Manage Kubernetes configuration with ConfigMaps, Secrets, namespaces, resource quotas, and environment-specific settings.
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Kubernetes Recipes reaches 400 production-ready articles. Explore new AI/GPU infrastructure, NVIDIA networking, ArgoCD GitOps, OpenShift, and RHACS security recipes.
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Expose pod and container metadata to applications using the Downward API. Access labels, annotations, resource limits, and node information from within.
Expose pod and container metadata to applications using the Downward API. Access labels, annotations, resource limits, and pod information from within.
Manage application configuration with environment variables and ConfigMaps. Learn injection methods, mounting as files, and dynamic configuration updates.
Pull container images from private registries using image pull secrets. Configure authentication for Docker Hub, GCR, ECR, ACR, and private registries.
Switch between multiple clusters efficiently. Configure kubeconfig, manage contexts, and set up secure multi-cluster access.
Run batch workloads and scheduled tasks with Jobs and CronJobs. Configure retries, parallelism, and completion tracking for reliable task execution.
Organize and manage Kubernetes resources with labels and annotations. Implement labeling strategies for selection, filtering, and metadata.
Set CPU and memory requests and limits effectively. Understand QoS classes, resource quotas, and optimize container resource allocation.
Set CPU and memory requests and limits for containers. Understand QoS classes, resource quotas, and best practices for right-sizing workloads.
Master Kubernetes ConfigMaps and Secrets for application configuration. Learn creation methods, mounting strategies, and security best practices.
Master Kubernetes namespace organization for multi-team environments. Learn resource quotas, network policies, and RBAC per namespace.
Master Kubernetes resource management with proper CPU and memory requests and limits. Avoid OOMKills, throttling, and resource contention.
Configure NicClusterPolicy node selectors and affinity rules to deploy MOFED drivers only on RDMA-capable nodes.
Configure OpenClaw's built-in cron scheduling and heartbeat system on Kubernetes for proactive notifications, periodic checks, and automated background.
Deploy and manage OpenClaw agent skills (tools, automations, integrations) on Kubernetes using ConfigMaps, PVCs, and git-sync for dynamic capability.
Manage OpenClaw agent workspaces (SOUL.md, skills, memory) with GitOps using Flux or ArgoCD, enabling version-controlled AI persona management on.
Configure CatalogSource resources in OpenShift to serve custom operator catalogs from private registries, air-gapped environments, or curated operator collections.
Understand OpenShift Container Platform version lifecycle, support phases, EUS releases, and upgrade planning for production clusters.
Configure an OpenShift Project Request Template so every new namespace automatically gets a ServiceAccount with imagePullSecrets for your private Quay registry.
Configure Kubernetes PriorityClasses for GPU workloads with training, serving, batch, and interactive tiers and preemption policies.
Configure ResourceQuota and LimitRange for GPU workloads with per-tenant caps on GPU, CPU, memory, and object counts.
Understand and configure the driver.kernelModuleType field in the NVIDIA GPU Operator ClusterPolicy to choose between auto, open, and proprietary kernel.
Handle Kubernetes API version changes and deprecations. Migrate resources to stable APIs and ensure cluster upgrade compatibility.
Reduce cloud costs in Kubernetes clusters. Right-size resources, use spot instances, implement autoscaling, and monitor spending effectively.
Manage Kubernetes configurations with Kustomize overlays. Customize base manifests for different environments without template duplication.
Control pod scheduling with taints and tolerations. Dedicate nodes for specific workloads, handle node conditions, and implement scheduling constraints.
Implement resource quotas to limit CPU, memory, and object counts per namespace. Ensure fair resource allocation across teams and environments.
Limit resource consumption per namespace with ResourceQuotas. Control CPU, memory, storage, and object counts to ensure fair cluster sharing.
Configure safe MOFED driver upgrade policies in the NVIDIA GPU Operator ClusterPolicy with rolling updates, node draining, and rollback procedures.
Deploy and configure NVIDIA DOCA Driver containers via NicClusterPolicy for RDMA, NFS-RDMA, and precompiled driver builds.
Build and deploy precompiled NVIDIA DOCA Driver containers on OpenShift using DriverToolKit, MachineConfig, and upgrade lifecycle.
Complete reference for the NVIDIA GPU Operator ClusterPolicy CRD covering driver, toolkit, device plugin, MOFED, GDS, MIG, and DCGM configuration options.
Configure the NVIDIA GPU Operator to deploy Mellanox OFED drivers for high-performance RDMA networking on Kubernetes GPU nodes with InfiniBand and RoCE support.
Maintain a version compatibility matrix for GPU Operator, Network Operator, drivers, firmware, CUDA, and OpenShift for safe upgrades.
Build NVIDIA MOFED and DOCA drivers for OpenShift using DriverToolKit, Buildah, and MachineConfig for RDMA and GPU networking.
Migrate from proprietary NVIDIA kernel modules and nvidia-peermem to open kernel modules with DMA-BUF for safer GPU upgrades.
Understand and manage Red Hat Enterprise Linux CoreOS (RHCOS) for OpenShift nodes including MachineConfig, ignition, OS updates, and node customization.
Step-by-step guide to migrate the NVIDIA GPU Operator from proprietary to open kernel modules on OpenShift, enabling DMA-BUF and GPUDirect Storage support.
Apply safe NCCL environment variable profiles for RDMA-capable and Ethernet-only GPU clusters to maximize collective communication throughput.
Use Crossplane to provision and manage cloud infrastructure resources like databases, storage, and networking using Kubernetes-native APIs and GitOps.
Configure ComputeDomains for robust and secure Multi-Node NVLink (MNNVL) workloads on NVIDIA GB200 and similar systems using DRA
Learn to use Kubernetes Dynamic Resource Allocation (DRA) for flexible GPU allocation, sharing, and configuration with the NVIDIA DRA Driver
Dynamically partition NVIDIA A100 and H100 GPUs using Multi-Instance GPU (MIG) technology with Dynamic Resource Allocation for flexible workload isolation
Orchestrate heterogeneous accelerator workloads combining GPUs, TPUs, FPGAs, and custom AI chips using Dynamic Resource Allocation
Configure Google Cloud TPUs in Kubernetes using DRA for flexible allocation, multi-slice workloads, and optimized machine learning training
Extend the Kubernetes API with custom API servers using the aggregation layer to add new resource types and functionality without modifying core components
Perform Kubernetes cluster upgrades with zero downtime. Learn upgrade strategies, pre-flight checks, rollback procedures, and best practices for.
Customize the Kubernetes scheduler with scheduling profiles, plugins, and advanced placement strategies for optimal pod placement and resource utilization
Extend Kubernetes API with Custom Resource Definitions. Define custom objects, configure validation schemas, and manage CRD lifecycle.
Manage resource cleanup with Kubernetes finalizers. Implement custom cleanup logic and understand how finalizers prevent premature resource deletion.
Implement leader election and distributed coordination with Kubernetes Lease objects. Build highly available controllers and prevent split-brain scenarios.
Automatically inject configurations into pods using admission controllers. Configure environment variables, volumes, and annotations at deployment time.
Our book includes an entire chapter dedicated to configuration with dozens more examples.
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