test:unsupported_cpu_linux --config=release_base

build:unsupported_gpu_linux --config=cuda
build:unsupported_gpu_linux --config=unsupported_cpu_linux
build:unsupported_gpu_linux --action_env=TF_CUDA_VERSION="11"
build:unsupported_gpu_linux --action_env=TF_CUDNN_VERSION="8"
build:unsupported_gpu_linux --repo_env=TF_CUDA_COMPUTE_CAPABILITIES="sm_35,sm_50,sm_60,sm_70,sm_75,compute_80"
build:unsupported_gpu_linux --config=tensorrt

# VERY basic check to ensure the [and-cuda] package variant is installable.
# Checks TFCI_BAZEL_COMMON_ARGS for "gpu" or "cuda", implying that the test is
# relevant. All of the GPU test machines have CUDA installed via other means,
# so I am not sure how to verify that the dependencies themselves are valid for
# the moment.
if [[ "$TFCI_BAZEL_COMMON_ARGS" =~ gpu|cuda ]]; then
  echo "Checking to make sure tensorflow[and-cuda] is installable..."

  }

  auto* gpu_options = config.mutable_gpu_options();
  gpu_options->set_allow_growth(gpu_memory_allow_growth);

  (*config.mutable_device_count())["CPU"] = num_cpu_devices;

  // TODO(b/113217601): This is needed for EagerContext::runner_ to use a
  // threadpool, so that we avoid the possibility of running the runner_ in the
  // threadpool of GPU event mgr, as that can trigger more callbacks to be

    and
    [GPU developer Dockerfile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dockerfiles/dockerfiles/devel-gpu.Dockerfile)
    for the required packages. Alternatively, use the said
    [tensorflow/build Docker images](https://hub.docker.com/r/tensorflow/build)
    (`tensorflow/tensorflow:devel` and `tensorflow/tensorflow:devel-gpu` are no

  # At most 1 GPU platform can be configured.
  gpu_platform_count = 0
  if environ_cp.get('TF_NEED_ROCM') == '1':
    gpu_platform_count += 1
  if environ_cp.get('TF_NEED_CUDA') == '1':
    gpu_platform_count += 1
  if gpu_platform_count >= 2:
    raise UserInputError('CUDA / ROCm are mututally exclusive. '
                         'At most 1 GPU platform can be configured.')

TEST(CAPI, Session_Min_GPU) {
  const string gpu_device = GPUDeviceName();
  // Skip this test if no GPU is available.
  if (gpu_device.empty()) return;

  RunMinTest(gpu_device, /*use_XLA=*/false);
}

TEST(CAPI, Session_Min_XLA_GPU) {
  const string gpu_device = GPUDeviceName();
  // Skip this test if no GPU is available.
  if (gpu_device.empty()) return;

# limitations under the License.
# ==============================================================================
source "${BASH_SOURCE%/*}/utilities/setup.sh"

# Record GPU count and CUDA version status
if [[ "$TFCI_NVIDIA_SMI_ENABLE" == 1 ]]; then
  tfrun nvidia-smi
fi

# Update the version numbers for Nightly only
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then

        `XNNPACK` delegate automatically when the model has a `fp32` operation.
*   GPU
    *   Allow GPU acceleration starting with internal graph nodes
    *   Experimental support for quantized models with the Android GPU delegate
    *   Add GPU delegate whitelist.
    *   Rename GPU whitelist -> compatibility (list).
    *   Improve GPU compatibility list entries from crash reports.
*   NNAPI
    *   Set default value for

        with:
          api-level: ${{ matrix.api-level }}
          arch: ${{ matrix.api-level == '34' && 'x86_64' || 'x86' }}
          force-avd-creation: false
          emulator-options: -no-window -gpu swiftshader_indirect -noaudio -no-boot-anim -camera-back none
          disable-animations: false
          script: echo "Generated AVD snapshot for caching."

      - name: Run Tests

  # package, ever.
  cat > $BATS_TEST_TMPDIR/ignore_these_deps <<EOF
benchmark
_test$
_test.py$
_test_cpu$
_test_cpu.py$
_test_gpu$
_test_gpu.py$
_test_lib$
//tensorflow/cc/saved_model:saved_model_test_files
//tensorflow/cc/saved_model:saved_model_half_plus_two
//tensorflow:no_tensorflow_py_deps
//tensorflow/tools/pip_package:win_pip_package_marker