WIP ML Build Docker container for ML repositories (Tensorflow, JAX and XLA).

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

```Python hl_lines="3  6-8"
{!../../docs_src/sub_applications/tutorial001.py!}
```

### Sub-application

Then, create your sub-application, and its *path operations*.

This sub-application is just another standard FastAPI application, but this is the one that will be "mounted":

```Python hl_lines="11  14-16"
{!../../docs_src/sub_applications/tutorial001.py!}
```

pyyaml >=5.3.1,<7.0.0
# For Material for MkDocs, Chinese search
jieba==0.42.1
# For image processing by Material for MkDocs
pillow==11.0.0
# For image processing by Material for MkDocs
cairosvg==2.7.1
mkdocstrings[python]==0.26.1
griffe-typingdoc==0.2.7
# For griffe, it formats with black
black==24.3.0
mkdocs-macros-plugin==1.0.5

#!/usr/bin/env/env python3
import boto3

s3 = boto3.client('s3',
                  endpoint_url='http://localhost:9000',
                  aws_access_key_id='minio',
                  aws_secret_access_key='minio123',
                  region_name='us-east-1')

r = s3.select_object_content(
    Bucket='mycsvbucket',
    Key='sampledata/TotalPopulation.csv.gz',
    ExpressionType='SQL',

# 응답 쿠키

## `Response` 매개변수 사용하기

*경로 작동 함수*에서 `Response` 타입의 매개변수를 선언할 수 있습니다.

그런 다음 해당 *임시* 응답 객체에서 쿠키를 설정할 수 있습니다.

```Python hl_lines="1  8-9"
{!../../docs_src/response_cookies/tutorial002.py!}
```

그런 다음 필요한 객체(`dict`, 데이터베이스 모델 등)를 반환할 수 있습니다.

그리고 `response_model`을 선언했다면 반환한 객체를 거르고 변환하는 데 여전히 사용됩니다.

**FastAPI**는 그 *임시* 응답에서 쿠키(또한 헤더 및 상태 코드)를 추출하고, 반환된 값이 포함된 최종 응답에 이를 넣습니다. 이 값은 `response_model`로 걸러지게 됩니다.

# 响应Cookies

## 使用 `Response` 参数

你可以在 *路径函数* 中定义一个类型为 `Response`的参数，这样你就可以在这个临时响应对象中设置cookie了。

```Python hl_lines="1  8-9"
{!../../docs_src/response_cookies/tutorial002.py!}
```

而且你还可以根据你的需要响应不同的对象，比如常用的 `dict`，数据库model等。

如果你定义了 `response_model`，程序会自动根据`response_model`来过滤和转换你响应的对象。

**FastAPI** 会使用这个 *临时* 响应对象去装在这些cookies信息 (同样还有headers和状态码等信息), 最终会将这些信息和通过`response_model`转化过的数据合并到最终的响应里。

## 🛠️

🚥 👆 ⏪ 🖖 🗃 & 👆 💭 👈 👆 💪 ⏬ 🤿 📟, 📥 📄 ⚒ 🆙 👆 🌐.

### 🕹 🌐 ⏮️ `venv`

👆 💪 ✍ 🕹 🌐 📁 ⚙️ 🐍 `venv` 🕹:

<div class="termy">

```console
$ python -m venv env
```

</div>

👈 🔜 ✍ 📁 `./env/` ⏮️ 🐍 💱 &amp; ⤴️ 👆 🔜 💪 ❎ 📦 👈 ❎ 🌐.

### 🔓 🌐

🔓 🆕 🌐 ⏮️:

//// tab | 💾, 🇸🇻

<div class="termy">

```console

You can declare a parameter in a *path operation function* or dependency to be of type `Request` and then you can access the raw request object directly, without any validation, etc.

You can import it directly from `fastapi`:

```python
from fastapi import Request
```

/// tip

ここで、タスク関数はファイル書き込みを実行します (メール送信のシミュレーション)。

また、書き込み操作では `async` と `await` を使用しないため、通常の `def` で関数を定義します。

```Python hl_lines="6-9"
{!../../docs_src/background_tasks/tutorial001.py!}
```

## バックグラウンドタスクの追加

*path operations 関数* 内で、`.add_task()` メソッドを使用してタスク関数を *background tasks* オブジェクトに渡します。

```Python hl_lines="14"
{!../../docs_src/background_tasks/tutorial001.py!}
```

`.add_task()` は以下の引数を受け取ります:

 & `--root-path` 📋 ⏸ 🎛 🚚 👈 `root_path`.

///

### ✅ ⏮️ `root_path`

👆 💪 🤚 ⏮️ `root_path` ⚙️ 👆 🈸 🔠 📨, ⚫️ 🍕 `scope` 📖 (👈 🍕 🔫 🔌).

📥 👥 ✅ ⚫️ 📧 🎦 🎯.

```Python hl_lines="8"
{!../../docs_src/behind_a_proxy/tutorial001.py!}
```

⤴️, 🚥 👆 ▶️ Uvicorn ⏮️:

<div class="termy">

```console
$ uvicorn main:app --root-path /api/v1