/**
     * Data transfer object representing process CPU statistics.
     */
    public static class ProcessCpuObj {
        /**
         * Default constructor.
         */
        public ProcessCpuObj() {
            // Default constructor
        }

        /** CPU usage percentage for the process. */
        public short percent;
        /** Total CPU time used by the process in milliseconds. */

A smaller CPU-only package is also available:

```
$ pip install tensorflow-cpu
```

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

*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.*

С другой стороны, если у вас 2 сервера и вы используете **100% их CPU и RAM**, в какой‑то момент один процесс попросит больше памяти, и сервер начнёт использовать диск как «память» (что в тысячи раз медленнее) или даже **упадёт**. Или процессу понадобятся вычисления, но ему придётся ждать освобождения CPU.

				"application/vnd.criticaltools.wbs+xml",
				"application/vnd.ctc-posml",
				"application/vnd.ctct.ws+xml",
				"application/vnd.cups-pdf",
				"application/vnd.cups-postscript",
				"application/vnd.cups-ppd",
				"application/vnd.cups-raster",
				"application/vnd.cups-raw",
				"application/vnd.curl.car",
				"application/vnd.curl.pcurl",
				"application/vnd.cybank",
				"application/vnd.data-vision.rdz",

## 资源利用率

您的服务器是一个**资源**，您可以通过您的程序消耗或**利用**CPU 上的计算时间以及可用的 RAM 内存。

您想要消耗/利用多少系统资源？ 您可能很容易认为“不多”，但实际上，您可能希望在不崩溃的情况下**尽可能多地消耗**。

如果您支付了 3 台服务器的费用，但只使用了它们的一点点 RAM 和 CPU，那么您可能**浪费金钱** 💸，并且可能 **浪费服务器电力** 🌎，等等。

在这种情况下，最好只拥有 2 台服务器并使用更高比例的资源（CPU、内存、磁盘、网络带宽等）。

另一方面，如果您有 2 台服务器，并且正在使用 **100% 的 CPU 和 RAM**，则在某些时候，一个进程会要求更多内存，并且服务器将不得不使用磁盘作为“内存” （这可能会慢数千倍），甚至**崩溃**。 或者一个进程可能需要执行一些计算，并且必须等到 CPU 再次空闲。

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"

        }
    }

    /**
     * Calibrates the CPU load.
     *
     * @return true if the CPU load is within the acceptable range, false otherwise.
     */
    public boolean calibrateCpuLoad() {
        return calibrateCpuLoad(0L);
    }

    /**
     * Calibrates the CPU load with a timeout.
     *
     * @param timeoutInMillis The timeout in milliseconds.

TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cuda
TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow"
TFCI_DOCKER_ARGS="--gpus all"
TFCI_LIB_SUFFIX="-gpu-linux-x86_64"
# TODO: Set back to 610M once the wheel size is fixed.

if [[ "$TFCI_WHL_NUMPY_VERSION" == 1 ]]; then
  # Uninstall tf nightly wheel built with numpy1.
  "$python" -m pip uninstall -y tf_nightly_numpy1
  # Install tf nightly cpu wheel built with numpy2.x from PyPI in numpy1.x env.
  "$python" -m pip install tf-nightly-cpu
  if [[ "$TFCI_WHL_IMPORT_TEST_ENABLE" == "1" ]]; then
    "$python" -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)'

* Use local disk for ConfigMap volume instead of tmpfs ([#25306](https://github.com/kubernetes/kubernetes/pull/25306), [@pmorie](https://github.com/pmorie))
* Alpha support for scheduling pods on machines with NVIDIA GPUs whose kubelets use the `--experimental-nvidia-gpus` flag, using the alpha.kubernetes.io/nvidia-gpu resource  ([#24836](https://github.com/kubernetes/kubernetes/pull/24836), [@therc](https://github.com/therc))