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license Release Version PRs Welcome Download Code

(English Documents Available)

Angel是一个基于参数服务器(Parameter Server)理念开发的高性能分布式机器学习和图计算平台,它基于腾讯内部的海量数据进行了反复的调优,并具有广泛的适用性和稳定性,模型维度越高,优势越明显。 Angel由腾讯和北京大学联合开发,兼顾了工业界的高可用性和学术界的创新性。

Angel的核心设计理念围绕模型。它将高维度的大模型合理切分到多个参数服务器节点,并通过高效的模型更新接口和运算函数,以及灵活的同步协议,轻松实现各种高效的机器学习和图算法。

Angel基于JavaScala开发,能在社区的Yarn上直接调度运行,并基于PS Service,支持Spark on Angel,集成了图计算和深度学习算法。

欢迎对机器学习、图计算有兴趣的同仁一起贡献代码,提交Issues或者Pull Requests。请先查阅: Angel Contribution Guide

Overview

Design

Quick Start

Deployment

Programming Guide

Algorithm

Community

FAQ

Support

  • QQ群:20171688

  • 微信答疑群:(加微信小助手,备注Angel答疑)

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