Angel, an LF AI Foundation Graduated Project, has released version 3.1.0 and we’re thrilled to see lots of momentum within this community. The Angel Project is a high-performance distributed machine learning platform based on Parameter Server, running on YARN and Apache Spark. It is tuned for performance with big data and provides advantages in handling higher dimension models. It supports big and complex models with billions of parameters, partitions parameters of complex models into multiple parameter-server nodes, and implements a variety of machine learning algorithms using efficient model-updating interfaces and functions, as well as flexible consistency models for synchronization.
In version 3.1.0, Angel adds a variety of improvements, including:
- Features in graph learning with the trend of graph data structure adopted for many applications such as social network analysis and recommendation systems
- Publishing a collection of well implemented graph algorithms such as traditional learning, graph embedding, and graph deep learning – These algorithms can be used directly in the production model by calling with simple configurations
- Providing an operator API for graph manipulations including building graph, and operating the vertices and edges
- Enabling the support of GPU devices within the PyTorch-on-Angel running mode – With this feature it’s possible to leverage the hardwares to speed up the computation intensive algorithms
The Angel Project invites you to adopt or upgrade Angel of version 3.1.0 in your application, and welcomes feedback. To learn more about the Angel 3.1.0 release, check out the full release notes. Want to get involved with Angel? Be sure to join the Angel-Announce and Angel Technical-Discuss mailing lists to join the community and stay connected on the latest updates.
Congratulations to the Angel team and we look forward to continued growth and success as part of the LF AI Foundation! To learn about hosting an open source project with us, visit the LF AI Foundation website.
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