#     elinux_aarch64:  Embedded Linux options for aarch64 (ARM64) CPU support.
#     elinux_armhf:    Embedded Linux options for armhf (ARMv7) CPU support.
#
# Release build options (for all operating systems)
#     release_base:                    Common options for all builds on all operating systems.
#     release_cpu_linux:               Toolchain and CUDA options for Linux CPU builds.

A smaller CPU-only TensorFlow package is also available:

```
 pip install tensorflow-cpu
```

To update TensorFlow to the latest version, add the `--upgrade` flag to the
commands above.

*Nightly binaries are available for testing using the
[tf-nightly](https://pypi.python.org/pypi/tf-nightly) and
[tf-nightly-cpu](https://pypi.python.org/pypi/tf-nightly-cpu) packages on PyPI.*

            if (logger.isDebugEnabled()) {
                logger.debug("Search Engine CPU: {}%", maxCpu);
            }
        } catch (final Exception e) {
            systemHelper.setSearchEngineCpuPercent((short) 0);
            consecutiveFailures++;
            if (consecutiveFailures <= 3) {
                logger.warn("Failed to get search engine CPU stats.", e);
            } else if (logger.isDebugEnabled()) {

你的伺服器（群）是可以被「消耗／利用」的「資源」，你的程式會使用 CPU 的計算時間，以及可用的 RAM 記憶體。

你想要消耗／利用多少系統資源？直覺上可能會想「不要太多」，但實際上，你大概會希望在「不當機」的前提下「盡可能用多一點」。

如果你花錢租了 3 台伺服器，卻只用了它們少量的 RAM 與 CPU，那你可能是在「浪費金錢」💸、也「浪費伺服器電力」🌎 等。

在那種情況下，可能更好的是只用 2 台伺服器，並以更高的比例使用它們的資源（CPU、記憶體、磁碟、網路頻寬等）。

另一方面，如果你有 2 台伺服器，且你使用了它們「100% 的 CPU 與 RAM」，某個時刻一個行程會要求更多記憶體，伺服器就得用磁碟當作「記憶體」（這可能慢上數千倍），甚至「當機」。或是某個行程需要做計算時，必須等到 CPU 再度空閒。

    }

    /**
     * Gets the current system CPU usage percentage.
     *
     * @return The system CPU usage percentage.
     */
    public short currentSystemCpuPercent() {
        return getSystemCpuPercent();
    }

    /**
     * Gets the search engine CPU usage percentage.
     *
     * @return The search engine CPU usage percentage.
     */

TFCI_BAZEL_COMMON_ARGS="--repo_env=HERMETIC_PYTHON_VERSION=$TFCI_PYTHON_VERSION --repo_env=USE_PYWRAP_RULES=True --config release_arm64_linux"
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_arm64
# Note: this is not set to "--cpu", because that changes the package name
# to tensorflow_cpu. These ARM builds are supposed to have the name "tensorflow"
# despite lacking Nvidia CUDA support.
TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow"

import jakarta.servlet.ServletResponse;
import jakarta.servlet.http.HttpServletRequest;
import jakarta.servlet.http.HttpServletResponse;

/**
 * Filter for CPU load-based request control.
 * Returns HTTP 429 (Too Many Requests) when CPU usage exceeds configurable thresholds.
 * Web and API requests have independent threshold settings.
 */
public class LoadControlFilter implements Filter {

In that case, it could be better to have only 2 servers and use a higher percentage of their resources (CPU, memory, disk, network bandwidth, etc).

TFCI_DOCKER_IMAGE=us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build:latest
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_REBUILD_ARGS="--target=devel ci/official/containers/ml_build"
TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-linux-x86_64"
TFCI_OUTPUT_DIR=build_output
TFCI_WHL_AUDIT_ENABLE=1
TFCI_WHL_AUDIT_PLAT=manylinux_2_27_x86_64
TFCI_WHL_BAZEL_TEST_ENABLE=1
TFCI_WHL_SIZE_LIMIT=300M

concurrency:
  group: ${{ github.workflow }}-${{ github.head_ref || github.ref }}
  # Don't cancel in-progress jobs for master branches.
  cancel-in-progress: ${{ github.ref != 'master' }}

jobs:
  #build-and-test-linux-x86-cpu:
  #  uses: ./.github/workflows/build-reusable.yml
  #  with:
  #    runner: 'linux-x86-n2-16'
  #    tfci: 'py313,linux_x86,rbe'

  #build-and-test-linux-x86-cuda:
  #  uses: ./.github/workflows/build-reusable.yml