eks-machine-learning-microservice

A summary of the project

This is a Machine Learning Microservice API project with 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.

• Some of the screenshots are listed in the files folder.

Setup Project Environment

• Clone the ptoject repo.

$ git clone git@github.com:Emmanuel-Dominic/DevOps_Microservices.git

• Change to the project directory.

$ cd project-ml-microservice-kubernetes

• Create a virtualenv with Python 3.7 and activate it.

$ python3.7 -m venv ~/.devops && python3 -m ~/.devops/bin/activate && \
  source ~/.devops/bin/activate

• Install the necessary system softwares.

$ ./setup_installations.sh

• Install the necessary project dependencies

$ make install

Project Setup

• Test project code using linting

$  make lint

• Run project unittests

$  make test

• Dockerize project and make a prediction (if it fails run with sudo)

$ ./run_docker.sh 

• Display docker log statements (if it fails run with sudo)

$ docker logs project-four

• Deploy containerized application to DockerHub (if it fails run with sudo)

$ ./upload_docker.sh 

• Create a Kubernetes cluster and deploy a container using Kubernetes

$ ./run_kubernetes.sh 

• Running application locally (Standalone application)

$ flask run 

OR

$ python app.py 

Install eks cluster

• Set your aws configuration using the awscli by running

$ sudo apt install awscli

$ aws configure

• deploy eks cluster

$ eks create cluster

Folder and File Structure Explained

• app.py: our flask application file
• test_app.py: file that exercise edge cases in code blocks
• Makefile: make utility file which defines set of tasks to be executed
• Dockerfile: file with instructions to build Docker images
• .hadolint.yaml: supports hadolint configurations like the ingnoring rules.
• .dockerignore: prevents files or folders from being listed in the build context
• requirements.txt: file with listed project dependecies
• docker_out.txt and kubernetes_out.txt:
  • These are output files with given provided exercise logs
• make_predictions.sh, run_docker.sh, run_kubernetes.sh, setup_installations.sh, upload_docker.sh:
  • These are script files with a sequence of commands run to perform a given task.

Author:

Matembu Emmanuel Dominic | Software/Devops Engineer