在pytorch的多卡训练中,通常有两种方式,一种是单机多卡模式(存在一个节点,通过torch.nn.DataParallel(model)实现),一种是多机多卡模式(存在一个节点或者多个节点,通过torch.nn.parallel.DistributedDataParallel(model),在单机多卡环境下使用第二种分布式训练模式具有更快的速度。pytorch在分布式训练过程中,对于数据的读取...
nn.parallel import DistributedDataParallel from torch_geometric.io import fs from torch_geometric.nn import GraphSAGE @@ -281,10 +282,10 @@ def run_training_proc( ) train_file = osp.join(root_dir, f'{args.dataset}-train-partitions', f'partition{data_pidx}.pt') train_idx = torch.load...
nn.parallel.DistributedDataParallel Partly supported Function is constrained nn.utils.clip_grad_norm_ Partly supported Function is constrained nn.utils.clip_grad_value_ Partly supported Function is constrained nn.utils.parameters_to_vector Supported nn.utils.vector_to_parameters Currently not support on...
("init model") model = TwoLinLayerNet().cuda() print("init ddp") ddp_model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[rank]) inp = torch.randn(10, 10).cuda() print("train") for _ in range(20): output = ddp_model(inp) loss = output[0] + output[1] loss...