v0.1.1

This patch release provides the following features.

• Offline inference: #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: #104
  • Add check for udd input
  • allow users to specify the delimiter

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.