nn.parallel import DistributedDataParallel as DDP def dist_training_loop(rank, world_size, dataloader, model, loss_fn, optimizer): dist.init_process_group('gloo', rank=rank, world_size=world_size) model = model.to(rank) ddp_model = DDP(model, device_ids=[rank]) optimizer = optimizer(ddp...
device_id = rank % torch.cuda.device_count() model = ToyModel().to(device_id) ddp_model = DDP(model, device_ids=[device_id]) loss_fn = nn.MSELoss() optimizer = optim.SGD(ddp_model.parameters(), lr=0.001) optimizer.zero_grad() outputs = ddp_model(torch.randn(20, 10)) labels ...
Does the Distributed Data Parallel (DDP) require additional GPU memory for maintaining a model? Here is the code: python print(f"before 1 {torch.cuda.memory_reserved()/(1024**2)}") ---> 0 model = model.to(self.device) print(f"before 2 {t...
The model is created as normal in each of the processes, but is sent to this device. A distributed version of the model that will process its shard of batch is created using DistributedDataParallel: model = model.to(device) ddp_model = DistributedDataParallel(model, device_ids=[local_rank]...
model = DDP(model, device_ids=[rank]) if rank == 0: write_dir = create_version_dir( os.path.join(write_dir, exp_name), prefix="run") for epoch in range(max_epochs): start_time = time.time() model.train() running_loss = 0 train_data.sampler.set_epoch(epoch) iter = ...
pytorch获取model的device信息,[源码解析]PyTorch分布式(17)—结合DDP和分布式RPC框架文章目录[源码解析]PyTorch分布式(17)---结合DDP和分布式RPC框架0x00摘要0x00综述0x01启动0x03支撑系统3.1功能3.2使用3.2.1混合模型3.2.2使用3.3定义3.4主要函数0x04HybridModel0x05训练
model = DDP(model, device_ids=[local_rank]) # define loss and optimizer criterion = torch.nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) model.train() t0 = time.perf_counter() summ = 0 ...
DDP(DistributedDataParallel) 注意一:使用分布式需设置`set_epoch` 注意二:相关概念说明 注意三:model先to(device)再DDP 注意四:batchsize含义有区别 基于horovod的分布式训练方法 为什么要使用分布式训练 对于懒癌星人,单卡训练能解决的问题,再多卡给我,我都懒得用。但是对于资深炼丹师,分布式训练带来的收益很大,可以...
加载模型时采用: net = torch.nn.parallel.DistributedDataParallel(net,device_ids=[args.local_rank],output_device=args.local_rank,find_unused_parameters=True) 此处local_rank断点调试发现可以传入值,想分多张卡去跑,发现local_rank是0的时候可以执行,但是继续到local_rank为1的时候报错如下: ...
load_checkpoint(args.model, None, None) unwrap_classes = (torchDDP, LocalDDP, MegatronFloat16Module) return unwrap_model(args.model, unwrap_classes)[0] def generate(self, input_ids=None, **kwargs): args = get_args() if parallel_state.get_data_parallel_world_size() > 1: ...