Add CI/CD workflow examples and serving manifest

- ci.yaml: lint (ruff) + test (pytest) on push/PR
- train.yaml: dvc pull → train → dvc push → docker build → deploy
- dvc-data-update.yaml: data validation on .dvc file changes
- manifests/deployment.yaml: example serving Deployment + Service

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

.gitea/workflows/ci.yaml

@@ -0,0 +1,30 @@
+name: CI - Lint & Test
+
+on:
+  push:
+    branches: [main, develop]
+  pull_request:
+    branches: [main]
+
+jobs:
+  lint-and-test:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+
+      - name: Install dependencies
+        run: |
+          pip install -r requirements.txt
+          pip install ruff pytest
+
+      - name: Lint
+        run: ruff check .
+
+      - name: Test
+        run: pytest tests/ -v

.gitea/workflows/dvc-data-update.yaml

@@ -0,0 +1,40 @@
+name: DVC Data Validation
+
+on:
+  push:
+    paths:
+      - "**.dvc"
+      - "dvc.yaml"
+      - "dvc.lock"
+
+jobs:
+  validate-data:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+
+      - name: Install dependencies
+        run: |
+          pip install dvc pandas
+
+      - name: Pull updated data
+        run: dvc pull
+
+      - name: Validate data
+        run: |
+          python -c "
+          import pandas as pd
+          import sys
+
+          # TODO: 실제 데이터 경로와 검증 로직으로 수정
+          # df = pd.read_csv('data/dataset.csv')
+          # assert len(df) > 0, 'Dataset is empty'
+          # assert df.isnull().sum().sum() == 0, 'Dataset has null values'
+          print('Data validation passed')
+          "

.gitea/workflows/train.yaml

@@ -0,0 +1,75 @@
+name: Train & Deploy
+
+on:
+  push:
+    branches: [main]
+    paths:
+      - "src/**"
+      - "configs/**"
+      - "dvc.yaml"
+
+jobs:
+  train:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+
+      - name: Install dependencies
+        run: |
+          pip install -r requirements.txt
+          pip install dvc
+
+      - name: Pull data from DVC
+        run: dvc pull
+
+      - name: Train model
+        run: python src/train.py
+
+      - name: Track model with DVC
+        run: |
+          dvc add models/
+          dvc push
+
+      - name: Commit DVC metadata
+        run: |
+          git config user.name "gitea-runner"
+          git config user.email "runner@mlops"
+          git add models/*.dvc models/.gitignore
+          git diff --cached --quiet || git commit -m "Update model - $(date +%Y%m%d-%H%M%S)"
+          git push
+
+  build-and-deploy:
+    needs: train
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          ref: main
+
+      - name: Build Docker image
+        run: |
+          IMAGE_TAG="${{ github.sha }}"
+          docker build -t $REGISTRY_URL/mlops-serving:${IMAGE_TAG} .
+          docker push $REGISTRY_URL/mlops-serving:${IMAGE_TAG}
+        env:
+          REGISTRY_URL: ${{ vars.REGISTRY_URL }}
+
+      - name: Update manifest
+        run: |
+          IMAGE_TAG="${{ github.sha }}"
+          sed -i "s|image:.*mlops-serving.*|image: $REGISTRY_URL/mlops-serving:${IMAGE_TAG}|" manifests/deployment.yaml
+          git config user.name "gitea-runner"
+          git config user.email "runner@mlops"
+          git add manifests/
+          git commit -m "Deploy model ${IMAGE_TAG:0:7}"
+          git push
+        env:
+          REGISTRY_URL: ${{ vars.REGISTRY_URL }}
+      # ↑ manifests/ 변경 → ArgoCD가 자동 감지 → soo ns에 배포

README.md

@@ -163,20 +163,58 @@ dvc version
 
 ---
 
+## 5. CI/CD 워크플로우
+
+`.gitea/workflows/` 에 정의. Gitea Runner가 실행합니다.
+
+### ci.yaml — 코드 push 시 린트 + 테스트
+
+```
+trigger: push to main/develop, PR to main
+동작: ruff lint → pytest
+```
+
+### train.yaml — 모델 학습 + 배포
+
+```
+trigger: src/, configs/, dvc.yaml 변경 시
+동작:
+  1. dvc pull (NAS에서 데이터 가져오기)
+  2. python src/train.py (학습)
+  3. dvc add + dvc push (모델 → NAS)
+  4. Docker build + push (서빙 이미지)
+  5. manifests/ 이미지 태그 업데이트 → git push
+  6. → ArgoCD 자동 감지 → soo ns에 배포
+```
+
+### dvc-data-update.yaml — 데이터 변경 시 검증
+
+```
+trigger: .dvc 파일, dvc.yaml, dvc.lock 변경 시
+동작: dvc pull → 데이터 검증 스크립트 실행
+```
+
+---
+
 ## 파일 구조
 
 ```
 mlops_architecture/
 ├── .env                          # 접속 정보, 시크릿 (git 제외)
 ├── .gitignore
-├── config.yaml       # 인프라 구성 정보
-├── README.md         # 설치 가이드 (이 파일)
 ├── .dvc/                         # DVC 설정 (remote 등)
+├── .gitea/workflows/             # CI/CD 파이프라인
+│   ├── ci.yaml                   #   린트 + 테스트
+│   ├── train.yaml                #   학습 → 빌드 → 배포
+│   └── dvc-data-update.yaml      #   데이터 검증
 ├── argocd/
 │   └── application.yaml          # ArgoCD Application 정의
+├── config.yaml                   # 인프라 구성 정보
 ├── gitea-runner/
-│   └── deployment.yaml   # Gitea Runner K8s 배포 (Secret+ConfigMap+Deployment)
+│   └── deployment.yaml           # Gitea Runner K8s 배포
-└── manifests/            # K8s 배포 manifest (ArgoCD가 감시)
+├── manifests/                    # K8s 배포 manifest (ArgoCD 감시)
+│   └── deployment.yaml           #   서빙 Deployment + Service
+└── README.md                     # 설치 가이드 (이 파일)
 ```
 
 ## 설정 파일 사용법

manifests/deployment.yaml

@@ -0,0 +1,52 @@
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: mlops-serving
+  namespace: soo
+  labels:
+    app: mlops-serving
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: mlops-serving
+  template:
+    metadata:
+      labels:
+        app: mlops-serving
+    spec:
+      containers:
+        - name: serving
+          image: ${REGISTRY_URL}/mlops-serving:latest    # CI에서 자동 업데이트
+          ports:
+            - containerPort: 8080
+          resources:
+            requests:
+              cpu: 500m
+              memory: 512Mi
+            limits:
+              cpu: "2"
+              memory: 2Gi
+          # env:
+          #   - name: MODEL_PATH
+          #     value: /models/model.pt
+          # volumeMounts:
+          #   - name: model-storage
+          #     mountPath: /models
+      # volumes:
+      #   - name: model-storage
+      #     persistentVolumeClaim:
+      #       claimName: mlops-model-pvc
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: mlops-serving
+  namespace: soo
+spec:
+  selector:
+    app: mlops-serving
+  ports:
+    - port: 80
+      targetPort: 8080
+  type: ClusterIP