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Angel-ML

angel

A Flexible and Powerful Parameter Server for large-scale machine learning

Model DevelopmentTraining toolkitsJava
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创建于 2017-04-25 · 更新于 2026-10-13 · 今日第 7229 名
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Angel-ML/angel 排名第 7,229，有 6,794 个星标。

同类别比较

Angel-ML/angel 排名第 7,229，此处有 8 个同类条目；NVIDIA/pix2pixHD 排名第 7,222。

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关键参数与规格
关键参数与规格
参数值
语言Java
类别Model Development
子类别Training toolkits
星标数6,794
分叉数1,586
贡献者49
创建日期2017-04-25
更新日期2026-10-13
1 天星标数0
7 天星标数1
排名7,229
上次排名7,389
排名变化160
7 天星标排名9,011
上次 7 天星标排名11,754
7 天星标排名变化2,743
总星标排名2,104
上次总星标排名2,097
总星标排名变化-7
国家USA
README 摘录

(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**. 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 Architecture Code Framework Design Spark on Angel Machine Learning Graph Computing Design Model Partitioner SyncController psFunc Core API Quick Start Quick Start Example Deployment Compilation Guide Running on Local Running on Yarn Configuration Details Resource Configuration Guide Programming Guide Spark on Angel Programming Guide Algorithm **Angel or Spark On Angel？ ** **Algorithm Parameter Description** **Angel** **Traditional Machine Learning Methods** **Spark on Angel** **Angel Mllib** **Angel

Graph** Community Mailing list: angel-tsc@lists. org Angel homepage in Linux FD: Committers & Contributors Contributing to Angel Roadmap FAQ Angel FAQ Papers 1. PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm. Node Dependent Local Smoothing for Scalable Graph Learning. PSGraph: How Tencent trains extremely large-scale graphs with Spark? DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions. LDA*: A Robust and Large-scale Topic Modeling System. Heterogeneity-aware Distributed Parameter Servers. Angel: a new large

-scale machine learning system. TencentBoost: A Gradient Boosting Tree System with Parameter Server.

常见问题

记录了多少星标和分叉？

Angel-ML/angel 有 6,794 个星标和 1,586 个分叉。

仓库排名是多少？

Angel-ML/angel 排名第 7,229。

记录的语言和类别是什么？

Angel-ML/angel 有 6,794 个星标，语言为 Java，类别为 Model Development。

哪个仓库排名最接近？

Angel-ML/angel 与 NVIDIA/pix2pixHD 比较，后者排名第 7,222，有 6,924 个星标。

README

(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

Design

Quick Start

Deployment

Programming Guide

Algorithm

Community

FAQ

Papers

  1. PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm. WWW, 2022
  2. Graph Attention Multi-Layer Perceptron. KDD, 2022
  3. Node Dependent Local Smoothing for Scalable Graph Learning. NeurlPS, 2021
  4. PSGraph: How Tencent trains extremely large-scale graphs with Spark?.ICDE, 2020.
  5. DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions. SIGMOD, 2018.
  6. LDA*: A Robust and Large-scale Topic Modeling System. VLDB, 2017
  7. Heterogeneity-aware Distributed Parameter Servers. SIGMOD, 2017
  8. Angel: a new large-scale machine learning system. National Science Review (NSR), 2017
  9. TencentBoost: A Gradient Boosting Tree System with Parameter Server. ICDE, 2017
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