## Deployment Concepts { #deployment-concepts }

Here you saw how to use multiple **workers** to **parallelize** the execution of the application, take advantage of **multiple cores** in the CPU, and be able to serve **more requests**.

L'idée de code **asynchrone** décrite ci-dessus est parfois aussi appelée **"concurrence"**. Ce qui est différent du **"parallélisme"**.

La **concurrence** et le **parallélisme** sont tous deux liés à l'idée de "différentes choses arrivant plus ou moins au même moment".

Mais les détails entre la **concurrence** et le **parallélisme** diffèrent sur de nombreux points.

Pour expliquer la différence, voici une histoire de burgers :

This idea of **asynchronous** code described above is also sometimes called **"concurrency"**. It is different from **"parallelism"**.

**Concurrency** and **parallelism** both relate to "different things happening more or less at the same time".

But the details between *concurrency* and *parallelism* are quite different.

To see the difference, imagine the following story about burgers:

### Parallele Hamburger

Stellen wir uns jetzt vor, dass es sich hierbei nicht um „nebenläufige Hamburger“, sondern um „parallele Hamburger“ handelt.

Sie gehen los mit Ihrem Schwarm, um paralleles Fast Food zu bekommen.

					updates <- cache.clone()
					return
				}
				cache.replace(v.Name, v.Parent, v.Entry)
				cache.Info.LastUpdate = time.Now()
			}
		}
	}()

	// Restrict parallelism for disk usage scanner
	// upto GOMAXPROCS if GOMAXPROCS is < len(disks)
	maxProcs := runtime.GOMAXPROCS(0)
	if maxProcs < len(disks) {
		disks = disks[:maxProcs]
	}

	// Start one scanner per disk

### Feature

- Parallelize calls to conversion webhooks when watching from etcd to prevent watchcache from lagging and helps clusters with slow conversion webhooks or transformers. 

        transformations deterministic. The option will now apply the same across
        all transformations.
    *   The optimization `parallel_batch` now becomes default if not disabled by
        users, which will parallelize copying of batch elements.
    *   Added the ability for `TensorSliceDataset` to identify and handle inputs
        that are files. This enables creating hermetic SavedModels when using
        datasets created from files.

# 並行與 async / await

有關*路徑操作函式*的 `async def` 語法的細節與非同步 (asynchronous) 程式碼、並行 (concurrency) 與平行 (parallelism) 的一些背景知識。

## 趕時間嗎?

<abbr title="too long; didn't read（文長慎入）"><strong>TL;DR:</strong></abbr>

如果你正在使用要求你以 `await` 語法呼叫的第三方函式庫，例如：

```Python
results = await some_library()
```

然後，使用 `async def` 宣告你的*路徑操作函式*：


```Python hl_lines="2"
@app.get('/')
async def read_results():
    results = await some_library()

# 並行処理と async / await

*path operation 関数*のための `async def` に関する詳細と非同期 (asynchronous) コード、並行処理 (Concurrency)、そして、並列処理 (Parallelism) の背景について。

## 急いでいますか？

<abbr title="too long; didn't read (長すぎて読めない人のための要約という意味のスラング)"><strong>TL;DR:</strong></abbr>

次のような、`await` を使用して呼び出すべきサードパーティライブラリを使用している場合:

```Python
results = await some_library()
```

以下の様に `async def` を使用して*path operation 関数*を宣言します。

```Python hl_lines="2"
@app.get('/')