https://github.com/tristcoil/test-ci-repo

testing of circleci
https://github.com/tristcoil/test-ci-repo

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testing of circleci

• Host: GitHub
• URL: https://github.com/tristcoil/test-ci-repo
• Owner: tristcoil
• Created: 2020-10-31T23:15:11.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2020-11-04T10:42:27.000Z (about 4 years ago)
• Last Synced: 2024-10-14T07:50:43.621Z (2 months ago)
• Language: Shell
• Size: 231 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

[![CircleCI](https://circleci.com/gh/tristcoil/test-ci-repo.svg?style=svg)](https://circleci.com/gh/tristcoil/test-ci-repo)

## Project Overview

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](https://www.kaggle.com/c/boston-housing). 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

Your project goal is to operationalize this working, machine learning microservice using [kubernetes](https://kubernetes.io/), 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](https://review.udacity.com/#!/rubrics/2576/view).

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

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## Setup the Environment

* Create a virtualenv and activate it

* 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