README.md

A scalable Deep learning REST API

Deploy machine learning model as a web service API

Stack:

• Keras: Machine learning
• Redis: an in-memory data structure store
• Flask: a micro web framework for Python

Workflow:

Installation

1. Install redis-server

   sudo apt-get install -y redis redis-server
   

   Test the redis server:

   $ redis-cli ping
   PONG
   

2. Install python, pip, install packages by:

   virtualenv venv                   # create virtual environment folder
   source ./venv/bin/activate        # activate env
   pip install -r requirements.txt   # install packages
   

3. Run the worker model

   python worker.py
   

4. Open another terminal, start the web server

   python server.py
   

5. Using cURL to test the api /predict

   curl -X POST http://localhost:5000/predict \
        -F 'image=@file.png'
   

   It's will show you the output like this:

   {
       "predictions": [
           {
               "label": "web_site",
               "probability": 0.8858472108840942
           },
           {
               "label": "bow_tie",
               "probability": 0.06905359774827957
           },
           {
               "label": "laptop",
               "probability": 0.015353902243077755
           },
           {
               "label": "monitor",
               "probability": 0.005411265417933464
           },
           {
               "label": "notebook",
               "probability": 0.0035434039309620857
           }
       ],
       "success": true
   }
   

Docker

docker-compose up

Visit: http://localhost:8000/predict

Test with Postman