Quanto dos recursos do sistema você quer consumir/utilizar? Pode ser fácil pensar "não muito", mas, na realidade, você provavelmente vai querer consumir **o máximo possível sem travar**.

	return resp.ValueOrZero(), err
}

// GetCPUs - fetch CPU information for a remote node.
func (client *peerRESTClient) GetCPUs(ctx context.Context) (info madmin.CPUs, err error) {
	resp, err := getCPUsHandler.Call(ctx, client.gridConn(), grid.NewMSS())
	return resp.ValueOrZero(), err
}

- Fix a race in the timeout handler that could lead to kube-apiserver crashes ([#108455](https://github.com/kubernetes/kubernetes/pull/108455), [@Argh4k](https://github.com/Argh4k))
- Fix container creation errors for pods with cpu requests bigger than 256 cpus ([#106570](https://github.com/kubernetes/kubernetes/pull/106570), [@odinuge](https://github.com/odinuge))

서버는 여러분이 프로그램으로 소비하거나 **활용(utilize)**할 수 있는 **리소스**입니다. CPU의 계산 시간과 사용 가능한 RAM 메모리가 대표적입니다.

시스템 리소스를 얼마나 소비/활용하고 싶으신가요? “많지 않게”라고 생각하기 쉽지만, 실제로는 **크래시하지 않는 선에서 가능한 한 많이** 사용하고 싶을 가능성이 큽니다.

서버 3대를 비용을 내고 쓰고 있는데 RAM과 CPU를 조금만 사용한다면, 아마 **돈을 낭비**하고 💸, **서버 전력도 낭비**하고 🌎, 기타 등등이 될 수 있습니다.

그 경우에는 서버를 2대만 두고, 각 서버의 리소스(CPU, 메모리, 디스크, 네트워크 대역폭 등)를 더 높은 비율로 사용하는 것이 더 나을 수 있습니다.

	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")

- Fixed a bug whereby the allocation of reusable CPUs and devices was not being honored when the TopologyManager was enabled ([#93189](https://github.com/kubernetes/kubernetes/pull/93189), [@klueska](https://github.com/klueska)) [SIG Node]

### Hardware attacks

Physical GPUs or TPUs can also be the target of attacks. [Published
research](https://scholar.google.com/scholar?q=gpu+side+channel) shows that it
might be possible to use side channel attacks on the GPU to leak data from other
running models or processes in the same system. GPUs can also have
implementation bugs that might allow attackers to leave malicious code running

     */
    public void find(char c) {
        int cpos = pos;
        while (cpos < input.length()) {
            if (input.charAt(cpos) == c) {
                break;
            }
            cpos++;
        }
        pos = cpos;
    }

    /**

  Args:
    environ_cp: copy of the os.environ.
    var_name: string for name of environment variable, e.g. "TF_NEED_CUDA".
    query_item: string for feature related to the variable, e.g. "CUDA for
      Nvidia GPUs".
    enabled_by_default: boolean for default behavior.
    question: optional string for how to ask for user input.
    yes_reply: optional string for reply when feature is enabled.

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs

## How to Test Your Changes to TensorFlow

You may check how your changes will affect TensorFlow by:

1. Creating a PR and observing the presubmit test results