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)