This document has instructions for running ResNet50* v1.5 int8 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.
DATASET_DIR to point to the TF records directory when running ResNet50 v1.5.
Quick Start Scripts
||Runs online inference (batch_size=1).|
||Runs batch inference (batch_size=128).|
||Measures the model accuracy (batch_size=100).|
||Uses numactl to run batch inference (batch_size=128) with one instance per socket for 1500 steps and 50 warm-up steps. If no
||Uses numactl to run online inference (batch_size=1) using four cores per instance for 1500 steps and 50 warm-up steps. If no
To run on bare metal, the following prerequisites must be installed in your environment:
- Python* 3
- Intel® Optimization for TensorFlow*
After installing the prerequisites, download and untar the model package. Set environment variables for the path to your
DATASET_DIR and an
OUTPUT_DIR where log files will be written, then run a quick start script.
DATASET_DIR is required to be set. For inference, just to evaluate performance on sythetic data, the
DATASET_DIR is not needed. Otherwise
DATASET_DIR needs to be set:
DATASET_DIR=<path to the dataset> OUTPUT_DIR=<directory where log files will be written> wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_3_0/resnet50v1-5-int8-inference.tar.gz tar -xzf resnet50v1-5-int8-inference.tar.gz cd resnet50v1-5-int8-inference quickstart/<script name>.sh
Documentation and Sources
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Product and Performance Information
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