local_rank指在一个node上进程的相对序号,local_rank在node之间相互独立 WORLD_SIZE全局进程总个数,即在一个分布式任务中rank的数量 Group进程组,一个分布式任务对应了一个进程组。只有用户需要创立多个进程组时才会用到group来管理,默认情况下只有一个group 如下图所示,共有3个节点(机器),每个节点上有4个GPU,每...
# local_rank这个参数是底层传的,我们只要预留好位置接受即可 parser = argparse.ArgumentParser() parser.add_argument('--local_rank', default=0, type=int, help='node rank for distributed training') args = parser.parse_args() print(args.local_rank) # if args.local_rank != -1: torch.cuda....
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 ...
parser.add_argument('--local_rank', type=int, help='local rank for dist') args = parser.parse_args() print(os.environ['MASTER_ADDR']) print(os.environ['MASTER_PORT']) world_size = torch.cuda.device_count() local_rank = args.local_rank dist.init_process_group(backend='nccl') torc...
1、local_rank = int(os.environ.get("LOCAL_RANK", -1)) - 在多卡训练场景下，存在多个进程，每个进程利用一张GPU进行训练。此代码用于获取某个进程使用的GPU编号。四卡训练时，四个进程的local_rank分别对应0、1、2和3。2、dist.init_process_group(backend="nccl") - 多卡训练前需执行初始...
🚀 The feature, motivation and pitch For a symmetry with torch.distributed.get_global_rank it would be useful to add torch.distributed.get_local_rank rather than have the user fish for it in the LOCAL_RANK env var. This feature is almost ...
The module torch.distributed.launch is deprecated and will be removed in future. Use torchrun. Note that --use_env is set by default in torchrun. If your script expects `--local_rank` argument to be set, please change it to read from `os.environ['LOCAL_RANK']` instead. See https:...
+ifargs.device =="xla": + device = xm.xla_device() + 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) mo...
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 (optional)– process group to scope ...
+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....