pre-1.0 to 1.6 kubelet is not supported.
* Fluentd was migrated to Daemon Set, which targets nodes with
  beta.kubernetes.io/fluentd-ds-ready=true label. If you use fluentd in your
  cluster please make sure that the nodes with version 1.6+ contains this
  label.

### kubectl
* Running `kubectl taint` (alpha in 1.5) against a 1.6 server requires upgrading kubectl to version 1.6

{
  "labels": {
    "critical": {
      "name": "type:critical",
      "colour": "#E84137",
      "description": "critical questions"
    },
    "question": {
      "name": "type:question",
      "colour": "#EDEDED",
      "description": "general questions"
    },
    "feature": {
      "name": "type:feature_request",
      "colour": "#43952A",
      "description": "feature request"
    },
    "invalid_question": {

        to a labeled dataset, in one function call. Note that it doesn't perform
        image data augmentation (which is meant to be done using preprocessing
        layers).
    *   **[`text_dataset_from_directory`](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text_dataset_from_directory)**
        takes you from a structured directory of text files to a labeled
        dataset, in one function call.

  - cAdvisor json endpoints have been deprecated since 1.15. ([#78504](https://github.com/kubernetes/kubernetes/pull/78504), [@dashpole](https://github.com/dashpole))

Sebastián Ramírez <******@****.***> 1771691198 -0800

     * labels.facet_filetype_title:labels.facet_filetype_html=filetype:html	labels.facet_filetype_word=filetype:word	labels.facet_filetype_excel=filetype:excel	labels.facet_filetype_powerpoint=filetype:powerpoint	labels.facet_filetype_odt=filetype:odt	labels.facet_filetype_ods=filetype:ods	labels.facet_filet...

  - Make "kubeadm upgrade apply" add the "node-role.kubernetes.io/control-plane" label on existing nodes that only have the "node-role.kubernetes.io/master" label during upgrade.
  - Please adapt your tooling built on top of kubeadm to use the "node-role.kubernetes.io/control-plane" label.

                    labels.add("label1");
                    labels.add("label2");
                }
                return labels;
            }
        };
        ComponentUtil.register(mockLabelTypeHelper, "labelTypeHelper");

        // Mock LanguageHelper
        LanguageHelper mockLanguageHelper = new LanguageHelper() {
            @Override
            public Script createScript(Map<String, Object> doc, String scriptString) {

	// managed values follow:
	labelSet map[string]struct{}
}

func (md *MetricDescriptor) getLabelSet() map[string]struct{} {
	if md.labelSet != nil {
		return md.labelSet
	}
	md.labelSet = make(map[string]struct{}, len(md.VariableLabels))
	for _, label := range md.VariableLabels {
		md.labelSet[label] = struct{}{}
	}
	return md.labelSet
}

                                  const char* label1, const char* label2);

// APIs for String Gauge with 3 labels.
typedef struct TFE_MonitoringStringGauge3 TFE_MonitoringStringGauge3;
TF_CAPI_EXPORT extern TFE_MonitoringStringGauge3* TFE_MonitoringNewStringGauge3(
    const char* name, TF_Status* out_status, const char* description,
    const char* label1, const char* label2, const char* label3);