Google Kubernetes Engine 說明文件
在 Google Cloud 提供技術的 Kubernetes 上部署、管理及調整容器化應用程式資源。
瞭解詳情
使用價值 $300 美元的免費抵免額，開始進行概念驗證
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取得 Gemini 2.0 Flash Thinking 的存取權
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每月免費使用 AI API 和 BigQuery 等熱門產品
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不會自動收費，也不會要求您一定要購買特定方案
繼續探索超過 20 項一律免費的產品
使用超過 20 項實用的免費產品，包括 AI API、VM 和 data warehouse 等。
訓練
訓練與教學課程
運用 Kubernetes Engine 建立架構
本課程結合了講座、示範和實作研究室，可協助您探索及部署解決方案元素，包括 Pod、容器、部署和服務等基礎架構元件，以及網路和應用程式服務。
訓練
訓練與教學課程
Kubernetes Qwik Start 實驗室
瞭解如何在 30 分鐘內透過 Kubernetes Engine 部署容器化應用程式。
訓練
訓練與教學課程
透過 vLLM 在 GKE 上使用 GPU 提供 LLM，用於 AI/機器學習推論
本教學課程示範如何在 GKE 上使用 GPU，執行大型語言模型 (LLM) 以進行 AI/機器學習推論。
訓練
訓練與教學課程
在 Google Cloud 控制台中建立叢集及部署工作負載
瞭解如何建立 Kubernetes 叢集，並在 Google Cloud 控制台中部署「Hello World」網頁應用程式。
訓練
訓練與教學課程
使用 Ingress 設定 HTTP(S) 負載平衡
本教學課程說明如何設定 Ingress 資源，以便執行使用外部 HTTP(S) 負載平衡器的網頁應用程式。
訓練
訓練與教學課程
使用靜態 IP 位址設定網域名稱
本教學課程將示範如何在靜態外部 IP 位址上，將網路應用程式公開到網際網路，並設定網域名稱的 DNS 記錄以指向應用程式。
用途
用途
Google Kubernetes Engine 的持續整合與持續推送軟體更新最佳做法
從原始碼控管到部署作業策略，瞭解持續整合及持續推送軟體更新至 GKE 的最佳做法。
CI/CD
GitOps
用途
用途
為 GKE 設定私人使用的公開 IP
為 Google Kubernetes Engine Pod 位址區塊套用私人使用的公開 IP 位址。
虛擬私有雲
網路
用途
用途
在 GKE 上執行最具成本效益的 Kubernetes 應用程式的最佳做法
善用 Google Cloud 提供的彈性，在 GKE 中執行成本效益最高的應用程式。
費用
用途
用途
Google Cloud 中 .NET 應用程式的翻新途徑
瞭解翻新單體式應用程式的結構化逐步流程。
Windows
.NET
遷移
除非另有註明，否則本頁面中的內容是採用創用 CC 姓名標示 4.0 授權，程式碼範例則為阿帕契 2.0 授權。詳情請參閱《Google Developers 網站政策》。Java 是 Oracle 和/或其關聯企業的註冊商標。
上次更新時間：2025-09-01 (世界標準時間)。
[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2025-09-01 (世界標準時間)。"],[],[],null,["# Google Kubernetes Engine documentation\n======================================\n\n[Read product documentation](/kubernetes-engine/docs/concepts/kubernetes-engine-overview)\nDeploy, manage, and scale containerized applications on Kubernetes, powered by Google Cloud.\n[Learn more](/kubernetes-engine/docs/concepts/kubernetes-engine-overview)\n[Get started for free](https://console.cloud.google.com/freetrial) \n\n#### Start your proof of concept with $300 in free credit\n\n- Get access to Gemini 2.0 Flash Thinking\n- Free monthly usage of popular products, including AI APIs and BigQuery\n- No automatic charges, no commitment \n[View free product offers](/free/docs/free-cloud-features#free-tier) \n\n#### Keep exploring with 20+ always-free products\n\n\nAccess 20+ free products for common use cases, including AI APIs, VMs, data warehouses,\nand more.\n\nDocumentation resources\n-----------------------\n\nFind quickstarts and guides, review key references, and get help with common issues. \nformat_list_numbered\n\n### Guides\n\n-\n\n [Quickstart: Create a cluster and deploy a workload](/kubernetes-engine/docs/quickstarts/create-cluster)\n\n-\n\n [Configuring cluster access for kubectl](/kubernetes-engine/docs/how-to/cluster-access-for-kubectl)\n\n-\n\n [Access Google Cloud APIs from GKE workloads](/kubernetes-engine/docs/how-to/workload-identity)\n\n-\n\n [GKE security documentation](/kubernetes-engine/docs/concepts/security-overview)\n\n-\n\n [GKE networking documentation](/kubernetes-engine/docs/concepts/network-overview)\n\ninfo\n\n### AI/ML on GKE tutorials\n\n-\n\n [AI/ML orchestration on GKE](/kubernetes-engine/docs/integrations/ai-infra)\n\n-\n\n [Core concept: About GPUs in GKE](/kubernetes-engine/docs/concepts/gpus)\n\n-\n\n [Core skill: Use GPUs in GKE](/kubernetes-engine/docs/how-to/gpus)\n\n-\n\n [Serve Llama open models using GPUs with vLLM](/kubernetes-engine/docs/tutorials/serve-llama-gpus-vllm)\n\n-\n\n [Serve Gemma open models using GPUs with vLLM](/kubernetes-engine/docs/tutorials/serve-gemma-gpu-vllm)\n\n-\n\n [Serve Gemma open models with Hugging Face TGI](/kubernetes-engine/docs/tutorials/serve-gemma-gpu-tgi)\n\n-\n\n [Serve an LLM with multiple GPUs in GKE](/kubernetes-engine/docs/tutorials/serve-multiple-gpu)\n\n-\n\n [Deploy GPUs for batch workloads with Dynamic Workload Scheduler](/kubernetes-engine/docs/how-to/provisioningrequest)\n\n-\n\n [About Ray on GKE](/kubernetes-engine/docs/add-on/ray-on-gke/concepts/overview)\n\ngroup_work\n\n### References and resources\n\n-\n\n [REST API](/kubernetes-engine/docs/reference/rest)\n\n-\n\n [API permissions](/kubernetes-engine/docs/reference/api-permissions)\n\n-\n\n [API organization and structure](/kubernetes-engine/docs/reference/api-organization)\n\n-\n\n [gcloud container commands](/sdk/gcloud/reference/container)\n\n-\n\n [Kubernetes documentation](http://kubernetes.io/docs/)\n\n-\n\n [kubectl commands](https://kubernetes.io/docs/reference/generated/kubectl/kubectl-commands)\n\n-\n\n [Release notes](/kubernetes-engine/docs/release-notes)\n\n-\n\n [Release schedule](/kubernetes-engine/docs/release-schedule)\n\n-\n\n [Security bulletins](/anthos/clusters/docs/security-bulletins)\n\n-\n\n [Security patching](/kubernetes-engine/docs/resources/security-patching)\n\n-\n\n [Pricing](/kubernetes-engine/pricing)\n\nRelated resources\n-----------------\n\nTraining and tutorials \nUse cases \nExplore self-paced training, use cases, reference architectures, and code samples with examples of how to use and connect Google Cloud services. Training \nTraining and tutorials\n\n### Architecting with Kubernetes Engine\n\n\nThis course features a combination of lectures, demos, and hands-on labs to help you explore and deploy solution elements---including infrastructure components like pods, containers, deployments, and services---along with networks and application services.\n\n\n[Learn more](http://cloud.google.com/training/course/architecting-with-google-kubernetes-engine) \nTraining \nTraining and tutorials\n\n### Kubernetes Qwik Start lab\n\n\nLearn how to deploy a containerized application with Kubernetes Engine in less than 30 minutes.\n\n\n[Learn more](https://www.cloudskillsboost.google/catalog_lab/911?qlcampaign=77-18-gcpd-236&utm_source=gcp&utm_medium=documentation&utm_campaign=kubernetes) \nTraining \nTraining and tutorials\n\n### Serve LLMs for AI/ML inference using GPUs on GKE with vLLM\n\n\nThis tutorial demonstrates how to use graphical processinng units (GPUs) on GKE to run large language models (LLMs) for AI/ML inference.\n\n\n[Learn more](/kubernetes-engine/docs/tutorials/serve-gemma-gpu-vllm) \nTraining \nTraining and tutorials\n\n### Create a cluster and deploy a workload in the Google Cloud console\n\n\nLearn how to create a Kubernetes cluster and deploy a 'hello world' web app in Google Cloud console.\n\n\n[Learn more](/kubernetes-engine/docs/quickstarts/create-cluster) \nTraining \nTraining and tutorials\n\n### Setting up HTTP(S) Load Balancing with Ingress\n\n\nThis tutorial shows how to run a web application behind an external HTTP(S) load balancer by configuring the Ingress resource.\n\n\n[Learn more](/kubernetes-engine/docs/tutorials/http-balancer) \nTraining \nTraining and tutorials\n\n### Configuring Domain Names with Static IP Addresses\n\n\nThis tutorial demonstrates how to expose your web application to the internet on a static external IP address and configure DNS records of your domain name to point to your application.\n\n\n[Learn more](/kubernetes-engine/docs/tutorials/configuring-domain-name-static-ip) \nUse case \nUse cases\n\n### Best practices for continuous integration and delivery to Google Kubernetes Engine\n\n\nLearn best practices for continuous integration and continuous delivery to GKE, from source control to deployment strategies.\n\nCI/CD GitOps\n\n\u003cbr /\u003e\n\n[Learn more](/solutions/best-practices-continuous-integration-delivery-kubernetes) \nUse case \nUse cases\n\n### Configuring privately used public IPs for GKE\n\n\nApply privately used public IP addresses for Google Kubernetes Engine pod address blocks.\n\nVPC Networking\n\n\u003cbr /\u003e\n\n[Learn more](/solutions/configuring-privately-used-public-ips-for-GKE) \nUse case \nUse cases\n\n### Best practices for running cost-optimized Kubernetes applications on GKE\n\n\nTake advantage of the elasticity provided by Google Cloud when running cost-optimized applications on GKE.\n\nCosts\n\n\u003cbr /\u003e\n\n[Learn more](/architecture/best-practices-for-running-cost-effective-kubernetes-applications-on-gke) \nUse case \nUse cases\n\n### Modernization path for .NET applications on Google Cloud\n\n\nLearn a gradual and structured process for modernizing monolithic applications.\n\nWindows .NET Migration\n\n\u003cbr /\u003e\n\n[Learn more](/solutions/modernization-path-dotnet-applications-google-cloud)\n\nRelated videos\n--------------\n\n### Try GKE for yourself\n\nCreate an account to evaluate how our products perform in real-world scenarios. \nNew customers also get $300 in free credits to run, test, and deploy workloads. \n[Try GKE free](https://console.cloud.google.com/freetrial)"]]
