Using inputs data in string format with universal-sentence-encoder model

Download the model

In this experiment we are going to use a TensorFlow model from .

curl --create-dir -o universal-sentence-encoder-multilingual/1/3.tar.gz
tar -xzf universal-sentence-encoder-multilingual/1/3.tar.gz -C universal-sentence-encoder-multilingual/1/
rm universal-sentence-encoder-multilingual/1/3.tar.gz
chmod -R 755 universal-sentence-encoder-multilingual
tree universal-sentence-encoder-multilingual/

└── 1
    ├── assets
    ├── saved_model.pb
    └── variables
        └── variables.index

Optionally build OVMS with CPU extension library for sentencepiece_tokenizer layer

Model universal-sentence-encoder-multilingual includes a layer SentencepieceTokenizer which is not supported by OpenVINO at the moment. It can be however implemented using a CPU extension, which is a dynamic library performing the execution of the model layer. The layer SentencepieceTokenizer expects on the input a list of strings. The CPU extension replaces the input format to an array with UINT8 precision with a shape [-1]. It is serialized representation of the list of strings in a form or bytes. When this extension is deployed in OpenVINO Model Server, you don’t need to worry about the serialization as it is handled internally. The model server accepts the input in a string format and performs the conversion to OpenVINO requirement transparently.

The image openvino/model_server:2023.3 and newer includes ready to use OpenVINO Model Server with the CPU extension. It can be also built from source using the commands:

git clone
cd model_server
make docker_build OV_USE_BINARY=0
cd ..

Start the model server in a container

When the new docker image is built, you can start the service with a command:

docker run -d --name ovms -p 9000:9000 -p 8000:8000 -v $(pwd)/universal-sentence-encoder-multilingual:/model openvino/model_server:latest --model_name usem --model_path /model --cpu_extension /ovms/lib/ --plugin_config '{"NUM_STREAMS": 1}' --port 9000 --rest_port 8000

Check the container logs to confirm successful start:

docker logs ovms

Send string data as inference request

OpenVINO Model Server can accept the input in a form of strings. Below is a code snipped based on tensorflow_serving_api python library:

data = np.array(["string1", "string1", "string_n"])
predict_request = predict_pb2.PredictRequest() = "my_model"
predict_response = prediction_service_stub.Predict(predict_request, 10.0)

Here is a basic client execution :

pip install --upgrade pip
pip install -r model_server/demos/universal-sentence-encoder/requirements.txt
python model_server/demos/universal-sentence-encoder/ --grpc_port 9000 --string "I enjoy taking long walks along the beach with my dog."
processing time 6.931 ms.
Output shape (1, 512)
Output subset [-0.00552395  0.00599533 -0.01480555  0.01098945 -0.09355522 -0.08445048
 -0.02802683 -0.05219319 -0.0675998   0.03127321 -0.03223499 -0.01282092
  0.06131846  0.02626886 -0.00983501  0.00298059  0.00141201  0.03229365
  0.06957124  0.01543707]

The same can be achieved using REST API interface and even a simple curl command:

curl -X POST http://localhost:8000/v1/models/usem:predict \
-H 'Content-Type: application/json' \
-d '{"instances": ["dog", "Puppies are nice.", "I enjoy taking long walks along the beach with my dog."]}'

Compare results with TFS

The same client code can be used to send the requests to TensorFlow Serving component. There is full compatibility in the API.

Start TFS container:

docker run -it -p 8500:8500 -p 9500:9500 -v $(pwd)/universal-sentence-encoder-multilingual:/models/usem -e MODEL_NAME=usem tensorflow/serving --port=9500 --rest_api_port=8500

Run the client

python model_server/demos/universal-sentence-encoder/ --grpc_port 9500 --input_name inputs --output_name outputs --string "I enjoy taking long walks along the beach with my dog."

processing time 12.167000000000002 ms.
Output shape (1, 512)
Output subset [-0.00552387  0.00599531 -0.0148055   0.01098951 -0.09355522 -0.08445048
 -0.02802679 -0.05219323 -0.06759984  0.03127313 -0.03223493 -0.01282088
  0.06131843  0.02626882 -0.00983502  0.00298053  0.00141208  0.03229369
  0.06957125  0.01543701]

NOTE: Do not use this model with --cache_dir, the model does not support caching.