}

  auto* gpu_options = config.mutable_gpu_options();
  gpu_options->set_allow_growth(gpu_memory_allow_growth);

  (*config.mutable_device_count())["CPU"] = num_cpu_devices;

  // TODO(b/113217601): This is needed for EagerContext::runner_ to use a
  // threadpool, so that we avoid the possibility of running the runner_ in the

	processLocksWriteTotalMD          = NewGaugeMD(processLocksWriteTotal, "Number of current WRITE locks on this peer")
	processCPUTotalSecondsMD          = NewCounterMD(processCPUTotalSeconds, "Total user and system CPU time spent in seconds")
	processGoRoutineTotalMD           = NewGaugeMD(processGoRoutineTotal, "Total number of go routines running")

      return false;
    }

    if (!finalizeReference(firstReference, finalizeReferentMethod)) {
      return false;
    }

    /*
     * Loop as long as we have references available so as not to waste CPU looking up the Method
     * over and over again.
     */
    while (true) {
      Reference<?> furtherReference = queue.poll();
      if (furtherReference == null) {
        return true;
      }

    }

    public void test_getUserAgentType_safariWithoutChrome() {
        getMockRequest().addHeader("user-agent",
                "Mozilla/5.0 (iPhone; CPU iPhone OS 14_6 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.1.1 Mobile/15E148 Safari/604.1");
        assertEquals(UserAgentType.SAFARI, userAgentHelper.getUserAgentType());
    }

        blocker = (Blocker) state;
      }
      spinCount++;
      if (spinCount > MAX_BUSY_WAIT_SPINS) {
        /*
         * If we have spun a lot, just park ourselves. This will save CPU while we wait for a slow
         * interrupting thread. In theory, interruptTask() should be very fast, but due to
         * InterruptibleChannel and JavaLangAccess.blockedOn(Thread, Interruptible), it isn't

```

</div>

Vous disposez maintenant d'un serveur FastAPI optimisé dans un conteneur Docker. Configuré automatiquement pour votre
serveur actuel (et le nombre de cœurs du CPU).

## Vérifier

Et comme la plupart du temps d'exécution est pris par du "vrai" travail (et non de l'attente), et que le travail dans un ordinateur est fait par un <abbr title="Central Processing Unit">CPU</abbr>, ce sont des problèmes dits "CPU bound".

---

Des exemples communs d'opérations "CPU bounds" sont les procédés qui requièrent des traitements mathématiques complexes.

Par exemple :

* Traitements d'**audio** et d'**images**.

    Optional<Boolean> alsoMakeDependents();

    /**
     * Returns the number of threads used for parallel builds.
     *
     * @return an {@link Optional} containing the number of threads (or "1C" for one thread per CPU core), or empty if not specified
     */
    @Nonnull
    Optional<String> threads();

    /**
     * Returns the id of the build strategy to use.
     *

    public boolean hasPrimaryKey() {
        return false;
    }

    @Override
    public boolean hasCompoundPrimaryKey() {
        return false;
    }

    @Override
    protected UniqueInfo cpui() {
        return null;
    }

    // ===================================================================================
    //                                                                           Type Name

The Resource Management Working Group graduated three features to beta in the 1.10 release.  First, [CPU Manager](https://kubernetes.io/docs/tasks/administer-cluster/cpu-management-policies/), which allows users to request exclusive CPU cores.  This helps performance in a variety of use-cases, including network latency sensitive applications, as well as applications that benefit from CPU cache residency.  Next, [Huge Pages](https://kubernetes.io/docs/tasks/manage-hugepages/scheduling-hugepages/),...