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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

Note: Docker tag or id should be always specified in the end of the docker command to avoid issues

  1. Build docker image from Dockerfile

    docker build -t "<app name>" -f docker-files/Dockerfile . eg: docker build -t "ml_app" -f docker-files/Dockerfile .

  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

  5. Docker Entry point The ENTRYPOINT specifies a command that will always be executed when the container starts. The CMD specifies arguments that will be fed to the ENTRYPOINT 1683

Docker has a default ENTRYPOINT which is /bin/sh -c but does not have a default CMD. --entrypoint in docker run will overwrite the default entry point docker run -it --entrypoint /bin/bash <image>

I WANT TO CONNECT FROM A CONTAINER TO A SERVICE ON THE HOST The host has a changing IP address (or none if you have no network access). From 18.03 onwards our recommendation is to connect to the special DNS name host.docker.internal, which resolves to the internal IP address used by the host. This is for development purpose and will not work in a production environment outside of Docker Desktop for Mac.

The gateway is also reachable as gateway.docker.internal.

To avoid ModuleNotFoundError: No module named 'api'

add this line to docker file ENV PYTHONPATH="$PYTHONPATH:/" and insure that init.py exists in necessary folders

Appendix