PaddleRec 0.1.0发布

功能：支持一键启动训练，四大可插拔组件，多种训练模式自动兼容

• Engine：覆盖不同运行平台，含cpu/gpu；单机、分布式（单机PS、单机多卡；PaddleCloud/MPI/K8S下的多机ps、多机多卡)；支持不同操作系统（ubuntu/centos/macos/windows)
• Trainer: 支持组件化自定义训练流程，含ps-transpiler、ps-pslib、collective
• Model: 支持快速构建新组网，已预置几十种经典模型
• Reader: 支持高效灵活的数据处理，含slot-feasign数据读取、自定义reader

模型库：

• 31+推荐算法，基本覆盖推荐各个模块的主流算法：内容理解模型（2），召回模型（7），排序模型（15），多目标模型（3），重排序模型（1），树模型（1），匹配模型（2）

文档：

• 快速开始：十分钟上手PaddleRec（AIStudio教程） 使用了MovieLens 1M数据集训练了召回+推荐模型，并模拟了在线推荐的全流程。使得用户通过该简单示例能够快速使用PaddleRec的数据处理、训练、预测等功能。
• 入门教程：数据准备、模型调参、启动训练、启动预测、快速部署
• 进阶教程：自定义数据处理、自定义模型、自定义训练流程

PaddleRec release 0.1.0

Major Features and Components:

• Start training with one-line command
• Training framework with four extensible modules supported
  • Engine: local training and distributed training supported on CPU/GPU on multiple platforms
  • Trainer: support user-defined training logics
  • Model: easy to develop user-defined models and plugin models
  • Reader: high performance data processing with user-defined processing functions.

Model zoos:

• more than 30 plugin deep learning algorithms in recommendation system pipelines, such as content understanding models, recall models, ranking models, multi-task models, reranking models, tree-based models and matching models, etc.

Documentation

• Quick start: 10 minutes hands on tutorial with movielens 1M dataset. Users can understand what is going on in recommender system offline training through data processing, training, validation.
• Basic tutorials, covering data preprocessing, model hyper-parameter tuning, training, prediction, deployment
• Advanced tutorials, including how to do user-defined data preprocessing, how to write a user-defined network, training pipeline customization.

Special Thanks to our Contributors

xiexionghang (for initial commit contribution)