Wide Deep Large Dataset FP32 Training Tensorflow* Container

Pull Command

docker pull intel/recommendation:tf-2.3.0-imz-2.0.0-wide-deep-large-ds-fp32-training

Description

This document has instructions for training Wide and Deep using a large dataset using Intel® Optimizations for TensorFlow*.

The large Kaggle Display Advertising Challenge Dataset will be used for training Wide and Deep. The data is from Criteo and has a field indicating if an ad was clicked (1) or not (0), along with integer and categorical features.

Download large Kaggle Display Advertising Challenge Dataset from Criteo Labs.

The directory where you've downloaded the train.csv and eval.csv files should be used as the DATASET_DIR when running quickstart scripts.

Quick Start Scripts

Script name Description
fp32_training_check_accuracy.sh Trains the model for a specified number of steps (default is 500) and then compare the accuracy against the specified target accuracy. If the accuracy is not met, then script exits with error code 1. The CHECKPOINT_DIR environment variable can optionally be defined to start training based on previous set of checkpoints.
fp32_training.sh Trains the model for 10 epochs. The CHECKPOINT_DIR environment variable can optionally be defined to start training based on previous set of checkpoints.

Docker

The model container used in the example below includes the scripts and libraries needed to run Wide and Deep Large Dataset FP32 training. To run one of the model quickstart scripts using this container, you'll need to provide volume mounts for the dataset, checkpoints, and an output directory where logs and the saved model will be written.

DATASET_DIR=<path to the dataset directory>
OUTPUT_DIR=<directory where the logs and the saved model will be written>
CHECKPOINT_DIR=<directory where checkpoint files will be read and written>

docker run \
  --env DATASET_DIR=${DATASET_DIR} \
  --env OUTPUT_DIR=${OUTPUT_DIR} \
  --env CHECKPOINT_DIR=${CHECKPOINT_DIR} \
  --env http_proxy=${http_proxy} \
  --env https_proxy=${https_proxy} \
  --volume ${DATASET_DIR}:${DATASET_DIR} \
  --volume ${OUTPUT_DIR}:${OUTPUT_DIR} \
  --volume ${CHECKPOINT_DIR}:${CHECKPOINT_DIR} \
  --privileged --init -t \
  intel/recommendation:tf-2.3.0-imz-2.0.0-wide-deep-large-ds-fp32-training \
  /bin/bash quickstart/<script name>.sh

Documentation and Sources

Get Started
Docker Repo
Main GitHub
Readme
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.


Related Containers and Solutions

Wide Deep Large Dataset FP32 Training TensorFlow* Model Package

Product and Performance Information

1

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804