"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",

// 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),
	}

          },
          "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",

          "expr": "sum by (server) (minio_cluster_replication_average_active_workers{job=\"$scrape_jobs\"})",
          "interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "{{server}}",
          "refId": "A"
        }
      ],
      "thresholds": [],
      "timeRegions": [],
      "title": "Avg. Active Workers",
      "tooltip": {
        "shared": true,
        "sort": 0,

          "expr": "sum by (server) (minio_cluster_replication_average_active_workers{job=\"$scrape_jobs\"})",
          "interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "{{server}}",
          "refId": "A"
        }
      ],
      "thresholds": [],
      "timeRegions": [],
      "title": "Avg. Active Workers",
      "tooltip": {
        "shared": true,
        "sort": 0,

  //  Worker0:
  //  cluster { job { name: "client" tasks { key: 0 value: "localhost:14526" } }
  //            job { name: "worker" tasks { key: 0 value: "localhost:14522" } }
  //          } job_name: "worker" protocol: "grpc"
  //

  // Create `worker_1` using single host server def `worker_1_server_def`.
  tensorflow::ServerDef worker_1_server_def =
      CreateSingleHostServerDef(cluster_server_def, 1);

Я подготовил для вас Docker-образ, в который включён Gunicorn управляющий процессами (воркерами) Uvicorn, в соответствии с концепциями рассмотренными в предыдущей главе: [Рабочие процессы сервера (воркеры) - Gunicorn совместно с Uvicorn](server-workers.md){.internal-link target=_blank}.

Этот образ может быть полезен для ситуаций описанных тут: [Множество процессов внутри контейнера для особых случаев](#_11).

            // all the tasks, then the thread will block waiting for all
            // those subtasks to finish.
            // This ensures the number of running workers is no more than
            // the defined parallism, while making sure the pool will not
            // be exhausted
            //
            return new ThreadPoolExecutor(

build:rbe_base --jobs=800
build:rbe_base --remote_executor=grpcs://remotebuildexecution.googleapis.com
build:rbe_base --remote_timeout=3600
build:rbe_base --spawn_strategy=remote,worker,standalone,local
# Attempt to minimize the amount of data transfer between bazel and the remote
# workers:
build:rbe_base --remote_download_toplevel
test:rbe_base --test_env=USER=anon

# TODO(kanglan): Check if we want to merge rbe_linux into rbe_linux_cpu.

	vars := mux.Vars(r)

	var args dsync.LockArgs
	var lockers []dsync.NetLocker
	for _, path := range strings.Split(vars["paths"], ",") {
		if path == "" {
			continue
		}
		args.Resources = append(args.Resources, path)
	}

	for _, lks := range z.serverPools[0].erasureLockers {
		lockers = append(lockers, lks...)
	}

	for _, locker := range lockers {
		locker.ForceUnlock(ctx, args)
	}
}