README.md

Wide & Deep

This document has instructions for how to run Wide & Deep for the following modes/precisions:

• FP32 inference

Instructions and scripts for model training and inference for other precisions are coming later.

FP32 Inference Instructions

1. Clone tensorflow/models as a tensorflow-models

   # 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 
   

2. Download and extract the pre-trained model.

   $ 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
   

3. Clone the intelai/models repo. This repo has the launch script for running the model, which we will use in the next step.

   $ git clone https://github.com/IntelAI/models.git
   

4. 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 --http_proxy and --https_proxy arguments.

   $ cd models
   $ python benchmarks/recommendation/tensorflow/wide_deep/inference/fp32/data_download.py --data_dir /home/<user>/widedeep_dataset
   

5. How to run

   • Running the model in online inference mode, set --batch-size = 1

     $ cd /home/<user>/models/benchmarks
     
     $ python launch_benchmark.py \ 
           --framework tensorflow \ 
           --model-source-dir /home/<user>/path/to/tensorflow-models \
           --precision fp32 \
           --mode inference \
           --model-name wide_deep \
           --batch-size 1 \
           --data-location /home/<user>/widedeep_dataset \
           --checkpoint /home/<user>/path/to/wide_deep_fp32_pretrained_model \
           --docker-image intel/intel-optimized-tensorflow:2.3.0 \
           --verbose
     

     The three locations used (model-source-dir, data-location, checkpoint) here, works better with docker if they are located in the local disk. The locations should be pointed as absolute path.

   • Running the model in batch inference mode, set --batch-size = 1024

     $ cd /home/<user>/models/benchmarks
     
     $ python launch_benchmark.py \ 
           --framework tensorflow \ 
           --model-source-dir /home/<user>/path/to/tensorflow-models \
           --precision fp32 \
           --mode inference \
           --model-name wide_deep \
           --batch-size 1024 \
           --data-location /home/<user>/path/to/dataset \
           --checkpoint /home/<user>/path/to/wide_deep_fp32_pretrained_model \
           --docker-image intel/intel-optimized-tensorflow:2.3.0 \
           --verbose
     

     The three locations used (model-source-dir, data-location, checkpoint) here, works better with docker if they are located in the local disk. The locations should be pointed as absolute path.

6. The log file is saved to the value of --output-dir.

   The tail of the log output when the script completes should look something like this:

   accuracy: 1.0
   accuracy_baseline: 1.0
   auc: 1.0
   auc_precision_recall: 0.0
   average_loss: 2.1470942e-05
   global_step: 9775
   label/mean: 0.0
   loss: 2.1470942e-05
   precision: 0.0
   prediction/mean: 2.1461743e-05
   recall: 0.0
   End-to-End duration is %s 36.5971579552
   Latency is: %s 0.00224784460139
   current path: /workspace/benchmarks
   search path: /workspace/benchmarks/*/tensorflow/wide_deep/inference/fp32/model_init.py
   Using model init: /workspace/benchmarks/classification/tensorflow/wide_deep/inference/fp32/model_init.py
   PYTHONPATH: :/workspace/models
   RUNCMD: python common/tensorflow/run_tf_benchmark.py         --framework=tensorflow         --model-name=wide_deep         --precision=fp32         --mode=inference         --model-source-dir=/workspace/models         --intelai-models=/workspace/intelai_models         --batch-size=1                  --data-location=/dataset         --checkpoint=/checkpoints