datawhalechina / torch-rechub
A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend.
Why this job: works in the areas we cover, but does not say which job it does
What it does
- Modular Design. Easy to add new models, datasets, and evaluation metrics.
- Based on PyTorch. Leverages PyTorch's dynamic graph and hardware acceleration capabilities. Supports CPU, NVIDIA CUDA GPU, AMD ROCm GPU, and Huawei Ascend NPU.
- Rich Model Library. Covers 30+ classic and cutting-edge recommendation algorithms (Matching, Ranking, Multi-task, Generative Recommendation, etc.).
- Standardized Pipeline. Provides unified data loading, training, and evaluation workflows.
- Easy Configuration. Adjust experiment settings via config files or command-line arguments.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 6 days ago
- Stars
- 1,241
- +2 this week
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v1.0.0
- Released 6 days ago
- Built with
- Jupyter Notebook
- 97% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 95% closed
- 3 still open
- Pull requests
- 205 merged
- 0 waiting
- Releases
- 13
- 13 in the last year
- Contributing guide (opens in new tab)
- No security policy
- Code of conduct (opens in new tab)
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More: languages, topics and project record
Languages
- Jupyter Notebook 97%
- Python 3%
Topics
- ctr-prediction
- pytorch
- recommendation-system
- recsys
- deep-learning
- recommendation-algorithms
- recommendation-engine
- recommender-system
- hstu
- generative-recommendation
- onnx
- llm
Project record
- JobOther AI tools
- StatusListed
- Commits702
- Forks162
- Watching18
- Stars in 28 days+12
- CreatedMay 12, 2022
- First seen by usNov 7, 2026
- CheckedNov 8, 2026