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....
= -1: torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) dist.init_process_group(backend='nccl') # 开启后端通信 ... # model采用DistributedDataParallel进行包装 model.to(device) # 封装之前要把模型移到对应的gpu num_gpus = torch.cuda.device_count() if...
# 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...
torch.cuda.set_device(local_rank) model = YourModel() # 如果需要加载模型 if args.resume_path: checkpoint = torch.load(args.resume_path, map_location=torch.device("cpu")) model.load_state_dict(checkpoint["state_dict"]) # 要在模型初始化或加载完后再进行 # SyncBatchNorm不是必选项, 可以将...
目前cann版本是6.3.RC2,pytorch-npu版本是1.11.0,之前在cuda环境下一个模型采用单机多卡的方式(torch.nn.DataParallel),现在参照官网示例采用hccl: torch.distributed.init_process_group(backend="nccl",rank=args.local_rank,world_size=1) 加载模型时采用: net = torch.nn.parallel.DistributedDataParallel(net,dev...
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...
torch.device(f"cuda:{int(os.environ['LOCAL_RANK'])}") so it follows that making this easier is good. I think we need to define it carefully though, because it does clash a bit with the 'local rank' concept relative to an established process group/devicemesh. ...
torch.distributed支持三个后端,每个后端具有不同的功能。下表显示哪些功能可用于CPU/CUDA张量。仅当用于构建PyTorch的实现支持时,MPI才支持CUDA。 PyTorch附带的后端 目前PyTorch分发版仅支持Linux。默认情况下,Gloo和NCCL后端构建并包含在PyTorch的分布之中(仅在使用CUDA构建时为NCCL)。MPI是一个可选的后端,只有从源代...
device_id = int(os.environ["LOCAL_RANK"]) Launch distributed training: Instantiate the TorchDistributor with the desired parameters and call .run(*args) to launch training. The following is a training code example: Copy Python from pyspark.ml.torch.distributor import TorchDistributor def tr...
set_tuple_item, ) from torch_geometric.io import fs from torch_geometric.profile import benchmark from torch_geometric.testing import ( onlyCUDA, @@ -1256,7 +1257,7 @@ def test_save_and_load(dtype, device, tmp_path): path = osp.join(tmp_path, 'edge_index.pt') torch.save(adj,...