Project Overview

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 my 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.

Project Tasks

The goal of the project 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 I:

• Test my project code using linting
• Complete a Dockerfile to containerize this application
• Deploy my 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 my code has been tested

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

---

Setup the Environment

• Create a virtualenv with Python 3.7 and activate it.

python3 -m pip install --user virtualenv
# You should have Python 3.7 available in your host. 
# Check the Python path using `which python3`
# Use a command similar to this one:
python3 -m virtualenv --python=<path-to-Python3.7> .devops
source .devops/bin/activate

• Run make install to install the necessary dependencies

Running app.py

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

Kubernetes Steps

• Setup and Configure Docker locally:

  • To install Docker on your machine, go to Docker

• Setup and Configure Kubernetes locally:

  • To onstall Kubernetes on linux:

    curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl"
    sudo install -o root -g root -m 0755 kubectl /usr/local/bin/kubectl

  or go here for other OS

    #Verify the installation with:`
    kubectl version --client
    #Create Flask app in Container: 
    docker build --tag=yourdockerhubusername/imagename:tag`
    docker run -p 8000:80 imagename

• Run via kubectl:

    #To xecute via Kubectl
    kubectl run preferedname --image=yourdockerhubusername/imagename:tag --port=80
    #Verify kubernetes pods started
    kubectl get pods
    #Forward the container port to a host
    kubectl port-forward pods/preferedname --address 0.0.0.0 8000:80
    #Verify port is forwarded by opening another terminal and execute
    curl localhost:8000
    #or
    run "make_prediction.sh"

---

Files In the Project

.circleci/config.yml: Contains CircleCI workflow configurations for testing and building our code. Can be extended to be deployed to a web server.

output_txt_files: Files containing outputs from the projuct run. Docker_out.txt contains docker run outputs and kubernetes_out.txt contains out from kubernetes

app.py: fask file that accepts json data, formatand scale the json input, and make predictions based on input.

Dockerfile: Our docker image setup file

make_prediction.sh: when executed, calls our running instance with json data needed to make predictions, and outputs the receuved prediction response.

Makefile: The Makefile includes instructions on environment setup and lint tests. Create and activate a virtual environment. Install dependencies in requirements.txt

requirements.txt: List of libraries needede for our app to run

run_docker.sh: Build image and add a descriptive tag, and Run flask app

run_kubernetes.sh: Run the Docker Hub container with kubernetes and Forward the container port to a host

upload_docker.sh: The file tags and uploads an image to Docker Hub