sourabhgupta385/operationalize-ml-microservice-api

This is one of the project in Udacity Cloud DevOps Engineer Nanodegree.

Project Overview

Operationalize a Python flask app—in a provided file, app.py—that serves out predictions (inference) about housing prices through API calls. Main aim of the project is to containerize the python flask-app and deploy the container in Kubernetes cluster. This project could be extended to any pre-trained machine learning model, such as those for image recognition and data labeling.

Project Tasks

• Test project code using linting
• Complete a Dockerfile to containerize this application
• Deploy 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

---

Setup the Environment

• Create a virtualenv and activate it

python3 -m venv <your_venv>
source <your_venv>/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

Files

• output_txt_files/docker_out.txt contains logs returned after running the app with Docker
• output_txt_files/kubernetes_out.txt containes logs and the prediction returned after running the app with Kubernetes(run_kubernetes.sh)
• run_docker.sh contains the steps to get Docker running the app locally
• run_kubernetes.sh contains the steps to get Kubernetes running the app locally
• upload_docker.sh contains the steps to upload the image to the Docker repository