README.md

Wide & Deep inference

This document has instructions for running Wide & Deep inference using Intel-optimized TensorFlow.

Dataset

Download and preprocess the income census data by running following python script, which is a standalone version of census_dataset.py Please note that below program requires requests module to be installed. You can install it using pip install requests. Dataset will be downloaded in directory provided using --data_dir. If you are behind corporate proxy, then you can provide proxy URLs using --http_proxy and --https_proxy arguments.

git clone https://github.com/IntelAI/models.git
cd models
python ./benchmarks/recommendation/tensorflow/wide_deep/inference/fp32/data_download.py --data_dir /home/<user>/widedeep_dataset

Quick Start Scripts

Script name	Description
inference_online.sh	Runs wide & deep model inference online mode (batch size = 1)
inference_batch.sh	Runs wide & deep model inference in batch mode (batch size = 1024)

Software requirements:

Install tensorflow

Pretrained Model

Download and extract the pretrained model. If you run on Windows, please use a browser to download the pretrained model using the link below. Set the directory path to the PRETRAINED_MODEL environment variable.

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/3_1/wide_and_deep.h5
export PRETRAINED_MODEL=$(pwd)/

Run benchmark on Linux

Install the Latest TensorFlow along with model dependencies under requirements.txt. Set the environment variables and run quickstart script on either Linux or Windows systems. See the list of quickstart scripts for details on the different options.

# cd to your AI Reference Models directory
cd models

export TF_USE_LEGACY_KERAS=0
export DATASET_DIR=<path to the Wide & Deep dataset directory>
export PRECISION=fp32
export OUTPUT_DIR=<path to the directory where log files will be written>
export PRETRAINED_MODEL=<pretrained model directory>
# For a custom batch size, set env var `BATCH_SIZE` or it will run with a default value.
export BATCH_SIZE=<customized batch size value>

# Install model specific dependencies:
pip install -r benchmarks/recommendation/tensorflow/wide_deep/inference/requirements.txt

# Run the quickstart scripts
./quickstart/recommendation/tensorflow/wide_deep/inference/cpu/<script name>.sh

Run accuracy on Linux

# cd to your AI Reference Models directory
cd models

export TF_USE_LEGACY_KERAS=0
export DATASET_DIR=<path to the Wide & Deep dataset directory>
export PRECISION=fp32
export OUTPUT_DIR=<path to the directory where log files will be written>
export PRETRAINED_MODEL=<pretrained model directory>
# For a custom batch size, set env var `BATCH_SIZE` or it will run with a default value.
export BATCH_SIZE=<customized batch size value>
# Install model specific dependencies:
pip install -r benchmarks/recommendation/tensorflow/wide_deep/inference/requirements.txt

./quickstart/recommendation/tensorflow/wide_deep/inference/cpu/accuracy.sh

Run benchmark on Windows

Using cmd.exe, run:

# cd to your AI Reference Models directory
cd models

set TF_USE_LEGACY_KERAS=0
set PRETRAINED_MODEL=<pretrained model directory>
set DATASET_DIR=<path to the Wide & Deep dataset directory>
set PRECISION=fp32
set OUTPUT_DIR=<directory where log files will be written>
# For a custom batch size, set env var `BATCH_SIZE` or it will run with a default value.
set BATCH_SIZE=<customized batch size value>
# Install model specific dependencies:
pip install -r benchmarks\recommendation\tensorflow\wide_deep\inference\requirements.txt
bash quickstart\recommendation\tensorflow\wide_deep\inference\cpu\<script name>.sh

Note: You may use cygpath to convert the Windows paths to Unix paths before setting the environment variables. As an example, if the dataset location on Windows is D:\<user>\widedeep_dataset, convert the Windows path to Unix as shown:

cygpath D:\<user>\widedeep_dataset
/d/<user>/widedeep_dataset

Then, set the DATASET_DIR environment variable set DATASET_DIR=/d/<user>/widedeep_dataset.

Additional Resources

• To run more advanced use cases, see the instructions for the available precisions FP32 for calling the launch_benchmark.py script directly.
• To run the model using docker, please see the Intel® Developer Catalog workload container:
  https://www.intel.com/content/www/us/en/developer/articles/containers/wide-deep-fp32-inference-tensorflow-container.html.