This document has instructions to run a Wide & Deep FP32 inference using Intel® Optimizations for TensorFlow*.
Download and preprocess the income census data by running the 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 is using
pip install requests. Dataset will be downloaded in directory provided using
--data_dir. If you are behind proxy then you can proxy urls using
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
||Runs Wide & Deep model inference online mode (batch size = 1)|
||Runs Wide & Deep model inference in batch mode (batch size = 1024)|
To run on bare metal, the following prerequisites must be installed in your environment:
- Python* 3
Download and untar the Wide & Deep FP32 inference model package:
wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_3_0/wide-deep-fp32-inference.tar.gz tar -xvf wide-deep-fp32-inference.tar.gz
# We are 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
Once your environment is setup, navigate back to the directory that contains the Wide & Deep FP32 inference model package, set environment variables pointing to your dataset and output directories, and then run a quickstart script.
DATASET_DIR=<path to the Wide & Deep dataset directory> OUTPUT_DIR=<directory where log files will be written> TF_MODEL_SOURCE_DIR=<path to tensorflow-models> quickstart/<script name>.sh
Documentation and Sources
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