/Deploy-machine-learning-model

Dockerize and deploy machine learning model as REST API using Flask

Primary LanguagePythonMIT LicenseMIT

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