Comments (4)
zheng-da
commented on May 20, 2024
Yes, adding GNN models for embeddings is in our roadmap. Currently, we are working on distributed training capabilities in DGL and we are implementing an efficient RGCN in DGL. Once we have these two ready, we'll move the RGCN model in DGL-KE.
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snash4
commented on May 20, 2024
Thanks for the response.
What is the possible date of release of DGL with distributed computing capabilities?
Further, can I have some more details on the distributed computing side of DGL, such as which distributed framework will be used in DGL?
Thanks
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zheng-da
commented on May 20, 2024
The distributed training capability will be released with DGL 0.5. The planned release date is the end of July.
We didn't put any design documentation yet. DGL builds its own distributed framework.
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snash4
commented on May 20, 2024
Thanks, eagerly waiting for the release.
Would love to contribute.
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Related Issues (20)
- why dgl-ke so much faster than openke in test set HOT 3
- Upgrade DGL dependency HOT 2
- Can DGL_KE models be implemented on dynamic knowledge graphs? HOT 1
- Force dtype to int64 to ensure that we don't index with non-long tensor
- IndexError: list index out of range when training on raw user defined knowledge graph HOT 4
- Support Adam or Adagrad HOT 8
- Can not install dgl 0.4.3 HOT 4
- DGLBACKEND s not recognized as an internal or external command HOT 1
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- [BUG] Quick start example code does not work HOT 4
- dgl.__version__ >= 0.8 breaks on partition.py HOT 2
- RuntimeError: Cannot re-initialize CUDA in forked subprocess HOT 1
- Multi-gpu training is not effective on specific cases
- `graph.HeteroGraph` Error happened when running example HOT 1
- !DGLBACKEND=pytorch dglke_train Not Working HOT 1
- pytorch dglke_train Not Working, Expected type graph.Graph but get graph.HeteroGraph HOT 3
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