TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys). It allows authors to train models with large embedding tables sharded across many GPUs.

• Parallelism primitives that enable easy authoring of large, performant multi-device/multi-node models using hybrid data-parallelism/model-parallelism.
• The TorchRec sharder can shard embedding tables with different sharding strategies including data-parallel, table-wise, row-wise, table-wise-row-wise, and column-wise sharding.
• The TorchRec planner can automatically generate optimized sharding plans for models.
• Pipelined training overlaps dataloading device transfer (copy to GPU), inter-device communications (input_dist), and computation (forward, backward) for increased performance.
• Optimized kernels for RecSys powered by FBGEMM.
• Quantization support for reduced precision training and inference.
• Common modules for RecSys.
• Production-proven model architectures for RecSys.
• RecSys datasets (criteo click logs and movielens)
• Examples of end-to-end training such the dlrm event prediction model trained on criteo click logs dataset.

[Install FBGEMM] (https://github.com/pytorch/FBGEMM/tree/main/fbgemm_gpu#build-notes) pip install -e .