The goal of this repo is demonstrate how to apply Hexagonal Architecture in a ML based system
The model used in this example has been taken from IntelAI
wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_8/rfcn_resnet101_fp32_coco_pretrained_model.tar.gz
tar -xzvf rfcn_resnet101_fp32_coco_pretrained_model.tar.gz -C tmp
rm rfcn_resnet101_fp32_coco_pretrained_model.tar.gz
chmod -R 777 tmp/rfcn_resnet101_coco_2018_01_28
mkdir -p tmp/model/1
mv tmp/rfcn_resnet101_coco_2018_01_28/saved_model/saved_model.pb tmp/model/1
rm -rf tmp/rfcn_resnet101_coco_2018_01_28
model_name=rfcn
cores_per_socket=`lscpu | grep "Core(s) per socket" | cut -d':' -f2 | xargs`
num_sockets=`lscpu | grep "Socket(s)" | cut -d':' -f2 | xargs`
num_physical_cores=$((cores_per_socket * num_sockets))
echo $num_physical_cores
docker rm -f tfserving
docker run \
--name=tfserving \
-d \
-p 8500:8500 \
-p 8501:8501 \
-v "$(pwd)/tmp/model:/models/$model_name" \
-e MODEL_NAME=$model_name \
-e OMP_NUM_THREADS=$num_physical_cores \
-e TENSORFLOW_INTER_OP_PARALLELISM=2 \
-e TENSORFLOW_INTRA_OP_PARALLELISM=$num_physical_cores \
intel/intel-optimized-tensorflow-serving:2.3.0
docker rm -f test-mongo
docker run --name test-mongo --rm --net host -d mongo:latestpython3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txtpython -m counter.entrypoints.webapp
ENV=prod python -m counter.entrypoints.webapp
curl -F "threshold=0.9" -F "file=@resources/images/boy.jpg" http://0.0.0.0:5000/object-count
curl -F "threshold=0.9" -F "file=@resources/images/cat.jpg" http://0.0.0.0:5000/object-count
curl -F "threshold=0.9" -F "file=@resources/images/food.jpg" http://0.0.0.0:5000/object-countpytest
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