Model Server demo with a direct import of TensorFlow model#
This guide demonstrates how to run inference requests for TensorFlow model with OpenVINO Model Server. As an example, we will use InceptionResNetV2 to perform classification of an image.
Prerequisites#
Docker installed
Python 3.7 or newer installed
Preparing to Run#
Clone the repository and enter image_classification_using_tf_model directory
git clone https://github.com/openvinotoolkit/model_server.git
cd model_server/demos/image_classification_using_tf_model/python
Download the InceptionResNetV2 model#
mkdir -p model/1
wget -P model/1 https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_resnet_v2_2018_04_27.tgz
tar xzf model/1/inception_resnet_v2_2018_04_27.tgz -C model/1
Run Openvino Model Server#
docker run -d -v $PWD/model:/models -p 9000:9000 openvino/model_server:latest --model_path /models --model_name resnet --port 9000
Run the client#
Install python dependencies:
pip3 install -r requirements.txt
Now you can run the client:
python3 image_classification_using_tf_model.py --help
usage: image_classification_using_tf_model.py [-h] [--grpc_address GRPC_ADDRESS] [--grpc_port GRPC_PORT] --image_input_path IMAGE_INPUT_PATH
Client for OCR pipeline
optional arguments:
-h, --help show this help message and exit
--grpc_address GRPC_ADDRESS
Specify url to grpc service. default:localhost
--grpc_port GRPC_PORT
Specify port to grpc service. default: 9000
--image_input_path IMAGE_INPUT_PATH
Image input path
Exemplary result of running the demo:
python3 image_classification_using_tf_model.py --grpc_port 9000 --image_input_path ../../common/static/images/zebra.jpeg
Image classified as zebra