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
- 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.Dataset.from_parquet(filenames)
ds = ds.batch(batch_size)
# ...
with tf.device('/gpu:0'):
embs = tf.nn.embedding_lookup_sparse(weights, input_ids)
# ...
Please see documentation for more information.
Install
Method 1: Install from PyPI
pip install {PACKAGE}
{PACKAGE} |
Dependency | Python | CUDA | GLIBC | Data Opt. | Embedding Opt. | Parallelism Opt. |
---|---|---|---|---|---|---|---|
hybridbackend-tf115-cu118 | TensorFlow 1.15 1 |
3.8 | 11.8 | >=2.31 | ✓ | ✓ | ✓ |
hybridbackend-tf115-cu100 | TensorFlow 1.15 | 3.6 | 10.0 | >=2.27 | ✓ | ✓ | ✗ |
hybridbackend-tf115-cpu | TensorFlow 1.15 | 3.6 | - | >=2.24 | ✓ | ✗ | ✗ |
hybridbackend-deeprec2212-cu114 | DeepRec 22.12 2 |
3.6 | 11.4 | >=2.27 | ✓ | ✓ | ✓ |
1
: Suggested docker image:nvcr.io/nvidia/tensorflow:23.02-tf1-py3
2
: Suggested docker image:registry.cn-shanghai.aliyuncs.com/pai-dlc/tensorflow-training:deeprec2212-gpu-py36-cu114-ubuntu18.04
Method 2: Build from source
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.
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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
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