listOf(
        // We pass the 'maxParallelForks' setting as 'workers.max' to limit the maximum number of executers even
        // if multiple test tasks run in parallel. We also pass it to the Gradle build as a maximum (maxParallelForks)
        // for each test task, such that we are independent of whatever default value is defined in the build itself.
        "-Dorg.gradle.workers.max=$maxParallelForks",
        "-PmaxParallelForks=$maxParallelForks",

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**

        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
    }

        "org/gradle/swiftpm/**",
        "org/gradle/model/**",
        "org/gradle/testkit/**",
        "org/gradle/testing/**",
        "org/gradle/vcs/**",
        "org/gradle/work/**",
        "org/gradle/workers/**",
        "org/gradle/util/**", // contains Path that clashes with `org.gradle.api.model.Path` imported above. This line should not appear before "org/gradle/api/**"
    )

    val excludes = listOf("**/internal/**")

          },
          "exemplar": true,
          "expr": "sum by (server) (minio_cluster_replication_average_active_workers{job=\"$scrape_jobs\"})",
          "interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "{{server}}",
          "refId": "A"
        }
      ],
      "title": "Avg. Active Workers",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",

  server_def.set_task_index(1);
  std::unique_ptr<tensorflow::GrpcServer> worker_server1;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server1)
                  .ok());
  ASSERT_TRUE(worker_server1->Start().ok());

  server_def.set_task_index(2);
  std::unique_ptr<tensorflow::GrpcServer> worker_server2;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(

        val expectedInstallationPaths = if (os == Os.WINDOWS) windowsPaths else linuxPaths

	totalFailedCount    MetricName = "total_failed_count"
	totalFailedBytes    MetricName = "total_failed_bytes"

	currActiveWorkers  MetricName = "current_active_workers"
	avgActiveWorkers   MetricName = "average_active_workers"
	maxActiveWorkers   MetricName = "max_active_workers"
	recentBacklogCount MetricName = "recent_backlog_count"
	currInQueueCount   MetricName = "last_minute_queued_count"

## Imagem Oficial do Docker com Gunicorn - Uvicorn

Há uma imagem oficial do Docker que inclui o Gunicorn executando com trabalhadores Uvicorn, conforme detalhado em um capítulo anterior: [Server Workers - Gunicorn com Uvicorn](server-workers.md){.internal-link target=_blank}.

// Return a shape op fetching the shape of `a`.
TFE_Op* ShapeOp(TFE_Context* ctx, TFE_TensorHandle* a);

// Return an allreduce op adding up input tensor `in` from `group_size` workers.
TFE_Op* AllReduceOp(TFE_Context* ctx, TFE_TensorHandle* in, int group_size);

// Return a SendOp op `op_name` with send input tensor `in` and attributes
// `send_device`, `recv_device`, and `send_device_incarnation` set.