kubectl源码分析之taint

kubectl滚动更新资源 rollout Manage the rollout of a resource. Valid resource types include: * deployments * daemonsets * statefulsets Examples: # Rollback to the previous deployment kubectl rollout undo deployment/abc # Check the rollout status of a da 阅读详情

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 目前刚开始写一个月,一共写了18篇原创文章,文章目录如下:

istio多集群探秘,部署了50次多集群后我得出的结论

istio多集群链路追踪,附实操视频

istio防故障利器,你知道几个,istio新手不要读,太难!

istio业务权限控制,原来可以这么玩

istio实现非侵入压缩,微服务之间如何实现压缩

不懂envoyfilter也敢说精通istio系列-http-rbac-不要只会用AuthorizationPolicy配置权限

不懂envoyfilter也敢说精通istio系列-02-http-corsFilter-不要只会vs

不懂envoyfilter也敢说精通istio系列-03-http-csrf filter-再也不用再代码里写csrf逻辑了

不懂envoyfilter也敢说精通istio系列http-jwt_authn-不要只会RequestAuthorization

不懂envoyfilter也敢说精通istio系列-05-fault-filter-故障注入不止是vs

不懂envoyfilter也敢说精通istio系列-06-http-match-配置路由不只是vs

不懂envoyfilter也敢说精通istio系列-07-负载均衡配置不止是dr

不懂envoyfilter也敢说精通istio系列-08-连接池和断路器

不懂envoyfilter也敢说精通istio系列-09-http-route filter

不懂envoyfilter也敢说精通istio系列-network filter-redis proxy

不懂envoyfilter也敢说精通istio系列-network filter-HttpConnectionManager

不懂envoyfilter也敢说精通istio系列-ratelimit-istio ratelimit完全手册

 

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type TaintOptions struct {//taint结构体
	PrintFlags *genericclioptions.PrintFlags
	ToPrinter  func(string) (printers.ResourcePrinter, error)

	resources      []string
	taintsToAdd    []v1.Taint
	taintsToRemove []v1.Taint
	builder        *resource.Builder
	selector       string
	overwrite      bool
	all            bool

	ClientForMapping func(*meta.RESTMapping) (resource.RESTClient, error)

	genericclioptions.IOStreams
}
//创建taint命令
func NewCmdTaint(f cmdutil.Factory, streams genericclioptions.IOStreams) *cobra.Command {
	options := &TaintOptions{//初始化结构体
		PrintFlags: genericclioptions.NewPrintFlags("tainted").WithTypeSetter(scheme.Scheme),
		IOStreams:  streams,
	}

	validArgs := []string{"node"}

	cmd := &cobra.Command{//创建cobra命令
		Use:                   "taint NODE NAME KEY_1=VAL_1:TAINT_EFFECT_1 ... KEY_N=VAL_N:TAINT_EFFECT_N",
		DisableFlagsInUseLine: true,
		Short:                 i18n.T("Update the taints on one or more nodes"),
		Long:                  fmt.Sprintf(taintLong, validation.DNS1123SubdomainMaxLength, validation.LabelValueMaxLength),
		Example:               taintExample,
		Run: func(cmd *cobra.Command, args []string) {
			cmdutil.CheckErr(options.Complete(f, cmd, args))//准备
			cmdutil.CheckErr(options.Validate())//校验
			cmdutil.CheckErr(options.RunTaint())//运行
		},
		ValidArgs: validArgs,//有效参数
	}

	options.PrintFlags.AddFlags(cmd)//打印选项

	cmdutil.AddValidateFlags(cmd)//校验选项
	cmd.Flags().StringVarP(&options.selector, "selector", "l", options.selector, "Selector (label query) to filter on, supports '=', '==', and '!='.(e.g. -l key1=value1,key2=value2)")//selector选项
	cmd.Flags().BoolVar(&options.overwrite, "overwrite", options.overwrite, "If true, allow taints to be overwritten, otherwise reject taint updates that overwrite existing taints.")//overwrite选项
	cmd.Flags().BoolVar(&options.all, "all", options.all, "Select all nodes in the cluster")//all选项
	return cmd
}
//准备方法
func (o *TaintOptions) Complete(f cmdutil.Factory, cmd *cobra.Command, args []string) (err error) {
	namespace, _, err := f.ToRawKubeConfigLoader().Namespace()//获取namespace
	if err != nil {
		return err
	}

	// retrieves resource and taint args from args
	// also checks args to verify that all resources are specified before taints
	taintArgs := []string{}//taint参数
	metTaintArg := false
	for _, s := range args {//区分taint和资源
		isTaint := strings.Contains(s, "=") || strings.HasSuffix(s, "-")
		switch {
		case !metTaintArg && isTaint:
			metTaintArg = true
			fallthrough
		case metTaintArg && isTaint:
			taintArgs = append(taintArgs, s)
		case !metTaintArg && !isTaint:
			o.resources = append(o.resources, s)
		case metTaintArg && !isTaint:
			return fmt.Errorf("all resources must be specified before taint changes: %s", s)
		}
	}

	o.ToPrinter = func(operation string) (printers.ResourcePrinter, error) {//printflag转printer
		o.PrintFlags.NamePrintFlags.Operation = operation
		return o.PrintFlags.ToPrinter()
	}

	if len(o.resources) < 1 {//资源小于1个报错
		return fmt.Errorf("one or more resources must be specified as <resource> <name>")
	}
	if len(taintArgs) < 1 {//taint参数小于1个报错
		return fmt.Errorf("at least one taint update is required")
	}

	if o.taintsToAdd, o.taintsToRemove, err = parseTaints(taintArgs); err != nil {//解析taint,区分添加和删除
		return cmdutil.UsageErrorf(cmd, err.Error())
	}
	o.builder = f.NewBuilder().
		WithScheme(scheme.Scheme, scheme.Scheme.PrioritizedVersionsAllGroups()...).
		ContinueOnError().
		NamespaceParam(namespace).DefaultNamespace()//构造result对象
	if o.selector != "" {
		o.builder = o.builder.LabelSelectorParam(o.selector).ResourceTypes("node")
	}
	if o.all {
		o.builder = o.builder.SelectAllParam(o.all).ResourceTypes("node").Flatten().Latest()
	}
	if !o.all && o.selector == "" && len(o.resources) >= 2 {
		o.builder = o.builder.ResourceNames("node", o.resources[1:]...)
	}
	o.builder = o.builder.LabelSelectorParam(o.selector).
		Flatten().
		Latest()

	o.ClientForMapping = f.ClientForMapping//设置clientFormapping
	return nil
}
//校验选项
func (o TaintOptions) validateFlags() error {
	// Cannot have a non-empty selector and all flag set. They are mutually exclusive.
	if o.all && o.selector != "" {//all和selector不能同时指定
		return fmt.Errorf("setting 'all' parameter with a non empty selector is prohibited.")
	}
	// If both selector and all are not set.
	if !o.all && o.selector == "" {//如果没有指定all和selector,资源数量不能小于2个
		if len(o.resources) < 2 {
			return fmt.Errorf("at least one resource name must be specified since 'all' parameter is not set")
		} else {
			return nil
		}
	}
	return nil
}

// Validate checks to the TaintOptions to see if there is sufficient information run the command.
//校验
func (o TaintOptions) Validate() error {
	resourceType := strings.ToLower(o.resources[0])
	validResources, isValidResource := []string{"node", "nodes"}, false
	for _, validResource := range validResources {//验证资源是否有效
		if resourceType == validResource {
			isValidResource = true
			break
		}
	}
	if !isValidResource {
		return fmt.Errorf("invalid resource type %s, only %q are supported", o.resources[0], validResources)
	}

	// check the format of taint args and checks removed taints aren't in the new taints list
	var conflictTaints []string
	for _, taintAdd := range o.taintsToAdd {//判断taintadd和remove是否冲突
		for _, taintRemove := range o.taintsToRemove {
			if taintAdd.Key != taintRemove.Key {
				continue
			}
			if len(taintRemove.Effect) == 0 || taintAdd.Effect == taintRemove.Effect {
				conflictTaint := fmt.Sprintf("{\"%s\":\"%s\"}", taintRemove.Key, taintRemove.Effect)
				conflictTaints = append(conflictTaints, conflictTaint)
			}
		}
	}
	if len(conflictTaints) > 0 {
		return fmt.Errorf("can not both modify and remove the following taint(s) in the same command: %s", strings.Join(conflictTaints, ", "))
	}
	return o.validateFlags()//校验选项
}
//运行
func (o TaintOptions) RunTaint() error {
	r := o.builder.Do()//获取result对象
	if err := r.Err(); err != nil {
		return err
	}

	return r.Visit(func(info *resource.Info, err error) error {//visit result对象
		if err != nil {
			return err
		}

		obj := info.Object//得到obj
		name, namespace := info.Name, info.Namespace//得到name和namespace
		oldData, err := json.Marshal(obj)//保存改变前的json
		if err != nil {
			return err
		}
		operation, err := o.updateTaints(obj)//执行更新
		if err != nil {
			return err
		}
		newData, err := json.Marshal(obj)//保存改变后的json
		if err != nil {
			return err
		}
		patchBytes, err := strategicpatch.CreateTwoWayMergePatch(oldData, newData, obj)//创建patch
		createdPatch := err == nil
		if err != nil {
			klog.V(2).Infof("couldn't compute patch: %v", err)
		}

		mapping := info.ResourceMapping()//获取mapping
		client, err := o.ClientForMapping(mapping)//获取client
		if err != nil {
			return err
		}
		helper := resource.NewHelper(client, mapping)//获取helper

		var outputObj runtime.Object
		if createdPatch {
			outputObj, err = helper.Patch(namespace, name, types.StrategicMergePatchType, patchBytes, nil)//应用patch到服务端
		} else {
			outputObj, err = helper.Replace(namespace, name, false, obj)//执行替换
		}
		if err != nil {
			return err
		}

		printer, err := o.ToPrinter(operation)// print flag转printer
		if err != nil {
			return err
		}
		return printer.PrintObj(outputObj, o.Out)//打印结果
	})
}
//更新taint
func (o TaintOptions) updateTaints(obj runtime.Object) (string, error) {
	node, ok := obj.(*v1.Node)//把对象转为node
	if !ok {
		return "", fmt.Errorf("unexpected type %T, expected Node", obj)
	}
	if !o.overwrite {//如果没有指定ovrewrite,检查是否有冲突
		if exists := checkIfTaintsAlreadyExists(node.Spec.Taints, o.taintsToAdd); len(exists) != 0 {
			return "", fmt.Errorf("Node %s already has %v taint(s) with same effect(s) and --overwrite is false", node.Name, exists)
		}
	}
	operation, newTaints, err := reorganizeTaints(node, o.overwrite, o.taintsToAdd, o.taintsToRemove)//重新组织taint
	if err != nil {
		return "", err
	}
	node.Spec.Taints = newTaints//更新taint
	return operation, nil
}
//重新组织taint
func reorganizeTaints(node *corev1.Node, overwrite bool, taintsToAdd []corev1.Taint, taintsToRemove []corev1.Taint) (string, []corev1.Taint, error) {
	newTaints := append([]corev1.Taint{}, taintsToAdd...)//新的taint
	oldTaints := node.Spec.Taints//老的taint
	// add taints that already existing but not updated to newTaints
	added := addTaints(oldTaints, &newTaints)//添加后的taint
	allErrs, deleted := deleteTaints(taintsToRemove, &newTaints)//删除后的taint
	if (added && deleted) || overwrite {
		return MODIFIED, newTaints, utilerrors.NewAggregate(allErrs)//返回
	} else if added {
		return TAINTED, newTaints, utilerrors.NewAggregate(allErrs)
	}
	return UNTAINTED, newTaints, utilerrors.NewAggregate(allErrs)
}

yolov8训练模型的一些心得-总结篇 先放在这,等过几天我都弄完了详细给大家说说(哈哈其实是给我自己说说,记录一下怕忘记) 阅读详情

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Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); 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