In this project, you will apply the skills you have acquired in this course to operationalize a Machine Learning Microservice API.

You are given a pre-trained, sklearn model that has been trained to predict housing prices in Boston according to several features, such as average rooms in a home and data about highway access, teacher-to-pupil ratios, and so on. You can read more about the data, which was initially taken from Kaggle, on the data source site. This project tests your ability to operationalize a Python flask app—in a provided file, app.py—that serves out predictions (inference) about housing prices through API calls. This project could be extended to any pre-trained machine learning model, such as those for image recognition and data labeling.

Your project goal is to operationalize this working, machine learning microservice using kubernetes, which is an open-source system for automating the management of containerized applications. In this project you will:

• Test your project code using linting
• Complete a Dockerfile to containerize this application
• Deploy your containerized application using Docker and make a prediction
• Improve the log statements in the source code for this application
• Configure Kubernetes and create a Kubernetes cluster
• Deploy a container using Kubernetes and make a prediction
• Upload a complete Github repo with CircleCI to indicate that your code has been tested

You can find a detailed project rubric, here.

The final implementation of the project will showcase your abilities to operationalize production microservices.

[X] All files are submitted.
[X] .circleci folder is included.
[X] Extend app.py to log a prediction value.
[X] The project shows the proper use of documentation.
[X] The project passes linting via a Makefile.
[X] Dockerfile is complete.
[X] Dockerfile passes linting via a Makefile.
[X] Log output is saved in docker_out.txt.
[X] run_docker.sh is complete.
[X] Docker image is uploaded to docker via upload_docker.sh.
[X] run_kubernetes.sh is complete.
[X] An output prediction is saved in kubernetes_out.txt.

• Remove old Docker

  sudo apt-get remove docker docker-engine docker.io containerd runc

• Update the apt package index:

  sudo apt-get update

• Install packages to allow apt to use a repository over HTTPS:

  sudo apt-get install \
      apt-transport-https \
      ca-certificates \
      curl \
      gnupg-agent \
      software-properties-common

• Add Docker’s official GPG key:

  curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -

• Use the following command to set up the stable repository.

  sudo add-apt-repository \
  "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
  $(lsb_release -cs) \
  stable"

• Update the apt package index.

  sudo apt-get update

• Install the latest version of Docker

  sudo apt-get -y  install docker-ce docker-compose

• Add your normal user to the group to run docker commands as non-privileged user.

  sudo usermod -aG docker $USER

• Download and install minikube to /usr/local/bin:

  curl -LO https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64 \
     && sudo install minikube-linux-amd64 /usr/local/bin/minikube

• Start minikube within VirtualBox

  sudo minikube start --vm-driver=none
  

• Make none the default driver:

  sudo minikube config set vm-driver none
  

• Download the latest release with the command:

  curl -LO https://storage.googleapis.com/kubernetes-release/release/`curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt`/bin/linux/amd64/kubectl
  

• Make the kubectl binary executable.

  chmod +x ./kubectl

• Move the binary in to your PATH.

  sudo mv ./kubectl /usr/local/bin/kubectl

• Test to ensure the version you installed is up-to-date:

  kubectl version

• Install socat Under Debian / Ubuntu Linux:

  sudo apt-get update && sudo apt-get install socat
  

• Install via pip repository

  pip install virtualenvwrapper

• Export env directory

  export WORKON_HOME=~/Envs

• Create env directory

  mkdir -p $WORKON_HOME

• Activate wrapper

  source /usr/local/bin/virtualenvwrapper.sh

• Create a virtualenv and activate it

  mkvirtualenv machine-learning-ms -p /usr/local/bin/python3.7

Run make install to install the necessary dependencies

• add W1202 to pylint Ignore for f-strings

Run make lint to lint dockerfile and app.py
Run upload_docker.sh for pushing your docker image to docker hub

1. Standalone: python3 app.py
2. Run in Docker: ./run_docker.sh
3. Run in Kubernetes: ./run_kubernetes.sh