The Deep Learning Recommendation Model (DLRM) is a recommendation model designed to make use of both categorical and numerical inputs.
We're constantly refining and improving our performance on AI and HPC workloads even on the same hardware with frequent updates to our software stack. For our latest performance data please refer to these pages for AI and HPC benchmarks.
Changelog
July 2022
- Start using Merlin Distributed Embeddings
March 2022
- Major performance improvements
- Support for BYO dataset
March 2021
- Initial release
Known issues
Checkpointing
TensorFlow runs into issues when trying to save model checkpoints for extremely large variables. We circumvent this by using a custom checkpoint format that splits the variables into pieces and stores each piece independently. However, this custom format cannot be used by the standard inference deployment frameworks such as ONNX.
Inference performance
Current inference performance was evaluated in python using TensorFlow 2.9.1. This provides ease of use and flexibility but is suboptimal in terms of performance. If you're interested in state-of-the-art performance for recommender system inference, please review our results in the MLPerf v0.7 benchmark where we used TensorRT. You might also want to check the source code of our MLPerf Inference submission.