Create cml.yaml

.github/workflows/cml.yaml

@@ -0,0 +1,27 @@
+name: model-pipeline
+on: [push]
+jobs:
+  run:
+    runs-on: ubuntu-latest
+    # optionally use a convenient Ubuntu LTS + DVC + CML image
+    # container: docker://ghcr.io/iterative/cml:0-dvc2-base1
+    steps:
+      - uses: actions/checkout@v2
+      # may need to setup NodeJS & Python3 on e.g. self-hosted
+      # - uses: actions/setup-node@v2
+      #   with:
+      #     node-version: '16'
+      # - uses: actions/setup-python@v2
+      #   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 model.py
+          echo "## Model metrics" > report.md
+          cat metrics.txt >> report.md
+          cml-send-comment report.md

model.py

@@ -16,3 +16,5 @@
 print(results.summary())
 print('Parameters: ', results.params)
 print('R2: ', results.rsquared)
+with open ("metrics.txt", "w") as f:
+  f.write(f"{results.summary().as_html()}")