README.md

NCF FP32 inference

Description

This document has instructions for running Neural Collaborative Filtering (NCF) FP32 inference using Intel-optimized TensorFlow.

Datasets

Download movielens 1M dataset

wget http://files.grouplens.org/datasets/movielens/ml-1m.zip
unzip ml-1m.zip

Set the DATASET_DIR to point to this directory when running NCF.

Quick Start Scripts

Script name	Description
fp32_online_inference.sh	Runs online inference (batch_size=1).
fp32_batch_inference.sh	Runs batch inference (batch_size=256).
fp32_accuracy.sh	Measures the model accuracy (batch_size=256).

Run the model

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

Setup using AI Kit	Setup without AI Kit
AI Kit does not currently support TF 1.15.2 models	To run without AI Kit you will need: Python 3 intel-tensorflow==1.15.2 numactl git google-api-python-client==1.6.7 google-cloud-bigquery==0.31.0 kaggle==1.3.9 numpy==1.16.3 oauth2client==4.1.2 pandas psutil>=5.6.7 py-cpuinfo==3.3.0 tar typing wget A clone of the Model Zoo repo git clone https://github.com/IntelAI/models.git

Running NCF also requires a clone of the TensorFlow models repository with at the v1.11 tag. Set the TF_MODELS_DIR env var to the path of your clone.

git clone https://github.com/tensorflow/models.git tf_models
cd tf_models
git checkout v1.11
export TF_MODELS_DIR=$(pwd)
cd ..

Download and extract the pretrained model and set the path to the PRETRAINED_MODEL env var.

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_5/ncf_fp32_pretrained_model.tar.gz
tar -xzvf ncf_fp32_pretrained_model.tar.gz
export PRETRAINED_MODEL=$(pwd)/ncf_trained_movielens_1m

After your environment is setup, set environment variables to the DATASET_DIR and an OUTPUT_DIR where log files will be written. Ensure that you already have the TF_MODELS_DIR and PRETRAINED_MODEL paths set from the previous commands. Once the environment variables are all set, you can run a quickstart script.

# cd to your model zoo directory
cd models

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

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

Additional Resources

• To run more advanced use cases, see the instructions here for calling the launch_benchmark.py script directly.
• To run the model using docker, please see the oneContainer workload container:
  https://software.intel.com/content/www/us/en/develop/articles/containers/ncf-fp32-inference-tensorflow-container.html.