Machine Learning CI/CD Pipeline Demo

A demo continuous delivery pipeline for machine learning models.

Here's the sentiment analysis model which we've deployed using this pipeline: https://pycon-ml-demo.herokuapp.com/

Getting started

1. Clone repo: git clone https://www.github.com/davified/ml-ci-cd-demo
2. Setup dev environment: bin/setup.sh
3. To activate virtual environment: source activate ml-ci-cd-demo (To deactivate virtual environment: source deactivate)
4. Commands that you can run locally:

• jupyter notebook (start jupyter notebook for development)
• bin/run_unit_tests.sh scikit-nlp-model
• bin/get_data.sh scikit-nlp-model
• bin/train.sh scikit-nlp-model
• bin/evaluate.sh scikit-nlp-model
• bin/smoke_test.sh MODEL_VERSION (e.g. bin/smoke_test.sh v2)

5. Commands that are meant to be run by CI

• bin/deploy_to_staging.sh
• bin/deploy_to_prod.sh
• bin/upload_artifact.sh

Deployment

All models are deployed to GCP ML Engine. As training and deployment are time-consuming steps which we may not want to run with every commit, we've made training and deployment manual steps in our CI pipelines.

As manual deployments is not a supported feature in TravisCI, we created a workaround to simulate manual deployments - by committing and push to 2 branches: deploy-to-staging and deploy-to-prod. To deploy to staging:

• merge all your changes onto the deploy-to-staging branch (by running git checkout deploy-to-staging && git merge master)
• push your changes (git push origin deploy-to-staging, or simply git push)

Some manual configuration:

For collaborators on this project:

• generate credentials for GCP project here -> select 'Service Account Key' -> enter details, select JSON and download it, and move/rename it to ./client_secret.json (i.e. ml-ci-cd-demo/client_secret.json)

For collaborators on this project:

• Sign up/sign in to your Google Cloud Platform account
• Create a project on the GCP console, and replace PROJECT_ID in bin/common.sh with your project name
• Generate client_secret.json credentials (see instructions in previous section)
• Encrypt client_secret.json: travis encrypt-file ./client_secret.json --add
• Click on these links and enable the following APIs:
  • Cloud Resource Manager API
  • ML Engine API

Resources

• Tasks board
• CI
• GCP project dashboard
• Demo client app
  • Source
  • Demo
• Server app
  • Source
  • Demo