Comments (2)
It totally makes sense to decouple networkx and DGLGraph. I don't feel it's necessary to support the non-batchable mode.
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Done in #90 .
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Related Issues (20)
- AttributeError: '_OpNamespace' 'graphbolt' object has no attribute 'set_max_uva_threads' HOT 2
- [GraphBolt] ogbn-arxiv accuracy values are lower than expected. HOT 10
- Code running for distributed graph training HOT 3
- [liburing] redefinition of 'struct in6_pktinfo' when building on ubi7 with gcc/g++ 9.5.0 HOT 2
- [GraphBolt] Fix the performance issue of `graphbolt::parallel_for`. HOT 1
- [GraphBolt] Edge feature fetching does not work.
- [GraphBolt] Store `ORIGINAL_EDGE_ID` in `FeatureStore`. HOT 6
- DGL installation does not install pyyaml and pydantic as dependancies HOT 1
- [GraphBolt] Enable `CPUCachedFeature` for r-gcn mag240M example. HOT 1
- [CI] Update CI compiler versions
- how to use gpu to generate graph ? (to generate edges and nodes ) HOT 3
- ImportError: Cannot load Graphbolt C++ library HOT 8
- Restrict `torch` versions in `setup.py` HOT 3
- [GraphBolt] WARNING: An experimental feature for CUDA allocations is turned on HOT 12
- sample_neighbor not behaving as expected on onDiskDataset HOT 17
- HeteroGraphConv's forward need to modify HOT 3
- HeteroGraphConv documentation sample code crash HOT 1
- DGL DataLoader does not maintain example order with shuffle=False when using multiple workers. HOT 1
- [DataLoader] User-defined Dataloader Problem HOT 1
- SDDMM operator fails in distributed environment
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from dgl.