https://github.com/hack-light/project-ml-microservice-k8s

https://github.com/hack-light/project-ml-microservice-k8s

Last synced: 5 days ago
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• Host: GitHub
• URL: https://github.com/hack-light/project-ml-microservice-k8s
• Owner: Hack-Light
• Created: 2022-07-24T07:28:29.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2022-07-29T10:29:57.000Z (over 2 years ago)
• Last Synced: 2023-03-07T16:07:20.909Z (almost 2 years ago)
• Language: Python
• Size: 43.1 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
[![Hack-Light](https://circleci.com/gh/Hack-Light/project-ml-microservice-k8s.svg?style=svg)](https://app.circleci.com/pipelines/github/Hack-Light/project-ml-microservice-k8s)

## 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.**

---

## 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. 

```bash

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= .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