In diesem Fall möchten Sie also **nicht** einen Prozessmanager wie Gunicorn mit Uvicorn-Workern oder Uvicorn mit seinen eigenen Uvicorn-Workern haben. Sie möchten nur einen **einzelnen Uvicorn-Prozess** pro Container haben (wahrscheinlich aber mehrere Container).

                                      int64_t init_timeout_in_ms, int retries,
                                      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,

	current     []jstream.KVS   // current block of results to be returned
	recordsRead int             // number of records read in current slice
	input       chan *queueItem // input for workers
	queue       chan *queueItem // output from workers in order
	err         error           // global error state, only touched by Reader.Read
	bufferPool  sync.Pool       // pool of []byte objects for input

					"ivy",
					"testing-native",
					"maven",
					"integ-test"
				]
			},
			{
				"parallelizationMethod":{
					"name":"TestDistribution"
				},
				"subprojects":[
					"workers",
					"model-core",
					"language-groovy",
					"build-init",
					"logging",
					"plugins-groovy",
					"kotlin-dsl",
					"plugins-java",
					"samples",
					"plugin-development",

	current      [][]string       // current block of results to be returned
	recordsRead  int              // number of records read in current slice
	input        chan *queueItem  // input for workers
	queue        chan *queueItem  // output from workers in order
	err          error            // global error state, only touched by Reader.Read
	bufferPool   sync.Pool        // pool of []byte objects for input

// newBatchJobPool creates a pool of job manifest workers of specified size
func newBatchJobPool(ctx context.Context, o ObjectLayer, workers int) *BatchJobPool {
	jpool := &BatchJobPool{
		ctx:          ctx,
		objLayer:     o,
		jobCh:        make(chan *BatchJobRequest, 10000),
		workerKillCh: make(chan struct{}, workers),
		jobCancelers: make(map[string]context.CancelFunc),
	}

可以通过多种方法来实现这一目标，我将在接下来的章节中向您详细介绍具体策略，例如在谈论 Docker 和容器时。

要考虑的主要限制是必须有一个**单个**组件来处理**公共IP**中的**端口**。 然后它必须有一种方法将通信**传输**到复制的**进程/worker**。

以下是一些可能的组合和策略：

* **Gunicorn** 管理 **Uvicorn workers**
     * Gunicorn 将是监听 **IP** 和 **端口** 的 **进程管理器**，复制将通过 **多个 Uvicorn 工作进程** 进行
* **Uvicorn** 管理 **Uvicorn workers**
     * 一个 Uvicorn **进程管理器** 将监听 **IP** 和 **端口**，并且它将启动 **多个 Uvicorn 工作进程**
* **Kubernetes** 和其他分布式 **容器系统**

* **JWT-Token**-Authentifizierung.
* **SQLAlchemy**-Modelle (unabhängig von Flask-Erweiterungen, sodass sie direkt mit Celery-Workern verwendet werden können).
* Grundlegende Startmodelle für Benutzer (ändern und entfernen Sie nach Bedarf).
* **Alembic**-Migrationen.
* **CORS** (Cross Origin Resource Sharing).
* **Celery**-Worker, welche Modelle und Code aus dem Rest des Backends selektiv importieren und verwenden können.

* Modelos **SQLAlchemy** (independente de extensões Flask, para que eles possam ser usados com _workers_ Celery diretamente).
* Modelos básicos para usuários (modifique e remova conforme suas necessidades).
* Migrações **Alembic**.
* **CORS** (_Cross Origin Resource Sharing_ - Compartilhamento de Recursos Entre Origens).
* _Worker_ **Celery** que pode importar e usar modelos e códigos do resto do _backend_ seletivamente.

    "crossVersionTests": false
  },
  {
    "name": "worker-processes",
    "path": "platforms/core-execution/worker-processes",
    "unitTests": true,
    "functionalTests": false,
    "crossVersionTests": false
  },
  {
    "name": "worker-services",
    "path": "platforms/core-runtime/worker-services",
    "unitTests": false,
    "functionalTests": false,