# All required CUDA packages
cuda-compat-12-8
cuda-command-line-tools-12-8
cuda-cudart-dev-12-8
cuda-nvcc-12-8
cuda-cupti-12-8
cuda-nvprune-12-8
cuda-libraries-12-8
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

# All required CUDA packages
cuda-compat-13-0
cuda-command-line-tools-13-0
cuda-cudart-dev-13-0
cuda-nvcc-13-0
cuda-cupti-13-0
cuda-nvprune-13-0
cuda-libraries-13-0
cuda-libraries-dev-13-0
cuda-nvml-dev-13-0
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

# All required CUDA packages
cuda-compat-12-1
cuda-command-line-tools-12-1
cuda-cudart-dev-12-1
cuda-nvcc-12-1
cuda-cupti-12-1
cuda-nvprune-12-1
cuda-libraries-12-1
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

# packages only provide the "0.10-2build1" version. We use patchelf to manipulate
# certain shared libraries during the wheel building process (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/build_pip_package.sh#L255-L262).
# When we use Patchelf versions <0.12, those shared libraries end up with a

# Tensorflow to the default, however test coverage wasn't enough to catch the
# errors.
# There is ongoing work on Bazel team's side to provide support for transitive
# shared libraries. As part of migrating to transitive shared libraries, we
# hope to provide a better mechanism for control over symbol exporting, and
# then tackle this issue again.
#
# TODO: Remove the following two lines once TF doesn't depend on Bazel wrapping

# All required CUDA packages
cuda-compat-12-1
cuda-command-line-tools-12-1
cuda-cudart-dev-12-1
cuda-nvcc-12-1
cuda-cupti-12-1
cuda-nvprune-12-1
cuda-libraries-12-1
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

# All required CUDA packages
cuda-compat-12-3
cuda-command-line-tools-12-3
cuda-cudart-dev-12-3
cuda-nvcc-12-3
cuda-cupti-12-3
cuda-nvprune-12-3
cuda-libraries-12-3
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

they need to be linked into the final binary, along with the libraries
they might depend on (in the case of puts, stdio). cmd/link has been
extended to understand basic ELF files, but it does not understand ELF
in the full complexity that modern C libraries embrace, so it cannot
in general generate direct references to the system libraries.

Instead, the build process generates an object file using dynamic

This will catch most license, Python coding style and BUILD file issues that
may exist in your changes.

#### Running unit tests

There are two ways to run TensorFlow unit tests.

1.  Using tools and libraries installed directly on your system.

    Refer to the
    [CPU-only developer Dockerfile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dockerfiles/dockerfiles/devel-cpu.Dockerfile)
    and

[TensorFlow](https://www.tensorflow.org/) is an end-to-end open source platform
for machine learning. It has a comprehensive, flexible ecosystem of
[tools](https://www.tensorflow.org/resources/tools),
[libraries](https://www.tensorflow.org/resources/libraries-extensions), and
[community](https://www.tensorflow.org/community) resources that lets
researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.