In this project, using a pre-trained, sklearn model that has been trained to predict housing prices in Boston, I operationalize a Python flask app using Docker and Kubernetes.
- app.py - Flask microservice app containing pretrained model for predicting houseing prices in Boston.
- requirements.txt - Contains list of python modules the app needs installed to run properly.
- Makefile - utility file which defines a set of tasks to be run to build/test the application.
- Dockerfile - Dockerfile to build containerized environment for the application.
- run_docker.sh - Script to build and run the application inside a docker container.
- upload_docker.sh - Script to upload Docker image to Docker repository.
- run_kubernetes.sh - Script to pull application Docker image to deploy to Kubernetes cluster.
- make_prediction.sh - Script to send request to deployed application to make a prediction.
- .circleci/config.yml - CicleCI configuration file for config CI process.
- Standalone:
python app.py - Run app task:
make [setup|install|test|lint|all] - Run in Docker:
./run_docker.sh - Upload Docker image:
./upload_docker.sh - Run in Kubernetes:
./run_kubernetes.sh