'kubernetes' 검색 결과

Manage credentials for Git access and deployments in KubeSphere DevOps. Covers SSH keys, passwords, API tokens, and Kubernetes connection settings, including their use in Jenkins pipelines and troubleshooting synchronization failures.

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• Register credentials for private Git repositories
• Use deployment credentials in Jenkins pipelines
• Investigate credentials missing from Jenkins

Inspect KubeSphere clusters with read-only commands. List clusters, view detailed configuration, identify host and member roles, and review connection types, readiness indicators, and KubeSphere and Kubernetes versions without changing cluster resources.

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• List clusters registered in KubeSphere
• Check cluster readiness
• Review KubeSphere and Kubernetes versions

Set up and manage a local Kubernetes development environment for OpenShell using k3d, Skaffold, and Helm. Build and deploy changes, inspect traces, configure optional authentication components, and test behavior across multiple gateway replicas.

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• Create or stop a local OpenShell development cluster
• Rebuild and deploy changes during development
• Inspect local execution traces

Set up NVIDIA infrastructure for synthetic data generation on MicroK8s or Azure AKS. Coordinate Kubernetes, inference services, and OSMO workflow execution, with prerequisite checks, deployment validation, capacity planning, and failure investigation before workloads are retried.

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• Set up a synthetic data generation environment on MicroK8s
• Deploy an Azure AKS environment with OSMO and inference services
• Check GPU quotas and capacity before scaling

Choose diagnostic tools and collect performance traces for production .NET problems such as high CPU usage, memory growth, or slow requests. Get environment-specific collection commands and guidance on which tools to use for later analysis.

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• Collecting traces during high CPU usage or slow requests
• Choosing diagnostic tools for Windows or Linux
• Collecting diagnostics from containers or Kubernetes

Configure mirrord to route selected Temporal tasks to a local worker. Generate Kubernetes configuration files, connection settings, and task filters, then check that the settings agree and troubleshoot common setup problems.

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• Route only test workflows to a local worker
• Configure a Temporal Cloud connection
• Filter tasks by workflow input

Set up one mirrord-up.yaml file to debug several related services locally at the same time. Define each application’s command, Kubernetes target, environment settings, and traffic mode, then manage the sessions together with mirrord up.

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• Debug an authentication service and dashboard together
• Create an initial multi-service configuration
• Route personal requests using a shared session key

Set up mirrord preview environments that run changed services in isolated Kubernetes pods. Configure traffic filters, share previews through HTTPS links, and connect previews to pull request workflows so teams can review changes using shared cluster dependencies.

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• Start a preview of a changed service in a shared cluster
• Share a preview with reviewers through an HTTPS link
• Create and stop previews for each pull request

Build GitLab workflows that automate building, testing, and deployment. The skill covers pipeline stages, dependency caching, runners that execute jobs, and manual approval before production deployment, with examples for Docker, Kubernetes, and Terraform.

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• Automating builds and tests in GitLab
• Reviewing caches to improve pipeline performance
• Separating staging and production deployments

Develop and troubleshoot distributed applications with Aspire. Coordinate services written in different languages through AppHost, configure connections and startup dependencies, inspect logs and metrics, and prepare deployment settings.

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• Creating an Aspire application from a template
• Connecting services written in different languages
• Diagnosing services through logs and traces

Use Stratus Red Team to simulate attack techniques across AWS, Azure, GCP, and Kubernetes. Prepare test resources, run and repeat simulations, check logs and alerts, and remove resources to assess whether security detections work.

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• Checking whether cloud detection rules trigger
• Testing log ingestion and alert routing
• Running joint attack and defense exercises

Guides migration of existing AI inference workloads to self-hosted GKE, Google’s managed Kubernetes service. Covers requirements, GPU sizing, deployment configuration, model storage, validation, and traffic cutover using gcloud and kubectl.

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• Migrating an existing Cloud Run AI service to GKE
• Planning a self-hosted replacement for Gemini API inference
• Sizing GPUs and preparing deployment configuration

Plans, executes, and validates Google Kubernetes Engine upgrades for Standard and Autopilot clusters. Creates environment-specific upgrade plans, checklists, and command-based runbooks, with guidance on maintenance timing, node upgrade strategies, workload readiness, and stalled upgrades.

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• Planning a GKE version upgrade
• Choosing release channels and maintenance schedules
• Preparing node pool upgrades for databases or GPU workloads

Diagnose why GKE Pods cannot authenticate to Google Cloud APIs. Inspect cluster settings, service account bindings, resource permissions, and metadata server health, then provide evidence and concrete fixes for a human to apply.

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• Investigate Pod authentication failures and 403 errors
• Check Kubernetes service account identity bindings
• Diagnose metadata server connectivity problems

Set up WizTelemetry Auditing to collect Kubernetes and KubeSphere audit events, records of system operations. Configure storage and retention, then query events by user, action, resource, response status, or time.

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• Deploy audit event collection to selected clusters
• Find operations by user, action, resource, and time
• Configure audit storage and retention

Install, upgrade, and remove the ingress-nginx-based KubeSphere Gateway extension, which manages external access to applications. Inspect gateways across cluster, workspace, and project scopes, and investigate stalled operations, Helm installation failures, or pod problems.

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• Installing or upgrading the Gateway extension
• Checking gateways by cluster, workspace, or project
• Investigating stalled gateway operations and pod failures

Set up KubeEye in KubeSphere to inspect Kubernetes clusters. Define checks for configurations, nodes, and services, schedule recurring or one-time inspections, and retrieve findings as HTML or spreadsheet reports.

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• Install KubeEye for cluster inspections
• Create checks for node health and configuration
• Schedule recurring cluster inspections

Design controlled failure experiments for distributed systems to examine how services recover. Produce failure injection scripts, monitoring plans, rollback procedures, and exercise guides, then turn findings into documented improvements.

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• Planning controlled failure experiments
• Testing recovery after Kubernetes Pod failures
• Checking service behavior under network delays

Set up and operate NVIDIA AI-Q Blueprint locally or on your own infrastructure. Choose a deployment mode, configure credentials, check service health, troubleshoot failures, and provide a verified server address for aiq-research.

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• Starting a backend for aiq-research
• Running AI-Q through a terminal or browser
• Deploying AI-Q to a Kubernetes cluster

Inspect live development or staging traffic to find an identifier that distinguishes one developer’s requests across services. Produce a matching mirrord HTTP filter and a report showing where the identifier survives, disappears, or cannot be observed.

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• Find a filter for personal requests on a shared staging cluster
• Check whether request IDs survive across services
• Build mirrord filters using JWT claims or cookies

Configure mirrord to route selected Kafka messages to a local application. Generate Kubernetes resource files and matching message filters, check their consistency, and troubleshoot connection or splitting issues, including Kafka Streams and AWS MSK IAM setups.

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• Set up Kafka message splitting for local debugging
• Filter messages by headers or payload fields
• Configure Kafka Streams or AWS MSK IAM connections

Deploy, scale, and manage Kubernetes workloads. Create deployments, services, and configurations, manage cluster resources, troubleshoot pods, and implement production-ready Kubernetes patterns. Use when working with Kubernetes clusters, K8s deployments, or container orchestration.

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Design, build, and operate Kubernetes operators. Use when extending Kubernetes with a custom controller, choosing a framework, designing CRDs, implementing reconciliation loops, or auditing an operator for anti-patterns.

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Expert knowledge for Azure Stack Edge development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when deploying IoT Edge modules, GPU/Kubernetes apps, DeepStream workloads, Arc GitOps, or local ARM integrations, and other Azure Stack Edge related development tasks. Not for Azure IoT Edge (use azure-iot-edge), Azure Kubernetes Service (AKS) (use azure-kubernetes-service), Azure Kubernetes Service Edge Essentials (use azure-aks-edge-essentials), Azure Virtual Machines (use azure-virtual-machines).

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