load(
    "@rules_ml_toolchain//third_party/gpus/cuda/hermetic:cuda_json_init_repository.bzl",
    "cuda_json_init_repository",
)

cuda_json_init_repository()

load(
    "@cuda_redist_json//:distributions.bzl",
    "CUDA_REDISTRIBUTIONS",
    "CUDNN_REDISTRIBUTIONS",
)
load(
    "@rules_ml_toolchain//third_party/gpus/cuda/hermetic:cuda_redist_init_repositories.bzl",

-   type: input id: Compiler attributes: label: GCC/Compiler version
    description: if compiling from source placeholder: validations: required:
    false

-   type: input id: Cuda attributes: label: CUDA/cuDNN version description:
    placeholder: validations: required: false

-   type: input id: Gpu attributes: label: GPU model and memory description: if

///

## Ordena los parámetros como necesites { #order-the-parameters-as-you-need }

/// tip | Consejo

Esto probablemente no es tan importante o necesario si usas `Annotated`.

///

Supongamos que quieres declarar el parámetro de query `q` como un `str` requerido.

# Config setting that is satisfied when TensorFlow is being built with CUDA
# support through e.g. `--config=cuda` (or `--config=cuda_clang` in OSS).
alias(
    name = "is_cuda_enabled",
    actual = if_oss(
        "@local_config_cuda//:is_cuda_enabled",
        "@local_config_cuda//cuda:using_config_cuda",
    ),
)

# Config setting that is satisfied when CUDA device code should be compiled

These "env" files match up with an environment matrix that roughly covers:

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs

## How to Test Your Changes to TensorFlow

You may check how your changes will affect TensorFlow by:

1. Creating a PR and observing the presubmit test results

      description: If compiling from source
  - type: input
    id: Compiler
    attributes:
      label: GCC/compiler version
      description: If compiling from source
  - type: input
    id: Cuda
    attributes:
      label: CUDA/cuDNN version
  - type: input
    id: Gpu
    attributes:
      label: GPU model and memory
      description: If compiling from source
  - type: textarea
    id: what-happened

/// note | Nota

Um parâmetro de path é sempre obrigatório, pois precisa fazer parte do path. Mesmo que você o declare como `None` ou defina um valor padrão, isso não afetaria nada, ele ainda seria sempre obrigatório.

///

## Ordene os parâmetros de acordo com sua necessidade { #order-the-parameters-as-you-need }

/// tip | Dica

load("@python_version_repo//:py_version.bzl", "REQUIREMENTS")
load("@rules_python//python:pip.bzl", "compile_pip_requirements")

# TODO(ybaturina): Remove once TF is migrated to CUDA 12.9.
genrule(
    name = "nvidia_constraints",
    srcs = ["nvidia-requirements.txt"],
    outs = ["nvidia-constraints.txt"],
    cmd = """sed -E "s/>=/==/" $(location nvidia-requirements.txt) > $@;""",
)

        flag.

        ```bash
        export flags="--config=linux --config=cuda -k"
        ```

    *   For TensorFlow versions prior v.2.18.0: Add CUDA paths to
        LD_LIBRARY_PATH and add the `cuda` option flag.

        ```bash
        export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH"

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