CI_DOCKER_BUILD_EXTRA_PARAMS="--build-arg py_major_minor_version=${{ matrix.pyver }} --build-arg is_nightly=${is_nightly} --build-arg tf_project_name=${tf_project_name}" \

TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=windows_x86_ml_actions
TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow"
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

        ctx, name, TF_FLOAT, dims, 2, data, size, &Deleter, copy, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TFE_TensorHandle* on_host =
        TFE_TensorHandleCopyToDevice(copy_aliased, ctx, "CPU:0", status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TF_Tensor* resolved = TFE_TensorHandleResolve(on_host, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

---

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

Por exemplo:

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

  std::string device_type = parsed_name.type;
  int device_id = 0;
  if (parsed_name.has_id) {
    device_id = parsed_name.id;
  }

  ctx.device_id = device_id;
  if (device_type == "CPU") {
    ctx.device_type = DLDeviceType::kDLCPU;
  } else if (device_type == "GPU") {
#if TENSORFLOW_USE_ROCM
    ctx.device_type = DLDeviceType::kDLROCM;
#else
    ctx.device_type = DLDeviceType::kDLCUDA;
#endif

          CI_DOCKER_BUILD_EXTRA_PARAMS="--build-arg py_major_minor_version=${{ matrix.pyver }} --build-arg is_nightly=${is_nightly} --build-arg tf_project_name=${tf_project_name}" \
          ./tensorflow/tools/ci_build/ci_build.sh cpu.arm64 bash tensorflow/tools/ci_build/rel/ubuntu/cpu_arm64_test_build.sh
      - name: Upload pip wheel to PyPI

			// callhome running on a different node.
			// sleep for some time and try again.
			duration := max(time.Duration(r.Float64()*float64(globalCallhomeConfig.FrequencyDur())),
				// Make sure to sleep at least a second to avoid high CPU ticks.
				time.Second)
			time.Sleep(duration)
		}
	}()
}

func runCallhome(ctx context.Context, objAPI ObjectLayer) bool {
	// Make sure only 1 callhome is running on the cluster.

 * java.util.LinkedHashMap}. 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
 */
@J2ktIncompatible // no support for access-order mode in LinkedHashMap delegate
@GwtIncompatible // not worth using in GWT for now

## Concepts de déploiement { #deployment-concepts }

Ici, vous avez vu comment utiliser plusieurs workers pour paralléliser l'exécution de l'application, tirer parti de plusieurs cœurs du CPU et être en mesure de servir davantage de requêtes.

這裡唯一新增的選項是 `--workers`，告訴 Uvicorn 要啟動 4 個工作處理序。

你也會看到它顯示每個處理序的 **PID**，`27365` 是父處理序（這是**處理序管理器**），另外每個工作處理序各有一個：`27368`、`27369`、`27370`、`27367`。

## 部署概念 { #deployment-concepts }

你已經看到如何使用多個 **workers** 來將應用的執行進行**平行化**，善用 CPU 的**多核心**，並能服務**更多請求**。

在上面的部署概念清單中，使用 workers 主要能幫助到**副本**這一塊，並對**重啟**也有一點幫助，但你仍需要處理其他部分：

* **安全 - HTTPS**
* **系統啟動時執行**
* ***重啟***
* 副本（正在執行的處理序數量）
* **記憶體**
* **啟動前的前置作業**