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5.6 Environment management: Docker

Slides

Installing Docker

To isolate more our project file from our system machine, there is an option named Docker. With Docker you are able to pack all your project is a system that you want and run it in any system machine. For example if you want Ubuntu 20.4 you can have it in a mac or windows machine or other operating systems.
To get started with Docker for the churn prediction project you can follow the instructions below.

Ubuntu

  • To install Docker run the command below.

    • bash
sudo apt-get install docker.io
      To run docker without sudo, follow this instruction.
    • Once our project was packed in a Docker container, we're able to run our project on any machine.
    • First we have to make a Docker image. In Docker image file there are settings and dependecies we have in our project. To find Docker images that you need you can simply search the Docker website.
    • Here a Docker file is written we'll explain it below.(There should be no comments in Docker file, So remove the comments if you want to copy it)
    • FROM python:3.8.12-slim                                                     # First install the python 3.8, the slim version have less size
RUN pip install pipenv                                                      # Install pipenv library in Docker 
WORKDIR /app                                                                # we have created a directory in Docker named app and we're using it as work directory 
COPY ["Pipfile", "Pipfile.lock", "./"]                                      # Copy the Pip files into our working derectory 
RUN pipenv install --deploy --system                                        # install the pipenv dependecies we had from the project and deploy them 
COPY ["*.py", "churn-model.bin", "./"]                                      # Copy any python files and the model we had to the working directory of Docker 
EXPOSE 9696                                                                 # We need to expose the 9696 port because we're not able to communicate with Docker outside it
ENTRYPOINT ["gunicorn", "--bind", "0.0.0.0:9696", "churn_serving:app"]      # If we run the Docker image, we want our churn app to be running
      If we don't put the last line ENTRYPOINT, we will be in a python shell. Note that in Docker we made put in double quotes, This is because of the spaces. We have to ignore spaces in a command and put the characters in double quotes.(See ENTRYPOINT for example)
    • After creating the Dockerfile and writing the settings we want in it, We need to build it with the command below.
    •  docker build -t churn-prediction .
      With -t command We're specifying the name churn-prediction for this Dockerfile.
    • To run it, Simply execute the command below:
    •  docker run -it -p 9696:9696 churn-prediction:latest
      Here we use the option -it in order to the Docker run from terminal and shows the result. The -p parameter is used to map the 9696 port of the Docker to 9696 port of our machine.(first 9696 is the port number of our machine and the last one is Docker container port.)
    • At last you've deployed your prediction app inside a Docker continer. Congratulations 🥳

Windows

To install the Docker you can just follow the instruction by Andrew Lock in this link: https://andrewlock.net/installing-docker-desktop-for-windows/

MacOS

Please send a PR!

Notes

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