print(f"rank = {rank} is initialized") # 单机多卡情况下,localrank = rank. 严谨应该是local_rank来设置device torch.cuda.set_device(rank)tensor= torch.tensor([1, 2, 3, 4]).cuda() print(tensor) 假设单机双卡的机器上运行,则「开两个终端」,同时运行下面的命令 # TCP方法 python3 test_ddp....
# torchrun 会处理环境变量以及 rank & world_size 设置 os.environ["MASTER_ADDR"] = "localhost" # 由于这里是单机实验所以直接写 localhost os.environ["MASTER_PORT"] = "12355" # 任意空闲端口 init_process_group(backend="nccl") torch.cuda.set_device(int(os.environ['LOCAL_RANK']))) class Tr...
environ['MASTER_PORT'] = '12355' def main(rank, world_size): dist.init_process_group('nccl', rank=rank, world_size=world_size) # 初始化通信 torch.cuda.set_device(rank) # 设置当前可见cuda只有cuda:{rank} trans = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 1.0)...
import torch.distributed as dist import torch.multiprocessing as mp def setup(rank, world_size): dist.init_process_group(backend='nccl', init_method='env://', world_size=world_size, rank=rank) torch.cuda.set_device(rank) def cleanup(): dist.destroy_process_group() def demo_basic(rank,...
help='node rank for distributed training') opt = parser.parse_args() # 初始化GPU通信方式(NCCL)和参数的获取方式(env代表通过环境变量)。 dist.init_process_group(backend='nccl', init_method='env://') torch.cuda.set_device(opt.local_rank) ...
rank = args.nr * args.gpus + gpu dist.init_process_group(backend='nccl', init_method='env://', world_size=args.world_size, rank=rank) torch.manual_seed(0) model = ConvNet() torch.cuda.set_device(gpu) model.cuda(gpu) batch_size = 100 # define loss function (criterion) and opti...
A workaround for your case would be setting the device at the very beginning: def setup(rank, world_size): # initialize the process group dist.init_process_group("nccl", rank=rank, world_size=world_size) torch.cuda.set_device(rank) # use local_rank for multi-node But this thing ...
And say, I'm doing model parallelism as explained in thistutorial- why doesn't it dotorch.cuda.set_device()when switching devices? Would it be possible to write a clear documentation on when to usetorch.cuda.set_device()? Currently, it seems to be used more as a band-aid when related...
将torch.cuda.amp.GradScaler替换为torchacc.torch_xla.amp.GradScaler: fromtorchacc.torch_xla.ampimportGradScaler 替换optimizer。 使用原生PyTorch optimizer性能会稍差,可将torch.optim的optimizer替换为syncfree optimizer来进一步提升训练速度。 fromtorchacc.torch_xla.ampimportsyncfree ...
(rank,world_size): print(f"Hello from process {rank} out of {world_size}")if __name__ == '__main__': world_size = 4 pool = mp.Pool(world_size) pool.map(train, range(world_size))需要注意的是,如果要在主进程中使用GPU,需要在创建子进程之前先调用torch.cuda.set_device()函数设置...