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;

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

// completed. This is only valid on local TFE_TensorHandles. The pointer
// returned will be on the device in which the TFE_TensorHandle resides (so e.g.
// for a GPU tensor this will return a pointer to GPU memory). The pointer is
// only guaranteed to be valid until TFE_DeleteTensorHandle is called on this
// TensorHandle. Only supports POD data types.

/// tip | "Tipp"

Das *Herunterfahren* würde erfolgen, wenn Sie die Anwendung **stoppen**.

Möglicherweise müssen Sie eine neue Version starten, oder Sie haben es einfach satt, sie auszuführen. 🤷

    def "matches google-chrome-for-testing"() {
        def line = '3723579 /usr/bin/google-chrome-for-testing --allow-pre-commit-input --disable-background-networking --disable-client-side-phishing-detection --disable-default-apps --disable-gpu --disable-hang-monitor --disable-popup-blocking --disab'

        def projectDir = "/whatever"

        expect:
        (line =~ KillLeakingJavaProcesses.generateLeakingProcessKillPattern(projectDir)).find()
    }

  TF_AddInput(desc, {zero, 0});
  TF_AddInput(desc, {input, 0});
  TF_SetAttrInt(desc, "num_split", 3);
  TF_SetAttrType(desc, "T", TF_INT32);
  // Set device to CPU since there is no version of split for int32 on GPU
  // TODO(iga): Convert all these helpers and tests to use floats because
  // they are usually available on GPUs. After doing this, remove TF_SetDevice
  // call in c_api_function_test.cc
  TF_SetDevice(desc, "/cpu:0");

* Do not query the metadata server to find out if running on GCE.  Retry metadata server query for gcr if running on gce. ([#28871](https://github.com/kubernetes/kubernetes/pull/28871), [@vishh](https://github.com/vishh))
* Fix GPU resource validation ([#28743](https://github.com/kubernetes/kubernetes/pull/28743), [@therc](https://github.com/therc))

)

config_setting(
    name = "with_xla_support",
    define_values = {"with_xla_support": "true"},
    visibility = ["//visibility:public"],
)

# By default, XLA GPU is compiled into tensorflow when building with
# --config=cuda even when `with_xla_support` is false. The config setting
# here allows us to override the behavior if needed.
config_setting(
    name = "no_xla_deps_in_cuda",

    * Fix stop hook failure on kubernetes-worker charm

    * Fix handling of juju kubernetes-worker.restart-needed state

    * Fix nagios checks in charms

  * Enable GPU mode if GPU hardware detected ([#43467](https://github.com/kubernetes/kubernetes/pull/43467), [@tvansteenburgh](https://github.com/tvansteenburgh))

    for (auto input : inputs) {
      TF_AddInput(desc, input);
    }
    // Set device to CPU because some ops inside the function might not be
    // available on GPU.
    TF_SetDevice(desc, "/cpu:0");
    *op = TF_FinishOperation(desc, s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
    ASSERT_NE(*op, nullptr);
  }

  FunctionDef fdef() {

* Support for the `alpha.kubernetes.io/nvidia-gpu` resource, which was deprecated in 1.10, has been removed. Please use the resource exposed by DevicePlugins instead (`nvidia.com/gpu`). ([#61498](https://github.com/kubernetes/kubernetes/pull/61498), [@mindprince](https://github.com/mindprince))