# an entry in tensorflow/.bazelrc for build:rbe_TFCI_BAZEL_TARGET...
#                                           ^^^^
# This env is only valid when RBE is available on the selected platform, since
# it enables a derived --config setting.  If RBE is not available (i.e. there
# is no --config setting), bazel would fail and quit. This script does a quick
# check This script checks for such errors early

export TF_VER_SUFFIX=$(awk -F\" '/#define TF_VERSION_SUFFIX/ {print $2}' tensorflow/core/public/version.h)
export TF_VER_PYTHON=$(awk -F\' '/_VERSION =/ {print $2}' tensorflow/tools/pip_package/setup.py)

# Derived helper variables.
export TF_VER_SHORT="${TF_VER_MAJOR}.${TF_VER_MINOR}"

    "somepath(//tensorflow/tools/pip_package:build_pip_package, " \
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cuda_driver + "\
    "@local_config_cuda//cuda:cudnn + "\
    "@local_config_cuda//cuda:curand + "\
    "@local_config_cuda//cuda:cusolver + "\
    "@local_config_tensorrt//:tensorrt)" --keep_going > $BATS_TEST_TMPDIR/out

// Create a packed TensorHandle with the given list of TensorHandles.
// If `handles` are on the same device, assign the same device to the packed
// handle; if `handles` are on different deivces, assign a CompositeDevice to
// it.
TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_CreatePackedTensorHandle(
    TFE_Context* ctx, TFE_TensorHandle** handles, int* num_handles,
    TF_Status* status);

    disallowed_deps = if_static(
        [],
        otherwise = [
            "@local_xla//xla/stream_executor/cuda:all_runtime",
            "@local_xla//xla/stream_executor/cuda:cuda_driver",
            "@local_xla//xla/stream_executor/cuda:cuda_platform",
            "@local_xla//xla/stream_executor/cuda:cudnn_plugin",
            "@local_xla//xla/stream_executor/cuda:cufft_plugin",

`tensorflow` pip package has a new, optional installation method for Linux that installs necessary Nvidia CUDA libraries through pip. As long as the Nvidia driver is already installed on the system, you may now run `pip install tensorflow[and-cuda]` to install TensorFlow's Nvidia CUDA library dependencies in the Python environment. Aside from the Nvidia driver, no other pre-existing Nvidia CUDA packages are necessary.

*   Enable JIT-compiled i64-indexed kernels on GPU for large tensors with more...

    sdkmanager 'cmake;3.6.4111459'; \
    sdkmanager 'emulator'; \
    sdkmanager 'system-images;android-27;google_apis;x86'; \
    sdkmanager 'sources;android-27'; \
    sdkmanager 'extras;google;Android_Emulator_Hypervisor_Driver'; \
    sdkmanager 'extras;google;auto'; \
    sdkmanager 'extras;google;google_play_services'; \
    sdkmanager 'extras;google;instantapps'; \
    sdkmanager 'extras;google;m2repository'; \

    --@local_config_cuda//:enable_cuda \
    "somepath(//tensorflow/tools/pip_package:wheel, " \
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cuda_driver + "\
    "@local_config_cuda//cuda:cudnn + "\
    "@local_config_cuda//cuda:curand + "\
    "@local_config_cuda//cuda:cusolver + "\
    "@local_config_tensorrt//:tensorrt)" --keep_going > $BATS_TEST_TMPDIR/out