"aquilo", "aquí", "ao", "aos", "as", "así", "á", "ben", "cando", "che", "co", "coa", "comigo", "con", "connosco", "contigo", "convosco", "coas", "cos", "cun", "cuns", "cunha", "cunhas", "da", "dalgunha", "dalgunhas", "dalgún", "dalgúns", "das", "de", "del", "dela", "delas", "deles", "desde", "deste", "do", "dos", "dun", "duns", "dunha", "dunhas", "e", "el", "ela", "elas", "eles", "en", "era", "eran", "esa", "esas", "ese", "eses", "esta", "estar", "estaba", "está", "están", "este", "estes", "estiven",...

nvidia-cublas-cu12>=12.5.3.2,<13.0
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

Debido a esto, el propio **FastAPI** proporciona una documentación de API alternativa (usando ReDoc), a la cual puedes acceder en <a href="http://127.0.0.1:8000/redoc" class="external-link" target="_blank">http://127.0.0.1:8000/redoc</a>:

<img src="/img/tutorial/path-params/image02.png">

Una ventaja particular que no es necesariamente obvia es que puedes **generar clientes** (a veces llamados <abbr title="Software Development Kits">**SDKs**</abbr> ) para tu API, para muchos **lenguajes de programación** diferentes.

## Generadores de Clientes OpenAPI

/// tip | Consejo

Para preguntas, házlas en <a href="https://github.com/fastapi/fastapi/discussions/new?category=questions" class="external-link" target="_blank">GitHub Discussions</a>, hay muchas más probabilidades de que recibas ayuda de parte de los [FastAPI Experts](fastapi-people.md#fastapi-experts){.internal-link target=_blank}.

Usa el chat solo para otras conversaciones generales.

///

    # via
    #   -r ci/official/requirements_updater/requirements.in
    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
nvidia-cublas-cu12==12.5.3.2 \
    --hash=sha256:4960f3dc5f39699acadf76fa6d94b10a2a00f2956c2c442efa299fb22b0748f3 \
    --hash=sha256:7d0191251180de606023d396b94d66f66470a0ae96d1dbb906c7656ea0f71eda \

    # via
    #   -r ci/official/requirements_updater/requirements.in
    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
nvidia-cublas-cu12==12.5.3.2 \
    --hash=sha256:4960f3dc5f39699acadf76fa6d94b10a2a00f2956c2c442efa299fb22b0748f3 \
    --hash=sha256:7d0191251180de606023d396b94d66f66470a0ae96d1dbb906c7656ea0f71eda \

    # via
    #   -r ci/official/requirements_updater/requirements.in
    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
nvidia-cublas-cu12==12.5.3.2 \
    --hash=sha256:4960f3dc5f39699acadf76fa6d94b10a2a00f2956c2c442efa299fb22b0748f3 \
    --hash=sha256:7d0191251180de606023d396b94d66f66470a0ae96d1dbb906c7656ea0f71eda \

* **Machine Learning**: normalmente requiere muchas multiplicaciones de "matrices" y "vectores". Piensa en una enorme hoja de cálculo con números y multiplicando todos juntos al mismo tiempo.

    # via
    #   -r ci/official/requirements_updater/requirements.in
    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
nvidia-cublas-cu12==12.5.3.2 \
    --hash=sha256:4960f3dc5f39699acadf76fa6d94b10a2a00f2956c2c442efa299fb22b0748f3 \
    --hash=sha256:7d0191251180de606023d396b94d66f66470a0ae96d1dbb906c7656ea0f71eda \