In this project, you will apply the skills you have acquired in this course to operationalize a Machine Learning Microservice API.
You are 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 tests your 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.
Files present in thiis repo are:
.
├── .circleci
│ └── config.yml # Project TODO
├── Dockerfile # Project TODO
├── LICENSE
├── Makefile
├── README.md # Project TODO
├── app.py # Project TODO
├── make_prediction.sh
├── model_data
│ ├── boston_housing_prediction.joblib
│ └── housing.csv
├── output_txt_files # Project TODO
│ ├── docker_out.txt
│ └── kubernetes_out.txt
├── requirements.txt
├── run_docker.sh # Project TODO
├── run_kubernetes.sh # Project TODO
└── upload_docker.sh # Project TODOYour 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 final implementation of the project will showcase your abilities to operationalize production microservices.
- Create a virtualenv and activate it.
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# Check/Update the requirements.txt
# Install the necessary dependencies
make install
python app.py# Update the Dockerfile
make lint# Update the run_docker.sh
chmod +x run_docker.sh
./run_docker.sh
docker exec -it [container-name] bash
# Check the ports in the make_prediction.sh file
chmod +x make_prediction.sh
./make_prediction.shSave the output to the nd9991-p4/output_txt_files/docker_out.txt file.
# Complete the upload_docker.sh file
# Create a repo in Docker hub
docker login
./upload_docker.sh# Start k8s in Docker Desktop > Preferences
kubectl get nodes
kubectl delete pods --all
# Finish the run_kuberenets.sh file
./run_kuberenets.sh
# Open another terminal tab
./make_prediction.sh
# Copy the result of the ./run_kuberenets.sh to the nd9991-p4/output_txt_files/kubernetes_out.txt filekubectl delete pods --all
# You can also reset the k8s cluster from the Docker Desktop preferences# Create nd9991-p4/.circleci/config.yml file
# Use the content from https://github.com/udacity/DevOps_Microservices/blob/master/Lesson-2-Docker-format-containers/class-demos/.circleci/config.yml file
# Add the project repo in the CircleCI portal
# Push the code
# Check the badge