dataflow graphs to represent computation, 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...

- Reduced server startup time. For IAM encryption with the root credentials, MinIO had
   to use a memory-hard function (Argon2) that (on purpose) consumes a lot of memory and CPU.
   The new KMS-based approach can use a key derivation function that is orders of magnitudes
   cheaper w.r.t. memory and CPU.
- Root credentials can now be changed easily. Before, a two-step process was required to

O **número de processos** nesta imagem é **calculado automaticamente** a partir dos **núcleos de CPU** disponíveis.

Isso significa que ele tentará **aproveitar** o máximo de **desempenho** da CPU possível.

Você também pode ajustá-lo com as configurações usando **variáveis de ambiente**, etc.

Mas isso também significa que, como o número de processos depende da CPU do contêiner em execução, a **quantidade de memória consumida** também dependerá disso.

        run: |
          CI_DOCKER_BUILD_EXTRA_PARAMS="--pull --build-arg py_major_minor_version=${{ matrix.pyver }} --build-arg is_nightly=1 --build-arg tf_project_name=tf_nightly_ci" \

 * java.util.LinkedHashSet}. Generally speaking, this class reduces object allocation and memory
 * consumption at the price of moderately increased constant factors of CPU. Only use this class
 * when there is a specific reason to prioritize memory over CPU.
 *
 * @author Louis Wasserman
 */
@GwtIncompatible // not worth using in GWT for now
final class CompactLinkedHashSet<E extends @Nullable Object> extends CompactHashSet<E> {

issues with Docker 1.9.1 can be found below.
  * CPU hardcapping will be enabled by default for containers with CPU limit set,
if supported by the kernel. You should either adjust your CPU limit, or set CPU
request only, if you want to avoid hardcapping. If the kernel does not support
CPU Quota, NodeStatus will contain a warning indicating that CPU Limits cannot
be enforced.

TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow"
TFCI_BUILD_PIP_PACKAGE_ADDITIONAL_WHEEL_NAMES="tensorflow_cpu"
TFCI_OUTPUT_DIR=build_output
TFCI_FIND_BIN=C:/tools/msys64/usr/bin/find.exe
TFCI_LIB_SUFFIX="-cpu-windows-x86_64"
# auditwheel is not supported for Windows
TFCI_WHL_AUDIT_ENABLE=0
TFCI_WHL_AUDIT_PLAT=0
# Tests are extremely slow at the moment
TFCI_WHL_BAZEL_TEST_ENABLE=0
TFCI_WHL_SIZE_LIMIT=450M

		m := collectLocalMetrics(types, collectMetricsOpts{
			hosts: map[string]struct{}{
				globalLocalNodeName: {},
			},
		})

		for _, hm := range m.ByHost {
			if hm.CPU != nil {
				v = *hm.CPU
				break
			}
		}

		return
	}

	return cachevalue.NewFromFunc(1*time.Minute,
		cachevalue.Opts{ReturnLastGood: true},
		loadCPUMetrics)
}

이 시나리오에서, (당신을 포함한) 각각의 청소부들은 프로세서가 될 것이고, 각자의 역할을 수행합니다.

실행 시간의 대부분이 대기가 아닌 실제 작업에 소요되고, 컴퓨터에서 작업은 <abbr title="Central Processing Unit">CPU</abbr>에서 이루어지므로, 이러한 문제를 "CPU에 묶였"다고 합니다.

---

CPU에 묶인 연산에 관한 흔한 예시는 복잡한 수학 처리를 필요로 하는 경우입니다.

예를 들어:

* **오디오** 또는 **이미지** 처리.
* **컴퓨터 비전**: 하나의 이미지는 수백개의 픽셀로 구성되어있고, 각 픽셀은 3개의 값 / 색을 갖고 있으며, 일반적으로 해당 픽셀들에 대해 동시에 무언가를 계산해야하는 처리.

---

Exemplos comuns de operações limitadas por CPU são coisas que exigem processamento matemático complexo.

Por exemplo:

* **Processamento de áudio** ou **imagem**