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Showing content from https://run-ai-docs.nvidia.com/self-hosted/infrastructure-setup/advanced-setup/cluster-config below:

Advanced Cluster Configurations | Run:ai Documentation

Advanced Cluster Configurations

Advanced cluster configurations can be used to tailor your NVIDIA Run:ai cluster deployment to meet specific operational requirements and optimize resource management. By fine-tuning these settings, you can enhance functionality, ensure compatibility with organizational policies, and achieve better control over your cluster environment. This article provides guidance on implementing and managing these configurations to adapt the NVIDIA Run:ai cluster to your unique needs.

After the NVIDIA Run:ai cluster is installed, you can adjust various settings to better align with your organization's operational needs and security requirements.

Modify Cluster Configurations

Advanced cluster configurations in NVIDIA Run:ai are managed through the runaiconfig Kubernetes Custom Resource . To edit the cluster configurations, run:

kubectl edit runaiconfig runai -n runai

To see the full runaiconfig object structure, use:

kubectl get crds/runaiconfigs.run.ai -n runai -o yaml

The following configurations allow you to enable or disable features, control permissions, and customize the behavior of your NVIDIA Run:ai cluster:

spec.global.affinity (object)

Sets the system nodes where NVIDIA Run:ai system-level services are scheduled. Using global.affinity will overwrite the node roles set using the Administrator CLI (runai-adm). Default: Prefer to schedule on nodes that are labeled with node-role.kubernetes.io/runai-system

spec.global.nodeAffinity.restrictScheduling (boolean)

Enables setting node roles and restricting workload scheduling to designated nodes Default: false

spec.global.tolerations (object)

Configure Kubernetes tolerations for NVIDIA Run:ai system-level services

spec.global.subdomainSupport (boolean)

spec.global.devicePluginBindings (boolean)

Instruct NVIDIA Run:ai fractions to use device plugin for host mount instead of NVIDIA Run:ai fractions using explicit host path mount configuration on the pod. See GPU fractions and dynamic GPU fractions. Default: false

spec.global.enableWorkloadOwnershipProtection (boolean)

Prevents users within the same project from deleting workloads created by others. This enhances workload ownership security and ensures better collaboration by restricting unauthorized modifications or deletions. Default: false

spec.project-controller.createNamespaces (boolean)

Allows Kubernetes namespace creation for new projects Default: true

spec.project-controller.createRoleBindings (boolean)

Specifies if role bindings should be created in the project's namespace Default: true

spec.project-controller.limitRange (boolean)

Specifies if limit ranges should be defined for projects Default: true

spec.project-controller.clusterWideSecret (boolean)

Allows Kubernetes Secrets creation at the cluster scope. See Credentials for more details. Default: true

spec.workload-controller.additionalPodLabels (object)

Set workload's Pod Labels in a format of key/value pairs. These labels are applied to all pods.

spec.workload-controller.failureResourceCleanupPolicy

NVIDIA Run:ai cleans the workload's unnecessary resources:

All - Removes all resources of the failed workload
None - Retains all resources
KeepFailing - Removes all resources except for those that encountered issues (primarily for debugging purposes)

Default: All

spec.workload-controller.GPUNetworkAccelerationEnabled

spec.mps-server.enabled (boolean)

spec.daemonSetsTolerations (object)

Configure Kubernetes tolerations for NVIDIA Run:ai daemonSets / engine

spec.runai-container-toolkit.logLevel (boolean)

Specifies the NVIDIA Run:ai-container-toolkit logging level: either 'SPAM', 'DEBUG', 'INFO', 'NOTICE', 'WARN', or 'ERROR' Default: INFO

spec.runai-container-toolkit.enabled (boolean)

node-scale-adjuster.args.gpuMemoryToFractionRatio (object)

A scaling-pod requesting a single GPU device will be created for every 1 to 10 pods requesting fractional GPU memory (1/gpuMemoryToFractionRatio). This value represents the ratio (0.1-0.9) of fractional GPU memory (any size) to GPU fraction (portion) conversion. Default: 0.1

spec.global.core.dynamicFractions.enabled (boolean)

spec.global.core.swap.enabled (boolean)

spec.global.core.swap.limits.cpuRam (string)

Sets the CPU memory size used to swap GPU workloads Default:100Gi

spec.global.core.swap.limits.reservedGpuRam (string)

Sets the reserved GPU memory size used to swap GPU workloads Default: 2Gi

spec.global.core.swap.biDirectional (string)

Sets the read/write memory mode of GPU memory swap to bi-directional (fully duplex). This produces higher performance (typically +80%) vs. uni-directional (simplex) read-write operations. For more details, see GPU memory swap. Default: false

spec.global.core.swap.mode (string)

Sets the GPU to CPU memory swap method to use UVA and optimized memory prefetch for optimized performance in some scenarios. For more details, see GPU memory swap. Default: None. The parameter is not set by default. To add this parameter set mode=mapped .

spec.global.core.nodeScheduler.enabled (boolean)

spec.global.core.timeSlicing.mode (string)

Sets the GPU time-slicing mode. Possible values:

timesharing - all pods on a GPU share the GPU compute time evenly.
strict - each pod gets an exact time slice according to its memory fraction value.
fair - each pod gets an exact time slice according to its memory fraction value and any unused GPU compute time is split evenly between the running pods.

Default: timesharing

spec.runai-scheduler.args.fullHierarchyFairness (boolean)

Enables fairness between departments, on top of projects fairness Default: true

spec.runai-scheduler.args.defaultStalenessGracePeriod

Sets the timeout in seconds before the scheduler evicts a stale pod-group (gang) that went below its min-members in running state:

0s - Immediately (no timeout)

Default: 60s

spec.pod-grouper.args.gangSchedulingKnative (boolean)

Enables gang scheduling for inference workloads.For backward compatibility with versions earlier than v2.19, change the value to false Default: false

spec.pod-grouper.args.gangScheduleArgoWorkflow (boolean)

Groups all pods of a single ArgoWorkflow workload into a single Pod-Group for gang scheduling Default: true

spec.runai-scheduler.args.verbosity (int)

Configures the level of detail in the logs generated by the scheduler service Default: 4

spec.limitRange.cpuDefaultRequestCpuLimitFactorNoGpu (string)

Sets a default ratio between the CPU request and the limit for workloads without GPU requests Default: 0.1

spec.limitRange.memoryDefaultRequestMemoryLimitFactorNoGpu (string)

Sets a default ratio between the memory request and the limit for workloads without GPU requests Default: 0.1

spec.limitRange.cpuDefaultRequestGpuFactor (string)

Sets a default amount of CPU allocated per GPU when the CPU is not specified Default: 100

spec.limitRange.cpuDefaultLimitGpuFactor (int)

Sets a default CPU limit based on the number of GPUs requested when no CPU limit is specified Default: NO DEFAULT

spec.limitRange.memoryDefaultRequestGpuFactor (string)

Sets a default amount of memory allocated per GPU when the memory is not specified Default: 100Mi

spec.limitRange.memoryDefaultLimitGpuFactor (string)

Sets a default memory limit based on the number of GPUs requested when no memory limit is specified Default: NO DEFAULT

NVIDIA Run:ai Services Resource Management

NVIDIA Run:ai cluster includes many different services. To simplify resource management, the configuration structure allows you to configure the containers CPU / memory resources for each service individually or group of services together.

Containers associated with the NVIDIA Run:ai Scheduler

Scheduler, StatusUpdater, MetricsExporter, PodGrouper, PodGroupAssigner, Binder

Containers associated with syncing updates between the NVIDIA Run:ai cluster and the NVIDIA Run:ai control plane

Agent, ClusterSync, AssetsSync

Containers associated with submitting NVIDIA Run:ai workloads

WorkloadController,

JobController

Apply the following configuration in order to change resources request and limit for a group of services:

spec:
  global:
   <service-group-name>: # schedulingServices | syncServices | workloadServices
     resources:
       limits:
         cpu: 1000m
         memory: 1Gi
       requests:
         cpu: 100m
         memory: 512Mi

Or, apply the following configuration in order to change resources request and limit for each service individually:

spec:
  <service-name>: # for example: pod-grouper
    resources:
      limits:
        cpu: 1000m
        memory: 1Gi
      requests:
        cpu: 100m
        memory: 512Mi

For resource recommendations, see Vertical scaling.

NVIDIA Run:ai Services Replicas

By default, all NVIDIA Run:ai containers are deployed with a single replica. Some services support multiple replicas for redundancy and performance.

To simplify configuring replicas, a global replicas configuration can be set and is applied to all supported services:

spec:
  global: 
    replicaCount: 1 # default

This can be overwritten for specific services (if supported). Services without the replicas configuration does not support replicas:

spec:
  <service-name>: # for example: pod-grouper
    replicas: 1 # default

The Prometheus instance in NVIDIA Run:ai is used for metrics collection and alerting.

The configuration scheme follows the official PrometheusSpec and supports additional custom configurations. The PrometheusSpec schema is available using the spec.prometheus.spec configuration.

A common use case using the PrometheusSpec is for metrics retention. This prevents metrics loss during potential connectivity issues and can be achieved by configuring local temporary metrics retention. For more information, see Prometheus Storage :

spec:  
  prometheus:
    spec: # PrometheusSpec
      retention: 2h # default 
      retentionSize: 20GB

In addition to the PrometheusSpec schema, some custom NVIDIA Run:ai configurations are also available:

Additional labels – Set additional labels for NVIDIA Run:ai's built-in alerts sent by Prometheus.
Log level configuration – Configure the logLevel setting for the Prometheus container.

spec:  
  prometheus:
    logLevel: info # debug | info | warn | error
    additionalAlertLabels:
      - env: prod # example

NVIDIA Run:ai Managed Nodes

To include or exclude specific nodes from running workloads within a cluster managed by NVIDIA Run:ai, use the nodeSelectorTerms flag. For additional details, see Kubernetes nodeSelector .

Label the nodes using the below:

key: Label key (e.g., zone, instance-type).
operator: Operator defining the inclusion/exclusion condition (In, NotIn, Exists, DoesNotExist).
values: List of values for the key when using In or NotIn.

The below example shows how to include NVIDIA GPUs only and exclude all other GPU types in a cluster with mixed nodes, based on product type GPU label:

spec:   
  global:
     managedNodes:
       inclusionCriteria:
          nodeSelectorTerms:
          - matchExpressions:
            - key: nvidia.com/gpu.product  
              operator: Exists

S3 and Git Sidecar Images

For air-gapped environments, when working with a Local Certificate Authority , it is required to replace the default sidecar images in order to use the Git and S3 data source integrations. Use the following configurations:

spec:
  workload-controller:    
    s3FileSystemImage:
      name: goofys       
      registry: runai.jfrog.io/op-containers-prod      
      tag: 3.12.24    
    gitSyncImage:      
      name: git-sync      
      registry: registry.k8s.io     
      tag: v4.4.0

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