Release v0.2.1

We are delighted to bring a number of improvements to GLT, alongside the 0.2.0 release. This release contains many new features, improvements/bug fixes and examples, which are summarized as follows:

1. Add support for single-node and distributed inbound sampling, provide users options of both inbound and outbound sampling.
2. Add chunk partitioning when partitioning graphs with large feature files, reduce the memory consumption of feature partitioning.
3. Add examples for the IGBH dataset
4. Fix bugs and improve system stability

What's Changed

• fix: clone the id chunk before pickle dump to avoid dumping the entire tensor by @LiSu in #44
• Update figure by @husimplicity in #45
• Feature: In bound sampling of single machine by @Jia-zb in #48
• fix bug 'index out of bounds for partition book List' for igbh-large … by @kaixuanliu in #49
• Fix igbh rgnn example by @LiSu in #50
• Add distributed in-sample functions by @husimplicity in #51
• [Example] clarify the setting of the number of servers and clients by @Zhanghyi in #52
• Fix igbh rgnn example by @Jia-zb in #53
• [bug] fix invalid configuration argument when samplers return torch.empty by @husimplicity in #54
• [Example] Separate server and client launch scripts for server-client mode distributed training by @Zhanghyi in #56
• Add IGBH multi-card single-node example & bug fix when mem-sharing graphs by @LiSu in #62
• Update the igbh readme of single-node multi-GPU training by @LiSu in #63
• Bump version to 0.2.1 by @LiSu in #64

New Contributors

• @Jia-zb made their first contribution in #48
• @Zhanghyi made their first contribution in #52

Full Changelog: v0.2.0...v0.2.1