torchrec

TorchRec is Meta's PyTorch library for training recommendation systems at scale. It is the engine behind the kind of "you might also like" model that runs on a feed of billions of items, packaged as a library you can use too. Free, BSD-3 licensed, used in production at Meta, Twitter, and Databricks.

Running this means GPU clusters, sharded embedding tables, and pipelined training. It gives you the parallelism primitives (model sharding, communication kernels, a planner that figures out the layout) but you bring the recommender model and the data pipeline. Single-GPU experiments work fine, the value shows up when your embedding table no longer fits in memory.

Solo: skip unless you are researching recsys at scale. Small teams: probably overkill, simpler libraries cover most product needs. Large teams shipping recommendations against tens of millions of items: this is what Meta built for itself, which is the strongest signal you will find.

The catch is that this is infrastructure, not a recommender. You still need to know what model you are training, how you are sharding it, and how to feed it. TorchRec makes the hard parts possible, not easy.