// ContainerResourceMetricSource indicates how to scale on a resource metric known to
// Kubernetes, as specified in requests and limits, describing each pod in the
// current scale target (e.g. CPU or memory).  The values will be averaged
// together before being compared to the target.  Such metrics are built in to
// Kubernetes, and have special scaling options on top of those available to

// ContainerResourceMetricSource indicates how to scale on a resource metric known to
// Kubernetes, as specified in requests and limits, describing each pod in the
// current scale target (e.g. CPU or memory).  The values will be averaged
// together before being compared to the target.  Such metrics are built in to
// Kubernetes, and have special scaling options on top of those available to

    TFE_TensorHandle* h, TF_Status* status);

// This function will block till the operation that produces `h` has
// completed. The memory returned might alias the internal memory used by
// TensorFlow. Hence, callers should not mutate this memory (for example by
// modifying the memory region pointed to by TF_TensorData() on the returned
// TF_Tensor).
TF_CAPI_EXPORT extern TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h,

      TFE_NewContextOptions(), TFE_DeleteContextOptions);
  std::unique_ptr<TF_Buffer, decltype(&TF_DeleteBuffer)> config(
      TF_CreateConfig(
          /*xla*/ false,
          /* gpu_memory_allow_growth */ true, /* num_cpu_devices */
          2),
      TF_DeleteBuffer);
  TFE_ContextOptionsSetConfig(opts.get(), config->data, config->length,
                              status.get());

        buf.append("},");
        buf.append("\"virtual_memory\":{");
        append(buf, "total", () -> processProbe.getTotalVirtualMemorySize());
        buf.append('}');
        buf.append("},");
    }

    private void appendOsStats(final StringBuilder buf) {
        buf.append("\"os\":{");
        final OsProbe osProbe = OsProbe.getInstance();
        buf.append("\"memory\":{");
        buf.append("\"physical\":{");

    ## Whether to automatically allocate NodePorts (only for LoadBalancers).
    # allocateLoadBalancerNodePorts: false

  resources:
    requests:
      cpu: 100m
      memory: 128Mi
    limits:
      cpu: 2000m
      memory: 1024Mi

  autoscaling:
    enabled: true
    minReplicas: 1
    maxReplicas: 5
    targetCPUUtilizationPercentage: 80
    targetMemoryUtilizationPercentage: {}

      autoscaleEnabled: true
      autoscaleMin: 1
      autoscaleMax: 5
      resources:
        requests:
          cpu: 100m
          memory: 128Mi
        limits:
          cpu: 2000m
          memory: 1024Mi
      cpu:
        targetAverageUtilization: 80
      memory: {}
        # targetAverageUtilization: 80

      serviceAnnotations: {}
      podAnnotations: {}

      autoscaleMin: 1
      autoscaleMax: 5

      cpu:
        targetAverageUtilization: 80
      memory: {}
        # targetAverageUtilization: 80

      resources:
        requests:
          cpu: 100m
          memory: 128Mi
        limits:
          cpu: 2000m
          memory: 1024Mi

      loadBalancerIP: ""
      loadBalancerSourceRanges: []
      serviceAnnotations: {}

// ContainerResourceMetricSource indicates how to scale on a resource metric known to
// Kubernetes, as specified in requests and limits, describing each pod in the
// current scale target (e.g. CPU or memory).  The values will be averaged
// together before being compared to the target.  Such metrics are built in to
// Kubernetes, and have special scaling options on top of those available to

```
./bin/spark-submit --class org.apache.spark.examples.SparkPi \
    --master yarn-client \
    --num-executors 1 \
    --driver-memory 512m \
    --executor-memory 512m \
    --executor-cores 1 \
    examples/jars/spark-examples*.jar 10
```

The job should produce an output as shown below. Note the value of pi in the output.

```