swaydevstan / containerizing-a-ml-microservice-api Goto Github PK

In this project, I have applied the skills I have acquired in the udacity cloud devops nanodegree course to operationalize a Machine Learning Microservice API.

I was 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 tested my 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.

The goal of the project was 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 I completed the following:

• Tested the project code using linting
• Completed a Dockerfile to containerize this application
• Deployed the containerized application using Docker and made a prediction
• Improved the log statements in the source code for this application
• Configured Kubernetes and created a Kubernetes cluster
• Deployed a container using Kubernetes and made a prediction
• Uploaded a complete Github repo with CircleCI to indicate that the code has been tested

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

• 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> .devops
source .devops/bin/activate

• Run make install to install the necessary dependencies

1. Run standalone if python is installed: python app.py
2. Run in Docker if docker is installed: ./run_docker.sh
3. Run in Kubernetes if kubernetes is installed: ./run_kubernetes.sh

• Setup and Configure Docker locally by installing docker desktop and minikube following the steps here : https://minikube.sigs.k8s.io/docs/start/
• Setup and Configure Kubernetes locally following the steps here: https://kubernetes.io/docs/tasks/tools/install-kubectl-linux/
• Create Flask app in Container: bash run_docker.sh
• Run via kubectl: bash run_kubernetes.sh