This document has instructions for running Wide & Deep FP32 inference using Intel® Optimizations for TensorFlow*.
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 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 firstname.lastname@example.org:IntelAI/models.git cd models python ./benchmarks/recommendation/tensorflow/wide_deep/inference/fp32/data_download.py --data_dir /home/<user>/widedeep_dataset
||Runs wide & deep model inference online mode (batch size = 1)|
||Runs wide & deep model inference in batch mode (batch size = 1024)|
Download and untar the Wide & Deep FP32 inference model package:
wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_2_0/wide-deep-fp32-inference.tar.gz tar -xvf wide-deep-fp32-inference.tar.gz
tensorflow/models as a
# 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
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Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.