Continuous Intelligence Workshop

A demo on how to apply continuous delivery principles to train, test and deploy ML models.

Setup

Note:

If you encounter any errors, please refer to FAQs for a list of common errors and how to fix them.
[Windows users] If you're new to Docker, please use Git Bash to run the commands below

Setup instructions

Please ensure you've completed the pre-requisite setup
Fork repository: https://github.com/davified/ci-workshop-app
Clone repository: git clone https://github.com/YOUR_USERNAME/ci-workshop-app
Start Docker on your desktop (Note: Wait for Docker to complete startup before running the subsequent commands. You'll know when startup is completed when the docker icon in your taskbar stops animating)
Build docker image

# [Mac/Linux users]
docker build . -t ci-workshop-app --build-arg user=$(whoami)

# [Windows users]
MSYS_NO_PATHCONV=1 docker build . -t ci-workshop-app --build-arg user=$(whoami)

Start docker container

# [Mac/Linux users]
docker run -it -v $(pwd):/home/ci-workshop-app -p 8080:8080 ci-workshop-app bash

# [Windows users]
winpty docker run -it -v C:\\Users\\path\\to\\your\\ci-workshop-app:/home/ci-workshop-app -p 8080:8080 ci-workshop-app bash
# Note: to find the path, you can run `pwd` in git bash, and manually replace forward slashes (/) with double backslashes (\\)

! Pre-workshop setup stops here

### Other useful docker commands ###
# See list of running containers
docker ps

# Start a bash shell in a running container when it’s running
docker exec -it <container-id> bash

Now you're ready to roll!

Common commands (run these in the container)

# Add some color to your terminal
source bin/color_my_terminal.sh

# Run unit tests
nosetests

# Train model
SHOULD_USE_MLFLOW=false python src/train.py

# Start flask app
python src/app.py

# Make requests to your app
# 1. In your browser, visit http://localhost:8080
# 2. In another terminal in the container, run:
bin/predict.sh http://localhost:8080

# You can also use this script to test your deployed application later:
bin/predict.sh http://my-app.herokuapp.com

IDE configuration

Please refer to FAQs for instructions on configuring VS Code or PyCharm.

Set up CD pipeline

Instructions for setting up your CD pipeline are in docs/CD.md. To keep this example simple, we will deploy to heroku.

Once the CD pipeline is set up, you only need to git add, git commit and git push your code changes, and the CD pipeline will do everything (train, test, deploy) for you.

Bonus: Set up CD pipeline

You can also configure your CD pipeline to deploy using kubernetes instead. See instructions here

sheroy / ci-workshop-app