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  • NVIDIA Deep Learning Examples
    NVIDIA Deep Learning Examples
    DLRM for PyTorch
    Resource
    NVIDIA Deep Learning Examples
    NVIDIA Deep Learning Examples
    DLRM for PyTorch

    The Deep Learning Recommendation Model (DLRM) is a recommendation model designed to make use of both categorical and numerical inputs.

  • Changelog

    October 2021

    • Added support for CUDA Graphs
    • Switched to PyTorch native AMP for mixed precision training
    • Unified the single-GPU and multi-GPU training scripts
    • Added support for BYO dataset
    • Updated performance results
    • Updated container version

    June 2021

    • Updated container version
    • Updated performance results

    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

    August 2020

    • Preprocessing with Spark 3 on GPU
    • Multiple performance optimizations
    • Automatic placement and load balancing of embedding tables
    • Improved README

    June 2020

    • Updated performance tables to include A100 results and multi-GPU setup
    • Multi-GPU optimizations

    May 2020

    • Performance optimizations

    April 2020

    • Initial release

    Known issues

    • Adam optimizer performance is not optimized.
    • For some seeds, the model's loss can become NaN due to aggressive learning rate schedule.
    • Custom dot interaction kernels for FP16 and TF32 assume that embedding size <= 128 and number of categorical variables < 32. Pass --interaction_op dot to use the slower native operation in those cases.