}
	}
	uaAppend("; ", "")

	if cpus, err := gopsutilcpu.Info(); err == nil && len(cpus) > 0 {
		cpuMap := make(map[string]struct{}, len(cpus))
		coreMap := make(map[string]struct{}, len(cpus))
		for i := range cpus {
			cpuMap[cpus[i].PhysicalID] = struct{}{}
			coreMap[cpus[i].CoreID] = struct{}{}
		}
		cpu := cpus[0]

   * using a secondary hash (Marsaglia XorShift) to try to find a
   * free slot.
   *
   * The table size is capped because, when there are more threads
   * than CPUs, supposing that each thread were bound to a CPU,
   * there would exist a perfect hash function mapping threads to
   * slots that eliminates collisions. When we reach capacity, we
   * search for this mapping by randomly varying the hash codes of

   * using a secondary hash (Marsaglia XorShift) to try to find a
   * free slot.
   *
   * The table size is capped because, when there are more threads
   * than CPUs, supposing that each thread were bound to a CPU,
   * there would exist a perfect hash function mapping threads to
   * slots that eliminates collisions. When we reach capacity, we
   * search for this mapping by randomly varying the hash codes of

   * using a secondary hash (Marsaglia XorShift) to try to find a
   * free slot.
   *
   * The table size is capped because, when there are more threads
   * than CPUs, supposing that each thread were bound to a CPU,
   * there would exist a perfect hash function mapping threads to
   * slots that eliminates collisions. When we reach capacity, we
   * search for this mapping by randomly varying the hash codes of

shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to...

	// is specified. It is empty if the KMS does not support
	// a default key.
	DefaultKey string

	// Details provides more details about the KMS endpoint status.
	// including uptime, version and available CPUs.
	// Could be more in future.
	Details kes.State
}

// DEK is a data encryption key. It consists of a
// plaintext-ciphertext pair and the ID of the key
// used to generate the ciphertext.
//

build:android_arm64 --config=android
build:android_arm64 --cpu=arm64-v8a
build:android_arm64 --fat_apk_cpu=arm64-v8a
build:android_x86 --config=android
build:android_x86 --cpu=x86
build:android_x86 --fat_apk_cpu=x86
build:android_x86_64 --config=android
build:android_x86_64 --cpu=x86_64
build:android_x86_64 --fat_apk_cpu=x86_64

# Build everything statically for Android since all static libs are later

	}
	info := getLocalServerProperty(globalEndpoints, &r, metrics)
	return madminServerProperties.NewJSONWith(&info), nil
}

// GetCPUsHandler - returns CPU info.
func (s *peerRESTServer) GetCPUsHandler(_ *grid.MSS) (*grid.JSON[madmin.CPUs], *grid.RemoteErr) {
	info := madmin.GetCPUs(context.Background(), globalLocalNodeName)
	return madminCPUs.NewJSONWith(&info), nil
}

## Zusammenfassung

Sie können **Gunicorn** (oder auch Uvicorn) als Prozessmanager mit Uvicorn-Workern verwenden, um **Multikern-CPUs** zu nutzen und **mehrere Prozesse parallel** auszuführen.

Sie können diese Tools und Ideen nutzen, wenn Sie **Ihr eigenes Deployment-System** einrichten und sich dabei selbst um die anderen Deployment-Konzepte kümmern.

## Recap

You can use **Gunicorn** (or also Uvicorn) as a process manager with Uvicorn workers to take advantage of **multi-core CPUs**, to run **multiple processes in parallel**.

You could use these tools and ideas if you are setting up **your own deployment system** while taking care of the other deployment concepts yourself.