Project Overview
In this project, 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. In this project the 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.
Setup the Environment
- Environment used is Ubuntu16 in cloud9.
- Create a virtualenv and activate it
- Run
make install
to install the necessary dependencies
Install Minikube through docker
curl -Lo minikube https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64 && chmod +x minikube
sudo mkdir -p /usr/local/bin/
sudo install minikube /usr/local/bin/
minikube start --driver=docker
Install hadolint:
sudo wget -O /bin/hadolint https://github.com/hadolint/hadolint/releases/download/v1.16.3/hadolint-Linux-x86_64 &&\ sudo chmod +x /bin/hadolint
Refer for other operating system
Install kubectl:Install via native package management:
sudo apt-get update && sudo apt-get install -y apt-transport-https gnupg2
curl -s https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -
echo "deb https://apt.kubernetes.io/ kubernetes-xenial main" | sudo tee -a /etc/apt/sources.list.d/kubernetes.list
sudo apt-get update
sudo apt-get install -y kubectl
Install via other package management:
snap install kubectl --classic
kubectl version --client
app.py
Running - Standalone:
python app.py
- Run in Docker:
./run_docker.sh
- Upload to Docker Hub:
./upload_docker.sh
- Run in Kubernetes:
./run_kubernetes.sh
Kubernetes Steps
- Setup and Configure Docker locally
- Setup and Configure Kubernetes locally
- Create Flask app in Container
- Run via kubectl