Continuous Machine Learning (CML) is an open-source library for implementing continuous integration & delivery (CI/CD) in machine learning projects. Use it to automate parts of your development workflow, including machine provisioning; model training and evaluation; comparing ML experiments across your project history, and monitoring changing datasets.

The iterative/setup-cml can be used as a GitHub Action to provide CML functions in your workflow. The action allows users to install CML without using the CML Docker container.

This action gives you:

• Access to all CML functions. For example:
  • cml comment create for publishing data visualization and metrics from your CI workflow as comments in a pull request.
  • cml pr create to open a pull request.
  • cml runner launch, a function that enables workflows to provision cloud and on-premise computing resources for training models.
• The freedom 🦅 to mix and match CML with your favorite data science tools and environments.

Note that CML does not include DVC and its dependencies (see the Setup DVC Action).

v1 of setup-cml was a wrapper around a set of npm installs. v2 installs CML from its pre-packaged binaries. Then attempts to run npm install --global canvas@2 vega@5 vega-cli@5 vega-lite@5 if you do not wish to install these tools pass vega: false to the action.

link to v1

This action is tested on ubuntu-latest, macos-latest and windows-latest.

Basic usage:

steps:
  - uses: actions/checkout@v3
  - uses: iterative/setup-cml@v2

A specific version can be pinned to your workflow.

steps:
  - uses: actions/checkout@v3
  - uses: iterative/setup-cml@v2
    with:
      version: 'v0.18.1'

Without vega tools

steps:
  - uses: actions/checkout@v3
  - uses: iterative/setup-cml@v2
    with:
      version: 'v0.20.0'
      vega: false

The following inputs are supported.

• version - (optional) The version of CML to install (e.g. '0.18.1'). Defaults to latest for the most recent CML release.
• vega - (optional) Whether to install vega dependencies. Defaults to true. runs command npm install --global canvas@2 vega@5 vega-cli@5 vega-lite@5

A sample CML report from a machine learning project displayed in a Pull Request.

Assume that we have a machine learning script, train.py which outputs an image plot.png:

steps:
  - uses: actions/checkout@v2
  - uses: iterative/setup-cml@v2
  - env:
      REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }} # Can use the default token for most functions
    run: |
      python train.py --output plot.png

      echo 'My first CML report' > report.md
      echo '![](./plot.png)' >> report.md
      cml comment create --publish report.md

In general GitHub's runner token can be given enough permissions to perform most functions. When using the cml runner launch command a PAT is required

CML provides several helper functions. See the docs.

To get started after cloning the repo, run npm ci (clean-install). Before pushing changes or opening a PR run npm run format && npm run lint to ensure that the code is formatted and linted.

run npm run build to compile the action.