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cml.yaml
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name: wine-quality
on: [push]
jobs:
run:
runs-on: ubuntu-latest
# optionally use a convenient Ubuntu LTS + DVC + CML image
container: ghcr.io/iterative/cml:0-dvc2-base1
steps:
- uses: actions/checkout@v3
# may need to setup NodeJS & Python3 on e.g. self-hosted
# - uses: actions/setup-node@v3
# with:
# node-version: '16'
# - uses: actions/setup-python@v4
# with:
# python-version: '3.x'
# - uses: iterative/setup-cml@v1
- name: Train model
env: repo_token: ${{ secrets.GITHUB_TOKEN }}
run: |
# Your ML workflow goes here
pip install -r requirements.txt
python train.py
echo "## MODEL METRICS" > report.md
cat metrics.txt >> report.md
echo "## Data Viz" >> report.md
cml-publish feature_importance.png --md >> report.md
cml-publish residuals.png --md >> report.md
cml-send-comment report.md