## Установка `PyJWT` { #install-pyjwt }

Нам необходимо установить `pyjwt` для генерации и проверки JWT-токенов на языке Python.

Убедитесь, что вы создали [виртуальное окружение](../../virtual-environments.md), активируйте его, а затем установите `pyjwt`:

<div class="termy">

```console
$ pip install pyjwt

---> 100%
```

</div>

libcufft-13-0
libcurand-13-0
libcusolver-dev-13-0
libcusparse-dev-13-0
libcublas-13-0
libcublas-dev-13-0
libnccl-dev=2.27.7-1+cuda13.0
libnccl2=2.27.7-1+cuda13.0
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-headers-cuda-13=9.15.1.9-1
libcudnn9-static-cuda-13=9.15.1.9-1
libcudnn9-dev-cuda-13=9.15.1.9-1

## Ejecútalo { #run-it }

/// info | Información

El paquete [`python-multipart`](https://github.com/Kludex/python-multipart) se instala automáticamente con **FastAPI** cuando ejecutas el comando `pip install "fastapi[standard]"`.

Sin embargo, si usas el comando `pip install fastapi`, el paquete `python-multipart` no se incluye por defecto.

### Instalar `pydantic-settings` { #install-pydantic-settings }

Primeiro, certifique-se de criar seu [ambiente virtual](../virtual-environments.md), ativá-lo e então instalar o pacote `pydantic-settings`:

<div class="termy">

```console
$ pip install pydantic-settings
---> 100%
```

</div>

Ele também vem incluído quando você instala os extras `all` com:

<div class="termy">

libcufft-12-8
libcurand-12-8
libcusolver-dev-12-8
libcusparse-dev-12-8
libcublas-12-8
libcublas-dev-12-8
libnccl-dev=2.25.1-1+cuda12.8
libnccl2=2.25.1-1+cuda12.8
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-dev-cuda-12=9.8.0.87-1

libcufft-12-1
libcurand-12-1
libcusolver-dev-12-1
libcusparse-dev-12-1
libcublas-12-1
libcublas-dev-12-1
libnccl-dev=2.18.3-1+cuda12.1
libnccl2=2.18.3-1+cuda12.1
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-dev-cuda-12=9.1.1.17-1

일반적인 선택은 Jinja2로, Flask와 다른 도구에서도 사용됩니다.

설정을 쉽게 할 수 있는 유틸리티가 있으며, 이를 **FastAPI** 애플리케이션에서 직접 사용할 수 있습니다(Starlette 제공).

## 의존성 설치 { #install-dependencies }

[가상 환경](../virtual-environments.md)을 생성하고, 활성화한 후 `jinja2`를 설치해야 합니다:

<div class="termy">

```console
$ pip install jinja2

---> 100%
```

</div>

## `Jinja2Templates` 사용하기 { #using-jinja2templates }

* `Jinja2Templates`를 가져옵니다.

libcufft-13-0
libcurand-13-0
libcusolver-dev-13-0
libcusparse-dev-13-0
libcublas-13-0
libcublas-dev-13-0
libnccl-dev=2.27.7-1+cuda13.0
libnccl2=2.27.7-1+cuda13.0
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-headers-cuda-13=9.12.0.46-1
libcudnn9-static-cuda-13=9.12.0.46-1
libcudnn9-dev-cuda-13=9.12.0.46-1

<div class="termy">

```console
$ pip install "fastapi[standard]"

---> 100%
```

</div>

////

//// tab | `uv`

如果你有 [`uv`](https://github.com/astral-sh/uv):

<div class="termy">

```console
$ uv pip install "fastapi[standard]"
---> 100%
```

</div>

////

### 從 `requirements.txt` 安裝 { #install-from-requirements-txt }

<div class="termy">

```console
$ pip install -r requirements.txt
---> 100%
Successfully installed fastapi pydantic
```

</div>

/// info | Informação

Há outros formatos e ferramentas para definir e instalar dependências de pacotes.

///

### Crie o código do **FastAPI** { #create-the-fastapi-code }