入门：使用和部署模型

name: Deploy Model (Template)

on:
  # the workflow is triggered whenever a tag is pushed to the repository
  push:
    tags:
      - '*'
jobs:
  # This job parses the git tag with the GTO GitHub Action to identify model registry actions. You can most likely copy this job and what precedes it verbatim into your own workflow
  parse:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: 'Parse GTO tag'
        id: gto
        uses: iterative/gto-action@v2
    outputs:
      event: ${{ steps.gto.outputs.event }}
      name: ${{ steps.gto.outputs.name }}
      stage: ${{ steps.gto.outputs.stage }}
      version: ${{ steps.gto.outputs.version }}

  deploy-model:
    needs: parse
    # using the outputs from the "parse" job, we run this job only for actions
    # in the model registry and only when the model was assigned to a stage called "prod"
    # You can replace the conditions with your own
    if:
      ${{ needs.parse.outputs.event == 'assignment' && needs.parse.outputs.stage
      == 'prod' }}
    runs-on: ubuntu-latest
    steps:
      - uses: iterative/setup-dvc@v1
      # this step uses DVC to download the model from the remote repository and deploys the model
      # Model deployment is mocked here as it is specific to each deployment environment
      # The DVC Studio token is used to avoid having to store specific remote storage credentials on GitHub
      - name: Get Model For Deployment
        run: |
          dvc config --global studio.token ${{ secrets.DVC_STUDIO_TOKEN }}
          dvc artifacts get  ${{ github.server_url }}/${{ github.repository }} ${{ needs.parse.outputs.name }} --rev ${{ needs.parse.outputs.version }}
          echo "The right model is available and you can use the rest of this command to deploy it. Good job!"

# Deploy Model (Template)

workflow:
  rules:
    # Run the pipeline whenever a tag is pushed to the repository
    - if: $CI_COMMIT_TAG

parse:
  # This job parses the model tag to identify model registry actions
  # You can most likely copy this job and what precedes it verbatim into your own workflow
  image: python:3.11-slim
  script:
    # Install GTO to parse model tags
    - pip install gto
    # This job parses the model tags to identify model registry actions
    - echo "CI_COMMIT_TAG - ${CI_COMMIT_TAG}"
    - echo MODEL_NAME="$(gto check-ref ${CI_COMMIT_TAG} --name)" >> parse.env
    - echo MODEL_VERSION="$(gto check-ref ${CI_COMMIT_TAG} --version)" >>
      parse.env
    - echo MODEL_EVENT="$(gto check-ref ${CI_COMMIT_TAG} --event)" >> parse.env
    - echo MODEL_STAGE="$(gto check-ref ${CI_COMMIT_TAG} --stage)" >> parse.env
    # Print variables saved to parse.env
    - cat parse.env
  artifacts:
    reports:
      dotenv: parse.env

deploy-model:
  needs:
    - job: parse
      artifacts: true
  image: python:3.11-slim
  script:
    # Check if the model is assigned to prod (variables from parse.env are only available in the 'script' section)
    # You can replace the conditions with your own
    - if [[ $MODEL_EVENT == 'assignment' && $MODEL_STAGE == 'prod' ]]; then echo
      "Deploy model"; else exit 1; fi
    # Install DVC
    - pip install dvc
    # Build commands to download and deploy the model
    - dvc config --global studio.token ${DVC_STUDIO_TOKEN}
    - dvc artifacts get  ${CI_REPOSITORY_URL} ${MODEL_NAME} --rev
      ${MODEL_VERSION}
    - echo "The right model is available and you can use the rest of this
      command to deploy it. Good job!"

on:
  # the workflow is triggered whenever a tag is pushed to the repository
  push:
    tags:
      - '*'

workflow:
  rules:
    # the workflow is triggered whenever a tag is pushed to the repository
    - if: $CI_COMMIT_TAG

# This job parses the git tag with the GTO GitHub Action to identify model registry actions
parse:
  runs-on: ubuntu-latest
  steps:
    - uses: actions/checkout@v3
    - name: 'Parse GTO tag'
      id: gto
      uses: iterative/gto-action@v2
  outputs:
    event: ${{ steps.gto.outputs.event }}
    name: ${{ steps.gto.outputs.name }}
    stage: ${{ steps.gto.outputs.stage }}
    version: ${{ steps.gto.outputs.version }}

parse:
  # This job parses the model tag to identify model registry actions
  image: python:3.11-slim
  script:
    # Install GTO to parse model tags
    - pip install gto
    # This job parses the model tags to identify model registry actions
    - echo "CI_COMMIT_TAG - ${CI_COMMIT_TAG}"
    - echo MODEL_NAME="$(gto check-ref ${CI_COMMIT_TAG} --name)" >> parse.env
    - echo MODEL_VERSION="$(gto check-ref ${CI_COMMIT_TAG} --version)" >>
      parse.env
    - echo MODEL_EVENT="$(gto check-ref ${CI_COMMIT_TAG} --event)" >> parse.env
    - echo MODEL_STAGE="$(gto check-ref ${CI_COMMIT_TAG} --stage)" >> parse.env
    # Print variables saved to parse.env
    - cat parse.env
  artifacts:
    reports:
      dotenv: parse.env

deploy-model:
  needs: parse
  # using the outputs from the "parse" job, we run this job only for actions
  # in the model registry and only when the model was assigned to a stage called "prod"
  # You can replace the conditions with your own
  if:
    ${{ needs.parse.outputs.event == 'assignment' && needs.parse.outputs.stage
    == 'prod' }}

deploy-model:
  needs:
    - job: parse
      artifacts: true
  image: python:3.11-slim
  script:
    # Check if the model is assigned to prod (variables from parse.env are only available in the 'script' section)
    # You can replace the conditions with your own
    - if [[ $MODEL_EVENT == 'assignment' && $MODEL_STAGE == 'prod' ]]; then echo
      "Deploy model"; else exit 1; fi

steps:
  - uses: iterative/setup-dvc@v1
  - name: Get Model For Deployment
    run: |
      dvc config --global studio.token ${{ secrets.DVC_STUDIO_TOKEN }}
      dvc artifacts get  ${{ github.server_url }}/${{ github.repository }} ${{ needs.parse.outputs.name }} --rev ${{ needs.parse.outputs.version }}
      echo "The right model is available and you can use the rest of this command to deploy it. Good job!"

  script:

  ...

  # Install DVC
  - pip install dvc
  # Build commands to download and deploy the model
  - dvc config --global studio.token ${DVC_STUDIO_TOKEN}
  - dvc artifacts get  ${CI_REPOSITORY_URL} ${MODEL_NAME} --rev ${MODEL_VERSION}
  - echo "The right model is available and you can use the rest of this command to deploy it. Good job!"