This repository contains a sample project using CML with Tensorboard.dev to track model training in real-time. When a pull request is made, the following steps occur:

• GitHub will deploy a runner machine with a specified CML Docker environment
• A Tensorboard.dev page will be created
• CML will report a link to the Tensorboard as a comment in the pull request
• The runner will execute a workflow to train a ML model (python train.py)

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

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

To access your Tensorboard credentials:

1. On your local machine, run tensorboard dev upload
2. Accept the TOS and follow the authentication procedure.
3. When you have authenticated, copy your credentials out of ~/.config/tensorboard/credentials/uploader-creds.json (this is the typical path for OSX/Linux systems). Paste these credentials as the secret CML_TENSORBOARD_CREDENTIALS.

Note that if you clone this project, you will have to configure your own TB credentials for the example.