CML with DVC use case

This repository contains a sample project using CML with DVC to push/pull data from cloud storage and track model metrics. When a pull request is made in this repository, the following will occur:

• GitHub will deploy a runner machine with a specified CML Docker environment
• DVC will pull data from cloud storage
• The runner will execute a workflow to train a ML model (python train.py)
• A visual CML report about the model performance with DVC metrics will be returned as a comment in the 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
GITHUB_TOKEN	This is set by default in every GitHub repository. It does not need to be manually added.
AWS_ACCESS_KEY_ID	AWS credential for accessing S3 storage
AWS_SECRET_ACCESS_KEY	AWS credential for accessing S3 storage

DVC works with many kinds of remote storage. To configure this example for your a different cloud storage provider, see our documentation on the CML repository.