}

    /**
     * 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"

	}
	if types.Contains(madmin.MetricsCPU) {
		m.Aggregated.CPU = &madmin.CPUMetrics{
			CollectedAt: UTCNow(),
		}
		cm, err := c.Times(false)
		if err != nil {
			m.Errors = append(m.Errors, fmt.Sprintf("%s: %v (cpuTimes)", byHostName, err.Error()))
		} else {
			// not collecting per-cpu stats, so there will be only one element
			if len(cm) == 1 {
				m.Aggregated.CPU.TimesStat = &cm[0]
			} else {

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 {

[main]
summary=Maximum server performance for MinIO

[vm]
transparent_hugepage=madvise

[sysfs]
/sys/kernel/mm/transparent_hugepage/defrag=defer+madvise
/sys/kernel/mm/transparent_hugepage/khugepaged/max_ptes_none=0

[cpu]
force_latency=1
governor=performance
energy_perf_bias=performance
min_perf_pct=100

[sysctl]
fs.xfs.xfssyncd_centisecs=72000
net.core.busy_read=50
net.core.busy_poll=50
kernel.numa_balancing=1

* Avoid CPU migrations by pinning your benchmarks to specific CPU cores. On Linux you can use `taskset`.
* Fix the CPU frequency to avoid Turbo Boost from kicking in and skewing your results. On Linux you can use `cpufreq-set` and the
  `performance` CPU governor.
* Vary the problem input size with `@Param`.

                            // the ID of the CPU socket
                            if (name.equals("physical id")) {
                                currentID = value;
                            }
                            // Number of cores not including hyper-threading
                            if (name.equals("cpu cores")) {
                                assert currentID.isEmpty() == false;

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).

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)'

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