datawhalechina/torch-rechub

Health v2 · maintenance only

Adoption not part of the score

1227 stars · 158 forks observed · 2026-10-11

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Torch-RecHub is a lightweight PyTorch framework for building recommendation system models with 30+ out-of-the-box algorithms covering ranking, matching, multi-task, and generative recommendation. It provides a standardized training/evaluation pipeline, ONNX export for deployment, and support for CPU, CUDA, ROCm, and Ascend NPU hardware.

Use cases

• train ctr prediction models like deepfm and din in pytorch
• build a two-tower matching retrieval model
• run multi-task recommendation experiments like mmoe and ple
• export a trained recommendation model to onnx for serving
• benchmark recommendation algorithms on a unified pipeline
• learn recommendation systems with reproducible pytorch examples

When to choose

• you want a simple, extensible pytorch framework for recommendation model experiments
• you need many classic and modern recsys models under one consistent trainer API
• you want easy ONNX export and hardware flexibility including Ascend NPU

When to avoid

• you need a full production recommender serving platform rather than a modeling framework
• your stack is TensorFlow-based
• you need non-recommendation deep learning tasks

Facets

framework · maturity active

machine-learning deep-learning data-science benchmarking machine-learning deep-learning data-science python cross-platform pytorch recommender-system ctr-prediction deepfm widedeep din multi-task-learning onnx-export matching generative-recommendation recsys recommendation recommendation-systems gpu

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