TF_CAPI_EXPORT extern TFE_ContextDevicePlacementPolicy
TFE_ContextGetDevicePlacementPolicy(TFE_Context* ctx);

// A tensorflow.ServerDef specifies remote workers (in addition to the current
// workers name). Operations created in this context can then be executed on
// any of these remote workers by setting an appropriate device.
//
// If the following is set, all servers identified by the
// ServerDef must be up when the context is created.

// 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),
	}

COPY ./app /code/app

# (1)!
CMD ["fastapi", "run", "app/main.py", "--port", "80", "--workers", "4"]
```

1. Hier verwenden wir die `--workers` Befehlszeilenoption, um die Anzahl der Worker auf 4 festzulegen.

Hier sind einige Beispiele, wann das sinnvoll sein könnte:

#### Eine einfache Anwendung { #a-simple-app }

Embedding a Java runtime in the distribution would provide some benefits, such as allowing the Launcher, Daemon and Workers to run on it, removing the prerequisite of an installed Java runtime.
However, this does not fully remove the prerequisite, as the Wrapper itself would still need an installed Java runtime to execute.

                "%windows.java26.openjdk.64bit%",
            )
        val expectedInstallationPaths = (if (os == Os.WINDOWS) windowsPaths else linuxPaths).joinToString(",")
        return listOf(
            "-Dorg.gradle.workers.max=%maxParallelForks%",
            "-PmaxParallelForks=%maxParallelForks% $PLUGINS_PORTAL_URL_OVERRIDE -Dscan.value.tcPipeline=master -s",
            "%additional.gradle.parameters%",

        subprojectName.contains("native") ||
            // Include precondition-tester here so we understand that tests do run on macOS intel as well
            subprojectName in listOf("file-watching", "snapshots", "workers", "logging", "precondition-tester")
    } else {
        true

对于 FastAPI 应用程序，使用像 `fastapi` 命令（运行 Uvicorn）这样的服务器程序，在**一个进程**中运行一次就可以同时为多个客户端提供服务。

但在许多情况下，您会希望同时运行多个工作进程。

### 多进程 - Workers { #multiple-processes-workers }

如果您的客户端数量多于单个进程可以处理的数量（例如，如果虚拟机不是太大），并且服务器的 CPU 中有 **多个核心**，那么您可以让 **多个进程** 同时运行同一个应用程序，并在它们之间分发所有请求。

当您运行同一 API 程序的**多个进程**时，它们通常称为 **workers**。

### 工作进程和端口 { #worker-processes-and-ports }

还记得文档 [关于 HTTPS](https.md) 中只有一个进程可以侦听服务器中的端口和 IP 地址的一种组合吗？

Але часто ви захочете запускати кілька процесів-працівників одночасно.

### Кілька процесів - працівники { #multiple-processes-workers }

	if err != nil {
		decomLogIf(ctx, fmt.Errorf("invalid workers value err: %v, defaulting to %d", err, len(pool.sets)))
		workerSize = len(pool.sets)
	}

	// Each decom worker needs one List() goroutine/worker
	// add that many extra workers.
	workerSize += len(pool.sets)

	wk, err := workers.New(workerSize)
	if err != nil {
		return err
	}

	var vc *versioning.Versioning

	"github.com/minio/minio-go/v7/pkg/tags"
	"github.com/minio/minio/internal/crypto"
	xhttp "github.com/minio/minio/internal/http"
	"github.com/minio/minio/internal/kms"
	"github.com/minio/pkg/v3/env"
	"github.com/minio/pkg/v3/workers"
)

// keyrotate:
//   apiVersion: v1
//   bucket: BUCKET
//   prefix: PREFIX
//   encryption:
//     type: sse-s3 # valid values are sse-s3 and sse-kms
//     key: <new-kms-key> # valid only for sse-kms