Example cml-runner workflow

This repository contains a sample project using CML to provision and launch a small EC2 instance and run a machine learning workflow on the instance:

• GitHub will deploy a runner machine and setup CML with the setup-CML GitHub Action
• The workflow uses cml-runner to provision and launch a t2.micro instance on AWS EC2
• The new t2.micro instance runs a workflow to pull a Docker container, install Python package requirements, and train a scikitlearn model.
• CML returns a summary of the model accuracy and a confusion matrix as a comment in your Pull Request.

The key file enabling these actions is .github/workflows/cml.yaml.

Secrets and environmental variables

In this example, .github/workflows/cml.yaml contains three environmental variables that are stored as repository secrets.

Secret	Description
PERSONAL_ACCESS_TOKEN	You must create a personal access token with repository and workflow permissions.
AWS_ACCESS_KEY_ID	AWS credential for accessing S3 storage
AWS_SECRET_ACCESS_KEY	AWS credential for accessing S3 storage

The cml-runner function currently works with AWS and Azure cloud service providers. For Azure, you'll want to substitute the AWS secrets for Azure's credential variables.