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Model Server demo with a direct import of TensorFlow model {#ovms_demo_tf_classification}

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