adding CI/CD with cml

.github/workflows/cml.yml

@@ -0,0 +1,32 @@
+name: test-mlops
+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
+      - env:
+          REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Train model
+        run: |
+          # Your ML workflow goes here
+          pip install -r requirements.txt
+          python train.py
+
+          echo "## Data VIZ" >> report.md
+          cml-publish feature_importance.png --md >> report.md
+          cml-publish residuals.png --md >> report.md
+
+          cml-send-connect report.md
+
+

train.py

@@ -23,7 +23,7 @@
 #################################
 
 # Fit a model on the train section
-regr = RandomForestRegressor(max_depth=2, random_state=seed)
+regr = RandomForestRegressor(max_depth=5, random_state=seed)
 regr.fit(X_train, y_train)
 
 # Report training set score