CD Deploy

.github/workflows/cd-deploy-workflow.yaml

@@ -15,60 +15,58 @@ jobs:
       - name: Checkout Repo
         uses: actions/checkout@v3
 
-      # Step 2: Set up Python 3.9 and create a virtual environment
-      - name: Setup Python
-        uses: actions/setup-python@v2
-        with:
-          python-version: '3.9'
-
-      # Step 3: Configure AWS Credentials
+      # Step 2: Configure AWS Credentials
       - name: Configure AWS Credentials
         uses: aws-actions/configure-aws-credentials@v1
         with:
           aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
           aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
           aws-region: ${{ secrets.AWS_REGION }}
 
-      # Step 4: Create the kaggle.json file in the working directory
+      # Step 3: Create the kaggle.json file in the working directory
       - name: Create kaggle.json
         run: |
           echo '${{ secrets.KAGGLE_JSON }}' > kaggle.json
 
-      # Step 5: Create a .env file for Docker Compose using the secrets
+      # Step 4: Create a .env file for Docker Compose using the secrets
       - name: Create .env file for Docker Compose
         run: |
           echo "MLFLOW_BACKEND_STORE_URI=${{ secrets.MLFLOW_BACKEND_STORE_URI }}" >> .env
           echo "MLFLOW_DEFAULT_ARTIFACT_ROOT=${{ secrets.MLFLOW_DEFAULT_ARTIFACT_ROOT }}" >> .env
           echo "MLFLOW_TRACKING_URI=${{ secrets.MLFLOW_TRACKING_URI }}" >> .env
           echo "PREFECT_API_KEY=${{ secrets.PREFECT_API_KEY }}" >> .env
           echo "PREFECT_WORKSPACE=${{ secrets.PREFECT_WORKSPACE }}" >> .env
+          echo "AWS_ACCESS_KEY_ID=${{ secrets.AWS_ACCESS_KEY_ID }}" >> .env
+          echo "AWS_SECRET_ACCESS_KEY=${{ secrets.AWS_SECRET_ACCESS_KEY }}" >> .env
           echo "AWS_ACCOUNT_ID=${{ secrets.AWS_ACCOUNT_ID }}" >> .env
+          echo "AWS_DEFAULT_REGION=${{ secrets.AWS_DEFAULT_REGION }}" >> .env
+          echo "AWS_REGION=${{ secrets.AWS_REGION }}" >> .env
           echo "TF_VAR_ecr_repository_name=${{ secrets.TF_VAR_ecr_repository_name }}" >> .env
           echo "TF_VAR_aws_region=${{ secrets.TF_VAR_aws_region }}" >> .env
           echo "TF_VAR_mlflow_models_bucket=${{ secrets.TF_VAR_mlflow_models_bucket }}" >> .env
           echo "TF_VAR_prediction_bucket=${{ secrets.TF_VAR_prediction_bucket }}" >> .env
 
-      # Step 6: Build the Docker Image
+      # Step 5: Build the Docker Image
       - name: Build Docker Image
         run: |
           docker build -f Dockerfile -t ${{ secrets.TF_VAR_ecr_repository_name }}:latest .
 
-      # Step 7: Log in to Amazon ECR
+      # Step 6: Log in to Amazon ECR
       - name: Log in to Amazon ECR
         run: |
           aws ecr get-login-password --region ${{ secrets.AWS_REGION }} | docker login --username AWS --password-stdin ${{ secrets.AWS_ACCOUNT_ID }}.dkr.ecr.${{ secrets.AWS_REGION }}.amazonaws.com
 
-      # Step 8: Tag the Docker Image
+      # Step 7: Tag the Docker Image
       - name: Tag Docker Image
         run: |
           docker tag ${{ secrets.TF_VAR_ecr_repository_name }}:latest ${{ secrets.AWS_ACCOUNT_ID }}.dkr.ecr.${{ secrets.AWS_REGION }}.amazonaws.com/${{ secrets.TF_VAR_ecr_repository_name }}:latest
 
-      # Step 9: Push the Docker Image to ECR
+      # Step 8: Push the Docker Image to ECR
       - name: Push Docker Image to ECR
         run: |
           docker push ${{ secrets.AWS_ACCOUNT_ID }}.dkr.ecr.${{ secrets.AWS_REGION }}.amazonaws.com/${{ secrets.TF_VAR_ecr_repository_name }}:latest
 
-      # Step 10: Run Docker Compose to bring up services
+      # Step 9: Run Docker Compose to bring up services
       - name: Run Docker Compose
         run: |
           docker compose up -d

docker-compose.yaml

@@ -1,4 +1,4 @@
-version: '3.8'
+version: '3.9'
 
 services:
   mlflow:
@@ -67,7 +67,7 @@ services:
       - "8081:8080"  # Changed to avoid port conflict with app service
     networks:
       - back-tier
-      - front-tier  
+      - front-tier
 
   grafana:
     image: grafana/grafana
@@ -89,6 +89,6 @@ services:
 networks:
   front-tier:
   back-tier:
-  
+
 volumes:
   grafana_data: {}