TF_CAPI_EXPORT extern bool TFE_ExecutorIsAsync(TFE_Executor*);

// Causes the calling thread to block till all ops dispatched in this executor
// have been executed. Note that "execution" here refers to kernel execution /
// scheduling of copies, etc. Similar to sync execution, it doesn't guarantee
// that lower level device queues (like GPU streams) have been flushed.
//

reproducing the executable from it.  However, as a special exception,
the materials to be distributed need not include anything that is
normally distributed (in either source or binary form) with the major
components (compiler, kernel, and so on) of the operating system on
which the executable runs, unless that component itself accompanies
the executable.

  It may happen that this requirement contradicts the license

Linux containers run using the same Linux kernel of the host (machine, virtual machine, cloud server, etc). This just means that they are very lightweight (compared to full virtual machines emulating an entire operating system).

Major Component, or to implement a Standard Interface for which an
implementation is available to the public in source code form.  A
"Major Component", in this context, means a major essential component
(kernel, window system, and so on) of the specific operating system
(if any) on which the executable work runs, or a compiler used to
produce the work, or an object code interpreter used to run it.

Contêineres Linux rodam usando o mesmo kernel Linux do hospedeiro (máquina, máquina virtual, servidor na nuvem, etc). Isso simplesmente significa que eles são muito leves (comparados com máquinas virtuais emulando um sistema operacional completo).

### Mehrere Prozesse – Worker { #multiple-processes-workers }

Wenn Sie mehr Clients haben, als ein einzelner Prozess verarbeiten kann (z. B. wenn die virtuelle Maschine nicht sehr groß ist) und die CPU des Servers **mehrere Kerne** hat, dann könnten **mehrere Prozesse** gleichzeitig mit derselben Anwendung laufen und alle Requests unter sich verteilen.

  ([#33684](https://github.com/kubernetes/kubernetes/pull/33684),
  [@fraenkel](https://github.com/fraenkel))
* Flag gate faster evictions based on node memory pressure using kernel memcg
  notifications - `--experimental-kernel-memcg-notification`.
  ([#38258](https://github.com/kubernetes/kubernetes/pull/38258),
  [@derekwaynecarr](https://github.com/derekwaynecarr))

```

</details>

## 什麼是容器 { #what-is-a-container }

容器（主要是 Linux 容器）是一種非常輕量的方式，用來封裝應用及其所有相依與必要檔案，並讓其與同一系統中的其他容器（其他應用或元件）隔離。

Linux 容器使用與主機（機器、虛擬機、雲端伺服器等）相同的 Linux kernel。這意味著它們非常輕量（相較於完整模擬整個作業系統的虛擬機）。

因此，容器只消耗很少的資源，與直接執行行程相當（而虛擬機會消耗更多）。

容器也有其各自隔離的執行行程（通常只有一個行程）、檔案系統與網路，簡化部署、安全性與開發等。

## 什麼是容器映像 { #what-is-a-container-image }

容器是由容器映像啟動執行的。

		Name:      readTotal,
		Help:      "Total read SysCalls to the kernel. /proc/[pid]/io syscr",
		Type:      counterMetric,
	}
}

func getMinIOProcessSysCallWMD() MetricDescription {
	return MetricDescription{
		Namespace: nodeMetricNamespace,
		Subsystem: sysCallSubsystem,
		Name:      writeTotal,
		Help:      "Total write SysCalls to the kernel. /proc/[pid]/io syscw",
		Type:      counterMetric,
	}
}

* **Deep Learning**: Dies ist ein Teilgebiet des maschinellen Lernens, daher gilt das Gleiche. Es ist nur so, dass es nicht eine einzige Tabelle mit Zahlen zum Multiplizieren gibt, sondern eine riesige Menge davon, und in vielen Fällen verwendet man einen speziellen Prozessor, um diese Modelle zu erstellen und / oder...