Angel's Code Framework

Overall, Angel's code framework consists of the following modules:

1. Angle-Core (Core Layer)

Angel's core layer consists of the following main components:

• PSServer
• PSAgent
• Worker
• AngelClient
• Network: RPC & RDMA
• Storage: memory & Disk
• ……

2. Angel-ML (Machine-Learning Layer)

Angel is oriented toward machine learning, therefore, all the machine-learning related elements are added to the core, in a separate directory, including:

• Matrix
• Vector
• Feature
• Optimizer
• Objective
• Metric
• psFunc

Notice that the ML layer in the core package contains mainly low-level, basic interfaces, whereas their extentions and implementations belong to the specific algorithm layer (MLLib, to be introduced below).

3. Angel-Client (Interface Layer)

Angel's interface layer supports plug-in extensions based on the bottom layer. Currently, there are two sets of API, one for Angel and the other for Spark.

• Angel API

  The API for Angel itself is designed to focus on PSModel. The overall task execution reflects the simplicity and directness principles in the first-generation MR. MLRunner calls Learner through Task driver, and learns the MLModel that contains one or more PSModel.

• Spark on Angel API

  Spark on Angel API focuses on Spark's integration and coordination on the Angel platform. The design of Angel's PS-Service makes it easy to develop Spark on Angel; in Spark, one can use Spark on Angel directly, with no need to change anything in the core.

4. Angle-MLLib (Algorithm Layer)

We developed a variety of algorithms based on Angel interfaces:

• SVM
• LR (with multiple optimization methods)
• KMeans
• GBDT
• LDA
• MF (Matrix Factorization)
• FM (Factorization Machines)

Angel's algorithm development takes a straight-forward approach that centers around models on the PS, constantly updating them. Algorithm implementations are comparatively flexible, aiming for the best performance.

Overall, Angel users can pursue a full range of modular designs by programming at different layers, in order to develop efficient machine-learning algorithms.