In this project, we are to operationalize a Machine Learning Microservice API.
We were 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 appβin a provided file, app.py
β 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.
Your 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
I worked on this project using Cloud9. You can go through this article on using Cloud9.
- Clone the repository
git clone https://github.com/CFCIfe/udapeople-microsvc.git
cd udapeople-microsvc
- Create a virtualenv with Python 3.7 and activate it.
python3 -m pip install virtualenv --user
python3 -m virtualenv ~/.devops
source ~/.devops/bin/activate
- Run Lint checks
# Install [hadolint](https://github.com/hadolint/hadolint)
wget -O /bin/hadolint https://github.com/hadolint/hadolint/releases/download/v1.16.3/hadolint-Linux-x86_64 &&\
chmod +x /bin/hadolint
make lint
- Run
make install
to install the necessary dependencies
- Run in Docker:
./run_docker.sh
- Run in Kubernetes:
./run_kubernetes.sh
Details of ./run_docker.sh
- Setup and Configure Docker locally using this link: https://www.docker.com/products/docker-desktop
- Builds an image of the flash app (
app.py
) and tags it. - Exposes port 80 for the container and maps to port 8000 of the host
- Setup and Configure Kubernetes locally here: https://kubernetes.io/docs/tasks/tools/install-kubectl-linux/
- Install Minikube here to run K8slocally: https://minikube.sigs.k8s.io/docs/start/
- Run via kubectl:
./run_kubernetes.sh
Special thanks to SwayDevStan on putting me through during this project. π