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 model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets.

On every pull request, CML helps you
automatically train and evaluate models, then generates a visual report with
results and metrics. Above, an example report for a
neural style transfer model.

We built CML with these principles in mind:

  • GitFlow for data
    Use GitLab or GitHub to manage ML experiments, track who trained ML
    models or modified data and when. Codify data and models with
    DVC instead of pushing to a Git repo.
  • Auto reports for ML experiments. Auto-generate reports with metrics and
    plots in each Git Pull Request. Rigorous engineering practices help your team
    make informed, data-driven decisions.
  • No additional services. Build you own ML platform using just GitHub or
    GitLab and your favorite cloud services: AWS, Azure, GCP. No databases,
    services or complex setup needed.

⁉️ Need help? Just want to chat about continuous integration for ML?
Visit our Discord channel!

Table of contents

  1. Usage
  2. Getting started
  3. Using CML with DVC
  4. Using self-hosted runners
  5. Using your own Docker image
  6. Examples


You'll need a GitHub or GitLab account to begin. Users may wish to familiarize
themselves with Github Actions or
GitLab CI/CD.
Here, will discuss the GitHub use case.

⚠️ GitLab users! Please see our
docs about configuring CML with GitLab.


The key file in any CML project is .github/workflows/cml.yaml.

name: your-workflow-name
on: [push]
    runs-on: [ubuntu-latest]
    container: docker://dvcorg/cml-py3:latest
      - uses: actions/[email protected]
      - name: cml_run
          repo_token: ${{ secrets.GITHUB_TOKEN }}
        run: |

          # Your ML workflow goes here

          # Write your CML report
          cat results.txt >>

CML Functions

CML provides a number of helper functions to help package outputs from ML
workflows, such as numeric data and data vizualizations about model performance,
into a CML report. The library comes pre-installed on our
custom Docker images.
In the above example, note the field container: docker://dvcorg/cml-py3:latest
specifies the CML Docker image with Python 3 will be pulled by the GitHub
Actions runner.

Below is a list of CML functions for writing markdown reports and delivering
those reports to your CI system (GitHub Actions or GitLab CI).

Function Description Inputs
cml-send-comment Return CML report as a comment in your GitHub/GitLab workflow. <path to report> --head-sha <sha>
cml-send-github-check Return CML report as a check in GitHub <path to report> --head-sha <sha>
cml-publish Publish an image for writing to CML report. <path to image> --title <image title> --md
cml-tensorboard-dev Return a link to a page --logdir <path to logs> --title <experiment title> --md

Customizing your CML report

CML reports are written in
GitHub Flavored Markdown. That means they can
contain images, tables, formatted text, HTML blocks, code snippets and more -
really, what you put in a CML report is up to you. Some examples:

📝 Text. Write to your report using whatever method you prefer. For example,
copy the contents of a text file containing the results of ML model training:

cat results.txt >>

🖼️ Images Display images using the markdown or HTML. Note that if an image
is an output of your ML workflow (i.e., it is produced by your workflow), you
will need to use the cml-publish function to include it a CML report. For
example, if graph.png is the output of my workflow python, run:

cml-publish graph.png --md >>

Getting started

  1. Fork our
    example project repository. ⚠️
    Note that if you are using GitLab,
    you will need to create a Personal Access Token
    for this example to work.

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

git clone<your-username>/example_cml
  1. To create a CML workflow, copy the following into a new file,
name: model-training
on: [push]
    runs-on: [ubuntu-latest]
    container: docker://dvcorg/cml-py3:latest
      - uses: actions/[email protected]
      - name: cml_run
          repo_token: ${{ secrets.GITHUB_TOKEN }}
        run: |
          pip install -r requirements.txt

          cat metrics.txt >>
          cml-publish confusion_matrix.png --md >>
  1. In your text editor of choice, edit line 16 of to depth = 5.

  2. Commit and push the changes:

git checkout -b experiment
git add . && git commit -m "modify forest depth"
git push origin experiment
  1. In GitHub, open up a Pull Request to compare the experiment branch to

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

Using CML with DVC

In many ML projects, data isn't stored in a Git repository and needs to be
downloaded from external sources. DVC is a common way to
bring data to your CML runner. DVC also lets you visualize how metrics differ
between commits to make reports like this:

The .github/workflows/cml.yaml file to create this report is:

name: train-test
on: [push]
    runs-on: [ubuntu-latest]
    container: docker://dvcorg/cml-py3:latest
      - uses: actions/[email protected]
      - name: cml_run
        shell: bash
          repo_token: ${{ secrets.GITHUB_TOKEN }}
          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
        run: |
          # Install requirements
          pip install -r requirements.txt

          # Pull data & run-cache from S3 and reproduce pipeline
          dvc pull data --run-cache
          dvc repro

          # Report metrics
          echo "## Metrics" >>
          git fetch --prune
          dvc metrics diff master --show-md >>

          # Publish confusion matrix diff
          echo -e "## Plots\n### Class confusions" >>
          dvc plots diff --target classes.csv --template confusion -x actual -y predicted --show-vega master > vega.json
          vl2png vega.json -s 1.5 | cml-publish --md >>

          # Publish regularization function diff
          echo "### Effects of regularization\n" >>
          dvc plots diff --target estimators.csv -x Regularization --show-vega master > vega.json
          vl2png vega.json -s 1.5 | cml-publish --md >>


If you're using DVC with cloud storage, take note of environmental variables for
your storage format.

S3 and S3 compatible storage (Minio, DigitalOcean Spaces, IBM Cloud Object Storage...)
# Github

:point_right: AWS_SESSION_TOKEN is optional.

  OSS_BUCKET: ${{ secrets.OSS_BUCKET }}
Google Storage

:warning: Normally, GOOGLE_APPLICATION_CREDENTIALS points to the path of the
json file that contains the credentials. However in the action this variable
CONTAINS the content of the file. Copy that json and add it as a secret.

Google Drive

:warning: After configuring your
Google Drive credentials
you will find a json file at
your_project_path/.dvc/tmp/gdrive-user-credentials.json. Copy that json and
add it as a secret.


Using self-hosted runners

GitHub Actions are run on GitHub-hosted runners by default. However, there are
many great reasons to use your own runners: to take advantage of GPUs; to
orchestrate your team's shared computing resources, or to train in the cloud.

☝️ Tip! Check out the
official GitHub documentation
to get started setting up your self-hosted runner.

Allocating cloud resources with CML

When a workflow requires computational resources (such as GPUs) CML can
automatically allocate cloud instances. For example, the following workflow
deploys a t2.micro instance on AWS EC2 and trains a model on the instance.
After the instance is idle for 120 seconds, it automatically shuts down.

name: train-my-model
on: [push]
    runs-on: [ubuntu-latest]
    container: docker://dvcorg/cml-cloud-runner
      - name: deploy
          repo_token: ${{ secrets.REPO_TOKEN }}
          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
        run: |
          echo "Deploying..."

          MACHINE="cml$(date +%s)"
          docker-machine create \
              --driver amazonec2 \
              --amazonec2-instance-type t2.micro \
              --amazonec2-region us-east-1 \
              --amazonec2-zone f \
              --amazonec2-vpc-id vpc-06bc773d85a0a04f7 \
              --amazonec2-ssh-user ubuntu \

          eval "$(docker-machine env --shell sh $MACHINE)"

          docker-machine ssh $MACHINE "sudo mkdir -p /docker_machine && sudo chmod 777 /docker_machine" && \
          docker-machine scp -r -q ~/.docker/machine/ $MACHINE:/docker_machine && \

          docker run --name runner -d \
            -v /docker_machine/machine:/root/.docker/machine \
            -e RUNNER_IDLE_TIMEOUT=120 \
            -e DOCKER_MACHINE=${MACHINE} \
            -e RUNNER_LABELS=cml \
            -e repo_token=$repo_token \
           dvcorg/cml-py3-cloud-runner && \

          sleep 20 && echo "Deployed $MACHINE"
          ) || (echo y | docker-machine rm $MACHINE && exit 1)
    needs: deploy-cloud-runner
    runs-on: [self-hosted, cml]

      - uses: actions/[email protected]
      - name: cml_run
          repo_token: ${{ secrets.GITHUB_TOKEN }}
        run: |
          pip install -r requirements.txt

          cat metrics.txt >>
          cml-publish confusion_matrix.png --md >>


You will need to
create a new personal access token,
REPO_TOKEN, with repository read/write access. REPO_TOKEN must be added as a
secret in your project repository.

Note that you will also need to provide access credentials for your cloud
compute resources as secrets. In the above example, AWS_ACCESS_KEY_ID and
AWS_SECRET_ACCESS_KEY are required to deploy EC2 instances.

Provisioning cloud compute

In the above example, we use
Docker Machine to provision
instances. Please see their documentation for further details.

Note several CML-specific arguments to docker run:

  • repo_token should be set to your repository's personal access token
  • RUNNER_REPO should be set to the URL of your project repository
  • The docker container should be given as dvcorg/cml-cloud-runner,
    dvcorg/cml-py3-cloud-runner, dvc/org/cml-gpu-cloud-runner, or

Using your own Docker image

In the above examples, CML is pre-installed in a custom Docker image, which is
pulled by a CI runner. If you are using your own Docker image, you will need to
install CML functions on the image:

npm i @dvcorg/cml

Note that you may need to install additional dependencies to use DVC plots and
Vega-Lite CLI commands. See our
base Dockerfile for details.

A library of CML projects

Here are some example projects using CML.