Running EfficientNet Inference on Intel® Data Center GPU Flex Series using Intel® Extension for TensorFlow*
This document has instructions for running EfficientNet inference using Intel® Extension for TensorFlow* with Intel® Data Center GPU Flex Series.
Item | Detail |
---|---|
Host machine | Intel® Data Center GPU Flex 170 |
Drivers | Latest Intel® Data Center GPU Flex Series Driver |
Software | Docker* |
Script name | Description |
---|---|
run_model |
Runs EfficientNet B0,B3,B4 batch inference for fp16 precision on Flex series 170 |
docker pull intel/image-recognition:tf-flex-gpu-efficientnet-inference
The EfficientNet inference container contains scripts,models and libraries needed to run fp16 inference. You will need to provide an output directory where log files will be written.
#Optional
export BATCH_SIZE=<provide batch size. Default is 128>
#Required
export OUTPUT_DIR=<path to output directory>
MODEL_NAME=<provide either EfficientNetB0,EfficientNetB3 or EfficientNetB4>
IMAGE_NAME=intel/image-recognition:tf-flex-gpu-efficientnet-inference
SCRIPT=run_model.sh
docker run \
--device=/dev/dri \
--ipc=host \
--env BATCH_SIZE=${BATCH_SIZE} \
--env MODEL_NAME=${MODEL_NAME} \
--env OUTPUT_DIR=${OUTPUT_DIR} \
--env http_proxy=${http_proxy} \
--env https_proxy=${https_proxy} \
--env no_proxy=${no_proxy} \
--volume ${OUTPUT_DIR}:${OUTPUT_DIR} \
--rm -it \
$IMAGE_NAME \
/bin/bash $SCRIPT
Support for Intel® Extension for TensorFlow* is found via the Intel® AI Analytics Toolkit. Additionally, the Intel® Extension for TensorFlow* team tracks both bugs and enhancement requests using GitHub issues. Before submitting a suggestion or bug report, please search the GitHub issues to see if your issue has already been reported.
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