A high-performance framework for training wide-and-deep recommender systems on heterogeneous cluster
Project description
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
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:
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
Hashes for hybridbackend_cpu_legacy-0.5.4-cp36-cp36m-manylinux_2_27_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6a51906d4719b662c5f2b0fca1663b0240350fd277b8b99f1e449551a36f05e9 |
|
MD5 | 59b86f07e4bf29f7c2f0807542dd2955 |
|
BLAKE2b-256 | fe990934bb7351ceaee07229689fdd2e7937aa05ffc5262d026d5c76f8e990c7 |