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Angel-ML

angel

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

Model DevelopmentTraining toolkitsJava
Open on GitHub
Momentum
+0stars in 24 hours0.0%
6.79k
Stars
1.59k
Forks
+1
This week
49
Contributors
Created 2017-04-25 · Updated 2026-10-13 · #7229 today
Top developers
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Angel-ML/angel is ranked 7,229 with 6,794 stars.

Same-category comparison

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Key parameters and specifications
Key parameters and specifications
ParameterValue
LanguageJava
CategoryModel Development
SubcategoryTraining toolkits
Stars6,794
Forks1,586
Contributors49
Created date2017-04-25
Updated date2026-10-13
Stars in 1 day0
Stars in 7 days1
Rank7,229
Previous rank7,389
Rank change160
7-day stars rank9,011
Previous 7-day stars rank11,754
7-day stars rank change2,743
Total stars rank2,104
Previous total stars rank2,097
Total stars rank change-7
CountryUSA
README summary

(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.

Frequently asked questions

How many stars and forks are recorded?

Angel-ML/angel has 6,794 stars and 1,586 forks.

What is the repository rank?

Angel-ML/angel is ranked 7,229.

What language and category are recorded?

Angel-ML/angel has 6,794 stars, uses Java and belongs to Model Development.

Which repository is closest in rank?

Angel-ML/angel is compared with NVIDIA/pix2pixHD, ranked 7,222 with 6,924 stars.

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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