test: 배포 테스트

.github/workflows/github-actions.yml

@@ -0,0 +1,55 @@
+name: CI/CD
+on:
+  push:
+    branches:
+      - dev
+      - main
+
+  workflow_dispatch:
+
+jobs:
+  AI-CI:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: 체크아웃
+        uses: actions/checkout@v2
+        with:
+          python-version: 3.12.4
+
+      - name: Configure AWS credentials
+        if: ${{ github.ref == 'refs/heads/dev' || github.ref == 'refs/heads/main' }}
+        uses: aws-actions/configure-aws-credentials@v1
+        with:
+          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY }}
+          aws-secret-access-key: ${{ secrets.AWS_SECRET_KEY }}
+          aws-region: ${{ secrets.AWS_REGION }}
+
+      - name: 배포 Dir 생성
+        if: ${{ github.ref == 'refs/heads/dev' || github.ref == 'refs/heads/main' }}
+        working-directory: ./
+        run: |
+          mkdir -p deploy-ai
+          echo "${{ secrets.ENV_PY }}" | base64 --decode >> ./deploy-ai/env
+          cp ./app/* ./deploy-ai
+          cp ./services/* ./deploy-ai
+          cp ./ai_server.py ./deploy-ai
+          cp ./docker-compose.yml ./deploy-ai
+          cp ./Dockerfile ./deploy-ai
+          cp ./requirements.txt ./deploy-ai
+
+      - name: S3 업로드 및 CD 요청
+        if: ${{ github.ref == 'refs/heads/dev' || github.ref == 'refs/heads/main' }}
+        working-directory: ./
+        run: |
+          cp utils/*.sh deploy-ai
+          cp utils/appspec.yml deploy-ai
+          cd deploy-ai && zip -r deploy-ai *
+          aws s3 cp deploy-ai.zip s3://${{ secrets.AWS_BUCKET_NAME }}/deploy-ai.zip
+
+          aws deploy create-deployment \
+          --application-name ${{ secrets.AWS_CODEDEPLOY_APP_NAME }} \
+          --deployment-config-name CodeDeployDefault.AllAtOnce \
+          --deployment-group-name ${{ secrets.AWS_CODEDEPLOY_GROUP_NAME_AI }} \
+          --file-exists-behavior OVERWRITE \
+          --s3-location bucket=${{ secrets.AWS_BUCKET_NAME }},bundleType=zip,key=deploy-ai.zip

Dockerfile

@@ -0,0 +1,11 @@
+# 베이스 이미지 선택
+FROM python:3.12.4-slim
+
+# 필요한 파일 복사
+COPY . .
+
+# 필요 패키지 설치
+RUN pip install python-dotenv
+RUN pip install --no-cache-dir -r requirements.txt
+
+ENTRYPOINT ["python3", "ai_server.py"]

app/es_vecDB_search.py

@@ -2,7 +2,7 @@
 from sentence_transformers import SentenceTransformer
 
 # Elasticsearch 연결 설정
-es = Elasticsearch([{'host': 'localhost', 'port': 9200, 'scheme': 'http'}])
+es = Elasticsearch([{'host': 'elasticsearch', 'port': 9200, 'scheme': 'http'}])
 
 # 사전 학습된 Sentence-BERT 모델 로드
 model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')

docker-compose.yml

@@ -0,0 +1,34 @@
+version: '3.8'
+
+services:
+  ai:
+    build: .
+    container_name: ai
+    restart: on-failure
+    env_file:
+      - .env
+    networks:
+      - dream
+      - datasource
+    ports:
+      - "8000:8000"
+
+  elasticsearch:
+    image: docker.elastic.co/elasticsearch/elasticsearch:8.5.0
+    container_name: elasticsearch
+    environment:
+      - discovery.type=single-node
+      - "ES_JAVA_OPTS=-Xms512m -Xmx512m"
+    networks:
+      - datasource
+    expose:
+      - "9200"
+      - "9300"
+    volumes:
+      - esdata:/usr/share/elasticsearch/data
+
+networks:
+  dream:
+    external: true
+  datasource:
+    external: true

services/es_vecDB_save.py

@@ -2,7 +2,7 @@
 from sentence_transformers import SentenceTransformer
 
 # Elasticsearch 연결 설정
-es = Elasticsearch([{'host': 'localhost', 'port': 9200, 'scheme': 'http'}])
+es = Elasticsearch([{'host': 'elasticsearch', 'port': 9200, 'scheme': 'http'}])
 
 # 사전 학습된 Sentence-BERT 모델 로드
 model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')

utils/appspec.yml

@@ -0,0 +1,18 @@
+version: 0.0
+os: linux
+files:
+  - source: /
+    destination: /home/ubuntu/deploy-ai
+    overwrite: yes
+
+permissions:
+  - object: /
+    pattern: "**"
+    owner: ubuntu
+    group: ubuntu
+
+hooks:
+  ApplicationStart:
+    - location: deploy.sh
+      timeout: 60
+      runas: ubuntu

utils/deploy.sh

@@ -0,0 +1,52 @@
+#!/bin/bash
+
+runNetwork(){
+  # 네트워크가 존재하는지 확인
+  if [ -z "$(docker network ls | grep harpsharp)" ]
+  then
+      echo "harpsharp 네트워크를 생성합니다."
+      docker network create dream
+  else
+      echo "harpsharp 네트워크가 이미 존재합니다."
+  fi
+
+  if [ -z "$(docker network ls | grep datasource)" ]
+    then
+        echo "harpsharp 네트워크를 생성합니다."
+        docker network create datasource
+    else
+        echo "harpsharp 네트워크가 이미 존재합니다."
+    fi
+}
+
+serviceDown(){
+  if [ $(docker ps -a -q -f name=$CONTAINER_NAME) ]
+  then
+      echo "컨테이너 $CONTAINER_NAME 종료 및 삭제 중..."
+
+      IMAGE_ID=$(docker images -q $CONTAINER_NAME)
+
+      if [ "$IMAGE_ID" ]; then
+          echo "이미지 $CONTAINER_NAME 삭제 중..."
+
+      docker rmi -f $IMAGE_ID
+      fi
+  fi
+}
+
+cleanUpImages(){
+  docker rmi $(docker images -f "dangling=true" -q)
+  sudo docker system prune -af
+}
+
+
+runNetwork
+
+cd /home/ubuntu/deploy-ai
+mv env .env
+serviecDown ai
+docker-compose up -d --build
+
+cleanUpImages
+
+