user  nginx;
worker_processes  auto;

error_log  /var/log/nginx/error.log warn;
pid        /var/run/nginx.pid;


events {
    worker_connections  1024;
}


http {
    include       /etc/nginx/mime.types;
    default_type  application/octet-stream;

    log_format  main  '$remote_addr - $remote_user [$time_local] "$request" '
                      '$status $body_bytes_sent "$http_referer" '

                                      bool clear_existing_contexts = false) = 0;

  // Initializes context for the local worker and no contexts will be created
  // for remote workers. Currently this only works for resetting context.
  // TODO(b/289445025): Consider removing this when we find a proper fix.
  virtual Status InitializeLocalOnlyContext(const ServerDef& server_def,

```console
$ pip install "hypercorn[trio]"
---> 100%
```

</div>

### Run with Trio

Then you can pass the command line option `--worker-class` with the value `trio`:

<div class="termy">

```console
$ hypercorn main:app --worker-class trio
```

</div>

And that will start Hypercorn with your app using Trio as the backend.

user  nginx;
worker_processes  auto;

error_log  /var/log/nginx/error.log warn;
pid        /var/run/nginx.pid;

events {
    worker_connections  4096;
}

http {
    include       /etc/nginx/mime.types;
    default_type  application/octet-stream;

    log_format  main  '$remote_addr - $remote_user [$time_local] "$request" '
                      '$status $body_bytes_sent "$http_referer" '

* **SQLAlchemy** models (Flask dan bağımsızdır. Celery worker'ları ile kullanılabilir).
* Kullanıcılar için temel başlangıç ​​modeli (gerektiği gibi değiştirin ve kaldırın).
* **Alembic** migration.
* **CORS** (Cross Origin Resource Sharing).
* **Celery** worker'ları ile backend içerisinden seçilen işleri çalıştırabilirsiniz.

* **安全密码**，默认使用密码哈希
* **JWT 令牌**身份验证
* **SQLAlchemy** 模型（独立于 Flask 扩展，可直接用于 Celery Worker）
* 基础的用户模型（可按需修改或删除）
* **Alembic** 迁移
* **CORS**（跨域资源共享）
* **Celery** Worker 可从后端其它部分有选择地导入并使用模型和代码
* REST 后端测试基于 Pytest，并与 Docker 集成，可独立于数据库实现完整的 API 交互测试。因为是在 Docker 中运行，每次都可从头构建新的数据存储（使用 ElasticSearch、MongoDB、CouchDB 等数据库，仅测试 API 运行）
* Python 与 **Jupyter Kernels** 集成，用于远程或 Docker 容器内部开发，使用 Atom Hydrogen 或 Visual Studio Code 的 Jupyter 插件
* **Vue** 前端：

        }

        def stylesheetFile = new File(getTemporaryDir(), "dslHtml.xsl")
        def xslthlConfigFile = getStylesheetHighlightFile().get().asFile.toURI()

        // TODO: Implement this with the worker API
        workerLeaseService.runAsIsolatedTask({
            source.visit { FileVisitDetails fvd ->
                if (fvd.isDirectory()) {
                    return
                }

```console
$ pip install "hypercorn[trio]"
---> 100%
```

</div>

### Запуск с Trio

Далее запустите Hypercorn с опцией `--worker-class` и аргументом `trio`:

<div class="termy">

```console
$ hypercorn main:app --worker-class trio
```

</div>

Hypercorn, в свою очередь, запустит ваше приложение использующее Trio.

```console
$ pip install "hypercorn[trio]"
---> 100%
```

</div>

### Exécuter avec Trio

Ensuite, vous pouvez passer l'option de ligne de commande `--worker-class` avec la valeur `trio` :

<div class="termy">

```console
$ hypercorn main:app --worker-class trio
```

</div>

Et cela démarrera Hypercorn avec votre application en utilisant Trio comme backend.

		// If the channel reaches above half capacity
		// we spawn more workers. The workers spawned
		// from this main worker routine will exit
		// once the channel drops below half capacity
		// and when it's been at least 30 seconds since
		// we launched a new worker.
		if mainWorker && len(h.logCh) > cap(h.logCh)/2 {
			nWorkers := atomic.LoadInt64(&h.workers)
			if nWorkers < h.maxWorkers {