kartikrameshiyer/project-ml-microservice-kubernetes

Overal Build

Project Overview

The project is 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. We need 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

Our 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

---

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: Install docker for mac. It will install Docker desktop , Kubernetes and kubectl by default. [https://www.docker.com ]

  Building docker image docker build -t ml-kubermnetes . To List the images created docker image ls To start the docker image docker run -p 8080:80 ml-kubermnetes
  Add all the above command in a file run_docker.sh

• Setup and Configure Kubernetes locally: Install kubectl and minikube. Start minikube: minikube start. Verify cluster: kubectl config view.

• Create Flask app in Container: Create a Docker file with workign directory WORKDIR /app Copy COPY . app.py /app/ to container. Install the dependencies. RUN pip install --upgrade pip &&\ pip install --trusted-host pypi.python.org -r requirements.txt Build the docker image, list docker images created and run the containerized Flask app by calling script: ./run_docker.sh. Make a prediction: Open a separate tab and call script: ./make_prediction.sh.

• Run via kubectl: Run the containerized application by calling script: ./run_kubernetes.sh. (Check pod status by executing kubectl get pod and rerun run_kubernetes.sh script when status changes from ContainerCreating to Running). Make a prediction: Open a separate tab and call script: ./make_prediction.sh.

• Delete the Kubernetes cluster: minikube delete (Or pause by running minikube stop).

Description of the files used in this project

• app.py - A Flask app created usign python to predict Boston Housing prices.
• requirements.txt - All the required packages to run the application sucessfully.
• Makefile - steps and commands used to install dependencies including pylint for application and hadolint for Docker file.
• Dockerfile - Contains the command to create and build a Docker Image.
• make_prediction.sh - Sample data passed to the API to geta prediction.
• run_docker.sh - The shell script includes steps to build , list and run containers.
• upload_docker.sh : This includes commands to define dockerpath, authenticate and tag image and push the docker image to the docker path (in the docker hub)
• run_kubernetes.sh : This includes commands to run the deployed docker image in a kubernetes cluster
• /output_txt_files - Folder contains the output logs of the Docker and Kubernetes.
• /.circleci/config.yml - Configuration file created to run CI build which includes the steps , installing dependencies.