set_default_deviceutilizes a torch function mode to override torch calls which have a device argument to use the default device. The issue arises in device_context.__exit__(None,None,None) where the implementat
一种是先调用t.cuda.set_device(1)指定使用第二块GPU,后续的.cuda()都无需更改,切换GPU只需修改这一行代码。 更推荐的方法是设置环境变量CUDA_VISIBLE_DEVICES,例如当export CUDA_VISIBLE_DEVICE=1(下标是从0开始,1代表第二块GPU),只使用第二块物理GPU,但在程序中这块GPU会被看成是第一块逻辑GPU,因此此时调...
set default device Better transformer 第一个重要的PyTorch里程碑是今年早些时候发布的PyTorch 2.0版本。在所有平台上下载超过2000万次,所以对我们来说是一个相当重大的发布。下载数量仍在增加。它添加了一系列非常重要的功能。其中一个重要的功能是MPS后端(apple等)。现在处于测试阶段,在覆盖范围和稳定性上有了很大的...
defexample(rank, world_size):# create default process groupdist.init_process_group("nccl", rank=rank, world_size=world_size)# create local modelmodel= nn.Linear(10,10).to(rank)# construct DDP modelddp_model = DDP(model, device_ids=[rank])#defineloss ...
model.to(device)#超参数batch_size=64lr=0.0001epoch=10trainloader,testloader,test_num=data_set....
I think it would be useful to have a cuda.set_default_device in pytorch, so that the GPU 0 is not always the default one.Contributor apaszke commented Nov 27, 2016 The only problem with setting the default device are slightly unclear semantics of this function, when used inside the ...
1self.device ='cpu'#torch.device('cpu')都行2torch.set_default_tensor_type('torch.FloatTensor') ③:构建的网络也要设置计算设备(与之前设置的device统一) 1model = UNET(in_channels=in_channels, out_channels=out_channels).to(self.device) ...
device:默认cuda,因为脚本是基于单GPU进行训练 parser = argparse.ArgumentParser() parser.add_argument('--num_classes', type=int, default=5) parser.add_argument('--epochs', type=int, default=30) parser.add_argument('--batch-size', type=int, default=16) ...
def initTrainDl(self): train_ds = LunaDataset( # ❶ val_stride=10, isValSet_bool=False, ) batch_size = self.cli_args.batch_size if self.use_cuda: batch_size *= torch.cuda.device_count() train_dl = DataLoader( # ❷ train_ds, batch_size=batch_size, # ❸ num_workers=self....
()ifworld_size==1:returndist.barrier()##WORLD_SIZE由torch.distributed.launch.py产生 具体数值为 nproc_per_node*node(主机数,这里为1)num_gpus=int(os.environ["WORLD_SIZE"])if"WORLD_SIZE"inos.environelse1is_distributed=num_gpus>1ifis_distributed:torch.cuda.set_device(args.local_rank)# 这里...