To work around this, you can use the <<worker_api.adoc#tasks_parallel_worker, Worker API>> with classloader or process isolation to encapsulate the library code.
The bytecode of the worker's classpath is not modified, so the self-checks should pass.
When process isolation is used, the worker action is executed in a separate worker process that doesn't have the Gradle Java agent installed.

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.

	pods := newPodList(nil, 1, v1.PodRunning, labelMap, rsSpec, "pod")
	manager.deletePod(logger, cache.DeletedFinalStateUnknown{Key: "foo", Obj: &pods.Items[0]})

	go manager.worker(ctx)

	expected := GetKey(rsSpec, t)
	select {
	case key := <-received:
		if key != expected {
			t.Errorf("Unexpected sync all for ReplicaSet %v, expected %v", key, expected)
		}

	dsc.expectations.DeleteExpectations(logger, key)

	dsc.queue.Add(key)
}

// Run begins watching and syncing daemon sets.
func (dsc *DaemonSetsController) Run(ctx context.Context, workers int) {
	defer utilruntime.HandleCrash()

	dsc.eventBroadcaster.StartStructuredLogging(3)
	dsc.eventBroadcaster.StartRecordingToSink(&v1core.EventSinkImpl{Interface: dsc.kubeClient.CoreV1().Events("")})

	// exactly one worker thread, especially syncClaim() is not reentrant.
	// Two syncClaims could bind two different claims to the same volume or one
	// claim to two volumes. The controller would recover from this (due to
	// version errors in API server and other checks in this controller),
	// however overall speed of multi-worker controller would be lower than if
	// it runs single thread only.

+
Your tests can distinguish between parallel test processes by using the value of the `org.gradle.test.worker` property, which is unique for each process. You can use this for anything you want, but it's particularly useful for filenames and other resource identifiers to prevent the kind of conflict we just mentioned.

            // 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(

    }

    @Override
    public GradleExecuter withWorkerDaemonsExpirationDisabled() {
        return withCommandLineGradleOpts("-Dorg.gradle.workers.internal.disable-daemons-expiration=true");
    }

    @Override
    public boolean usesSharedDaemons() {
        return isSharedDaemons();
    }

    @Override
    public File getDaemonBaseDir() {

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).

	}

	parseCpuList()

	m.before()
	defer m.after()

	// Run tests, examples, and benchmarks unless this is a fuzz worker process.
	// Workers start after this is done by their parent process, and they should
	// not repeat this work.
	if !*isFuzzWorker {
		deadline := m.startAlarm()
		haveExamples = len(m.examples) > 0