*       then sequentially accessing it could result in other processes dying. This is solvable
   *       via madvise(2), but that obviously doesn't exist in java.
   *   <li>Ordinary copy. Kernel copies bytes into a kernel buffer, from a kernel buffer into a
   *       userspace buffer (byte[] or ByteBuffer), then copies them from that buffer into the
   *       destination channel.
   * </ol>
   *

issues with Docker 1.9.1 can be found below.
  * CPU hardcapping will be enabled by default for containers with CPU limit set,
if supported by the kernel. You should either adjust your CPU limit, or set CPU
request only, if you want to avoid hardcapping. If the kernel does not support
CPU Quota, NodeStatus will contain a warning indicating that CPU Limits cannot
be enforced.

      - name: Enable KVM group perms
        # https://github.blog/changelog/2023-02-23-hardware-accelerated-android-virtualization-on-actions-windows-and-linux-larger-hosted-runners/
        run: |
          echo 'KERNEL=="kvm", GROUP="kvm", MODE="0666", OPTIONS+="static_node=kvm"' | sudo tee /etc/udev/rules.d/99-kvm4all.rules
          sudo udevadm control --reload-rules
          sudo udevadm trigger --name-match=kvm

Wenn Ihre Anwendung also viel Speicher verbraucht (z. B. bei Modellen für maschinelles Lernen) und Ihr Server über viele CPU-Kerne, **aber wenig Speicher** verfügt, könnte Ihr Container am Ende versuchen, mehr Speicher als vorhanden zu verwenden, was zu erheblichen Leistungseinbußen (oder sogar zum Absturz) führen kann. 🚨

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).

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).

compilation and installation of the executable. However, as a special
exception, the source code 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.

If distribution of executable or object code is made by offering access

Mit **FastAPI** bekommen Sie alle Funktionen von **Pydantic** (da FastAPI für die gesamte Datenverarbeitung Pydantic nutzt):

* **Kein Kopfzerbrechen**:
    * Keine neue Schemadefinition-Mikrosprache zu lernen.
    * Wenn Sie Pythons Typen kennen, wissen Sie, wie man Pydantic verwendet.

Wenn das Programm nun Dinge in den Arbeitsspeicher lädt, zum Beispiel ein Modell für maschinelles Lernen in einer Variablen oder den Inhalt einer großen Datei in einer Variablen, verbraucht das alles **einen Teil des Arbeitsspeichers (RAM – Random Access Memory)** des Servers.