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

### Concorrência + Paralelismo: Web + Aprendizado de Máquina { #concurrency-parallelism-web-machine-learning }

Com **FastAPI** você pode levar a vantagem da concorrência que é muito comum para desenvolvimento web (o mesmo atrativo de NodeJS).

## Görüşler

"_[...] Bugünlerde **FastAPI**'ı çok fazla kullanıyorum. [...] Aslında bunu ekibimin **Microsoft'taki Machine Learning servislerinin** tamamında kullanmayı planlıyorum. Bunlardan bazıları **Windows**'un ana ürünlerine ve **Office** ürünlerine entegre ediliyor._"

     *
     * @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")) {

    @TempDir
    Path projectRoot;

    /**
     * Provides file system configurations for testing both Windows and Unix path behaviors.
     * This allows us to test cross-platform path handling on any development machine.
     */
    static Stream<Arguments> fileSystemConfigurations() {
        return Stream.of(
                Arguments.of("Unix", Configuration.unix(), "/"),

### Memória do servidor { #server-memory }

pkg debug/elf, const EM_MIPS_X Machine
pkg debug/elf, const EM_MMA Machine
pkg debug/elf, const EM_NCPU Machine
pkg debug/elf, const EM_NDR1 Machine
pkg debug/elf, const EM_NONE Machine
pkg debug/elf, const EM_PARISC Machine
pkg debug/elf, const EM_PCP Machine
pkg debug/elf, const EM_PPC Machine
pkg debug/elf, const EM_PPC64 Machine
pkg debug/elf, const EM_RCE Machine
pkg debug/elf, const EM_RH32 Machine

parts of the aggregate.

  6. Conveying Non-Source Forms.

  You may convey a covered work in object code form under the terms
of sections 4 and 5, provided that you also convey the
machine-readable Corresponding Source under the terms of this License,
in one of these ways:

    a) Convey the object code in, or embodied in, a physical product
    (including a physical distribution medium), accompanied by the

    if (parent == null) {
      /*
       * The given directory is a filesystem root. All zero of its ancestors exist. This doesn't
       * mean that the root itself exists -- consider x:\ on a Windows machine without such a drive
       * -- or even that the caller can create it, but this method makes no such guarantees even for
       * non-root files.
       */
      return;
    }
    parent.mkdirs();

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).