hybridbackend-cpu-legacy 0.5.4.post1

A high-performance framework for training wide-and-deep recommender systems on heterogeneous cluster

HybridBackend

HybridBackend is a high-performance framework for training wide-and-deep recommender systems on heterogeneous cluster.

Features

• Memory-efficient loading of categorical data

Install

Using pip packages

GLIBC	CUDA	Python	Tensorflow	Command
>= 2.4	-	3.6	>=1.15, < 2.0	pip install hybridbackend-cpu
>= 2.4	-	3.6	>=1.14, < 1.15	pip install hybridbackend-cpu-legacy

Build from source

See Building Instructions.

Usage

Please see documentation for more information.

License

HybridBackend is licensed under the Apache 2.0 License.

Community

• Please see Contributing Guide before your first contribution.

• Please register as an adopter if your organization is interested in adoption. We will discuss new feature requirements with registered adopters in advance.

• Please cite HybridBackend in your publications if it helps:

  @article{zhang2022picasso,
    title={PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems},
    author={Zhang, Yuanxing and Chen, Langshi and Yang, Siran and Yuan, Man and Yi, Huimin and Zhang, Jie and Wang, Jiamang and Dong, Jianbo and Xu, Yunlong and Song, Yue and others},
    journal={arXiv preprint arXiv:2204.04903},
    year={2022}
  }
  

Contact Us

If you would like to share your experiences with others, you are welcome to contact us in DingTalk: