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....
torch.cuda.set_device(int(os.environ['LOCAL_RANK']))) class Trainer: def __init__( self, model: torch.nn.Module, train_data: DataLoader, optimizer: torch.optim.Optimizer, save_every: int, snapshot_path: str, # 保存 snapshots 的位置 ) -> None: self.gpu_id = int(os.environ['LOCA...
self.generator = generator self.local_rank = local_rank self.daemon = True self.start() def run(self): torch.cuda.set_device(self.local_rank) for item in self.generator: self.queue.put(item) self.queue.put(None) def next(self): next_item = self.queue.get() if next_item is None:...
So, say, if I'm setting up a DDP in the program. Do I have to calltorch.cuda.set_device(local_rank)at some point aftertorch.distributed.init_process_group()since otherwise the default device will becpuand the whole program will be slower because of that. ...
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. ...
out = self.fc(out) return out def main(rank): epochs = 2 batch_size = 100 dist.init_process_group("gloo", rank=rank, world_size=3) torch.cuda.set_device(rank) trans = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))]) data_set = torchvision.datasets...
'''基于bert上层的各类魔改,如last2layer_average, token_first_last_average'''class Model(BaseModel): # 需要继承BaseModel def __init__(self): super().__init__() self.bert = build_transformer_model(config_path, checkpoint_path) def forward(self): pass ...
device=cuda:0), %category : Long(4:32768, 32768:1, requires_grad=0, device=cuda:0), %FC_layer.2.0.weight : Float(5:128, 128:1, 1:1, requires_grad=1, device=cuda:0), %FC_layer.2.0.bias : Float(5:1, requires_grad=1, device=cuda:0), %402 : Float(64:3, 3:1, 1...
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...
client.setEndpoint("175805416243***.cn-beijing.pai-eas.aliyuncs.com"):需要将括号里的配置设置为您的服务Endpoint。例如175805416243***.cn-beijing.pai-eas.aliyuncs.com。 client.setModelName("alirec_rank_with_fg"):需要将括号里的配置设置为您的服务名称。 不使用...