/deploy-tensorflow-serving-flask

In this tutorial, 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.

Primary LanguagePython

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:

      1. RUN CMD as administrator and execute the below command
  2. docker run -p 8501:8501 --name=pets -v "C:\pets:/models/pets/1" -e MODEL_NAME=pets tensorflow/serving

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

    1. Conda create –n flaskapp python=3.7 2. Conda activate flaskapp 3. pip install tensorflow==2.1.0 flask flask-bootstrap requests pillow 4. Python app.py
    2. Access the app by going to localhost:5000 in your browser

Assumptions are that you’re running Windows with python 3.7

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