Quickstart Guide

OpenVINO Model Server can perform inference using pre-trained models in either OpenVINO IR, ONNX, PaddlePaddle[https://github.com/PaddlePaddle/Paddle] or TensorFlow format [https://www.tensorflow.org/]. You can get them by:

This guide uses a face detection model in IR format.

To quickly start using OpenVINO™ Model Server follow these steps:

  1. Prepare Docker

  2. Download or build the OpenVINO™ Model server

  3. Provide a model

  4. Start the Model Server Container

  5. Prepare the Example Client Components

  6. Download data for inference

  7. Run inference

  8. Review the results

Step 1: Prepare Docker

Install Docker Engine, including its post-installation steps, on your development system. To verify installation, test it using the following command. If it displays a test image and a message, it is ready.

$ docker run hello-world

Step 2: Download the Model Server

Download the Docker image that contains OpenVINO Model Server:

docker pull openvino/model_server:latest

Step 3: Provide a Model

Store components of the model in the model/1 directory. Here is an example command using curl and a face detection model:

curl --create-dirs https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/2/face-detection-retail-0004/FP32/face-detection-retail-0004.xml https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/2/face-detection-retail-0004/FP32/face-detection-retail-0004.bin -o model/1/face-detection-retail-0004.xml -o model/1/face-detection-retail-0004.bin

Note

For ONNX models additional steps are required. For a detailed description refer to our ONNX format example.

OpenVINO Model Server expects a particular folder structure for models - in this case model directory has the following content:

model/
└── 1
    ├── face-detection-retail-0004.bin
    └── face-detection-retail-0004.xml

Sub-folder 1 indicates the version of the model. If you want to upgrade the model, other versions can be added in separate subfolders (2,3…). For more information about the directory structure and how to deploy multiple models at a time, check out the following sections:

Step 4: Start the Model Server Container

Start the container:

docker run -d -u $(id -u):$(id -g) -v $(pwd)/model:/models/face-detection -p 9000:9000 openvino/model_server:latest \
--model_path /models/face-detection --model_name face-detection --port 9000 --shape auto

During this step, the model folder is mounted to the Docker container. This folder will be used as the model storage from which the server will access models.

Step 5: Prepare the Example Client Components

Client scripts are available for quick access to the Model Server. Run an example command to download all required components:

curl --fail https://raw.githubusercontent.com/openvinotoolkit/model_server/releases/2022/1/demos/common/python/client_utils.py -o client_utils.py https://raw.githubusercontent.com/openvinotoolkit/model_server/releases/2022/1/demos/face_detection/python/face_detection.py -o face_detection.py https://raw.githubusercontent.com/openvinotoolkit/model_server/releases/2022/1/demos/common/python/requirements.txt -o client_requirements.txt

For more information, check these links:

Step 6: Download Data for Inference

Put the files in a separate folder to provide inference data, as inference will be performed on all the files it contains.

You can download example images for inference. This example uses the file people1.jpeg. Run the following command to download the image:

curl --fail --create-dirs https://raw.githubusercontent.com/openvinotoolkit/model_server/releases/2022/1/demos/common/static/images/people/people1.jpeg -o images/people1.jpeg

Step 7: Run Inference

Go to the folder with the client script and install dependencies. Create a folder for inference results and run the client script:

pip install -r client_requirements.txt

mkdir results

python face_detection.py --batch_size 1 --width 600 --height 400 --input_images_dir images --output_dir results --grpc_port 9000

Step 8: Review the Results

You will see the inference output:

Start processing 1 iterations with batch size 1
Request shape (1, 3, 400, 600)
Response shape (1, 1, 200, 7)
image in batch item 0 , output shape (3, 400, 600)
detection 0 [[[0.         1.         1.         0.55241716 0.3024692  0.59122956
   0.39170963]]]
x_min 331
y_min 120
x_max 354
y_max 156...

In the results folder, you can find files containing inference results. In our case, it will be a modified input image with bounding boxes indicating detected faces.

Inference results

Note: Similar steps can be performed with an ONNX model. Check the inference use case example with a public ResNet model in ONNX format or TensorFlow model demo.

Congratulations, you have completed the Quickstart guide. Try Model Server demos or explore more features to create your application.