------------------------------------------------------------------

=== Load balancing and caches.

By default the tests run on multiple processes using all the available cores on all
available CPUs. Not including hyper-threading.
If you want to explicitly specify the number of JVMs you can do so on the command
line:

----------------------------
./gradlew test -Dtests.jvms=8
----------------------------

* заголовки
* заголовок авторизації
* заголовок `Authorization`
* направлений заголовок

* система впровадження залежностей
* залежність
* залежний
* залежний

* I/O-обмежений
* CPU-обмежений
* рівночасність
* паралелізм
* багатопроцесорність

* змінна оточення
* змінна оточення
* `PATH`
* змінна `PATH`

* автентифікація
* постачальник автентифікації
* авторизація

TF_CAPI_EXPORT void TF_SetXlaMinClusterSize(int size);

// Gets/Sets TF/XLA flag for whether(true) or not(false) to disable constant
// folding. This is for testing to ensure that XLA is being tested rather than
// Tensorflow's CPU implementation through constant folding.
TF_CAPI_EXPORT unsigned char TF_GetXlaConstantFoldingDisabled();
TF_CAPI_EXPORT void TF_SetXlaConstantFoldingDisabled(
    unsigned char should_enable);

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

Ve yürütme süresinin çoğu gerçek işten (bekleme yerine) oluştuğu ve bilgisayardaki işi bir <abbr title="Central Processing Unit - Merkezi İşlem Birimi">CPU</abbr> yaptığı için, bu sorunlara "CPU bound" denir.

---

CPU’ya bağlı işlemlerin yaygın örnekleri, karmaşık matematiksel işlem gerektiren iş yükleridir.

Örneğin:

* **Ses** veya **görüntü işleme**.

  (*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 {

 * Generally speaking, this class reduces object allocation and memory consumption at the price of
 * moderately increased constant factors of CPU. Only use this class when there is a specific reason
 * to prioritize memory over CPU.
 *
 * @author Louis Wasserman
 * @author Jon Noack
 */
@GwtIncompatible // not worth using in GWT for now

* header
* headers
* authorization header
* `Authorization` header
* forwarded header

* dependency injection system
* dependency
* dependable
* dependant

* I/O bound
* CPU bound
* concurrency
* parallelism
* multiprocessing

* env var
* environment variable
* `PATH`
* `PATH` variable

* authentication
* authentication provider
* authorization

このシナリオでは、各清掃員 (あなたを含む) がプロセッサであり、それぞれが自分の役割を果たします。

そして実行時間の大半は (待ちではなく) 実作業が占め、コンピュータでの作業は <abbr title="Central Processing Unit - 中央処理装置">CPU</abbr> によって行われます。これらの問題は「CPU バウンド」と呼ばれます。

---

CPU バウンドな操作の一般的な例は、複雑な数値処理が必要なものです。

例えば:

* **オーディオ** や **画像処理**。
* **コンピュータビジョン**: 画像は数百万のピクセルで構成され、各ピクセルには 3 つの値/色があり、通常、それらのピクセル上で同時に何かを計算する必要があります。

* the headers
* the authorization header
* the `Authorization` header
* the forwarded header

* the dependency injection system
* the dependency
* the dependable
* the dependant

* I/O bound
* CPU bound
* concurrency
* parallelism
* multiprocessing

* the env var
* the environment variable
* the `PATH`
* the `PATH` variable

* the authentication
* the authentication provider