Project Overview

Operationalize a Machine Learning Microservice API.

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 the 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 Goals

The project is to operationalize a working machine learning microservice using kubernetes, which is an open-source system for automating the management of containerized applications. The project does the following:

• Test the code using linting
• Containerize an application using a Dockerfile.
• Deploy the containerized application using Docker and make a prediction.
• Showcase the log statements after running application.
• Uses Kubernetes to create a Kubernetes cluster.
• Deploys a container using Kubernetes and makes a prediction.

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Setup the Environment

• Install minikube for either Linix or Windows or Mac. A local kubernetes for learning and developing Kubernetes.
• Install kubectl for either Linix or Windows or Mac. A cmd tool used for running cmds against Kubernetes clusters.
• Install docker desktop for either Linix or Windows or Mac. An application that is used to build and share containerized applications and microservices.
• Signup on DockerHub to publish image after successful build.
• Create a virtualenv with Python 3.7 and activate it. Refer to this link for help on specifying the Python version in the virtualenv.

python3 -m pip install --user virtualenv
# You should have Python 3.7 available in your host. 
# Check the Python path using `which python3`
# Use a command similar to this one:
python3 -m virtualenv --python=<path-to-Python3.7> .mlmicroserviceapp
source .mlmicroserviceapp/bin/activate

For Windows to create a virtualenv follow this link virtualenv

• 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

Docker Steps

• Setup and configure Docker using link above.
• Change tag and app name in ./run_docker.sh.
• Run ./run_docker.sh

Publish to DockerHub

• Setup and configure Docker using link above.
• Setup a DockerHub account and create a Repository.
• Publish image.

Kubernetes Steps

• Setup and Configure Docker locally
• Setup and Configure Kubernetes locally
• Create Flask app in Container
• Run via kubectl

Useful Troubleshooting Commands and Links

For publishing image follow this link -> tutorial

# List the images built
docker image ls
# List all the pods including the status
kubectl get pods
# Check status of a pod by replacing podname with the actual name.
kubectl describe pod [podname]