"article"
);
suggester.indexer().indexFromDocument(reader, 4, 50);
```

### Search Analytics

```java
// Track user queries for analytics
QueryLog userQuery = new QueryLog("machine learning tutorials", "user456");
suggester.indexer().indexFromQueryLog(userQuery);

// Get trending searches
PopularWordsResponse trending = suggester.popularWords()
    .setSize(10)
    .execute()
    .getResponse();

### Memory per Process { #memory-per-process }

Now, when the program loads things in memory, for example, a machine learning model in a variable, or the contents of a large file in a variable, all that **consumes a bit of the memory (RAM)** of the server.

{* ../../docs_src/path_params/tutorial005.py hl[18,21,23] *}

İstemci tarafında şuna benzer bir JSON yanıtı ile karşılaşırsınız:

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## Yol İçeren Yol Parametreleri

Farz edelim ki elinizde `/files/{file_path}` isminde bir *yol operasyonu* var.

* **Maschinelles Lernen**: Normalerweise sind viele „Matrix“- und „Vektor“-Multiplikationen erforderlich. Stellen Sie sich eine riesige Tabelle mit Zahlen vor, in der Sie alle Zahlen gleichzeitig multiplizieren.

**FastAPI** is all based on these type hints, they give it many advantages and benefits.

But even if you never use **FastAPI**, you would benefit from learning a bit about them.

/// note

If you are a Python expert, and you already know everything about type hints, skip to the next chapter.

///

## Motivation { #motivation }

Let's start with a simple example:

### 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**.

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).

## 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._"

*   Added warmup capabilities to `tf.keras.optimizers.schedules.CosineDecay` learning rate scheduler. You can now specify an initial and target learning rate, and our scheduler will perform a linear interpolation between the two after which it will begin a decay phase.

### Memória do servidor

Por exemplo, se seu código carrega um modelo de Machine Learning com **1 GB de tamanho**, quando você executa um processo com sua API, ele consumirá pelo menos 1 GB de RAM. E se você iniciar **4 processos** (4 trabalhadores), cada um consumirá 1 GB de RAM. Então, no total, sua API consumirá **4 GB de RAM**.