Celnet-hub / DevOps-ML

The 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

Project Overview

This project was done applying the skills acquired in a course to titled "Operationalize a Machine Learning Microservice API".

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.

Project Tasks

The 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, the following tasks were done:

• Test project code using linting
• Complete a Dockerfile to containerize this application
• Deploy 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

Setup the Environment

• Create a virtualenv with Python 3.7 and activate it. Refer to this link for help on specifying the Python version in the virtualenv.

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

Files

• app.py: serves out predictions (inference) about housing prices through API calls.
• Makefile: used to build alias commands to install and run specific tasks.
• Dokerfile: used to define some set of instructions needed for Docker to build docker imaged automatically.
• run_docker.sh: a shell script used to perform the following:
  • Build the docker image with a tag
  • Run the containerized flask application
  • Map the container's port to the host port
• upload_docker.sh: a shell script that is used to upload docker image to docker hub
• docker_out.txt: used to store the output of running the docker container
• run_kubernetes.sh: a shell script used to run the docker container with kubectl
• make_prediction.sh: a shell script that simulates predictions
• kubernetes.out.txt: used to hold the output of run_kubernetes.sh