Project Overview

In this project, 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. In this project the 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.

Setup the Environment

• Environment used is Ubuntu16 in cloud9.
• Create a virtualenv and activate it
• Run make install to install the necessary dependencies

Install Minikube through docker

• curl -Lo minikube https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64 && chmod +x minikube
• sudo mkdir -p /usr/local/bin/
• sudo install minikube /usr/local/bin/
• minikube start --driver=docker

Install hadolint:

sudo wget -O /bin/hadolint https://github.com/hadolint/hadolint/releases/download/v1.16.3/hadolint-Linux-x86_64 &&\ sudo chmod +x /bin/hadolint

Install kubectl: Refer for other operating system

Install via native package management:

• sudo apt-get update && sudo apt-get install -y apt-transport-https gnupg2
• curl -s https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -
• echo "deb https://apt.kubernetes.io/ kubernetes-xenial main" | sudo tee -a /etc/apt/sources.list.d/kubernetes.list
• sudo apt-get update
• sudo apt-get install -y kubectl

  Install via other package management:

• snap install kubectl --classic
• kubectl version --client

Running app.py

1. Standalone: python app.py
2. Run in Docker: ./run_docker.sh
3. Upload to Docker Hub: ./upload_docker.sh
4. Run in Kubernetes: ./run_kubernetes.sh

Kubernetes Steps

• Setup and Configure Docker locally
• Setup and Configure Kubernetes locally
• Create Flask app in Container
• Run via kubectl