by iterative.ai
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Get Started with CML on GitHub

Here, we'll walk through a tutorial to start using CML. For simplicity, we'll show the demo in GitHub Actions, but instructions are pretty similar for all the supported CI systems.

  1. Fork our example project repository.

    fork cml project

    The following steps can all be done in the GitHub browser interface. However, to follow along the commands, we recommend cloning your fork to your local workstation:

    git clone https://github.com/<your-username>/example_cml
  2. To create a CML workflow, copy the following into a new file, .github/workflows/cml.yaml:

    name: CML
    on: [push]
    jobs:
      run:
        runs-on: ubuntu-latest
        container: docker://ghcr.io/iterative/cml:0-dvc2-base1
        steps:
          - uses: actions/checkout@v2
          - name: Train model
            env:
              REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
            run: |
              pip install -r requirements.txt
              python train.py
    
              cat metrics.txt >> report.md
              cml publish confusion_matrix.png --md >> report.md
              cml send-comment report.md
  3. In your text editor of choice, edit line 16 of train.py 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, open up a Pull Request to compare the experiment branch to master.

    make pr

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

    cml first report

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 vizualizations, 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 publish and cml send-comment 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:

steps:
  - uses: iterative/setup-cml@v1
  - uses: actions/checkout@v2
  - env:
      REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
    run: |
      # train will generate plot.png
      python train.py

      echo "# My first CML report" >> report.md
      cml publish plot.png --md --title="Confusion Matrix" >> report.md
      cml send-comment report.md
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