uses: actions/upload-artifact@v3
        with:
          name: coverage-html
          path: htmlcov

  # https://github.com/marketplace/actions/alls-green#why
  check:  # This job does nothing and is only used for the branch protection
    if: always()
    needs:
      - coverage-combine
    runs-on: ubuntu-latest
    steps:
      - name: Dump GitHub context
        env:

Sie erfordern in der Regel komplexere Konfigurationen und einen Nachrichten-/Job-Queue-Manager wie RabbitMQ oder Redis, ermöglichen Ihnen jedoch die Ausführung von Hintergrundtasks in mehreren Prozessen und insbesondere auf mehreren Servern.

    done_translation_message = f"~There's a new translation PR to be reviewed: #{pr.number} by @{pr.user.login}~ Good job! This is done. 🍰☕"

    # Normally only one language, but still
    for lang in langs:
        if lang not in lang_to_discussion_map:
            log_message = f"Could not find discussion for language: {lang}"

### Example Tools to Run at Startup

Some examples of the tools that can do this job are:

* Docker
* Kubernetes
* Docker Compose
* Docker in Swarm Mode
* Systemd
* Supervisor
* Handled internally by a cloud provider as part of their services
* Others...

      - master
  pull_request:
    types:
      - opened
      - synchronize
jobs:
  changes:
    runs-on: ubuntu-latest
    # Required permissions
    permissions:
      pull-requests: read
    # Set job outputs to values from filter step
    outputs:
      docs: ${{ steps.filter.outputs.docs }}
    steps:
    - uses: actions/checkout@v4
    # For pull requests it's not necessary to checkout the code but for master it is

* **PGAdmin** PostgreSQL database admin tool'u, PHPMyAdmin ve MySQL ile kolayca değiştirilebilir.
* **Flower** ile Celery job'larını monitörleme.
* **Traefik** ile backend ve frontend arasında yük dengeleme, böylece her ikisini de aynı domain altında, path ile ayrılmış, ancak farklı kapsayıcılar tarafından sunulabilirsiniz.

They tend to require more complex configurations, a message/job queue manager, like RabbitMQ or Redis, but they allow you to run background tasks in multiple processes, and especially, in multiple servers.

    author_link: https://medium.com/@mike.p.moritz
    link: https://medium.com/@mike.p.moritz/using-docker-compose-to-deploy-a-lightweight-python-rest-api-with-a-job-queue-37e6072a209b
    title: Using Docker Compose to deploy a lightweight Python REST API with a job queue
  - author: '@euri10'
    author_link: https://gitlab.com/euri10
    link: https://gitlab.com/euri10/fastapi_cheatsheet

* 👷 Update GitHub Action latest-changes. PR [#3573](https://github.com/tiangolo/fastapi/pull/3573) by [@tiangolo](https://github.com/tiangolo).
* 👷 Rename and clarify CI workflow job names. PR [#3570](https://github.com/tiangolo/fastapi/pull/3570) by [@tiangolo](https://github.com/tiangolo).

In this scenario, each one of the cleaners (including you) would be a processor, doing their part of the job.

And as most of the execution time is taken by actual work (instead of waiting), and the work in a computer is done by a <abbr title="Central Processing Unit">CPU</abbr>, they call these problems "CPU bound".

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