.backward() optimizer.step() print(f"Process {rank}, Epoch {epoch}, Loss: {loss.item()}") dist.destroy_process_group...
清理和关闭进程组dist.destroyprocessgroup()if __name相关文章推荐 文心一言接入指南:通过百度智能云千帆大模型平台API调用 本文介绍了如何通过百度智能云千帆大模型平台接入文心一言,包括创建千帆应用、API授权、获取访问凭证及调用API接口的详细流程。文心一言作为百度的人工智能大语言模型,拥有强大的语义理解与生成能力,...
from torch.distributed import init_process_group, destroy_process_group def ddp_setup(rank, world_size): """ setup the distribution process group Args: rank: Unique identifier of each process world_size: Total number of processes """ # MASTER Node(运行 rank0 进程,多机多卡时的主机)用来协调...
['WORLD_SIZE']) dist.init_process_group("nccl", rank=rank, world_size=world_size) def cleanup(): dist.destroy_process_group() def main(local_rank, nnodes, args): rank = int(os.environ['RANK']) * nnodes + local_rank world_size = nnodes * int(os.environ['WORLD_SIZE']) print...
dist.destroy_process_group() class ToyModel(nn.Module): def __init__(self) -> None: super().__init__() self.layer = nn.Linear(1, 1) def forward(self, x): return self.layer(x) class MyDataset(Dataset): def __init__(self): ...
dist.init_process_group("nccl",rank=rank,world_size=world_size)defcleanup():dist.destroy_process_group() 复制 2.1 定义我们的手写数字分类的玩具模型。 classNet(nn.Module):def__init__(self):super(Net,self).__init__()self.conv1=nn.Conv2d(1,32,3,1)self.conv2=nn.Conv2d(32,64,3,1...
训练结束后,使用dist.destroy_process_group()来清理分布式进程组。 结论 将PyTorch中的分布式训练代码转换为单机模式通常涉及移除或修改与分布式相关的初始化、通信和数据分发代码。同时,理解分布式训练的基础可以帮助你在需要时高效地设置和运行分布式训练环境。希望本文能为你在这两个方向上的工作提供有价值的参考。相关...
dist.destroy_process_group() 最后一个疑问是,我怎样把我的数据和模型发送到另一个 GPU 上? 这正是 DistributedDataParallel 模块发挥作用的地方, 它将您的模型复制到每个 GPU 上 ,并且当 loss.backward() 被调用进行反向传播的时候,所有这些模型副本的梯度将被同步地平均/下降 (reduce)。这确保每个设备在执行优...
🐛 Describe the bug I seem to have found an issue that can occur when destroying the default process group and attempting to reinitialize it immediately after. This can lead to a race condition where not all workers have finished destroyi...
torch.cuda.set_device(rank)defcleanup():# 销毁进程组dist.destroy_process_group()defget_model(): model = LeNet(100).cuda() model = DDP(model, device_ids=[torch.cuda.current_device()])returnmodeldefget_dataloader(train=True): transform = transforms.Compose([ ...