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ROBE-Z in DLRM

Running the UMA/RMA 1). create the Kaggle/CriteTB dataset . Generally this happens on the first run.

  1. Download the repo https://github.com/apd10/universal_memory_allocation
  • and run python3 setup install in that repo

  1. A) run the bench/train_tb_rma_final.sh to run 1000x compression on official MLPerf DLRM Model run the bench/train_tb_rma_final.chunk32.sh to run 1000x compression on official MLPerf DLRM Model using Z = 32

    B) run the bench/train_kaggle_rma.sh to run 1000x compression on kaggle model (embedding size 16)

    • You can change the --rma-size to use required memory

Deep Learning Recommendation Model for Personalization and Recommendation Systems: Refer to the Original dlrm repo for dlrm description

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.