Advanced.md

Wide & Deep FP32 inference - Advanced Instructions

This document has advanced instructions for running Wide & Deep FP32 inference, which provides more control over the individual parameters that are used. For more information on using /benchmarks/launch_benchmark.py, see the launch benchmark documentation.

Prior to using these instructions, please follow the setup instructions from the model's README and/or the AI Kit documentation to get your environment setup (if running on bare metal) and download the dataset, pretrained model, etc. If you are using AI Kit, please exclude the --docker-image flag from the commands below, since you will be running the the TensorFlow conda environment instead of docker.

Any of the launch_benchmark.py commands below can be run on bare metal by removing the --docker-image arg. Ensure that you have all of the required prerequisites installed in your environment before running without the docker container.

If you are new to docker and are running into issues with the container, see this document for troubleshooting tips.

Once your environment is setup, navigate to the benchmarks directory of the model zoo and set environment variables pointing to the directory for the dataset, pretrained model, and an output directory where log files will be written.

# cd to the benchmarks directory in the model zoo
cd benchmarks

export DATASET_DIR=<path to the dataset>
export OUTPUT_DIR=<directory where log files will be written>
export PRETRAINED_MODEL=<path to the pretrained model>
export TF_MODELS_DIR=<path to your clone of the TensorFlow models repo>

The following commonds are examples on how Wide & Deep can be run:

• Running the model for online inference:

  python launch_benchmark.py \
    --framework tensorflow \
    --model-source-dir $TF_MODELS_DIR \
    --precision fp32 \
    --mode inference \
    --model-name wide_deep \
    --batch-size 1 \
    --data-location $DATASET_DIR \
    --checkpoint $PRETRAINED_MODEL \
    --docker-image intel/intel-optimized-tensorflow:latest \
    --output-dir $OUTPUT_DIR \
    --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:

  python launch_benchmark.py \
    --framework tensorflow \
    --model-source-dir $TF_MODELS_DIR \
    --precision fp32 \
    --mode inference \
    --model-name wide_deep \
    --batch-size 1024 \
    --data-location $DATASET_DIR \
    --checkpoint $PRETRAINED_MODEL \
    --docker-image intel/intel-optimized-tensorflow:latest \
    --output-dir $OUTPUT_DIR \
    --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.