4.2 checkpoint的保存与加载 4.3 dist.init_process_group的init_method 方式 4.4 进程内指定显卡 4.5 CUDA初始化的问题 本篇主要讲解单卡到分布式中DDP(DistributeDataParallel )的使用基础,包括如何使用DDP和相关的一些基础问题。 主要内容如下: 1 基本使用 2
checkpoint = torch.load(model_save_path, map_location=device) model.load_state_dict(checkpoint['model']) else: save_path = 'initial_weights.pth' if opts.local_rank == 0: torch.save(model.state_dict(), save_path) dist.barrier() # 这里注意,一定要指定map_location参数,否则会导致第一块GP...
model.load_state_dict(checkpoint['model']) model=DDP(model,device_ids=[gpu]) returnmodel 1. 2. 3. 4. 5. 6. 7. 二,将map_location指定为local_rank对应的GPU: defload_checkpoint(path): #加载到CPU checkpoint=torch.load(path,map_location='cuda:{}'.format(local_rank)) model=Net() mode...
model = TheModelClass(*args, **kwargs) optimizer = TheOptimizerClass(*args, **kwargs) checkpoint = torch.load(PATH) model.load_state_dict(checkpoint["model_state_dict"]) optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) epoch = checkpoint["epoch"] loss = checkpoint["loss"] ...
CHECKPOINT_PATH="./model.checkpoint"ifrank ==0: torch.save(ddp_model.state_dict(), CHECKPOINT_PATH)#barrier()其他保证rank 0保存完成dist.barrier() map_location= {"cuda:0": f"cuda:{local_rank}"} model.load_state_dict(torch.load(CHECKPOINT_PATH, map_location=map_location))#后面正常训练代...
model.load_state_dict(load_weights_dict, strict=False) # 如果是多卡训练,加载weights后要设置DDP模式,其后先定义一下optimizer和scheduler,之后再加载断点中保存的optimizer和scheduler以及设置epoch, optimizer.load_state_dict(load_ckpt['optimizer']) # 加载优化器状态 ...
PyTorch 的分布式训练方式主要有 DP (DataParallel)、DDP (先进的深度学习模型参数正以指数级速度增长:...
dist.barrier()# 这里注意,一定要指定map_location参数,否则会导致第一块GPU占用更多资源model.load_state_dict(torch.load(checkpoint_path, map_location=device)) 如果需要冻结模型权重,和单GPU基本没有差别。如果不需要冻结权重,可以选择是否同步BN层。然后再把模型...
defmain():load_checkpoint(checkpoint_path)initialize()train()deftrain():forbatchiniter(dataset):train_step(batch)ifshould_checkpoint:save_checkpoint(checkpoint_path) 3.3 小结 不难发现,TE的设计理念主要就是回答了之前提到的4个难点。 难点1 :需要一个节点/进程之间彼此发现的机制。
🐛 Describe the bug In instances where torch.compile is combined with DDP and checkpointing, the following error is raised: torch.utils.checkpoint.CheckpointError: torch.utils.checkpoint: A different number of tensors was saved during the...