'kubernetes' 검색 결과

Audit and strengthen security for applications running on GKE. Configure pod traffic restrictions, sandbox isolation, namespace security standards, and Secret Manager volume mounts to protect workloads at the application and namespace level.

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• Auditing GKE security settings
• Restricting communication within a namespace
• Isolating risky pods and applying security standards

Create and validate Terraform alerts for GKE using PromQL, a monitoring query language. Cover response times, errors, traffic, memory usage, and cluster health, with checks for alert timing, duplicate signals, and metric ingestion costs.

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• Create alerts for GKE service latency and errors
• Detect frequent container restarts and unhealthy nodes
• Review existing Terraform alert configurations

Run batch processing, scientific simulations, and machine learning training on Google Kubernetes Engine. Configure parallel jobs, queues that manage resource access, and distributed computation, with guidance on scaling idle capacity down and recovering from interruptions.

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• Run data processing jobs in parallel
• Share computing resources through job queues
• Configure distributed simulations with MPI

Deploy AI models on GKE with suitable GPUs or TPUs and model servers. Generate deployment configuration, adjust automatic scaling, and tune inference—the process of producing model responses—for memory use, throughput, and response time.

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• Deploying Gemma or Llama on GKE
• Choosing GPUs or TPUs for model serving
• Configuring automatic scaling for LLM requests

Configure Backup for GKE to protect Kubernetes configurations and persistent data. Set backup schedules, retention periods, and customer-managed encryption keys, then prepare restore plans with explicit policies for handling existing resources.

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• Setting scheduled backups and retention periods
• Backing up persistent data and Secrets
• Encrypting backups with customer-managed keys

Diagnoses storage failures in GKE, Google's managed Kubernetes service, including stuck Pods, disk attachment errors, storage pressure, and missing volume expansion. It provides diagnostic commands and reviewable configuration patches for deployment through GitOps.

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• Diagnose Pods stuck in ContainerCreating
• Investigate slow disks and node storage pressure
• Find why expanded storage is missing inside a container

Plan and troubleshoot large language and vision model training and inference. This guide uses hardware measurements to identify bottlenecks, address memory shortages and unstable training, and improve checkpointing, recovery, and performance across multiple GPUs and machines.

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• Diagnosing slow training and GPU memory shortages
• Investigating loss spikes and invalid numerical values
• Debugging communication hangs across GPUs and machines

Design service monitoring with Prometheus, write PromQL queries to analyze metrics, and build Grafana dashboards. Configure actionable alerts and manage metric labels so monitoring stays useful as the volume of collected data grows.

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• Monitor service latency, traffic, and errors
• Review PromQL queries and precomputed metrics
• Group related alerts and filter brief spikes

Manage KubeSphere’s Traefik-based Gateway API extension for external access to applications. Install, upgrade, or remove it across selected clusters, and inspect GatewayProxy resources, which manage proxy instances, to investigate deployment failures and unhealthy gateways.

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• Install or upgrade the extension on selected clusters
• Inspect GatewayProxy status by scope
• Investigate Traefik startup and update failures

Maintain and validate OpenShell builds for Windows on x64 and ARM64 using MSVC, Microsoft's C++ toolchain. Check compilation, build executables, run tests on native hosts, and report artifacts and unsupported driver behavior.

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• Investigating Windows compilation failures
• Building x64 and ARM64 release executables
• Checking errors for unsupported drivers

Investigate insecure deserialization, where restoring objects from stored or transmitted data can execute unwanted code. Covers multiple languages, input format recognition, vulnerable processing points, verification methods, and documentation of affected libraries and entry points.

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• Reviewing code that restores objects from external data
• Identifying serialized data in cookies and request bodies
• Assessing message consumers and admission webhooks

Create and validate mirrord.json settings for isolated database copies used in development and testing. Configure connection sources, copying scope, migrations, and authentication so experiments can run without affecting shared databases.

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• Preparing isolated databases for development and testing
• Choosing which schemas and data to copy
• Configuring connection sources and IAM authentication

Investigate cloud configuration weaknesses across AWS, GCP, and Azure, including public storage, exposed services, and leaked credentials. Find candidates in public information and JavaScript, then verify data access and permissions to establish practical impact.

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• Checking public access to S3, GCS, and Azure Blob storage
• Reviewing exposed serverless and managed services
• Assessing CloudWatch RUM guest role permissions

Deploy code changes to a live hero training run by starting a new run from a permanent checkpoint, a saved training state. Compare matching training steps against agreed criteria and roll back if the trial fails.

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• Deploy backend or optimizer changes to a live training run
• Compare loss and throughput across matching training steps
• Rehearse checkpoint handoff and rollback procedures

Build production systems for machine learning models, from deployment to monitoring and retraining. The skill also guides LLM integration and retrieval augmented generation, which uses retrieved information to support answers, with scripts and architecture references.

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• Deploying machine learning models to production
• Setting up model monitoring and retraining
• Building applications that answer using retrieved information

Deploy and manage NVIDIA RAG Blueprint, a system that retrieves information to support AI answers. Check the running environment, configure models and search features, troubleshoot unhealthy services, and verify affected workflows after changes.

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• Deploy NVIDIA RAG Blueprint
• Configure search and document ingestion
• Change models or enable image features

Run DOCA service containers on BlueField using YAML deployment files and standalone kubelet, the agent that starts containers. Check service readiness and troubleshoot startup failures, image downloads, missing mounts, and repeated restarts.

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• Starting a DOCA service container on BlueField
• Investigating image download failures or restart loops
• Checking whether a running container's service is ready

Operate NVIDIA DOCA Management Service to read, change, and monitor BlueField or ConnectX settings locally or remotely. Get guidance on deployment, authentication, management requests, logs, and diagnosing failures across the service and its backend.

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• Read or change local BlueField settings
• Choose deployment and authentication for remote management
• Read, update, and subscribe to settings through gNMI

Plan, create, and configure production-ready Azure Kubernetes Service (AKS) clusters. Covers Day-0 checklist, SKU selection (Automatic vs Standard), networking options (private API server, Azure CNI Overlay, egress configuration), security, and operations (autoscaling, upgrade strategy, cost analysis). WHEN: create AKS environment, provision AKS, enable AKS observability, design AKS networking, choose AKS SKU, secure AKS, optimize AKS, AKS spot nodes, AKS cluster-autoscaler, rightsize AKS pod, pod rightsizing, over-provisioned AKS pod, pod resource requests and limits, Vertical Pod Autoscaler, VPA recommendations.

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Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

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Assess and migrate cross-cloud workloads to Azure with reports and code conversion. Supports Lambda→Functions, Beanstalk/Heroku/App Engine→App Service, Fargate/Kubernetes/Cloud Run/Spring Boot→Container Apps. WHEN: migrate Lambda to Functions, AWS to Azure, migrate Beanstalk, migrate Heroku, migrate App Engine, Cloud Run migration, Fargate to ACA, ECS/Kubernetes/GKE/EKS to Container Apps, Spring Boot to Container Apps, cross-cloud migration.

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Builds and deploys containerized workloads on Elastic Kubernetes Service (EKS), Elastic Container Service (ECS), Fargate, and ECR (Elastic Container Registry). Covers general EKS knowledge, Karpenter, AWS Load Balancer Controller and leveraging various open source Kubernetes projects with EKS. Covers general ECS knowledge, task definitions, Fargate services, ECS Exec, ECS Express Mode and ECS Managed Instances. Covers general Elastic Beanstalk knowledge, Elastic Beanstalk configuration and platforms supported by Elastic Beanstalk. Covers general ECR knowledge, ECR repository setup and lifecycle policies. Includes recommending, enabling, and reading Amazon ECS Action Logs to troubleshoot control-plane failures (deployment rollback/circuit-breaker, task placement, scaling, task replacement). Applies when deploying, debugging, or optimizing containers on AWS. Should be used instead of relying on internal knowledge for these services.

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KubeKey (kk) Usage Guide. For Kubernetes cluster lifecycle management including creation, scaling and deletion. Use this skill whenever the user mentions kubekey, kk commands, cluster setup, cluster management, adding nodes, deleting nodes, creating inventory files, configuring clusters, installing Kubernetes, or deploying K8s. Also use this skill when the user asks about inventory.yaml, config.yaml formats, CNI configuration, container runtime setup, offline installation, or artifact operations. Provides detailed guidance on kk subcommand usage, config file formats, inventory structure and common workflows.

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Kubernetes operations including manifests, Helm charts, operators, troubleshooting, and resource management

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