Хмарний вітрильник

dvc add data.csv

dvc push

dvc pull

mlflow server --backend-store-uri sqlite:///mlflow.db --default-artifact-root ./mlruns --host 0.0.0.0 --port 5000

stages:
  - build
  - test
  - deploy

build_job:
  stage: build
  script:
    - echo "Build stage..."
    - mvn package

test_job:
  stage: test
  script:
    - echo "Test stage..."
    - mvn test

deploy_job:
  stage: deploy
  script:
    - echo "Deploy stage..."
    - ./deploy.sh

build_job:
  stage: build
  cache:
    key: ${CI_COMMIT_REF_SLUG}
    paths:
      - .m2/repository
  script:
    - mvn package

test_job_a:
  stage: test
  script:
    - echo "Running Test A..."
    - mvn test -Dtest=TestA

test_job_b:
  stage: test
  script:
    - echo "Running Test B..."
    - mvn test -Dtest=TestB

deploy_job:
  stage: deploy
  script:
    - echo "Deploy to prod with token: $DEPLOY_TOKEN"
    - ./deploy.sh
  only:
    - main

deploy_production:
  stage: deploy
  script:
    - echo "Deploying to production..."
    - ./prod_deploy.sh
  when: manual
  only:
    - main

stages:
  - build
  - deploy

variables:
  IMAGE_NAME: myapp:${CI_COMMIT_SHORT_SHA}

build:
  stage: build
  script:
    - docker build -t $IMAGE_NAME .
    - echo $CI_REGISTRY_PASSWORD | docker login -u $CI_REGISTRY_USER --password-stdin $CI_REGISTRY
    - docker push $IMAGE_NAME

deploy:
  stage: deploy
  only:
    - main
  script:
    - ssh user@server 'docker pull $IMAGE_NAME && docker-compose up -d'