hybridbackend-cu114 0.6.0a2
A high-performance framework for training wide-and-deep recommender systems on heterogeneous cluster
License: Apache License 2.0
Keywords: deep learning,recommendation system

# HybridBackend

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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 18.04 | 11.4 | 3.6    | 1.15.5     | registry.cn-shanghai.aliyuncs.com/pai-dlc/hybridbackend:0.6-tf1.15-py3.6-cu114-ubuntu18.04

See [PAI DLC](https://www.aliyun.com/activity/bigdata/pai-dlc) for more
information.

### Using pip packages

GLIBC    | CUDA | Python | Tensorflow      | Command
-------- | ---- | ------ | --------------- | ------------
`>= 2.7` | 11.4 | 3.6    | `>=1.15, < 2.0` | `pip install hybridbackend-cu114`
`>= 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](https://github.com/alibaba/HybridBackend/blob/main/BUILD.md).

## Usage

A minimal example:

```python
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](https://hybridbackend.readthedocs.io/en/latest/) for
more information.

## License

HybridBackend is licensed under the [Apache 2.0 License](LICENSE).

## Community

- Please see [Contributing Guide](https://github.com/alibaba/HybridBackend/blob/main/CONTRIBUTING.md)
before your first contribution.

- Please [register as an adopter](https://github.com/alibaba/HybridBackend/blob/main/ADOPTERS.md)
if your organization is interested in adoption. We will discuss
[RoadMap](https://github.com/alibaba/HybridBackend/blob/main/ROADMAP.md) with
registered adopters in advance.

- Please cite HybridBackend in your publications if it helps:

  ```text
  @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:

[<img src="https://github.com/alibaba/HybridBackend/raw/main/images/dingtalk.png" alt="dingtalk" width="200"/>](https://h5.dingtalk.com/circle/healthCheckin.html?dtaction=os&corpId=ding14f3e2ea4b79994cadf6428847a62d4a&51951ad=a84b419&cbdbhh=qwertyuiop)