#   container and start fresh, run "docker rm -f tf". Removing the container
#   destroys some temporary bazel data and causes longer builds.
#
#   You will need the NVIDIA Container Toolkit for GPU testing:
#   https://github.com/NVIDIA/nvidia-container-toolkit
#
#   Note: if you interrupt a bazel command on docker (ctrl-c), you
#   will need to run `docker exec tf pkill bazel` to quit bazel.
#

*   Performance improvements
*   Many bugfixes
*   Many documentation fixes
*   TensorBoard fixes: graphs with only one data point, Nan values, reload
    button and auto-reload, tooltips in scalar charts, run filtering, stable
    colors
*   Tensorboard graph visualizer now supports run metadata. Clicking on nodes
    while viewing a stats for a particular run will show runtime statistics,

    print('Found NCCL %s in:' % config['nccl_version'])
    print('    %s' % config['nccl_library_dir'])
    print('    %s' % config['nccl_include_dir'])

  print('\n')

  environ_cp['CUDA_TOOLKIT_PATH'] = config['cuda_toolkit_path']
  return True


def get_gcc_compiler(environ_cp):
  gcc_env = environ_cp.get('CXX') or environ_cp.get('CC') or which('gcc')
  if gcc_env is not None:

    google-auth google-auth-httplib2 grpcio httplib2 idna ipython-genutils \
    kiwisolver macholib matplotlib nose numpy packaging pandas pickleshare pip \
    prompt-toolkit protobuf psutil pyasn1 pyasn1-modules pycparser Pygments \
    pyparsing pyreadline python-dateutil pytz pywin32 requests rsa setuptools \
    simplegeneric six Tempita traitlets uritemplate urllib3 virtualenv wcwidth \

build:cuda_clang_official --config=cuda_clang
build:cuda_clang_official --action_env=TF_CUDA_VERSION="12"
build:cuda_clang_official --action_env=TF_CUDNN_VERSION="8"
build:cuda_clang_official --action_env=CUDA_TOOLKIT_PATH="/usr/local/cuda-12.3"
build:cuda_clang_official --action_env=GCC_HOST_COMPILER_PATH="/dt9/usr/bin/gcc"
build:cuda_clang_official --action_env=CLANG_CUDA_COMPILER_PATH="/usr/lib/llvm-17/bin/clang"

*   [TensorFlow model optimization roadmap](https://www.tensorflow.org/model_optimization/guide/roadmap)
*   [TensorFlow White Papers](https://www.tensorflow.org/about/bib)
*   [TensorBoard Visualization Toolkit](https://github.com/tensorflow/tensorboard)
*   [TensorFlow Code Search](https://cs.opensource.google/tensorflow/tensorflow)

Learn more about the
[TensorFlow community](https://www.tensorflow.org/community) and how to