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Running DLRM-v1 inference on Intel® Data Center GPU Flex Series using Intel® Extension for PyTorch*

Overview

This document has instructions for running DLRM-v1 inference using Intel® Extension for PyTorch on Intel® Flex Series GPU.

Requirements

Item Detail
Host machine Intel® Data Center GPU Flex Series 170
Drivers GPU-compatible drivers need to be installed: Download Driver
Software Docker*

Quick Start Scripts

Script name Description
run_model.sh Batch inference with FP16 precision on Flex series 170

Note

At the moment sample does not support FP32 precision (export PRECISION=fp16).

Datasets and Pre-trained Model

Refer to the link to download and prepare datasets and pre-trained models. Set DATASET_DIR and CKPT_DIR to point to the corresponding directories.

Run Using Docker

Set up Docker Image

docker pull intel/recommendation:pytorch-flex-gpu-dlrm-v1-inference

Run Docker Image

The DLRM-v1 inference container includes scripts, model and libraries needed to run FP16 inference. To run the run_model.sh quickstart script using this container, you will need to provide an output directory where log files will be written.

#Optional
export PRECISION=fp16
export BATCH_SIZE=<provide batch size otherwise (default: 32768)>
export NUM_ITERATIONS=<provide num_iterations otherwise (default: 20)>

#Required
export OUTPUT_DIR=<path to output directory>
export SCRIPT=run_model.sh
export MULTI_TILE=False
export PLATFORM=Flex
export DATASET_DIR=<path to processed dataset directory>
export CKPT_DIR=<path to pre-trained model>

IMAGE_NAME=intel/recommendation:pytorch-flex-gpu-dlrm-v1-inference
DOCKER_ARGS="--rm -it"

docker run \
  --privileged \
  --device=/dev/dri \
  --ipc=host \
  --env PRECISION=${PRECISION} \
  --env NUM_ITERATIONS=${NUM_ITERATIONS} \
  --env OUTPUT_DIR=${OUTPUT_DIR} \
  --env MULTI_TILE=${MULTI_TILE} \
  --env PLATFORM=${PLATFORM} \
  --env DATASET_DIR=${DATASET_DIR} \
  --env CKPT_DIR=${CKPT_DIR} \
  --env http_proxy=${http_proxy} \
  --env https_proxy=${https_proxy} \
  --env no_proxy=${no_proxy} \
  --volume ${OUTPUT_DIR}:${OUTPUT_DIR} \
  --volume ${DATASET_DIR}:${DATASET_DIR} \
  --volume ${CKPT_DIR}:${CKPT_DIR} \
  ${DOCKER_ARGS} \
  ${IMAGE_NAME} \
  /bin/bash $SCRIPT

Documentation and Sources

GitHub* Repository

Support

Support for Intel® Extension for PyTorch* is found via the Intel® AI Analytics Toolkit. Additionally, the Intel® Extension for PyTorch* team tracks both bugs and enhancement requests using GitHub issues. Before submitting a suggestion or bug report, please search the GitHub issues to see if your issue has already been reported.

License Agreement

LEGAL NOTICE: By accessing, downloading or using this software and any required dependent software (the “Software Package”), you agree to the terms and conditions of the software license agreements for the Software Package, which may also include notices, disclaimers, or license terms for third party software included with the Software Package. Please refer to the license file for additional details.