fix some broken links (#17857)
huccshen
Kubernetes Prow Robot
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d1363e5827
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@ -62,7 +62,7 @@ However, we were still using JSON as a format in which data was stored in etcd,
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We made tens of optimizations throughout the Kubernetes codebase during the last three releases, including:
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- optimizing the scheduler (which resulted in 5-10x higher scheduling throughput)
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- switching all controllers to a new recommended design using shared informers, which reduced resource consumption of controller-manager - for reference see [this document](https://github.com/kubernetes/community/blob/master/contributors/devel/controllers.md)
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- switching all controllers to a new recommended design using shared informers, which reduced resource consumption of controller-manager - for reference see [this document](https://github.com/kubernetes/community/blob/master/contributors/devel/sig-api-machinery/controllers.md)
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- optimizing individual operations in the API server (conversions, deep-copies, patch)
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- reducing memory allocation in the API server (which significantly impacts the latency of API calls)
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We want to emphasize that the optimization work we have done during the last few releases, and indeed throughout the history of the project, is a joint effort by many different companies and individuals from the whole Kubernetes community.
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@ -12,7 +12,7 @@ _Editor's note: Today's post is a joint post from the deep learning team at Baid
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Two open source communities—PaddlePaddle, the deep learning framework originated in Baidu, and Kubernetes®, the most famous containerized application scheduler—are announcing the Elastic Deep Learning (EDL) feature in PaddlePaddle’s new release codenamed Fluid.
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Fluid EDL includes a [Kubernetes controller](https://github.com/kubernetes/community/blob/master/contributors/devel/controllers.md), [_PaddlePaddle auto-scaler_](https://github.com/PaddlePaddle/cloud/tree/develop/doc/edl/experiment#auto-scaling-experiment), which changes the number of processes of distributed jobs according to the idle hardware resource in the cluster, and a new fault-tolerable architecture as described in the [PaddlePaddle design doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/cluster_train/README.md).
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Fluid EDL includes a [Kubernetes controller](https://github.com/kubernetes/community/blob/master/contributors/devel/sig-api-machinery/controllers.md), [_PaddlePaddle auto-scaler_](https://github.com/PaddlePaddle/cloud/tree/develop/doc/edl/experiment#auto-scaling-experiment), which changes the number of processes of distributed jobs according to the idle hardware resource in the cluster, and a new fault-tolerable architecture as described in the [PaddlePaddle design doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/cluster_train/README.md).
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Industrial deep learning requires significant computation power. Research labs and companies often build GPU clusters managed by SLURM, MPI, or SGE. These clusters either run a submitted job if it requires less than the idle resource, or pend the job for an unpredictably long time. This approach has its drawbacks: in an example with 99 available nodes and a submitted job that requires 100, the job has to wait without using any of the available nodes. Fluid works with Kubernetes to power elastic deep learning jobs, which often lack optimal resources, by helping to expose potential algorithmic problems as early as possible.
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@ -252,7 +252,7 @@ It’s finally possible to have dots in Go package names. In this section’s ex
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Kubernetes 1.9 includes a number of example projects which can serve as a blueprint for your own projects:
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- [k8s.io/sample-apiserver](https://github.com/kubernetes/sample-apiserver) is a simple user-provided API server that is integrated into a cluster via [API aggregation](/docs/concepts/api-extension/apiserver-aggregation/).
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- [k8s.io/sample-controller](https://github.com/kubernetes/sample-controller) is a full-featured [controller](https://github.com/kubernetes/community/blob/master/contributors/devel/controllers.md) (also called an operator) with shared informers and a workqueue to process created, changed or deleted objects. It is based on CustomResourceDefinitions and uses [k8s.io/code-generator](https://github.com/kubernetes/code-generator) to generate deepcopy functions, typed clientsets, informers, and listers.
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- [k8s.io/sample-controller](https://github.com/kubernetes/sample-controller) is a full-featured [controller](https://github.com/kubernetes/community/blob/master/contributors/devel/sig-api-machinery/controllers.md) (also called an operator) with shared informers and a workqueue to process created, changed or deleted objects. It is based on CustomResourceDefinitions and uses [k8s.io/code-generator](https://github.com/kubernetes/code-generator) to generate deepcopy functions, typed clientsets, informers, and listers.
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@ -81,7 +81,7 @@ Vos commits sont sur la branche master, qui est utilisée pour le développement
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Si vous souhaitez que vos commits apparaissent dans la documentation d'une version Kubernetes déjà publiée, vous devez proposer que vos commits soit sélectionnée dans la branche de publication.
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Par exemple, supposons que la branche master est utilisée pour développer Kubernetes 1.10, et vous voulez transférer vos commits sur la branche release-1.9.
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Pour savoir comment faire cela, consultez [Propose a Cherry Pick](https://github.com/kubernetes/community/blob/master/contributors/devel/cherry-picks.md).
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Pour savoir comment faire cela, consultez [Propose a Cherry Pick](https://github.com/kubernetes/community/blob/master/contributors/devel/sig-release/cherry-picks.md).
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Surveillez votre pull request cherry-pick jusqu'à ce qu'elle soit mergée dans la branche release.
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@ -63,7 +63,7 @@ terpisah, hal tersebut disebut _Webhook_. Layanan terpisah tersebut disebut sebu
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Di dalam model _Webhook_, Kubernetes membuat sebuah _network request_ kepada sebuah layanan terpisah.
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Di dalam model _Binary Plugin_, Kubernetes mengeksekusi sebuah program.
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_Binary Plugin_ digunakan oleh kubelet (misalnya [_Plugin Flex Volume_](https://github.com/kubernetes/community/blob/master/contributors/devel/flexvolume.md)
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_Binary Plugin_ digunakan oleh kubelet (misalnya [_Plugin Flex Volume_](https://github.com/kubernetes/community/blob/master/contributors/devel/sig-storage/flexvolume.md)
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dan oleh [_Plugin_ Jaringan](/docs/concepts/cluster-administration/network-plugins/)) dan oleh kubectl.
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Berikut ini adalah diagram yang menunjukkan bagaimana titik-titik perluasan berinteraksi dengan _control plane_ Kubernetes.
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@ -58,7 +58,7 @@ Kubernetes上でうまく動くクライアントプログラムを書くため
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Webhookのモデルでは、Kubernetesは外部のサービスを呼び出します。
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*バイナリプラグイン* モデルでは、Kubernetesはバイナリ（プログラム）を実行します。
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バイナリプラグインはkubelet（例、[FlexVolumeプラグイン](https://github.com/kubernetes/community/blob/master/contributors/devel/flexvolume.md)、[ネットワークプラグイン](/docs/concepts/cluster-administration/network-plugins/)）、またkubectlで利用されています。
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バイナリプラグインはkubelet（例、[FlexVolumeプラグイン](https://github.com/kubernetes/community/blob/master/contributors/devel/sig-storage/flexvolume.md)、[ネットワークプラグイン](/docs/concepts/cluster-administration/network-plugins/)）、またkubectlで利用されています。
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下図は、それぞれの拡張ポイントが、Kubernetesのコントロールプレーンとどのように関わっているかを示しています。
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