maxReplicas: {{ .Values.autoscaling.maxReplicas }}
  metrics:
    {{- if .Values.autoscaling.targetCPUUtilizationPercentage }}
    - type: Resource
      resource:
        name: cpu
        target:
          averageUtilization: {{ .Values.autoscaling.targetCPUUtilizationPercentage }}
          type: Utilization
    {{- end }}
    {{- if .Values.autoscaling.targetMemoryUtilizationPercentage }}

      brokenPodLabelValue: "true"

    # Set to `type: RuntimeDefault` to use the default profile if available.
    seccompProfile: {}

    resources:
      requests:
        cpu: 100m
        memory: 100Mi

    resourceQuotas:
      enabled: false
      pods: 5000

    # The number of pods that can be unavailable during rolling update (see

			cpuMap[cpus[i].PhysicalID] = struct{}{}
			coreMap[cpus[i].CoreID] = struct{}{}
		}
		cpu := cpus[0]
		uaAppend(" CPU ", fmt.Sprintf("(total_cpus:%d, total_cores:%d; vendor:%s; family:%s; model:%s; stepping:%d; model_name:%s)",
			len(cpuMap), len(coreMap), cpu.VendorID, cpu.Family, cpu.Model, cpu.Stepping, cpu.ModelName))
	}
	uaAppend(")", "")

	return strings.Join(userAgentParts, "")
}

// 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

@test "Wheel conforms to upstream size limitations" {
    WHEEL_MEGABYTES=$(stat --format %s "$TF_WHEEL" | awk '{print int($1/(1024*1024))}')
    # Googlers: search for "test_tf_whl_size"
    case "$TF_WHEEL" in
        # CPU:
        *cpu*manylinux*) LARGEST_OK_SIZE=220 ;;
        # GPU:
        *manylinux*)     LARGEST_OK_SIZE=580 ;;
        # Unknown:
        *)
            echo "The wheel's name is in an unknown format."
            exit 1

        - name: HOST_IP
          valueFrom:
            fieldRef:
              fieldPath: status.hostIP
        - name: ISTIO_CPU_LIMIT
          valueFrom:
            resourceFieldRef:
              resource: limits.cpu
        - name: PROXY_CONFIG
          value: |
                 {{ protoToJSON .ProxyConfig }}
        - name: ISTIO_META_POD_PORTS
          value: "[]"

		m := collectLocalMetrics(types, collectMetricsOpts{
			hosts: map[string]struct{}{
				globalLocalNodeName: {},
			},
		})

		for _, hm := range m.ByHost {
			if hm.CPU != nil {
				v = *hm.CPU
				break
			}
		}

		return
	}

	return cachevalue.NewFromFunc(1*time.Minute,
		cachevalue.Opts{ReturnLastGood: true},
		loadCPUMetrics)
}

        fieldRef:
          fieldPath: spec.serviceAccountName
    - name: HOST_IP
      valueFrom:
        fieldRef:
          fieldPath: status.hostIP
    - name: ISTIO_CPU_LIMIT
      valueFrom:
        resourceFieldRef:
          resource: limits.cpu
    - name: PROXY_CONFIG
      value: |
             {{ protoToJSON .ProxyConfig }}
    - name: ISTIO_META_POD_PORTS
      value: |-
        [

        fieldRef:
          fieldPath: spec.serviceAccountName
    - name: HOST_IP
      valueFrom:
        fieldRef:
          fieldPath: status.hostIP
    - name: ISTIO_CPU_LIMIT
      valueFrom:
        resourceFieldRef:
          resource: limits.cpu
    - name: PROXY_CONFIG
      value: |
             {{ protoToJSON .ProxyConfig }}
    - name: ISTIO_META_POD_PORTS
      value: |-
        [

    flags->tf_xla_cpu_global_jit = true;
    flags->tf_xla_min_cluster_size = 1;
  } else {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::OFF);
  }

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

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