hybridbackend-tf115-cu100 0.6.1a0.dev20220727104822

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

• Communication-efficient training and evaluation at scale

• GPU-efficient orchestration of embedding layers

• Easy to use with existing AI workflows

Install

Using container images

Linux Distro	CUDA	Python	Tensorflow	URL
Ubuntu 20.04	11.4	3.8	1.15.5	registry.cn-shanghai.aliyuncs.com/pai-dlc/hybridbackend:0.6-tf1.15-py3.8-cu114-ubuntu20.04

See PAI DLC for more information.

Using pip packages

CUDA 11.4

Features:

All

Requires:

• NVIDIA TensorFlow 1.15
• Python 3.8
• CUDA 11.4 or above
• Ubuntu 20.04 or above

pip install nvidia-pyindex
pip install hybridbackend-tf115-cu114

CUDA 10.0

Features:

• Memory-efficient loading of categorical data
• GPU-efficient orchestration of embedding layers

Requires:

• TensorFlow GPU 1.15
• Python 3.6
• CUDA 10.0 or above
• Ubuntu 18.04 or above

pip install hybridbackend-tf115-cu100

CPU

Features:

• Memory-efficient loading of categorical data

Requires:

• TensorFlow 1.15
• Python 3.6
• Ubuntu 18.04 or above

pip install hybridbackend-tf115-cpu

Build from source

See Building Instructions.

Usage

A minimal example:

import tensorflow as tf
import hybridbackend.tensorflow as hb

def model_fn(features, labels, mode, params):
  # ..
  dense_features = hb.keras.layers.DenseFeatures(columns)
  # ...
# ...
estimator = hb.estimator.Estimator(model_fn, model_dir=model_dir)
estimator.train_and_evaluate(train_spec, eval_spec)

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 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: