https://github.com/dojutsu-user/project-ml-microservices-kubernotes

Udacity Project 4
https://github.com/dojutsu-user/project-ml-microservices-kubernotes

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Udacity Project 4

• Host: GitHub
• URL: https://github.com/dojutsu-user/project-ml-microservices-kubernotes
• Owner: dojutsu-user
• Created: 2020-03-10T16:13:23.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2022-06-22T01:25:32.000Z (over 2 years ago)
• Last Synced: 2024-10-14T16:48:09.298Z (3 months ago)
• Language: Python
• Homepage:
• Size: 218 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# project-ml-microservices-kubernotes

[![CircleCI](https://circleci.com/gh/dojutsu-user/project-ml-microservices-kubernotes.svg?style=svg)](https://circleci.com/gh/dojutsu-user/project-ml-microservices-kubernotes)

## 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, 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.

## Steps (Without Docker)

* Create a virtual environment and activate it:

    ```

    $ make setup

    ```

* Install all the dependencies:

	```

    (.devops) $ make install

    ```

* Start the webapp

	```

    (.devops) $ python app.py

	```

## Steps (With Docker)

* Install [Docker](https://docs.docker.com/install/).

* Run the bash script:

	```

    $ bash run_docker.sh

    ```

## Steps (With Kubernetes)

* Install [Minikube](https://kubernetes.io/docs/tasks/tools/install-minikube/).

* Start Minikube:

	```

    $ minikube start

    ```

* Run the bash script:

	```

    bash run_kubernetes.sh

    ```

## Files

* `docker_out.txt`: Logs generated when the webapp is deployed with docker.

* `kubernetes_out.txt`: Logs generated when the webapp is deployed with kubernetes.

* `make_prediction.sh`: Bash script to make POST request to `localhost:8000` to make prediction with sample input.

* `run_kubernetes.sh`: Bash script to start the webapp with kubernetes.

* `run_docker.sh`: Bash script to start the webapp with docker.

* `upload_docker.sh`: Bash script to upload docker image to docker hub.