Pull Command
docker pull intel/recommendation:tf-latest-wide-deep-large-ds-fp32-training
Description
This document has instructions to train Wide & 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.
- Download the large version of train dataset from: https://storage.googleapis.com/dataset-uploader/criteo-kaggle/large_version/train.csv
- Download the large version of evaluation dataset from: https://storage.googleapis.com/dataset-uploader/criteo-kaggle/large_version/eval.csv
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 |
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 |
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-latest-wide-deep-large-ds-fp32-training \
/bin/bash quickstart/<script name>.sh
Documentation and Sources
Get Started
Docker* Repository
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