How to serve tflite format model in docker for API

I have custom yolov8 model and converted into .tflite format. Now I have to serve this model into mobile application for this I have to serve this model via docker. Give insights for serving it into docker. Thanks


Serving a custom YOLOv8 model in .tflite format via Docker for use in a mobile application involves several steps. Here’s a high-level overview of the process:

Prepare the TensorFlow Lite model: Ensure your YOLOv8 model is correctly converted to the .tflite format.
Set up TensorFlow Serving: TensorFlow Serving HealthInsuranceMarket can be used to serve a TFLite model. It’s a lesser-known feature, but you can enable it with the --prefer_tflite_model=true flag1.
Create a Docker container: You’ll need to create a Dockerfile that sets up TensorFlow Serving with the necessary configurations to serve your .tflite model.
Expose the API: Configure the Docker container to expose an API endpoint that mobile applications can interact with to send data to the model and receive predictions.
Deploy the Docker container: Once your Docker container is set up, you can deploy it to a server where it can be accessed by your mobile application.
Integrate with the mobile application: On the mobile application side, you’ll need to implement functionality to communicate with the API, sending image data and receiving predictions.
Here’s an example Docker command to run TensorFlow Serving with a TFLite model:

docker run -t --rm -p 8501:8501 \
    -v "/path/to/your/model_directory:/models/your_model" \
    -e MODEL_NAME=your_model \
    tensorflow/serving \

Replace /path/to/your/model_directory with the path to the directory containing your .tflite model, and your_model with the name of your model.

Remember to test the entire pipeline thoroughly to ensure that the model is served correctly and the mobile application can receive accurate predictions from the API.

I hope the information may help you.