server["Server on http://127.0.0.1:8000/app"]

browser --> proxy
proxy --> server
```

/// tip

The IP `0.0.0.0` is commonly used to mean that the program listens on all the IPs available in that machine/server.

///

The docs UI would also need the OpenAPI schema to declare that this API `server` is located at `/api/v1` (behind the proxy). For example:

```JSON hl_lines="4-8"
{
    "openapi": "3.1.0",

   *
   * @since 8.0
   */
  public static final byte NUL = 0;

  /**
   * Start of Heading: A communication control character used at the beginning of a sequence of
   * characters which constitute a machine-sensible address or routing information. Such a sequence
   * is referred to as the "heading." An STX character has the effect of terminating a heading.
   *
   * @since 8.0
   */
  public static final byte SOH = 1;

        if (q20.ans != null) {
            return q20.ans;
        }
        throw q1x.uhe;
    }

    /**
     * Determines the address of a host given it's host name. The name can be a
     * machine name like "jcifs.samba.org",  or an IP address like "192.168.1.15".
     *
     * @param hostname NetBIOS or DNS hostname to resolve
     * @throws java.net.UnknownHostException if there is an error resolving the name
     */

### Serverspeicher

Wenn Ihr Code beispielsweise ein Machine-Learning-Modell mit **1 GB Größe** lädt und Sie einen Prozess mit Ihrer API ausführen, verbraucht dieser mindestens 1 GB RAM. Und wenn Sie **4 Prozesse** (4 Worker) starten, verbraucht jeder 1 GB RAM. Insgesamt verbraucht Ihre API also **4 GB RAM**.

def is_linux():
  return platform.system() == 'Linux'


def is_macos():
  return platform.system() == 'Darwin'


def is_ppc64le():
  return platform.machine() == 'ppc64le'


def is_s390x():
  return platform.machine() == 's390x'


def is_cygwin():
  return platform.system().startswith('CYGWIN_NT')


def get_input(question):
  try:
    try:
      answer = raw_input(question)

To improve this, we’re working on automation tools 🤖 to manage translations more efficiently. Once ready, documentation will be machine-translated and still reviewed by at least two native speakers ✅ before publishing. This will allow us to keep translations up-to-date while reducing the review burden on maintainers.

What’s changing now:

pkg crypto/x509, type RevocationList struct, RawTBSRevocationList []uint8 #50674
pkg crypto/x509, type RevocationList struct, Signature []uint8 #50674
pkg debug/elf, const EM_LOONGARCH = 258 #46229
pkg debug/elf, const EM_LOONGARCH Machine #46229
pkg debug/elf, const R_LARCH_32 = 1 #46229
pkg debug/elf, const R_LARCH_32 R_LARCH #46229
pkg debug/elf, const R_LARCH_64 = 2 #46229
pkg debug/elf, const R_LARCH_64 R_LARCH #46229

under Section 2) in object code or executable form under the terms of
Sections 1 and 2 above provided that you also do one of the following:

    a) Accompany it with the complete corresponding machine-readable
    source code, which must be distributed under the terms of Sections 1
    and 2 above on a medium customarily used for software interchange; or,

    b) Accompany it with a written offer, valid for at least three

     *
     * @param hook The shutdown hook to add.
     */
    public void addShutdownHook(final Runnable hook) {
        shutdownHookList.add(hook);
    }

    /**
     * Gets the hostname of the local machine.
     *
     * @return The hostname.
     */
    public String getHostname() {
        final Map<String, String> env = getEnvMap();
        if (env.containsKey("COMPUTERNAME")) {

Si tu aplicación es **simple**, probablemente esto **no será un problema**, y puede que no necesites especificar límites de memoria estrictos. Pero si estás **usando mucha memoria** (por ejemplo, con modelos de **Machine Learning**), deberías verificar cuánta memoria estás consumiendo y ajustar el **número de contenedores** que se ejecutan en **cada máquina** (y tal vez agregar más máquinas a tu cluster).