Dockerize and deploy machine learning model as REST API using Flask

A simple Flask application that can serve predictions machine learning model. Reads a pickled sklearn model into memory when the Flask app is started and returns predictions through the /predict endpoint. You can also use the /train endpoint to train/retrain the model.

Steps for deploying ML model

1. Install Flask and Docker

2. Serialise your scikit-learn model (this can be done using Pickle, or JobLib)

3. [optional] add column names list to scikit object ex: rf.columns = ['Age', 'Sex', 'Embarked', 'Survived']

4. Create a separate flask_api.py file which will build the web service using Flask

   1. To run python flask_api.py
   2. Go to http address to check if its working

5. Create a dockerfile which does the below items

   1. Install ubuntu, python and git
   2. Clone code repo from git or move local python code to /app in container
   3. Set WORKDIR to /app
   4. Install packages in requirements.xt
   5. Expose the port for flask enpoint
   6. Define ENTRYPOINT as python main.py 9999

6. Build docker image

7. Run docker container

8. Make a http POST call with some data, and receive the prediction back using postman or python requests library.

9. Push the docker container to docker registry / ship to production

10. Install PIP requirements

    FYI: The code requries Python 3.6+ to run

    pip install -r requirements.txt
    

11. Running API

    python main.py <port>
    

12. Endpoints

    /predict (POST)

    Returns an array of predictions given a JSON object representing independent variables. Here's a sample input:

    [
        {"Age": 14, "Sex": "male", "Embarked": "S"},
        {"Age": 68, "Sex": "female", "Embarked": "C"},
        {"Age": 45, "Sex": "male", "Embarked": "C"},
        {"Age": 32, "Sex": "female", "Embarked": "S"}
    ]
    

    and sample output:

    {"prediction": [0, 1, 1, 0]}
    

    /train (GET)

    Trains the model. This is currently hard-coded to be a random forest model that is run on a subset of columns of the titanic dataset.

    /wipe (GET)

    Removes the trained model.

Docker commands

1. Build docker image from Dockerfile

   docker build -t "<app name>" . eg: docker build -t "ml_app" .

2. Run the docker container after build

   docker run -p 9999:9999 ml_app # -p to make the port externally avaiable for browsers

3. Show all running containers

   docker ps

   a. Kill and remove running container

   docker rm <containerid> -f

4. Open bash in a running docker container (optional)

   docker exec -ti <containerid> bash

Appendix

• https://blogs.technet.microsoft.com/machinelearning/2018/03/15/demystifying-docker-for-data-scientists-a-docker-tutorial-for-your-deep-learning-projects/
• http://docs.python-requests.org/en/latest/user/quickstart/#more-complicated-post-requests
• https://www.ibm.com/developerworks/webservices/library/ws-restful/
• https://docs.docker.com/develop/develop-images/dockerfile_best-practices/
• https://ropenscilabs.github.io/r-docker-tutorial/04-Dockerhub.html
• https://blog.hyperiondev.com/index.php/2018/02/01/deploy-machine-learning-model-flask-api/
• https://medium.com/@amirziai/a-flask-api-for-serving-scikit-learn-models-c8bcdaa41daa