Resource
The Deep Learning Recommendation Model (DLRM) is a recommendation model designed to make use of both categorical and numerical inputs.
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Changelog
April 2020
- Initial release
May 2020
- Performance optimizations
June 2020
- Updated performance tables to include A100 results and multi-GPU setup
- Multi-GPU optimizations
August 2020
- Preprocessing with Spark 3 on GPU
- Multiple performance optimizations
- Automatic placement and load balancing of embedding tables
- Improved README
March 2021
- Added NVTabular as a new preprocessing option
- Added a new dataset - xlarge, which uses a frequency threshold of 2
- Introduced a new GPU - A100 80GB, and its performance results
- Updated Spark preprocessing
- Added Adam as an optional optimizer for embedding and MLPs, for multi-GPU training
- Improved README
June 2021
- Updated container version
- Updated performance results
Known issues
- Adam performance is not optimized.
- For some seeds, the model's loss can become NaN due to aggressive scheduling rate policy.