Create CD pipeline

Author: rgallardone

.github/workflows/cd.yml

@@ -0,0 +1,86 @@
+name: 'Continuous Deployment'
+
+on:
+  push:
+    branches:
+      - main
+      - develop
+      - release/*
+
+jobs:
+  deployment:
+    runs-on: ubuntu-latest
+    environment: dev
+    env:
+      branch: main
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Get the branch name
+        id: get_branch_name
+        run: |
+          echo "branch=${GITHUB_HEAD_REF:-${GITHUB_REF#refs/heads/}}" >> $GITHUB_OUTPUT
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.9'
+
+      - name: Authenticate to GCP
+        uses: 'google-github-actions/auth@v2'
+        with:
+          credentials_json: '${{ secrets.CD_SA_KEYS }}'
+
+      - name: Install dependencies
+        run: |
+          pip install -r requirements.txt -r requirements-dev.txt
+
+      - name: Run training script
+        run: |
+          python train.py
+
+      - name: Authenticate Docker to GAR
+        uses: docker/login-action@v3
+        with:
+          registry: '${{ vars.GCP_REGION }}-docker.pkg.dev'
+          username: _json_key
+          password: ${{ secrets.CD_SA_KEYS }}
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v6
+        with:
+          push: true
+          tags: '${{ vars.GAR_REPOSITORY }}/${{ vars.GAR_IMAGE_NAME }}-${{ steps.get_branch_name.outputs.branch }}'
+
+      - name: Deploy the service to Cloud Run
+        id: 'deploy'
+        uses: 'google-github-actions/deploy-cloudrun@v2'
+        with:
+          service: '${{ vars.GCR_SERVICE_NAME }}-${{ steps.get_branch_name.outputs.branch }}'
+          image: '${{ vars.GAR_REPOSITORY }}/${{ vars.GAR_IMAGE_NAME }}-${{ steps.get_branch_name.outputs.branch }}'
+          region: '${{ vars.GCP_REGION }}'
+          flags: '--allow-unauthenticated'
+
+    outputs:
+      service_url: ${{ steps.deploy.outputs.url }}
+
+  stress_test:
+    runs-on: ubuntu-latest
+    needs: deployment
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.9'
+
+      - name: Install dependencies
+        run: |
+          pip install -r requirements-test.txt
+
+      - name: Run stress test
+        run: |
+          make stress-test API_URL=${{ needs.deployment.outputs.service_url }}

Makefile

@@ -23,7 +23,7 @@ install:		## Install dependencies
 	pip install -r requirements-test.txt
 	pip install -r requirements.txt
 
-STRESS_URL = https://delay-model-dpmrk4cwxq-uw.a.run.app
+STRESS_URL = $(API_URL)
 .PHONY: stress-test
 stress-test:
 	# change stress url to your deployed app

docs/challenge.md

@@ -133,3 +133,5 @@ The CI workflows focus on running the tests and assesing the quality of the code
 The CD workflows focus on training the model, deploying the API and running the stress testing against it. These workflows only run when there's a push to the `main`, `develop` or `release` branches on the repository.
 
 * Undesirable model tracking
+* env variables ci/cd
+* makefile argument

train.py

@@ -0,0 +1,27 @@
+import pandas as pd
+
+from challenge.model import DelayModel
+
+print("Loading data...")
+# Read the data
+df = pd.read_csv("data/data.csv")
+print("-> Data loaded")
+
+# Create the model
+model = DelayModel()
+
+print("Preprocessing data...")
+# Preprocess the data
+X_train, y_train = model.preprocess(df, "delay")
+print("-> Preprocessed data")
+
+
+print("Training model...")
+# Train the model
+model.fit(X_train, y_train)
+print("-> Model trained")
+
+print("Saving model...")
+# Store the model
+model.save("challenge/tmp/model_checkpoint.pkl")
+print("-> Model saved")