🎉 See our ongoing recommendation framework TorchEasyRec ! 🎉 This evolution of EasyRec is built on PyTorch, featuring GPU acceleration and hybrid parallelism for enhanced performance.
EasyRec implements state of the art machine learning models used in common recommedation tasks: candidate generation(matching), scoring(ranking), and multi-task learning. It improves the efficiency of generating high performance models by simple configuration and hyper parameter tuning(HPO).
- MaxCompute / DataScience / DLC / Local
- TF1.12-1.15 / TF2.x / PAI-TF
- MaxCompute Table
- HDFS files
- OSS files
- Kafka Streams
- Local CSV
- Flexible feature config and simple model config
- Efficient and robust feature generation[used in taobao]
- Nice web interface in development
- EarlyStop / Best Checkpoint Saver
- Hyper Parameter Search / AutoFeatureCross
- In development: NAS, Knowledge Distillation, MultiModal
- Support large scale embedding, incremental saving
- Many parallel strategies: ParameterServer, Mirrored, MultiWorker
- Easy deployment to EAS: automatic scaling, easy monitoring
- Consistency guarantee: train and serving
- DSSM / MIND / DropoutNet / CoMetricLearningI2I / PDN
- W&D / DeepFM / MultiTower / DCN / DIN / BST
- MMoE / ESMM / DBMTL / PLE
- CMBF / UNITER
- More models in development
- Easy to implement customized models
- Not need to care about data pipelines
- Run
knn algorithmof vectors in distribute environment
- DingDing Group: 32260796. (EasyRec usage general discussion.)
- DingDing Group: 37930014162, click this url or scan QrCode to join
