Wide & Deep Large Dataset FP32 Training TensorFlow* Model Package

Published: 10/23/2020

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Download Command

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_3_0/wide-deep-large-ds-fp32-training.tar.gz

Description

This document has instructions to run a Wide & Deep FP32 large dataset using Intel® Optimizations for TensorFlow*

The large Kaggle* Display Advertising Challenge Dataset will be used for training Wide & 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.

 

Bare Metal

To run on bare metal, the following prerequisites must be installed in your environment:

Download and untar the model package and then run a quickstart script with enviornment variables that point to the dataset, a checkpoint directory, and an output directory where log files 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>

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_3_0/wide-deep-large-ds-fp32-training.tar.gz
tar -xvf wide-deep-large-ds-fp32-training.tar.gz
cd wide-deep-large-ds-fp32-training

quickstart/<script name>.sh

The script will write a log file and the saved model to the OUTPUT_DIR and checkpoints will be written to the CHECKPOINT_DIR.


Documentation and Sources

Get Started
Main GitHub* Repository
Readme
Release Notes
Get Started Guide

Code Sources
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.


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Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.