sysCPUAvgIdleMD   = NewGaugeMD(sysCPUAvgIdle, "Average CPU idle time")
	sysCPUAvgIOWaitMD = NewGaugeMD(sysCPUAvgIOWait, "Average CPU IOWait time")
	sysCPULoadMD      = NewGaugeMD(sysCPULoad, "CPU load average 1min")
	sysCPULoadPercMD  = NewGaugeMD(sysCPULoadPerc, "CPU load average 1min (percentage)")
	sysCPUNiceMD      = NewGaugeMD(sysCPUNice, "CPU nice time")
	sysCPUStealMD     = NewGaugeMD(sysCPUSteal, "CPU steal time")

   
   
   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.

- Nightly builds:
  - [Linux CPU-only](https://storage.googleapis.com/tensorflow-nightly/github/tensorflow/lib_package/libtensorflow-cpu-linux-x86_64.tar.gz)
  - [Linux GPU](https://storage.googleapis.com/tensorflow-nightly/github/tensorflow/lib_package/libtensorflow-gpu-linux-x86_64.tar.gz)

            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()) {

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;