We are excited to announce [TorchRec](https://github.com/pytorch/torchrec), a PyTorch domain library for Recommendation Systems. This new library provides common sparsity and parallelism primitives, enabling researchers to build state-of-the-art personalization models and deploy them in production.
Modeling primitives, such as embedding bags and jagged tensors, that enable easy authoring of large, performant multi-device/multi-node models using hybrid data-parallelism and model-parallelism.
Optimized RecSys kernels powered by [FBGEMM](https://github.com/pytorch/FBGEMM) , including support for sparse and quantized operations.
A sharder which can partition embedding tables with a variety of different strategies including data-parallel, table-wise, row-wise, table-wise-row-wise, and column-wise sharding.
A planner which can automatically generate optimized sharding plans for models.
Pipelining to overlap dataloading device transfer (copy to GPU), inter-device communications (input_dist), and computation (forward, backward) for increased performance.
GPU inference support.
Common modules for RecSys, such as models and public datasets (Criteo & Movielens).
See our [announcement](https://pytorch.org/blog/introducing-torchrec/) and [docs](https://pytorch.org/torchrec/)