* Açılışta çalıştırma
* Yeniden başlatmalar
* Replikasyon (çalışan process sayısı)
* Bellek
* Başlatmadan önceki adımlar

Note: Linux and Mac only. Sorry Windows.

IMPORTANT: The 2.0 version of the profiler doesn't seem to be with compatible
with JMH as of 2021-04-30.

The async profiler is neat because it does not suffer from the safepoint
bias problem. And because it makes pretty flame graphs!

Let user processes read performance stuff:
```
sudo bash
echo 0 > /proc/sys/kernel/kptr_restrict
echo 1 > /proc/sys/kernel/perf_event_paranoid
exit
```

# Obtener Usuario Actual { #get-current-user }

En el capítulo anterior, el sistema de seguridad (que se basa en el sistema de inyección de dependencias) le estaba dando a la *path operation function* un `token` como un `str`:

{* ../../docs_src/security/tutorial001_an_py310.py hl[12] *}

Pero eso aún no es tan útil. Vamos a hacer que nos dé el usuario actual.

## Crear un modelo de usuario { #create-a-user-model }

    Random random = new Random();
    int nKeys = keys.size();
    long blah = 0;
    for (int i = 0; i < reps; i++) {
      Integer key = keys.get(random.nextInt(nKeys));
      // This range is [-5, 4] - slight negative bias so we often hit zero, which brings the
      // auto-removal of zeroes into play.
      int delta = random.nextInt(10) - 5;
      blah += delta;
      if (delta >= 0) {
        multiset.add(key, delta);

Este es solo un **tutorial rápido / recordatorio** sobre las anotaciones de tipos en Python. Cubre solo lo mínimo necesario para usarlas con **FastAPI**... que en realidad es muy poco.

**FastAPI** se basa completamente en estas anotaciones de tipos, dándole muchas ventajas y beneficios.

Pero incluso si nunca usas **FastAPI**, te beneficiaría aprender un poco sobre ellas.

/// note | Nota

"translate","type","usemap","valign","value","width","wrap","xmlns","slot"]),P=i(["accent-height","accumulate","additive","alignment-baseline","amplitude","ascent","attributename","attributetype","azimuth","basefrequency","baseline-shift","begin","bias","by","class","clip","clippathunits","clip-path","clip-rule","color","color-interpolation","color-interpolation-filters","color-profile","color-rendering","cx","cy","d","dx","dy","diffuseconstant","direction","display","divisor","dur","edgemode","...

///

Como SQLModel se basa en SQLAlchemy, puedes usar fácilmente **cualquier base de datos soportada** por SQLAlchemy (lo que las hace también soportadas por SQLModel), como:

* PostgreSQL
* MySQL
* SQLite
* Oracle
* Microsoft SQL Server, etc.

Ayrıca, aynı anda hem paralellik hem de asenkronluk kullanabildiğiniz için, test edilen çoğu NodeJS framework’ünden daha yüksek ve C’ye daha yakın derlenen bir dil olan Go ile başa baş performans elde edersiniz [(hepsi Starlette sayesinde)](https://www.techempower.com/benchmarks/#section=data-r17&hw=ph&test=query&l=zijmkf-1).

### Eşzamanlılık paralellikten daha mı iyi? { #is-concurrency-better-than-parallelism }

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        *   The new backend is used for 8 bits full integer post-training
            quantization
        *   The new backend removes the redundant rescales and fixes some bugs
            (shared weight/bias, extremely small scales, etc)
        *   Set `experimental_new_quantizer` in tf.lite.TFLiteConverter to False
            to disable this change
*   `tf.keras`
    *   `tf.keras.metrics.AUC` now support logit predictions.