README.md

ResNet50 v1.5 BFloat16 inference

Description

This document has instructions for running ResNet50 v1.5 BFloat16 inference using Intel-optimized TensorFlow.

Datasets

Download and preprocess the ImageNet dataset using the instructions here. After running the conversion script you should have a directory with the ImageNet dataset in the TF records format.

Set the DATASET_DIR to point to this directory when running ResNet50 v1.5.

Quick Start Scripts

Script name	Description
bfloat16_online_inference.sh	Runs online inference (batch_size=1).
bfloat16_batch_inference.sh	Runs batch inference (batch_size=128).
bfloat16_accuracy.sh	Measures the model accuracy (batch_size=100).

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
To run using AI Kit you will need: numactl wget Activate the tensorflow conda environment conda activate tensorflow	To run without AI Kit you will need: Python 3 [intel-tensorflow>=2.5.0](https://pypi.org/project/intel-tensorflow/) git numactl wget A clone of the Model Zoo repo git clone https://github.com/IntelAI/models.git

After finishing the setup above, download the pretrained model and set the PRETRAINED_MODEL environment var to the path to the frozen graph:

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_8/resnet50_v1_5_bfloat16.pb
export PRETRAINED_MODEL=$(pwd)/resnet50_v1_5_bfloat16.pb

Set environment variables for the path to your DATASET_DIR for ImageNet and an OUTPUT_DIR where log files will be written. Navigate to your model zoo directory and then run a quickstart script.

# cd to your model zoo directory
cd models

export PRETRAINED_MODEL=<path to the frozen graph downloaded above>
export DATASET_DIR=<path to the ImageNet TF records>
export OUTPUT_DIR=<directory where log files and checkpoints will be written>

./quickstart/image_recognition/tensorflow/resnet50v1_5/inference/cpu/bfloat16/<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/resnet50v1-5-bfloat16-inference-tensorflow-container.html.