[Feat/19] 진행률 및 Redis pub/sub 연결 구현 #29

Workflow file for this run

	name: Build and Deploy

	on:
	push:
	branches:
	- main
	- master
	pull_request:
	branches:
	- main
	- master
	workflow_dispatch:
	inputs:
	environment:
	description: 'Deployment environment'
	required: true
	default: 'production'
	type: choice
	options:
	- production

	env:
	AWS_REGION: ${{ secrets.AWS_REGION \|\| 'ap-northeast-2' }}
	CODE_DEPLOY_APPLICATION_NAME: ${{ secrets.CODE_DEPLOY_APPLICATION_NAME }}
	CODE_DEPLOY_DEPLOYMENT_GROUP: ${{ secrets.CODE_DEPLOY_DEPLOYMENT_GROUP }}

	jobs:
	test:
	name: Run Tests
	runs-on: ubuntu-latest
	steps:
	- name: Checkout code
	uses: actions/checkout@v4
	with:
	submodules: recursive
	token: ${{ secrets.PAT_TOKEN \|\| secrets.GITHUB_TOKEN }}

	- name: Set up Python
	uses: actions/setup-python@v5
	with:
	python-version: '3.10'
	cache: 'pip'

	- name: Verify Python version
	run: \|
	python --version
	python -c "import sys; assert sys.version_info[:2] == (3, 10), f'Expected Python 3.10, got {sys.version}'"

	- name: Clean up disk space
	run: \|
	# 디스크 공간 확인
	df -h
	# pip 캐시 정리
	pip cache purge \|\| true
	# 기타 캐시 정리
	sudo rm -rf /usr/share/dotnet
	sudo rm -rf /opt/ghc
	sudo rm -rf /usr/local/share/boost
	sudo rm -rf "$AGENT_TOOLSDIRECTORY"
	# 디스크 공간 재확인
	df -h

	- name: Install numpy first (pre-built wheel)
	run: \|
	python -m pip install --upgrade pip setuptools wheel
	python -m pip install --no-cache-dir --only-binary numpy "numpy>=1.26,<2.0"

	- name: Install dependencies
	run: \|
	python -m pip install --no-cache-dir --no-build-isolation -r echoshot_ai_server/requirements.txt

	- name: Install PyTorch (CPU for testing)
	run: \|
	pip install --no-cache-dir torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1

	# 테스트 비활성화화
	# - name: Run tests
	# run: \|
	# pytest echoshot_ai_server/ -v --tb=short \|\| true
	# continue-on-error: true

	build-and-deploy:
	name: Build and Deploy
	runs-on: ubuntu-latest
	needs: test
	if: github.event_name == 'push' && (github.ref == 'refs/heads/main' \|\| github.ref == 'refs/heads/master')
	steps:
	- name: Checkout code
	uses: actions/checkout@v4
	with:
	submodules: recursive
	token: ${{ secrets.PAT_TOKEN \|\| secrets.GITHUB_TOKEN }}

	# 1) Docker Hub 로그인
	- name: Login to Docker Hub
	uses: docker/login-action@v3
	with:
	username: ${{ secrets.DOCKERHUB_USERNAME }}
	password: ${{ secrets.DOCKERHUB_TOKEN }}

	# 2) 베이스 이미지 Pull (OpenCV CUDA 포함)
	- name: Pull base image
	run: \|
	echo "베이스 이미지를 pull합니다..."
	docker pull ${{ secrets.DOCKERHUB_USERNAME }}/opencv-cuda-t4:4.10.0 \|\| echo "베이스 이미지가 없습니다. 먼저 빌드해야 합니다."

	# 3) GPU 버전 Docker 이미지 빌드 (베이스 이미지 사용)
	- name: Build GPU Docker image
	run: \|
	echo "GPU 버전 애플리케이션 이미지를 빌드합니다..."
	docker build \
	--build-arg DOCKERHUB_USERNAME=${{ secrets.DOCKERHUB_USERNAME }} \
	--build-arg OPENCV_VERSION=4.10.0 \
	-t ${{ secrets.DOCKERHUB_USERNAME }}/echoshot-worker:gpu-fsrcnn-t4 \
	-t ${{ secrets.DOCKERHUB_USERNAME }}/echoshot-worker:latest \
	.

	# 4) Docker Hub로 푸시
	- name: Push Docker images
	run: \|
	echo "이미지를 Docker Hub에 푸시합니다..."
	docker push ${{ secrets.DOCKERHUB_USERNAME }}/echoshot-worker:gpu-fsrcnn-t4
	docker push ${{ secrets.DOCKERHUB_USERNAME }}/echoshot-worker:latest

	- name: Configure AWS credentials
	uses: aws-actions/configure-aws-credentials@v4
	with:
	aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
	aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
	aws-region: ${{ env.AWS_REGION }}

	- name: Create deployment package
	run: \|
	# 서브모듈 초기화 확인
	if [ -d "EchoShotX-ai-private" ]; then
	echo "서브모듈이 확인되었습니다."
	ls -la EchoShotX-ai-private/
	else
	echo "WARNING: 서브모듈이 없습니다."
	fi

	mkdir -p deploy-package
	cp -r echoshot_ai_server deploy-package/
	cp -r scripts deploy-package/
	cp -r EchoShotX-ai-private deploy-package/ # 서브모듈 포함
	cp appspec.yml deploy-package/
	cp Dockerfile deploy-package/
	cp docker-compose.yml deploy-package/ # Docker Compose 파일 포함
	cd deploy-package
	zip -r ../deploy-package.zip .
	cd ..

	- name: Upload to S3
	run: \|
	# S3 버킷 이름 설정 (Secret이 있으면 사용, 없으면 자동 생성)
	if [ -n "${{ secrets.CODEDEPLOY_S3_BUCKET }}" ]; then
	S3_BUCKET="${{ secrets.CODEDEPLOY_S3_BUCKET }}"
	else
	S3_BUCKET="codedeploy-echoshot-$(echo ${{ env.AWS_REGION }} \| tr -d '-')"
	# S3 버킷이 없으면 생성 (선택적)
	aws s3 mb s3://$S3_BUCKET --region ${{ env.AWS_REGION }} \|\| true
	fi

	S3_KEY="deployments/echoshot-worker/${{ github.sha }}.zip"

	# 배포 패키지 업로드
	aws s3 cp deploy-package.zip s3://$S3_BUCKET/$S3_KEY

	echo "S3_BUCKET=$S3_BUCKET" >> $GITHUB_ENV
	echo "S3_KEY=$S3_KEY" >> $GITHUB_ENV

	- name: Create CodeDeploy deployment
	id: create-deployment
	run: \|
	DEPLOYMENT_ID=$(aws deploy create-deployment \
	--application-name ${{ env.CODE_DEPLOY_APPLICATION_NAME }} \
	--deployment-group-name ${{ env.CODE_DEPLOY_DEPLOYMENT_GROUP }} \
	--s3-location bucket=${{ env.S3_BUCKET }},key=${{ env.S3_KEY }},bundleType=zip \
	--query 'deploymentId' \
	--output text)

	echo "deployment_id=$DEPLOYMENT_ID" >> $GITHUB_OUTPUT
	echo "Deployment created: $DEPLOYMENT_ID"

	- name: Wait for deployment
	run: \|
	DEPLOYMENT_ID="${{ steps.create-deployment.outputs.deployment_id }}"
	MAX_WAIT=1800 # 30분
	ELAPSED=0
	INTERVAL=30

	while [ $ELAPSED -lt $MAX_WAIT ]; do
	STATUS=$(aws deploy get-deployment \
	--deployment-id $DEPLOYMENT_ID \
	--query 'deploymentInfo.status' \
	--output text)

	echo "Deployment status: $STATUS"

	if [ "$STATUS" = "Succeeded" ]; then
	echo "Deployment succeeded!"
	exit 0
	elif [ "$STATUS" = "Failed" ] \|\| [ "$STATUS" = "Stopped" ]; then
	echo "Deployment failed!"
	exit 1
	fi

	sleep $INTERVAL
	ELAPSED=$((ELAPSED + INTERVAL))
	done

	echo "Deployment timeout!"
	exit 1

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[Feat/19] 진행률 및 Redis pub/sub 연결 구현 #29

Workflow file

[Feat/19] 진행률 및 Redis pub/sub 연결 구현 #29

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Workflow file for this run