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Angel 1.0.0
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@andyyehoo andyyehoo released this 15 Jun 16:09
· 2817 commits to master since this release
v1.0.0
5f2f459

Release v1.0.0

  1. ParameterServer功能

    • 基于Matrix/Vector的模型自动切分和管理，兼顾稀疏和稠密两种格式
    • 支持对Model进行Push和Pull操作，可以自定义复杂的psFunc
    • 提供多种同步控制机制（BSP/SSP/ASP）

  2. 开发运行

    • 语言支持：系统基于Scala和Java开发，用户可以自由选择其中一种
    • 部署方便：可以直接在Yarn社区版本中运行，也支持本地调试模式
    • 数据切分: 自动切分读取训练数据，默认兼容了Hadoop FS接口
    • 增量训练：训练过程中会自动Checkpoint，而且支持加载模型后，增量训练

  3. PS Service

    • 只启动PSServer和PSAngent，为其他分布式计算平台提供PS服务
    • 基于PS-Service，不需要修改Spark核心代码，直接开发Spark-on-Angel算法，该模式无缝支持Breeze数值运算库

  4. 算法库

    • 集成Logistic Regression，SVM，KMeans，LDA，MF，GBDT等机器学习算法
    • 多种优化方法，包括ADMM，OWLQN， LBFGS和GD
    • 支持多种损失函数、评估指标，包含L1、L2正则项

  5. 算法优化

    • LDA采用了F+LDA算法用于加速采样的速度，同时利用流式参数获取的方法减少网络参数获取的延迟
    • GBDT使用两阶段树分裂算法，将部分计算转移到PS，减少网络传输，提升速度

Release v1.0.0

  1. ParameterServer Functionalities

    • Automatically partitions and manages models whose parameters can be represented as matrix/vector, supporting sparse and dense types
    • Supports push/pull operations and customized psFunc
    • Provides multiple concurrency control mechanisms（BSP/SSP/ASP）

  2. Development & Execution

    • Language support: the system is developed with Scala and Java; users can use either one of them as choice
    • Deployment: can be deployed to Yarn or run on local
    • Data partitioning: automatically partitions and reads training data, compatible with Hadoop FS interface by default
    • Incremental training: automatically generates checkpoint during training, supporting incremental training with reloaded model

  3. PS Service

    • Only starts up PSServer and PSAgent, providing PS service for other distributed computing platforms
    • Spark-on-Angel algorithms can be developed based on PS-Service without changing Spark source code, supporting Breeze NumericOps seamlessly

  4. Algorithms Library

    • Contains algorithms such as Logistic Regression, SVM, KMeans, LDA, MF, GBDT, etc.
    • Supports various optimization methods, including ADMM, OWLQN, LBFGS and GD
    • Supports various loss functions and metrics, L1/L2 regularization

  5. Algorithm Optimization

    • LDA: F+LDA accelerates sampling, while retrieving parameters in a streaming fashion to reduce network latency
    • GBDT: utilize two-stage tree-splitting to transfer parts of computing load to PS, reducing network communications and improve for speed
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