
Optimized RecSys kernels powered by 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).

To showcase the flexibility of this tooling, let’s look at the following code snippet, pulled from our DLRM Event Prediction example:
```python
Specify the sparse embedding layers
eb_configs = [
   EmbeddingBagConfig(
       name=f"t_{feature_name}",
       embedding_dim=64,
       num_embeddings=100_000,