Project Tasks

in this project, the 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, this processes will be carried out: 1 Test project code using linting 2 Complete a Dockerfile to containerize this application 3 Deploy containerized application using Docker and make a prediction 4 Improve the log statements in the source code for this application 5 Configure Kubernetes and create a Kubernetes cluster 6 Deploy a container using Kubernetes and make a prediction 7 Upload 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.

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Setup the Environment

• 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

Running app.py

1. Standalone: python app.py
2. Run in Docker: ./run_docker.sh
3. Run in Kubernetes: ./run_kubernetes.sh

Kubernetes Steps

1 Setup and Configure Docker locally

• Install docker [refer to link - https://docs.docker.com/engine/install/ubuntu/ for reference].
• Run ./run_docker.sh after configuration.
• Run docker ps [this is to check if docker is running].
• Run ./make_prediction.sh on another terminal to make prediction.
• copy/paste the logging info at terminal to output_txt_files/docker_out.txt

2 Setup and Configure Kubernetes locally

• Install Minikube refer to link - https://minikube.sigs.k8s.io/docs/start/ for reference].
• Run ./run_kubernetes.sh after configuration
• Run ./make_prediction.sh on another terminal to make prediction.
• copy/paste the logging info at terminal to output_txt_files/docker_out.txt

3 Delete Cluster

• If you want to delete the kubernetes cluster just run this command 'minikube delete'.
• You can also stop the kubernetes cluster with this command 'minikube stop'.

4 CircleCI Integration

• Create a CircleCI Account (use your Github account for a better integration)
• Create a config.yml file
• Add a status badge using this template: replace <github_username> and with your data. e.g:
• paste the link on top of the readme file.