CUDA UVA允许用户创建超出 GPU内存大小的数据,同时利用 GPU 内核进行快速计算。将整个图结构及其特征存储在UVA后,可通过GPU内核快速提取子图特征,这对于大规模GNN训练非常有效[1][2]。在v0.8版本中,用户可以通过在DataLoader中设置use_uva开启UVA模式,如下所示: g=...# some DGLGraph datatrain_nids=...# train...
NVIDIA CUDA 12.1.1 DGL 1.1.2 1.1.1 PyTorch 23.09 23.07 Known Issues ‣ When cpu sampling is enabled (use_uva=False and num_workers>0), DGL sampling process would initialize cuda instance (issue-6561), which could result in a segmentation fault with the current cuda driver in the ...
The tensors that are used as node features must be contiguous and cannot be views of other tensors when the use_uva flag is set to True in the dgl.dataloading.Dataloader class. When you attempt to use a graph with a non-contiguous or view tensors for edata or ndata, aDGLError wi...
{"person": all_train_id}, sampler, batch_size=args.batch_size, num_workers=0, use_uva=True, shuffle=False, drop_last=False,) log_every = int(len(dataloader)// 20) tic = time.time() for step2, (input_nodes, seeds, blocks) in enumerate(dataloader): seeds = seeds['person'].long...
#include "./cuda/max_uva_threads.h" #endif #include "./cnumpy.h" #include "./detect_io_uring.h" #include "./expand_indptr.h" #include "./feature_cache.h" #include "./index_select.h" #include "./io_uring.h" #include "./partitioned_cache_policy.h" #include "./random.h" ...
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