A Flexible and Powerful Parameter Server for large-scale machine learning
Under Other
By Angel-ML

machine-learning model scala spark spark-streaming online-learning parameter-server high-dimensional

(ZH-CN Version)

Angel is a high-performance distributed machine learning and graph computing platform based on the philosophy of Parameter Server. It is tuned for performance with big data from Tencent and has a wide range of applicability and stability, demonstrating increasing advantage in handling higher dimension model. Angel is jointly developed by Tencent and Peking University, taking account of both high availability in industry and innovation in academia.

With model-centered core design concept, Angel partitions parameters of complex models into multiple parameter-server nodes, and implements a variety of machine learning algorithms and graph algorithms using efficient model-updating interfaces and functions, as well as flexible consistency model for synchronization.

Angel is developed with Java and Scala. It supports running on Yarn. With PS Service abstraction, it supports Spark on Angel. Graph computing and deep learning frameworks support is under development and will be released in the future.

We welcome everyone interested in machine learning or graph computing to contribute code, create issues or pull requests. Please refer to Angel Contribution Guide for more detail.

Introduction to Angel


Quick Start

Programming Guide






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  2. Jiawei Jiang, Bin Cui, Ce Zhang, Lele Yu. Heterogeneity-aware Distributed Parameter Servers. SIGMOD, 2017

  3. Jie Jiang, Lele Yu, Jiawei Jiang, Yuhong Liu and Bin Cui. Angel: a new large-scale machine learning system. National Science Review (NSR), 2017

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  6. Jiawei Jiang, Pin Xiao, Lele Yu, Xiaosen Li.PSGraph: How Tencent trains extremely large-scale graphs with Spark?.ICDE, 2020.