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![](assets/angel_logo.png)


[![license](http://img.shields.io/badge/license-Apache2.0-blue.svg?style=flat)](https://github.com/tencent/angel/blob/master/LICENSE)
[![Release Version](https://img.shields.io/badge/release-2.0.0-red.svg)](https://github.com/tencent/angel/releases)
[![license](http://img.shields.io/badge/license-BSD3-brightgreen.svg?style=flat)](https://github.com/tencent/angel/blob/master/LICENSE)
[![Release Version](https://img.shields.io/badge/release-1.4.0-red.svg)](https://github.com/tencent/angel/releases)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/tencent/angel/pulls)

[(English Documents Available)](./README_en.md)
**Angel** is a high-performance distributed machine learning 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.

**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。
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 using efficient model-updating interfaces and functions, as well as flexible consistency model for synchronization.

**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算法。
**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 with be released in the future.

**Angel**基于**Java**和**Scala**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**，集成了部分图计算和深度学习算法。
We welcome everyone interested in machine learning to contribute code, create issues or pull requests. Please refer to [Angel Contribution Guide](https://github.com/Tencent/angel/blob/master/CONTRIBUTING.md) for more detail.

欢迎对机器学习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](https://github.com/Tencent/angel/blob/master/CONTRIBUTING.md)
## Introduction to Angel

## Overview
* [Architecture](./docs/overview/architecture_en.md)
* [Code Framework](./docs/overview/code_framework_en.md)
* [Design](./docs/overview/design_philosophy_en.md)
* [Spark on Angel](./docs/overview/spark_on_angel_en.md)

* [架构设计](./docs/overview/architecture.md)
* [代码结构](./docs/overview/code_framework.md)
* [设计理念](./docs/overview/design_philosophy.md)
* [Spark on Angel](./docs/overview/spark_on_angel.md)

## Quick Start
* [Quick Start](./docs/tutorials/angel_ps_quick_start_en.md)
* [Spark on Angel Quick Start](./docs/tutorials/spark_on_angel_quick_start_en.md)

## Design
* [模型格式](./docs/design/model_format.md)
* [模型切分（modelPartitioner）](./docs/design/model_partitioner.md)
* [异步控制（syncController）](./docs/design/sync_controller.md)
* [定制函数（psFunc）](./docs/design/psfFunc.md)
* [核心接口](./docs/apis/core_api.md)
* [周边辅助](./docs/assistant/hobby_api.md)

## Programming Guide

* [Angel编程手册](./docs/programmers_guide/angel_programing_guide.md)
* [Spark on Angel编程手册](./docs/programmers_guide/spark_on_angel_programing_guide.md)
* [Angel Programming Guide](./docs/programmers_guide/angel_programing_guide_en.md)
* [Spark on Angel Programming Guide](./docs/programmers_guide/spark_on_angel_programing_guide_en.md)

## Deep Learning Architexture
* [Angel中的计算图](./docs/basic/computinggraph_on_angel.md)
* [Angel中的层](./docs/basic/layers_on_angel.md)
* [Angel中优化器](./docs/basic/optimizer_on_angel.md)
* [Angel中的损失函数](./docs/basic/lossfunc_on_angel.md)
* [Angel中的传输函数](./docs/basic/transfunc_on_angel.md)
* [Angel中的学习率Decay](./docs/basic/batchsize_decay.md)
## Design

* [Model Partitioner](./docs/design/model_partitioner_en.md)
* [SyncController](./docs/design/sync_controller_en.md)
* [psFunc](./docs/design/psfFunc_en.md)
* [Core API](./docs/apis/core_api_en.md)

## Quick Start
* [Angel入门](./docs/tutorials/angel_ps_quick_start.md)
* [Spark on Angel入门](./docs/tutorials/spark_on_angel_quick_start.md)
* [Angel Json配置](./docs/basic/json_conf.md)

## Algorithm
* [**Angel or Spark On Angel？**](./docs/algo/angel_or_spark_on_angel.md)
* [**Algorithm Parameter Description**](./docs/algo/model_config_details.md)
* **Angel**
* **Traditional Machine Learning Methods**
* [Logistic Regression(LR)](./docs/algo/lr_on_angel.md)
* [Support Vector Machine(SVM)](./docs/algo/svm_on_angel.md)
* [Factorization Machine(FM)](./docs/algo/fm_on_angel.md)
* [Linear Regression](./docs/algo/linear_on_angel.md)
* [Robust Regression](./docs/algo/robust_on_angel.md)
* [Softmax Regression](./docs/algo/softmax_on_angel.md)
* [KMeans](./docs/algo/kmeans_on_angel.md)
* [GBDT](./docs/algo/gbdt_on_angel.md)
* [LDA\*](./docs/algo/lda_on_angel.md) ([WrapLDA](./docs/algo/wrap_lda_on_angel.md))
* **Deep Learning Methods**
* [Deep Neural Network(DNN)](./docs/algo/dnn_on_angel.md)
* [Mix Logistic Regression(MLR)](./docs/algo/mlr_on_angel.md)
* [Deep And Wide(DAW)](./docs/algo/daw_on_angel.md)
* [Deep Factorization Machine(DeepFM)](./docs/algo/deepfm_on_angel.md)
* [Neural Factorization Machine(NFM)](./docs/algo/nfm_on_angel.md)
* [Product Neural Network(PNN)](./docs/algo/pnn_on_angel.md)
* **Spark on Angel**
* [FTRL](./docs/algo/ftrl_lr_spark.md)
* [Logistic Regression(LR)](./docs/algo/sona/lr_sona.md)
* [Word2Vec](./docs/algo/sona/word2vec_sona.md)
* [LINE](./docs/algo/sona/line_sona.md)
* [KCORE](./docs/algo/sona/kcore_sona.md)
* [Louvain](./docs/algo/sona/louvain_sona.md)
* [FTRLFM](./docs/algo/ftrl_fm_spark_en.md)
* [GBDT](./docs/algo/sona/feature_gbdt_sona.md)

* [**Algorithm Parameter Description**](./docs/algo/model_config_details.md)
* [Logistic Regression](./docs/algo/lr_on_angel_en.md)
* [Matrix Factorization](./docs/algo/mf_on_angel_en.md)
* [SVM](./docs/algo/svm_on_angel_en.md)
* [KMeans](./docs/algo/kmeans_on_angel_en.md)
* [GBDT](./docs/algo/gbdt_on_angel_en.md)
* [LDA](./docs/algo/lda_on_angel_en.md)
* [LR (Spark on Angel)](./docs/algo/spark_on_angel_optimizer_en.md)
* [Linear Regression](./docs/algo/linear_on_angel_en.md)
* [Robust Regression](./docs/algo/robust_on_angel_en.md)
* [Softmax Regression](./docs/algo/softmax_on_angel_en.md)

## Deployment

* [下载和编译](./docs/deploy/source_compile.md)
* [本地运行](./docs/deploy/local_run.md)
* [Yarn运行](./docs/deploy/run_on_yarn.md)
* [系统配置](./docs/deploy/config_details.md)
* [资源配置指南](./docs/deploy/resource_config_guide.md)
* [使用OpenBlas给算法加速](./docs/deploy/blas_for_densematrix.md)

## Community
* Mailing list: [email protected]
* Angel homepage in Linux FD: https://lists.deeplearningfoundation.org/g/angel-main
* [TSC members & Committers](./Community.md)
* [Contributing to Angel](./CONTRIBUTING.md)
* [Roadmap](https://github.com/Angel-ML/angel/wiki/Roadmap)
* [Compilation Guide](./docs/deploy/source_compile_en.md)
* [Running on Local](./docs/deploy/local_run_en.md)
* [Running on Yarn](./docs/deploy/run_on_yarn_en.md)
* [Configuration Details](./docs/deploy/config_details_en.md)
* [Resource Configuration Guide](./docs/deploy/resource_config_guide_en.md)

## FAQ
* [工程类问题](https://github.com/Tencent/angel/wiki/%E5%B7%A5%E7%A8%8B%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)
* [算法类问题](https://github.com/Tencent/angel/wiki/%E7%AE%97%E6%B3%95%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)

## Support

* **QQ群**：20171688
* [Angel FAQ](https://github.com/Tencent/angel/wiki/Angel%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)

## Papers
1. Lele Yu, Bin Cui, Ce Zhang, Yingxia Shao. [LDA*: A Robust and Large-scale Topic Modeling System](http://www.vldb.org/pvldb/vol10/p1406-yu.pdf). VLDB, 2017
2. Jiawei Jiang, Bin Cui, Ce Zhang, Lele Yu. [Heterogeneity-aware Distributed Parameter Servers](http://dl.acm.org/citation.cfm?id=3035933). SIGMOD, 2017
3. Jie Jiang, Lele Yu, Jiawei Jiang, Yuhong Liu and Bin Cui. [Angel: a new large-scale machine learning system](https://academic.oup.com/nsr/article/3052720). National Science Review (NSR), 2017
4. Jie Jiang, Jiawei Jiang, Bin Cui and Ce Zhang. [TencentBoost: A Gradient Boosting Tree System with Parameter Server](http://ieeexplore.ieee.org/abstract/document/7929984/). ICDE, 2017
2. Jiawei Jiang, Bin Cui, Ce Zhang, Lele Yu. [Heterogeneity-aware Distributed Parameter Servers](http://net.pku.edu.cn/~cuibin/Papers/2017%20sigmod.pdf). SIGMOD, 2017
3. Jie Jiang, Lele Yu, Jiawei Jiang, Yuhong Liu and Bin Cui. [Angel: a new large-scale machine learning system](http://net.pku.edu.cn/~cuibin/Papers/2017NSRangel.pdf). National Science Review (NSR), 2017
4. Jie Jiang, Jiawei Jiang, Bin Cui and Ce Zhang. [TencentBoost: A Gradient Boosting Tree System with Parameter Server](http://net.pku.edu.cn/~cuibin/Papers/2017%20ICDE%20boost.pdf). ICDE, 2017
5. Jiawei Jiang, Bin Cui, Ce Zhang and Fangcheng Fu. [DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions](https://dl.acm.org/citation.cfm?id=3196892). SIGMOD, 2018.

## Presentation

1. [Angel: A Machine Learning Framework for High Dimensionality](https://cdn.oreillystatic.com/en/assets/1/event/273/Angel_%E9%9D%A2%E5%90%91%E9%AB%98%E7%BB%B4%E5%BA%A6%E7%9A%84%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%AE%A1%E7%AE%97%E6%A1%86%E6%9E%B6%20_Angel_%20A%20machine%20learning%20framework%20for%20high%20dimensionality_%20%E8%AE%B2%E8%AF%9D.pdf). Strata China, 2017

2. [方圆并济：基于 Spark on Angel 的高性能机器学习](./docs/slides/Angel_QCon_2017.pdf). QCon ShangHai China, 2017

3. [基于Angel和Spark Streaming的高维度Online Learning](./docs/slides/Angel_GIAC_2017.pdf). GIAC China, 2017
121 changes: 121 additions & 0 deletions README_cn.md
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![](assets/angel_logo.png)


[![license](http://img.shields.io/badge/license-Apache2.0-blue.svg?style=flat)](https://github.com/tencent/angel/blob/master/LICENSE)
[![Release Version](https://img.shields.io/badge/release-2.0.0-red.svg)](https://github.com/tencent/angel/releases)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/tencent/angel/pulls)

[(English Documents Available)](./README_en.md)

**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算法。

**Angel**基于**Java**和**Scala**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**，集成了部分图计算和深度学习算法。

欢迎对机器学习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](https://github.com/Tencent/angel/blob/master/CONTRIBUTING.md)

## Overview

* [架构设计](./docs/overview/architecture.md)
* [代码结构](./docs/overview/code_framework.md)
* [设计理念](./docs/overview/design_philosophy.md)
* [Spark on Angel](./docs/overview/spark_on_angel.md)


## Design
* [模型格式](./docs/design/model_format.md)
* [模型切分（modelPartitioner）](./docs/design/model_partitioner.md)
* [异步控制（syncController）](./docs/design/sync_controller.md)
* [定制函数（psFunc）](./docs/design/psfFunc.md)
* [核心接口](./docs/apis/core_api.md)
* [周边辅助](./docs/assistant/hobby_api.md)

## Programming Guide

* [Angel编程手册](./docs/programmers_guide/angel_programing_guide.md)
* [Spark on Angel编程手册](./docs/programmers_guide/spark_on_angel_programing_guide.md)

## Deep Learning Architexture
* [Angel中的计算图](./docs/basic/computinggraph_on_angel.md)
* [Angel中的层](./docs/basic/layers_on_angel.md)
* [Angel中优化器](./docs/basic/optimizer_on_angel.md)
* [Angel中的损失函数](./docs/basic/lossfunc_on_angel.md)
* [Angel中的传输函数](./docs/basic/transfunc_on_angel.md)
* [Angel中的学习率Decay](./docs/basic/batchsize_decay.md)

## Quick Start
* [Angel入门](./docs/tutorials/angel_ps_quick_start.md)
* [Spark on Angel入门](./docs/tutorials/spark_on_angel_quick_start.md)
* [Angel Json配置](./docs/basic/json_conf.md)

## Algorithm
* [**Angel or Spark On Angel？**](./docs/algo/angel_or_spark_on_angel.md)
* [**Algorithm Parameter Description**](./docs/algo/model_config_details.md)
* **Angel**
* **Traditional Machine Learning Methods**
* [Logistic Regression(LR)](./docs/algo/lr_on_angel.md)
* [Support Vector Machine(SVM)](./docs/algo/svm_on_angel.md)
* [Factorization Machine(FM)](./docs/algo/fm_on_angel.md)
* [Linear Regression](./docs/algo/linear_on_angel.md)
* [Robust Regression](./docs/algo/robust_on_angel.md)
* [Softmax Regression](./docs/algo/softmax_on_angel.md)
* [KMeans](./docs/algo/kmeans_on_angel.md)
* [GBDT](./docs/algo/gbdt_on_angel.md)
* [LDA\*](./docs/algo/lda_on_angel.md) ([WrapLDA](./docs/algo/wrap_lda_on_angel.md))
* **Deep Learning Methods**
* [Deep Neural Network(DNN)](./docs/algo/dnn_on_angel.md)
* [Mix Logistic Regression(MLR)](./docs/algo/mlr_on_angel.md)
* [Deep And Wide(DAW)](./docs/algo/daw_on_angel.md)
* [Deep Factorization Machine(DeepFM)](./docs/algo/deepfm_on_angel.md)
* [Neural Factorization Machine(NFM)](./docs/algo/nfm_on_angel.md)
* [Product Neural Network(PNN)](./docs/algo/pnn_on_angel.md)
* **Spark on Angel**
* [FTRL](./docs/algo/ftrl_lr_spark.md)
* [Logistic Regression(LR)](./docs/algo/sona/lr_sona.md)
* [Word2Vec](./docs/algo/sona/word2vec_sona.md)
* [LINE](./docs/algo/sona/line_sona.md)
* [KCORE](./docs/algo/sona/kcore_sona.md)
* [Louvain](./docs/algo/sona/louvain_sona.md)
* [FTRLFM](./docs/algo/ftrl_fm_spark_en.md)
* [GBDT](./docs/algo/sona/feature_gbdt_sona.md)


## Deployment

* [下载和编译](./docs/deploy/source_compile.md)
* [本地运行](./docs/deploy/local_run.md)
* [Yarn运行](./docs/deploy/run_on_yarn.md)
* [系统配置](./docs/deploy/config_details.md)
* [资源配置指南](./docs/deploy/resource_config_guide.md)
* [使用OpenBlas给算法加速](./docs/deploy/blas_for_densematrix.md)

## Community
* Mailing list: [email protected]
* Angel homepage in Linux FD: https://lists.deeplearningfoundation.org/g/angel-main
* [TSC members & Committers](./Community.md)
* [Contributing to Angel](./CONTRIBUTING.md)
* [Roadmap](https://github.com/Angel-ML/angel/wiki/Roadmap)

## FAQ
* [工程类问题](https://github.com/Tencent/angel/wiki/%E5%B7%A5%E7%A8%8B%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)
* [算法类问题](https://github.com/Tencent/angel/wiki/%E7%AE%97%E6%B3%95%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)

## Support

* **QQ群**：20171688

## Papers
1. Lele Yu, Bin Cui, Ce Zhang, Yingxia Shao. [LDA*: A Robust and Large-scale Topic Modeling System](http://www.vldb.org/pvldb/vol10/p1406-yu.pdf). VLDB, 2017
2. Jiawei Jiang, Bin Cui, Ce Zhang, Lele Yu. [Heterogeneity-aware Distributed Parameter Servers](http://dl.acm.org/citation.cfm?id=3035933). SIGMOD, 2017
3. Jie Jiang, Lele Yu, Jiawei Jiang, Yuhong Liu and Bin Cui. [Angel: a new large-scale machine learning system](https://academic.oup.com/nsr/article/3052720). National Science Review (NSR), 2017
4. Jie Jiang, Jiawei Jiang, Bin Cui and Ce Zhang. [TencentBoost: A Gradient Boosting Tree System with Parameter Server](http://ieeexplore.ieee.org/abstract/document/7929984/). ICDE, 2017
5. Jiawei Jiang, Bin Cui, Ce Zhang and Fangcheng Fu. [DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions](https://dl.acm.org/citation.cfm?id=3196892). SIGMOD, 2018.

## Presentation

1. [Angel: A Machine Learning Framework for High Dimensionality](https://cdn.oreillystatic.com/en/assets/1/event/273/Angel_%E9%9D%A2%E5%90%91%E9%AB%98%E7%BB%B4%E5%BA%A6%E7%9A%84%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%AE%A1%E7%AE%97%E6%A1%86%E6%9E%B6%20_Angel_%20A%20machine%20learning%20framework%20for%20high%20dimensionality_%20%E8%AE%B2%E8%AF%9D.pdf). Strata China, 2017

2. [方圆并济：基于 Spark on Angel 的高性能机器学习](./docs/slides/Angel_QCon_2017.pdf). QCon ShangHai China, 2017

3. [基于Angel和Spark Streaming的高维度Online Learning](./docs/slides/Angel_GIAC_2017.pdf). GIAC China, 2017
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