🎉 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 deep learning models used in common recommendation 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).
Running Platform:
- Local / MaxCompute / EMR-DataScience / DLC
- TF1.12-1.15 / TF2.x / PAI-TF
- MaxCompute Table
- HDFS files / Hive Table
- OSS files
- CSV files / Parquet files
- Datahub / Kafka Streams
- Flexible feature config and simple model config
- Build models by combining some components
- Efficient and robust feature generation[used in taobao]
- Nice web interface in development
- EarlyStop / Best Checkpoint Saver
- Hyper Parameter Search / AutoFeatureCross / Knowledge Distillation / Features Selection
- In development: NAS
- Support large scale embedding and online learning
- 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 / FiBiNet / MaskNet / PPNet / CDN
- DIN / BST / CL4SRec
- MMoE / ESMM / DBMTL / AITM / PLE
- HighwayNetwork / CMBF / UNITER
- More models in development
- Support component-based development
- Easy to implement customized models and components
- Not need to care about data pipelines
- Run knn algorithm of vectors in distribute environment
- Home
- FAQ
- EasyRec Framework(PPT)
Any contributions you make are greatly appreciated!
- Please report bugs by submitting a GitHub issue.
- Please submit contributions using pull requests.
- please refer to the Development document for more details.
If EasyRec is useful for your research, please cite:
@article{Cheng2022EasyRecAE,
title={EasyRec: An easy-to-use, extendable and efficient framework for building industrial recommendation systems},
author={Mengli Cheng and Yue Gao and Guoqiang Liu and Hongsheng Jin and Xiaowen Zhang},
journal={ArXiv},
year={2022},
volume={abs/2209.12766}
}
- DingDing Group: 32260796. click this url or scan QrCode to join
- DingDing Group2: 37930014162, click this url or scan QrCode to join
- Email Group: easy_rec@service.aliyun.com.
- If you need EasyRec enterprise service support, or purchase cloud product services, you can contact us by DingDing Group.
EasyRec is released under Apache License 2.0. Please note that third-party libraries may not have the same license as EasyRec.