au12113 / udapredict Goto Github PK

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. 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.

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

You can find a detailed project rubric, here.

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

• Create a virtualenv and activate it
  • Create a virtual environment: python -m venv ~/<environment_name>
  • Activate the virtual environment: source ~/<environment_name>/activate
• Run make install to install the necessary dependencies
• Run make lint to linting app.py with pylint and Dockerfile with hadolint
• [ Recommend ] Windows users install Chocolatey following the instructions, on Chocolatey's page

1. Standalone: python app.py In case of error when running on port 80: sudo python app.py
2. Build image and run in Docker: ./run_docker.sh
3. Run in Kubernetes:
   1. Upload recent image: ./upload_docker.sh
   2. Get latest version and run in Kubernetes: ./run_kubernetes.sh

• Setup and Configure Docker locally
  • Linux :
    • Get Docker repository

        $ sudo apt-get update
        $ sudo apt-get install apt-transport-https ca-certificates curl gnupg lsb-release
        $ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
        $ echo "deb [arch=amd64 signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
    

    • Install Docker Engine

        $ sudo apt-get update
        $ sudo apt-get install docker-ce docker-ce-cli containerd.io
    

  • Windows :
    • Docker Desktop
• Setup and Configure Kubernetes locally

  • Install kubectl

    • Linux
      • Get repository and install

        $ curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl"
        $ sudo install -o root -g root -m 0755 kubectl /usr/local/bin/kubectl
      

      • Test to ensure the version of kubectl

        $ kubectl version --client
      

    • Windows

      choco install kubernetes-cli
      

  • Install minikube

    • Linux

      $ curl -LO https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64
      $ sudo install minikube-linux-amd64 /usr/local/bin/minikube
      

    • Windows choco install minikube
• Build Flask app to Docker image and upload to Docker Hub

  $ docker build --tag udapredict .
  $ ./upload_docker.sh
  

• Run via kubectl

  $ minikube start
  $ ./run_kubernetes.sh
  

  (run prediction on separate terminal)
  $ ./make_prediction.sh
  

.
|-- app.py
|-- Makefile 
|-- requirements.txt : required python library that apply in Makefile
|-- Dockerfile
|-- run_docker.sh 
|-- make_prediction.sh : script to request prediction 
|-- run_kubernetes.sh : change docker path to your own
|-- upload_docker.sh : change docker path and authentication to your own 
|-- .circleci
|   |-- config.yml
|
|-- model_data
|   |-- boston_housing_prediction.joblib : pretrained model
|   |-- housing.csv
|
|-- output_txt_files: directory to collect log
    |-- docker_out.txt
    |-- kubernetes_out.txt