-   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

| `minio_node_cpu_avg_system`          | CPU system time.                           |
| `minio_node_cpu_avg_system_avg`      | CPU system time (avg).                     |
| `minio_node_cpu_avg_system_max`      | CPU system time (max).                     |
| `minio_node_cpu_avg_idle`            | CPU idle time.                             |

		cpuUser:           "CPU user time",
		cpuSystem:         "CPU system time",
		cpuIdle:           "CPU idle time",
		cpuIOWait:         "CPU ioWait time",
		cpuSteal:          "CPU steal time",
		cpuNice:           "CPU nice time",
		cpuLoad1:          "CPU load average 1min",
		cpuLoad5:          "CPU load average 5min",
		cpuLoad15:         "CPU load average 15min",

  TF_SetAttrType(desc, "T", TF_INT32);
  // Set device to CPU since there is no version of split for int32 on GPU
  // TODO(iga): Convert all these helpers and tests to use floats because
  // they are usually available on GPUs. After doing this, remove TF_SetDevice
  // call in c_api_function_test.cc
  TF_SetDevice(desc, "/cpu:0");
  *op = TF_FinishOperation(desc, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

load(
    "@rules_ml_toolchain//third_party/gpus/cuda/hermetic:cuda_json_init_repository.bzl",
    "cuda_json_init_repository",
)

cuda_json_init_repository()

load(
    "@cuda_redist_json//:distributions.bzl",
    "CUDA_REDISTRIBUTIONS",
    "CUDNN_REDISTRIBUTIONS",
)
load(
    "@rules_ml_toolchain//third_party/gpus/cuda/hermetic:cuda_redist_init_repositories.bzl",

  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.

   
   
   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)

///

### 공식 도커 이미지에 있는 프로세스 개수

이 이미지에 있는 **프로세스 개수**는 가용한 CPU **코어들**로 부터 **자동으로 계산**됩니다.

이것이 의미하는 바는 이미지가 CPU로부터 **최대한의 성능**을 **쥐어짜낸다**는 것입니다.

여러분은 이 설정 값을 **환경 변수**나 기타 방법들로 조정할 수 있습니다.

그러나 프로세스의 개수가 컨테이너가 실행되고 있는 CPU에 의존한다는 것은 또한 **소요되는 메모리의 크기** 또한 이에 의존한다는 것을 의미합니다.

	}
	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 {