https://github.com/awslabs/dgl-ke - v0.1.1

This patch release provides the following features. * Offline inference: https://github.com/awslabs/dgl-ke/pull/103 * Reorganize documentation. * Fix bugs: * #85 , Force user to provide dataset name when using udd or raw_udd. (This avoid set 'FB15k' as dataset name of user defined data. * Minor improvements: * remove unnecessary eval log: https://github.com/awslabs/dgl-ke/pull/104 * Add check for udd input * allow users to specify the delimiter

- Python
Published by zheng-da almost 6 years ago

https://github.com/awslabs/dgl-ke - v0.1.0

We are happy to announce the first release of DGL-KE, a lightning-speed package for learning knowledge graph embeddings. DGL-KE was previously incubated under the DGL repository. It is now a standalone package with more efficient and more scalable training. The key highlights are:

• Effortlessly generate knowledge graph embedding with one line of code.
• Support for giant graphs with millions of nodes and billions of edges for various hardware:
  • multi-CPU machines,
  • multi-GPU machines,
  • a cluster of machines.
• Support for both Pytorch and MXNet.

- Python
Published by zheng-da about 6 years ago