Skip to content

Latest commit

 

History

History
95 lines (77 loc) · 4.38 KB

CONTAINER.md

File metadata and controls

95 lines (77 loc) · 4.38 KB

PyTorch DLRM inference

Description

This document has instructions for running DLRM Inference using Intel Extension for PyTorch.

Pull Command

docker pull intel/recommendation:centos-pytorch-cpu-dlrm-inference

Quick Start Scripts

Script name Description
inference_performance.sh Run inference to verify performance for the specified precision (fp32, int8, bf32 or bf16).
accuracy.sh Measures the inference accuracy for the specified precision (fp32, int8, bf32 or bf16).

Datasets

Criteo Terabyte Dataset

The Criteo Terabyte Dataset is used to run DLRM. To download the dataset, you will need to visit the Criteo website and accept their terms of use: https://labs.criteo.com/2013/12/download-terabyte-click-logs/. Copy the download URL into the command below as the <download url> and replace the <dir/to/save/dlrm_data> to any path where you want to download and save the dataset.

export DATASET_DIR=<dir/to/save/dlrm_data>
mkdir ${DATASET_DIR} && cd ${DATASET_DIR}
curl -O <download url>/day_{$(seq -s , 0 23)}.gz
gunzip day_*.gz

The raw data will be automatically preprocessed and saved as day_*.npz to the DATASET_DIR when DLRM is run for the first time. On subsequent runs, the scripts will automatically use the preprocessed data.

Pre-trained Model

Download the DLRM PyTorch weights (tb00_40M.pt, 90GB) from the MLPerf repo and set the WEIGHT_PATH to point to the weights file.

export WEIGHT_PATH=<path to the tb00_40M.pt file>

Docker Run

(Optional) Export related proxy into docker environment.

export DOCKER_RUN_ENVS="-e ftp_proxy=${ftp_proxy} \
  -e FTP_PROXY=${FTP_PROXY} -e http_proxy=${http_proxy} \
  -e HTTP_PROXY=${HTTP_PROXY} -e https_proxy=${https_proxy} \
  -e HTTPS_PROXY=${HTTPS_PROXY} -e no_proxy=${no_proxy} \
  -e NO_PROXY=${NO_PROXY} -e socks_proxy=${socks_proxy} \
  -e SOCKS_PROXY=${SOCKS_PROXY}"

To run DLRM inference, set environment variables to specify the dataset directory, precision,pre-trained model, and an output directory.

export DATASET_DIR=<path to the dataset>
export OUTPUT_DIR=<directory where log files will be written>
export SCRIPT=quickstart/<specify the script to run>
export PRECISION=<specify the precision to run>
export BERT_WEIGHT=<specify path to pre-trained model>

IMAGE_NAME=intel/recommendation:centos-pytorch-cpu-dlrm-inference
DOCKER_ARGS="--privileged --init -it"
WORKDIR=/workspace/pytorch-dlrm-inference

docker run --rm \
  --env DATASET_DIR=${DATASET_DIR} \
  --env PRECISION=${PRECISION} \
  --env WEIGHT_PATH=${WEIGHT_PATH} \
  --env OUTPUT_DIR=${OUTPUT_DIR} \
  --env http_proxy=${http_proxy} \
  --env https_proxy=${https_proxy} \
  --env no_proxy=${no_proxy} \
  --volume ${DATASET_DIR}:${DATASET_DIR} \
  --volume ${OUTPUT_DIR}:${OUTPUT_DIR} \
  --volume ${WEIGHT_PATH}:${WEIGHT_PATH} \
  ${DOCKER_RUN_ENVS} \
  --shm-size 8G \
  -w ${WORKDIR} \
  ${DOCKER_ARGS} \
  $IMAGE_NAME \
  /bin/bash $SCRIPT

Documentation and Sources

Get Started​

Docker* Repository

Main GitHub*

Release Notes

Get Started Guide

Code Sources

Dockerfile

Report Issue

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.

View All Containers and Solutions 🡢