## Run Gunicorn with Uvicorn Workers

Then you can run Gunicorn with:

<div class="termy">

```console
$ gunicorn main:app --workers 4 --worker-class uvicorn.workers.UvicornWorker --bind 0.0.0.0:80

[19499] [INFO] Starting gunicorn 20.1.0
[19499] [INFO] Listening at: http://0.0.0.0:80 (19499)
[19499] [INFO] Using worker: uvicorn.workers.UvicornWorker
[19511] [INFO] Booting worker with pid: 19511

- login: Smlep
  count: 11
  avatarUrl: https://avatars.githubusercontent.com/u/16785985?v=4
  url: https://github.com/Smlep
- login: hard-coders
  count: 10
  avatarUrl: https://avatars.githubusercontent.com/u/9651103?u=95db33927bbff1ed1c07efddeb97ac2ff33068ed&v=4
  url: https://github.com/hard-coders
- login: alejsdev
  count: 10
  avatarUrl: https://avatars.githubusercontent.com/u/90076947?u=1ee3a9fbef27abc9448ef5951350f99c7d76f7af&v=4

  - Deployment:
    - deployment/index.md
    - deployment/versions.md
    - deployment/https.md
    - deployment/manually.md
    - deployment/concepts.md
    - deployment/cloud.md
    - deployment/server-workers.md
    - deployment/docker.md
  - How To - Recipes:
    - how-to/index.md
    - how-to/general.md
    - how-to/graphql.md
    - how-to/custom-request-and-route.md
    - how-to/conditional-openapi.md

Gunicornを以下のように起動させることができます:

<div class="termy">

```console
$ gunicorn main:app --workers 4 --worker-class uvicorn.workers.UvicornWorker --bind 0.0.0.0:80

[19499] [INFO] Starting gunicorn 20.1.0
[19499] [INFO] Listening at: http://0.0.0.0:80 (19499)
[19499] [INFO] Using worker: uvicorn.workers.UvicornWorker
[19511] [INFO] Booting worker with pid: 19511
[19513] [INFO] Booting worker with pid: 19513

    <img src="https://github.com/tiangolo/fastapi/workflows/Test/badge.svg?event=push&branch=master" alt="Test">
</a>
<a href="https://coverage-badge.samuelcolvin.workers.dev/redirect/tiangolo/fastapi" target="_blank">
    <img src="https://coverage-badge.samuelcolvin.workers.dev/tiangolo/fastapi.svg" alt="Əhatə">
</a>
<a href="https://pypi.org/project/fastapi" target="_blank">

    <img src="https://github.com/tiangolo/fastapi/workflows/Test/badge.svg?event=push&branch=master" alt="Test">
</a>
<a href="https://coverage-badge.samuelcolvin.workers.dev/redirect/tiangolo/fastapi" target="_blank">
    <img src="https://coverage-badge.samuelcolvin.workers.dev/tiangolo/fastapi.svg" alt="Coverage">
</a>
<a href="https://pypi.org/project/fastapi" target="_blank">

    <img src="https://github.com/tiangolo/fastapi/workflows/Test/badge.svg?event=push&branch=master" alt="Test">
</a>
<a href="https://coverage-badge.samuelcolvin.workers.dev/redirect/tiangolo/fastapi" target="_blank">
    <img src="https://coverage-badge.samuelcolvin.workers.dev/tiangolo/fastapi.svg" alt="Coverage">
</a>
<a href="https://pypi.org/project/fastapi" target="_blank">

像 Kubernetes 这样的分布式容器管理系统通常有一些集成的方法来处理**容器的复制**，同时仍然支持传入请求的**负载均衡**。 全部都在**集群级别**。

在这些情况下，你可能希望从头开始构建一个 **Docker 镜像**，如[上面所解释](#dockerfile)的那样，安装依赖项并运行 **单个 Uvicorn 进程**，而不是运行 Gunicorn 和 Uvicorn workers这种。


### 负载均衡器

使用容器时，通常会有一些组件**监听主端口**。 它可能是处理 **HTTPS** 的 **TLS 终止代理** 或一些类似的工具的另一个容器。

由于该组件将接受请求的**负载**并（希望）以**平衡**的方式在worker之间分配该请求，因此它通常也称为**负载均衡器**。

!!! tip

To see the difference, imagine the following story about burgers:

### Concurrent Burgers

You go with your crush to get fast food, you stand in line while the cashier takes the orders from the people in front of you. 😍

<img src="/img/async/concurrent-burgers/concurrent-burgers-01.png" class="illustration">

Then it's your turn, you place your order of 2 very fancy burgers for your crush and you. 🍔🍔

    <img src="https://github.com/tiangolo/fastapi/workflows/Test/badge.svg?event=push&branch=master" alt="Test">
</a>
<a href="https://coverage-badge.samuelcolvin.workers.dev/redirect/tiangolo/fastapi" target="_blank">
    <img src="https://coverage-badge.samuelcolvin.workers.dev/tiangolo/fastapi.svg" alt="Coverage">
</a>
<a href="https://pypi.org/project/fastapi" target="_blank">