Pull Command
docker pull intel/image-recognition:tf-latest-resnet50v1-5-bfloat16-inference
Description
This document has instructions for running ResNet50* v1.5 bfloat16 inference using Intel® Optimization for TensorFlow*.
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 |
Runs online inference (batch_size=1). |
bfloat16_batch_inference |
Runs batch inference (batch_size=128). |
bfloat16_accuracy |
Measures the model accuracy (batch_size=100). |
Docker*
The model container includes the scripts and libraries needed to run ResNet50 v1.5 bfloat16 inference. To run one of the quick start scripts using this container, you'll need to provide volume mounts for the dataset and an output directory.
DATASET_DIR=<path to the dataset>
OUTPUT_DIR=<directory where log files will be written>
docker run \
--env DATASET_DIR=${DATASET_DIR} \
--env OUTPUT_DIR=${OUTPUT_DIR} \
--env http_proxy=${http_proxy} \
--env https_proxy=${https_proxy} \
--volume ${DATASET_DIR}:${DATASET_DIR} \
--volume ${OUTPUT_DIR}:${OUTPUT_DIR} \
--privileged --init -t \
intel/image-recognition:tf-latest-resnet50v1-5-bfloat16-inference \
/bin/bash quickstart/<script name>.sh
Documentation and Sources
Get Started
Docker* Repository
Main GitHub*
Readme
Release Notes
Get Started Guide
Code Sources
Dockerfile
Report Issue
License Agreement
LEGAL NOTICE: By accessing, downloading or using this software and any required dependent software (the “Software Package”), you agree to the terms and conditions of the software license agreements for the Software Package, which may also include notices, disclaimers, or license terms for third party software included with the Software Package. Please refer to the license file for additional details.