cuda-libraries-dev-12-1
cuda-nvml-dev-12-1
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=9.8.0.87-1

nvidia-cuda-cupti-cu12>=12.5.82,<13.0
nvidia-cuda-nvcc-cu12>=12.5.82,<13.0
nvidia-cuda-nvrtc-cu12>=12.5.82,<13.0
nvidia-cuda-runtime-cu12>=12.5.82,<13.0
# The upper bound is set for the CUDNN API compatibility.
# See
# https://docs.nvidia.com/deeplearning/cudnn/backend/latest/developer/forward-compatibility.html#cudnn-api-compatibility
nvidia-cudnn-cu12>=9.3.0.75,<10.0
nvidia-cufft-cu12>=11.2.3.61,<12.0
nvidia-curand-cu12>=10.3.6.82,<11.0

cuda-libraries-dev-12-3
cuda-nvml-dev-12-3
libcufft-12-3
libcurand-12-3
libcusolver-dev-12-3
libcusparse-dev-12-3
libcublas-12-3
libcublas-dev-12-3
libnccl-dev=2.19.3-1+cuda12.3
libnccl2=2.19.3-1+cuda12.3
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-dev-cuda-12=9.1.1.17-1

# Testing a Database { #testing-a-database }

You can study about databases, SQL, and SQLModel in the <a href="https://sqlmodel.tiangolo.com/" class="external-link" target="_blank">SQLModel docs</a>. 🤓

There's a mini <a href="https://sqlmodel.tiangolo.com/tutorial/fastapi/" class="external-link" target="_blank">tutorial on using SQLModel with FastAPI</a>. ✨

///

### Проверьте документацию { #check-the-docs }

Теперь вы можете запустить приложение и перейти по адресу <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>.

Вы увидите документацию, включающую раздел «Callbacks» для вашей *операции пути*, который показывает, как должен выглядеть *внешний API*:

	// the kernel is arbitrarily okay with < 248 bytes. That
	// matches what the docs above say:
	// "When using an API to create a directory, the specified
	// path cannot be so long that you cannot append an 8.3 file
	// name (that is, the directory name cannot exceed MAX_PATH
	// minus 12)." Since MAX_PATH is 260, 260 - 12 = 248.
	//
	// The MSDN docs appear to say that a normal path that is 248 bytes long

cuda-libraries-dev-12-8
cuda-nvml-dev-12-8
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

* 따라서 이미지, 동영상, 큰 이진코드와 같은 대용량 파일들을 많은 메모리를 소모하지 않고 처리하기에 적합합니다.
* 업로드 된 파일의 메타데이터를 얻을 수 있습니다.
* <a href="https://docs.python.org/3/glossary.html#term-file-like-object" class="external-link" target="_blank">file-like</a>  `async` 인터페이스를 갖고 있습니다.
* file-like object를 필요로하는 다른 라이브러리에 직접적으로 전달할 수 있는 파이썬 <a href="https://docs.python.org/3/library/tempfile.html#tempfile.SpooledTemporaryFile" class="external-link" target="_blank">`SpooledTemporaryFile`</a> 객체를 반환합니다.

### Confira a documentação { #check-the-docs }

Agora você pode iniciar a sua aplicação e ir até <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>.

Você verá que a sua documentação possui as *operações de rota* normais e agora também possui alguns **webhooks**:

O **FastAPI** irá **extrair** os dados para **cada campo** dos **cookies** recebidos na requisição e lhe fornecer o modelo Pydantic que você definiu.

## Verifique a Documentação { #check-the-docs }

Você pode ver os cookies definidos na IU da documentação em `/docs`:

<div class="screenshot">
<img src="/img/tutorial/cookie-param-models/image01.png">
</div>

/// info | Informação