Here, we'll walk through a tutorial to start using CML. For simplicity, we'll show the demo in GitHub Actions, but these instructions are valid for all supported CI systems (with exceptions as noted!).
Fork our example project repository.
⚠️ If you are using GitLab, you'll 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 workstation:
git clone https://github.com/<your-username>/example_cml
To create a CML workflow, copy the following into a new file,
.github/workflows/cml.yaml
:
name: train-my-model
on: [push]
jobs:
run:
runs-on: [ubuntu-latest]
container: docker://dvcorg/cml-py3:latest
steps:
- uses: actions/checkout@v2
- name: cml_run
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
train.py
to depth = 5
.Commit and push the changes:
git checkout -b experiment
git add . && git commit -m "modify forest depth"
git push origin experiment
In GitHub, open up a Pull Request to compare the experiment
branch to
master
.
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.
In the above example, we got the CML functions 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 functions in your GitHub Actions workflow. The action allows users to install CML without using the CML Docker container.
This action gives you:
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 function that enables workflows to provision cloud and
on-premise computing resources for training modelsNote that CML does not include DVC and its dependencies- for that, you want the Setup DVC Action.
This action has been tested on ubuntu-latest
and macos-latest
.
Basic usage:
steps:
- uses: actions/checkout@v2
- uses: iterative/cml-action@v1
A specific version can be pinned to your workflow.
steps:
- uses: actions/checkout@v2
- uses: iterative/setup-cml@v1
with:
version: '1.0.1'
The following inputs are supported.
version
- (optional) The version of CML to install. A value of latest
will
install the latest version of CML functions. Defaults to latest
.Setup CML has no outputs.
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: actions/checkout@v2
- uses: iterative/setup-cml@v1
with:
version: latest
- run: |
# train will generate plot.png
python train.py
echo 'My first CML report' > report.md
cml-publish plot.png --md > report.md
cml-send-comment report.md