storopoli / TF-Deploy

Deploy a Pre-trained TensorFlow model with the help of TensorFlow Serving with Docker. Create a visual web interface using Flask web framework which will serve to get predictions from the served TensorFlow model and help end-users to consume through API calls.

Deploying-Deep-Learning-Models-using-TensorFlow-Serving-with-Docker-and-Flask

In this project, we will deploy a Pre-trained TensorFlow model with the help of TensorFlow Serving with Docker, and we will also create a visual web interface using Flask web framework which will serve to get predictions from the served TensorFlow model and help end-users to consume through API calls.

Execution Steps:

1. Setting up tensorflow serving server on localhost:

   sudo docker run -d -p 8501:8501 --name=pets -v "$(full_path_to_directory)/pets:/models/pets/1" -e MODEL_NAME=pets tensorflow/serving

2. Setup virtual environment and installing flask and its dependencies:

   python3 -m venv env
   source env/bin/activate
   
   python3 -m pip install requirements.txt

3. Run the Flask app:

   python app.py

4. Access the app by going to localhost:5000 in your browser

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Ref: https://www.coursera.org/projects/deploy-models-tensorflow-serving-flask