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

Project description

HybridBackend

cibuild readthedocs PRs Welcome license

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

Features

  • Memory-efficient loading of categorical data

  • GPU-efficient orchestration of embedding layers

  • Communication-efficient training and evaluation at scale

  • Easy to use with existing AI workflows

Usage

A minimal example:

import tensorflow as tf
import hybridbackend.tensorflow as hb

ds = hb.data.ParquetDataset(filenames, batch_size=batch_size)
ds = ds.apply(hb.data.parse())
# ...

with tf.device('/gpu:0'):
  embs = tf.nn.embedding_lookup_sparse(weights, input_ids)
  # ...

Please see documentation for more information.

Install

Method 1: Pull container images from PAI DLC

docker pull registry.cn-shanghai.aliyuncs.com/pai-dlc/hybridbackend:{TAG}

{TAG} TensorFlow Python CUDA OS Columnar Data Loading Embedding Orchestration Hybrid Parallelism
0.7-tf1.15-py3.8-cu116-ubuntu20.04 1.15 3.8 11.6 Ubuntu 20.04

Method 2: Install from PyPI

pip install {PACKAGE}

{PACKAGE} TensorFlow Python CUDA GLIBC Columnar Data Loading Embedding Orchestration Hybrid Parallelism
hybridbackend-tf115-cu114 * 1.15 3.8 11.4 >=2.31
hybridbackend-tf115-cu100 1.15 3.6 10.0 >=2.27
hybridbackend-tf115-cpu 1.15 3.6 - >=2.24

* nvidia-pyindex must be installed first

Method 3: Build from source

See Building Instructions.

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 RoadMap with registered adopters in advance.

  • Please cite HybridBackend in your publications if it helps:

    @inproceedings{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},
      booktitle={2022 IEEE 38th International Conference on Data Engineering (ICDE)},
      year={2022},
      organization={IEEE}
    }
    

Contact Us

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

dingtalk

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