This project applies the skills acquired in the Udacity Cloud DevOps Engineering Nanodegree course to operationalize a Machine Learning Microservice API.
It includes 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 shows 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.
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
You can find a detailed project rubric, here.
The implementation of this project showcases my abilities to operationalize production microservices.
To run this project, you must first include the .devops folder for this project.
- 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/Scripts/activate
- Run
make install
to install the necessary dependencies
- Standalone:
python app.py
- Run in Docker:
./run_docker.sh
- Run in Kubernetes:
./run_kubernetes.sh
- Make Predictions:
./make_prediction.sh
- Docker Output file
./outout_txt_files/docker_out.txt
- Kubernetes Output file
./outout_txt_files/kubernetes_out.txt
- Requirements file states the requirements for dependency installations
./requirements.txt
- File that contains commands to upload docker images
./upload_docker.sh
- Create working directory and install dependencies
./Dockerfile
- File for linting and tests
./Makefile
- Setup and Configure Docker locally
docker build --tag=latest .
- Setup and Configure Kubernetes locally
minikube start
- Create Flask app in Container
docker run -p 8080:80 latest
- Run via kubectl
kubectl run app1 --image=$dockerpath --port=80
- Check pod status
kubectl get pod
- Stop Kubernetes Cluster
minikube close
- Delete Kubernetes Cluster
minikube delete
https://github.com/SammyBloom/Operationalize-A-Machine-Learning-MicroService-API