These "env" files match up with an environment matrix that roughly covers:

-   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

  (*sess_options.config.mutable_device_count())["CPU"] = num_cpus;
  status->status =
      tensorflow::DeviceFactory::AddCpuDevices(sess_options, prefix, &devices);

  // Remove the device that has the host device name since host device is alreay
  // in an initialized context.
  for (auto d = devices.begin(); d != devices.end();) {
    if (absl::StrContains(d->get()->name(), "CPU:0")) {
      d = devices.erase(d);
    } else {

        final long freeMemory = Runtime.getRuntime().freeMemory();
        final String msg = String.format(
                "%d words from %d %s: {\"time\":{\"parse\":%d,\"index\":%d},\"cpu\":%f,\"mem\":{\"heap\":\"%dmb\",\"used\":\"%dmb\"}}",
                items.length, size, type, parseTime, indexTime, cpuLoad, maxMemory / (1024 * 1024),
                (maxMemory - freeMemory) / (1024 * 1024));

    // we try hard to make them robust in practice.  We could additionally try to add in a system
    // load timeout multiplier.  Or we could try to use a CPU time bound instead of wall clock time
    // bound.  But these ideas are harder to implement.  We do not try to detect or handle a
    // user-specified -XX:+DisableExplicitGC.
    //
    // TODO(user): Consider using

               "intervalFactor": 2,
               "legendFormat": "{{cluster}} - {{namespace}}\n"
            }
         ],
         "title": "CPU Usage",
         "type": "timeseries"
      },
      {
         "datasource": {
            "type": "datasource",
            "uid": "-- Mixed --"
         },
         "fieldConfig": {
            "defaults": {

    // we try hard to make them robust in practice.  We could additionally try to add in a system
    // load timeout multiplier.  Or we could try to use a CPU time bound instead of wall clock time
    // bound.  But these ideas are harder to implement.  We do not try to detect or handle a
    // user-specified -XX:+DisableExplicitGC.
    //
    // TODO(user): Consider using

///

### 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.

   * using a secondary hash (Marsaglia XorShift) to try to find a
   * free slot.
   *
   * The table size is capped because, when there are more threads
   * than CPUs, supposing that each thread were bound to a CPU,
   * there would exist a perfect hash function mapping threads to
   * slots that eliminates collisions. When we reach capacity, we
   * search for this mapping by randomly varying the hash codes of

	g := errgroup.WithNErrs(len(buckets))
	bucketMetas := make([]BucketMetadata, len(buckets))
	for index := range buckets {
		index := index
		g.Go(func() error {
			// Sleep and stagger to avoid blocked CPU and thundering
			// herd upon start up sequence.
			time.Sleep(25*time.Millisecond + time.Duration(rand.Int63n(int64(100*time.Millisecond))))

// If source and destination are the same device, then this creates a new handle
// that shares the underlying buffer. Otherwise, it currently requires at least
// one of the source or destination devices to be CPU (i.e., for the source or
// destination tensor to be placed in host memory).
// If async execution is enabled, the copy may be enqueued and the call will
// return "non-ready" handle. Else, this function returns after the copy has