通过local_rank来确定该进程的设备:torch.cuda.set_device(opt.local_rank) 数据加载部分我们在该教程的第一篇里介绍过,主要时通过torch.utils.data.distributed.DistributedSampler来获取每个gpu上的数据索引,每个gpu根据索引加载对应的数据,组合成一个batch,与此同时Dataloader里的shuffle必须设置为None。 多机多卡训练 ...
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)...
+ 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.DistributedD...
+ifenable_torchacc_compiler(): + dist.init_process_group(backend="xla", init_method="env://") + device = xm.xla_device() + xm.set_replication(device, [device]) +else: args.local_rank =int(os.environ["LOCAL_RANK"]) device = torch.device(f"cuda:{args.local_rank}") dist....
rank = int(os.environ["RANK"]) local_rank = int(os.environ['LOCAL_RANK']) world_size = int(os.environ['WORLD_SIZE']) print(f'rank: {rank}, local_rank: {local_rank}, world_size: {world_size}\n') torch.cuda.set_device(int(os.environ['LOCAL_RANK'])) ...
data_loader_train = torch.utils.data.DataLoader(dataset=data_set, batch_size=batch_size, sampler=train_sampler) net = ConvNet() net = net.cuda() net = torch.nn.parallel.DistributedDataParallel(net, device_ids=[rank]) criterion = torch.nn.CrossEntropyLoss() opt = torch.optim.Adam(net.pa...
importosimporttorchimporttorch.distributedasdistimporttorch.utils.benchmarkasbenchmarkos.environ['CUDA_VISIBLE_DEVICES']=os.environ['LOCAL_RANK']dist.init_process_group(backend="nccl")x=torch.randn(1024,1024,device='cuda')ifdist.get_rank()==0:dist.send(x[0],1)elifdist.get_rank()==1:dist...
get_device_properties(torch.device(cuda)))" _CudaDeviceProperties(name='NVIDIA A100-SXM4-40GB', major=8, minor=0, total_memory=40536MB, multi_processor_count=108) git clone https://github.com/microsoft/DeepSpeed/ cd DeepSpeed rm -rf build TORCH_CUDA_ARCH_LIST=“8.0” DS_BUILD_CPU_ADAM...
In addition to being mutable, Tensors also have a set of dynamically determined properties (i.e. properties that can vary from run to run) this includes:dtype - their data type int, float, double, etc. device - where the Tensor lives, e.g. the CPU, or CUDA GPU 0 rank - the ...
parser.add_argument('--local_rank', type=int, default=-1) train中添加 import torch.distributed as dist from torch.utils.data.distributed import DistributedSampler 在有写操作时,注意判断local_rank 初始化 dist.init_process_group(backend='nccl') torch.cuda.set_device(self.opt.local_rank) torch...