Get Started with CML on GitHub

Here, we'll walk through a tutorial to start using CML with GitHub Actions.

1. Fork our example project repository.

   

$ git clone https://github.com/<your-username>/example_cml
$ cd example_cml

2. To create a CML workflow, copy the following into a new file at .github/workflows/cml.yaml:

   name: CML
   on: [push]
   jobs:
     train-and-report:
       runs-on: ubuntu-latest
       container: docker://ghcr.io/iterative/cml:0-dvc2-base1
       steps:
         - uses: actions/checkout@v3
         - name: Train model
           env:
             REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           run: |
             pip install -r requirements.txt
             python train.py  # generate plot.png
   
             # Create CML report
             cat metrics.txt >> report.md
             echo '![](./plot.png "Confusion Matrix")' >> report.md
             cml comment create report.md

3. In your text editor, open train.py and modify line 15 to depth = 5.

4. Commit and push the changes:

   $ git checkout -b experiment
   $ git add . && git commit -m "modify forest depth"
   $ git push origin experiment

5. In GitHub, create a Pull Request to compare the experiment branch to master.

   Ensure the target is your fork (under your username).

   

   Shortly, you should see a comment appear in the Pull Request with your CML report. This is a result of the cml comment create command in your workflow.

   

This is the gist of the CML workflow: when you push changes to your GitHub repository, the workflow in your .github/workflows/cml.yaml file gets run and a report generated.

CML commands let you display relevant results from the workflow, like model performance metrics and visualizations, in GitHub checks and comments. What kind of workflow you want to run, and want to put in your CML report, is up to you.

Final Solution

An example of what your repository should look like now can be found at iterative/cml_base_case.

Setup Action

In the above example, we got the CML commands thanks to our Docker container. But there's another way for GitHub Actions users to get CML: the setup-cml Action!

The iterative/setup-cml action is a JavaScript workflow that provides CML commands in your GitHub Actions workflow. The action allows users to install CML without using the CML Docker container.

This action gives you:

• Commands like cml comment create for publishing data visualization and metrics from your CI workflow as comments in a pull request.
• cml runner, a command 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 — for that, you want the Setup DVC Action.

Usage

This action has been tested on ubuntu-latest and macos-latest.

Basic usage:

steps:
  - uses: iterative/setup-cml@v1

A specific version can be pinned to your workflow.

steps:
  - uses: iterative/setup-cml@v1
    with:
      version: '1.0.1'

Inputs

The following inputs are supported.

• version - (optional) The version of CML to install. The default value of latest will install the latest version of CML.

Outputs

Setup CML has no outputs.

A complete workflow

Assume that we have a machine learning script, train.py, that outputs an image plot.png. A potential workflow will look like this:

name: CML
on: [push]
jobs:
  train-and-report:
    runs-on: ubuntu-latest
    steps:
      - uses: iterative/setup-cml@v1
      - uses: actions/checkout@v3
      - name: Train model
        env:
          REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        run: |
          pip install -r requirements.txt
          python train.py  # generate plot.png

          echo "# CML report" >> report.md
          echo '![](./plot.png "Confusion Matrix")' >> report.md
          cml comment create report.md