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https://github.com/tristcoil/udacity_devops_machine_learning_microservice

runs dockerized machine learning API
https://github.com/tristcoil/udacity_devops_machine_learning_microservice

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runs dockerized machine learning API

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[![CircleCI](https://circleci.com/gh/tristcoil/Udacity_DevOps_machine_learning_microservice.svg?style=svg)](https://circleci.com/gh/tristcoil/Udacity_DevOps_machine_learning_microservice)



## 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 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



## Sources:



#### original udacity project repo:

- git clone https://github.com/udacity/DevOps_Microservices.git



#### install docker:

- https://docs.docker.com/engine/install/ubuntu/

- https://www.digitalocean.com/community/questions/how-to-fix-docker-got-permission-denied-while-trying-to-connect-to-the-docker-daemon-socket



#### installing minikube:

- https://minikube.sigs.k8s.io/docs/start/



#### hadolint:

- https://github.com/hadolint/hadolint/

- https://www.haskell.org/platform/#linux-ubuntu

- https://stackoverflow.com/questions/62369711/how-to-install-hadolint-on-ubuntu



#### Docker commands:

- https://docs.docker.com/engine/reference/commandline/image_rm/

- https://docs.docker.com/engine/reference/commandline/image_push/

- https://docs.docker.com/docker-hub/repos/

- https://docs.docker.com/engine/reference/commandline/login/



#### Kubernetes:

- https://kubernetes.io/docs/tutorials/kubernetes-basics/deploy-app/deploy-interactive/

- https://kubernetes.io/docs/tutorials/kubernetes-basics/explore/explore-interactive/

- https://kubernetes.io/docs/tutorials/kubernetes-basics/expose/expose-interactive/



#### knowledge base:

- https://knowledge.udacity.com/questions/303115

- https://knowledge.udacity.com/questions/329275

- https://knowledge.udacity.com/questions/351451

- https://knowledge.udacity.com/questions/332693

- https://knowledge.udacity.com/questions/330414

- https://knowledge.udacity.com/questions/321217



#### circleci:

- https://s3.amazonaws.com/video.udacity-data.com/topher/2019/May/5cda0d76_config/config.yml

- https://circleci.com/docs/2.0/status-badges/



#### git

- used commands generated by github.com when creating new repository



#### fixing circleci yaml error:

- https://stackoverflow.com/questions/32441291/unknown-could-not-find-expected-while-scanning-a-simple-key-at-line-3-c



- https://codebeautify.org/yaml-validator

- https://circleci.com/docs/2.0/language-python/

- https://en.it1352.com/article/d00d2697f0994df79fcb8bd1ad3167c9.html



- https://stackoverflow.com/questions/16482875/you-cannot-define-a-mapping-item-when-in-a-sequence-when-running-phpunit-in-sy

- https://en.it1352.com/article/d00d2697f0994df79fcb8bd1ad3167c9.html



- https://discuss.circleci.com/t/incorrect-validation-of-config-yml/22793

- https://github.com/circleci/local-cli/issues/21

- https://github.com/CircleCI-Public/circleci-cli/issues/326

- http://pylint.pycqa.org/en/latest/#note-for-windows-users

- https://stackoverflow.com/questions/55478876/circleci-cannot-find-a-definition-for-job-named-publish-latest

- https://github.com/CircleCI-Public/circleci-cli/issues/11