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CML with Tensorboard use case

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.

Secrets and environmental variables

In this example, .github/workflows/cml.yaml contains two 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.
CML_TENSORBOARD_CREDENTIALS Tensorboard credentials

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.

Cloning this project

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