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.
- 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
eval.csv files should be used as the
DATASET_DIR when running quickstart scripts.
Quick Start Scripts
||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
||Trains the model for 10 epochs. The
To run on bare metal, the following prerequisites must be installed in your environment:
- Python* 3
Download and untar the model package and then run a quickstart script with environment 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
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