optimizer.step() print(f"Process {rank}, Epoch {epoch}, Loss: {loss.item()}") dist.destroy_process_group() 修改main函数增加world_size参数并调整进程初始化以传递world_size。 def main(): num_processes = 4 world_size = num_processes data = torch.randn(100, 10) target = torch.randn(100...
optimizer.step() print(f"Process {rank}, Epoch {epoch}, Loss: {loss.item()}") dist.destroy_process_group() 修改main函数增加world_size参数并调整进程初始化以传递world_size。 def main(): num_processes = 4 world_size = num_processes data = torch.randn(100, 10) target = torch.randn(100...
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...
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("world size:", ...
destroy_process_group() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 使用DistributedDataParallel包装模型,这样模型才能在各个进程间同步参数 self.model = DDP(model, device_ids=[gpu_id]) # model 要用 DDP 包装一下 1. 包装后 model 变成了一个 DDP 对象,要访问其参数得这样写self.model.mo...
for epoch in range(10):for images, labels in train_loader:images = images.to(rank)labels = labels.to(rank)optimizer.zero_grad()output = ddp_model(images)loss = loss_fn(output, labels)loss.backward()optimizer.step() 清理和关闭进程组dist.destroyprocessgroup()if __name...
dist.destroy_process_group() 修改main函数增加world_size参数并调整进程初始化以传递world_size。 def main(): num_processes = 4 world_size = num_processes data =torch.randn(100, 10) target = torch.randn(100, 1) mp.spawn(train, args=(world_size, data, target, 10), nprocs=num_processes,...
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...
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([ ...
wconstabadded a commit that referenced this issueMar 22, 2024 [C10D] Document destroy_process_group usage… c243484 wconstabadded a commit that referenced this issueMay 8, 2024 [C10D] Document destroy_process_group usage… 6852561 pytorchmergebotclosed this ascompletedin26b942cMay 9, 2024...