domino_admin_toolkit.analyzers.karpenter_analyzer module

Karpenter-specific analyzers for validating node provisioning and capacity management.

This module contains analyzers that validate Karpenter’s resource estimation accuracy, NodePool capacity utilization, and hardware tier mapping configuration.

pydantic model domino_admin_toolkit.analyzers.karpenter_analyzer.HardwareTierMappingAnalyzer

Bases: AnalyzerBase

Validates hardware tier to NodePool mapping configuration

Evaluates:

• Required hardware tiers have corresponding NodePools

• GPU and Neuron tiers have proper taints configured

• Instance type consistency within hardware tiers

Fields:

• required_hardware_tiers (list)

field required_hardware_tiers: list = ['compute', 'gpu', 'neuron']

List of required hardware tiers that must have proper nodepool mapping

analyze(data)

Ensures hardware tiers are properly mapped to Karpenter nodepools

Return type:

list[CheckResult]

name: ClassVar[str] = 'HardwareTierMappingAnalyzer'

pydantic model domino_admin_toolkit.analyzers.karpenter_analyzer.NodepoolCapacityUtilizationAnalyzer

Bases: AnalyzerBase

Validates NodePool capacity utilization to prevent resource exhaustion

Evaluates:

• Current utilization against defined limits

• Warning and critical threshold breaches

• Active node count vs expected capacity

Fields:

• critical_utilization_pct (float)

• warning_utilization_pct (float)

field critical_utilization_pct: float = 90.0

Utilization percentage threshold for critical alerts

field warning_utilization_pct: float = 80.0

Utilization percentage threshold for warnings

analyze(data)

Monitors nodepool capacity utilization to prevent job failures

Return type:

list[CheckResult]

name: ClassVar[str] = 'NodepoolCapacityUtilizationAnalyzer'

pydantic model domino_admin_toolkit.analyzers.karpenter_analyzer.ResourceEstimationAccuracyAnalyzer

Bases: AnalyzerBase

Validates Karpenter resource estimation accuracy against actual node capacity

Evaluates:

• Resource efficiency to avoid excessive waste

• Node readiness state after provisioning

• Reasonable overprovisioning bounds for cost control

Fields:

• max_overprovisioning_ratio (float)

• min_efficiency_ratio (float)

field max_overprovisioning_ratio: float = 100.0

Maximum ratio of actual to requested resources (cost control threshold)

field min_efficiency_ratio: float = 0.01

Minimum ratio of requested to actual resources (1% efficiency minimum)

analyze(data)

Validates resource efficiency for NodeClaims

Return type:

list[CheckResult]

name: ClassVar[str] = 'ResourceEstimationAccuracyAnalyzer'