n_gpu=torch.cuda.device_count()torch.distributed.init_process_group("nccl",world_size=n_gpus,rank=args.local_rank) 1.2.2.2.2 第二步 torch.cuda.set_device(args.local_rank)该语句作用相当于CUDA_VISIBLE_DEVICES环境变量 1.2.2.2.3 第三步 model=DistributedDataParallel(model.cuda(args.local_rank)...
= -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...
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) train_dataset = ... train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset...
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...
如果想要进一步指定要运行的 GPU,可以通过 CUDA_VISIBLE_DEVICES 设置GPU可见性,比如 CUDA_VISIBLE_DEVICES=2,3 torchrun --standalone --nproc_per_node=gpu multi_gpu_torchrun.py 1. 这样会把本机上的 GPU2 和 GPU3 看做 GPU0 和 GPU1 运行 ...
() + xm.set_replication(device, [device]) + train_device_loader = pl.MpDeviceLoader(train_device_loader, device) + model = model.to(device) +else: device = torch.device(f"cuda:{args.local_rank}") torch.cuda.set_device(device) model = model.cuda() model = torch.nn.parallel....
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...
local_rank], >>> output_device=args.local_rank) classmethod convert_sync_batchnorm(module, process_group=None)[source] Helper function to convert torch.nn.BatchNormND layer in the model to torch.nn.SyncBatchNorm layer. Parameters: module (nn.Module)– containing module process_group (...
("--local_rank", type=int) args = parser.parse_args() dist.init_process_group(backend='nccl', init_method='env://') torch.cuda.set_device(args.local_rank) n_sample = 100 n_dim = 10 batch_size = 25 X = torch.randn(n_sample, n_dim) Y = torch.randint(0, 2, (n_sample,...
then run time env MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ BUILD_CAFFE2_OPS=0 USE_CUDA=0 USE_MKLDNN=0 USE_DISTRIBUTED=1 python setup.py develop hvaara mentioned this issue Nov 14, 2024 Does not work on macOS with device="mps": "Can't infer missing attention mask on mps ...