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...
import torch_geometric.nn File "C:\Users\wanyu\anaconda3\envs\graph\lib\site-packages\torch_geometric\nn_init_.py", line 2, in from .data_parallel import DataParallel File "C:\Users\wanyu\anaconda3\envs\graph\lib\site-packages\torch_geometric\nn\data_parallel.py", line 5, in ...
nn.parallel import DistributedDataParallel import torch_geometric from torch_geometric.io import fs # Allow computation on objects that are larger than GPU memory # https://docs.rapids.ai/api/cudf/stable/developer_guide/library_design/#spilling-to-host-memory @@ -138,14 +139,14 @@ def load_...
Tensor.geometric_ Partly supported Function is constrained Tensor.log_normal_ Partly supported Function is constrained Tensor.map_ Partly supported Currently not support on GRAPH mode Tensor.diagonal_scatter Supported Tensor.apply_ Partly Supported Currently not support on GRAPH mode Tensor.nanmedian Part...
DataParallel 3 from .reshape import Reshape 4 from .conv import * # noqa 5 from .norm import * # noqa ~/Documents/pytorch_geometric/torch_geometric/nn/data_parallel.py in <module> 3 4 import torch ---> 5 from torch_geometric.data import Batch 6 7 ~/Documents/pytorch_geometric/torch_ge...
torch_geometric/nn/__init__.py", line 2, in <module> from .data_parallel import DataParallel File "/Users/urialon/miniconda3/envs/pytorch/lib/python3.6/site-packages/torch_geometric/nn/data_parallel.py", line 5, in <module> from torch_geometric.data import Batch File "/Users/urialon/...
("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.sum().backward() if __name__ == "__main__": os...