sitwolf / kubernetes-machine-learning-microservices

Project Overview

This project operationalizes a Machine Learning Microservice API.

A pre-trained, sklearn model is provided, 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. A Python flask app—in a provided file, app.py— 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.

Project Tasks

This project operationalizes this working, machine learning microservice using kubernetes, which is an open-source system for automating the management of containerized applications. In this project the following options are available:

• Testing the project code using linting
• A Dockerfile that containerizes this application
• Log statements in the source code for this application
• Deployment using Kubernetes
• Make a prediction

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Setup the Environment

Virtualenv

• 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

Docker

• Install Docker. Docker enables separating the application from your infrastructure.

Hadolint

Install Hadolint. Hadolint enables linting of Docker imgaes

wget -O /bin/hadolint https://github.com/hadolint/hadolint/releases/download/v2.8.0/hadolint-Linux-x86_64
sudo chmod +x /bin/hadolint

Minikube

• Install Minikube. Minikube is local Kubernetes, focusing on making it easy to develop for Kubernetes.

Kubectl

• Install Kubectl. The kubectl command line tool lets you control Kubernetes clusters. For more information of Kubernetes, see this link.

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Deploying app.py

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

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Makefile

A provided Makefile provides a template for installing dependencies, setup and linting.

cat Makefile

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Supplemental material

• When deploying on Cloud9, a supplemental file is provided to increase memory limits

(base) ec2-user:~ $ df -h
(base) ec2-user:~ $ ./resize.sh
(base) ec2-user:~ $ df -h