create cml.yaml

.github/workflows/cml.yaml

@@ -0,0 +1,33 @@
+name: your-workflow-name
+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

requirements.txt

@@ -1,5 +1,5 @@
 pandas
-sklearn
+scikit-learn
 matplotlib
 seaborn
 numpy