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)

Run the model

Setup your environment using the instructions below, depending on if you are using AI Kit:

Setup using AI Kit on Linux	Setup without AI Kit on Linux	Setup without AI Kit on Windows
To run using AI Kit on Linux you will need: git numactl wget Activate the `tensorflow` conda environment conda activate tensorflow	To run without AI Kit on Linux you will need: Python 3 intel-tensorflow>=2.5.0 git numactl wget A clone of the Model Zoo repo git clone https://github.com/IntelAI/models.git	To run without AI Kit on Windows you will need: Intel Model Zoo on Windows Systems prerequisites git A clone of the Model Zoo repo git clone https://github.com/IntelAI/models.git

After you've completed the setup above, 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/v1_8/wide_deep_fp32_pretrained_model.tar.gz
tar -xzvf wide_deep_fp32_pretrained_model.tar.gz
export PRETRAINED_MODEL=$(pwd)/wide_deep_fp32_pretrained_model

Wide & Deep inference also uses code from the TensorFlow models repo. Clone the repo using the PR in the snippet below and save the directory path to the TF_MODELS_DIR environment variable.

# We going to use a branch based on older version of the tensorflow model repo.
# Since, we need to to use logs utils on that branch, which were removed from
# the latest master
git clone https://github.com/tensorflow/models.git tensorflow-models
cd tensorflow-models
git fetch origin pull/7461/head:wide-deep-tf2
git checkout wide-deep-tf2

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.

Run on Linux

# cd to your model zoo directory
cd models

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>
export TF_MODELS_DIR=<path to tensorflow-models 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>

./quickstart/recommendation/tensorflow/wide_deep/inference/cpu/fp32/<script name>.sh

Run on Windows

Using cmd.exe, run:

# cd to your model zoo directory
cd models

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>
set TF_MODELS_DIR=<path to tensorflow-models directory>
# 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>

bash quickstart\recommendation\tensorflow\wide_deep\inference\cpu\fp32\<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://software.intel.com/content/www/us/en/develop/articles/containers/wide-deep-fp32-inference-tensorflow-container.html.