debug-inference

name	debug-inference
description	Troubleshoot failed or slow InferenceService deployments on OpenShift AI. Use when: - "My InferenceService won't start" - "Model deployment is stuck" - "Inference endpoint returns errors" - "Model is slow / high latency" - "GPU scheduling failed for my model" Progressive diagnosis: status conditions, events, pod logs, GPU health, and observability analysis. NOT for deploying models (use /model-deploy). NOT for creating runtimes (use /serving-runtime-config).
model	inherit
color	yellow
license	Apache-2.0
allowed-tools	resources_get resources_list pods_list pods_log events_list list_inference_services get_inference_service get_model_endpoint get_deployment_info analyze_vllm chat_vllm get_gpu_info analyze_openshift query_tempo_tool get_trace_details_tool execute_promql korrel8r_get_correlated

/debug-inference Skill

Troubleshoot failed, stuck, or slow InferenceService deployments on Red Hat OpenShift AI. Performs progressive diagnosis through status conditions, events, pod logs, related resources, and optional observability analysis. Follows a 6-step diagnosis pattern with human-in-the-loop confirmation at each step.

Prerequisites

Required MCP Server: openshift (OpenShift MCP Server)

Required MCP Tools (from openshift):

• resources_get - Get ServingRuntime, NIM Account CR, InferenceService details
• resources_list - List InferenceServices (OpenShift fallback)
• pods_list - Find predictor/transformer pods
• pods_log - Retrieve container logs
• events_list - Check events for errors

Preferred MCP Server: rhoai (RHOAI MCP Server) — used when available, automatic OpenShift fallback on failure

Preferred MCP Tools (from rhoai):

• list_inference_services - List deployed models with structured status data
• get_inference_service - Get detailed deployment status (conditions, endpoint, ready state)
• get_model_endpoint - Quick check if endpoint is available (early diagnostic)

Optional MCP Server: ai-observability (AI Observability MCP)

Optional MCP Tools (from ai-observability):

• get_deployment_info - Check model initialization status
• analyze_vllm - Analyze vLLM performance bottlenecks (latency, throughput, errors, token rates)
• chat_vllm - Conversational follow-up on vLLM metrics during diagnosis
• get_gpu_info - GPU inventory and utilization
• analyze_openshift - Check GPU health with "GPU & Accelerators" category
• query_tempo_tool - Trace request latency by service/operation/time range
• get_trace_details_tool - Get detailed span-level info for a specific trace ID
• execute_promql - Run custom PromQL queries for metrics not covered by standard analysis
• korrel8r_get_correlated - Correlate signals (logs, traces, metrics, alerts) across a pod/namespace for root cause analysis

Common prerequisites (KUBECONFIG, OpenShift+RHOAI cluster, KServe, verification protocol): See skill-conventions.md.

Fallback templates: See openshift-fallback-templates.md for OpenShift YAML templates used when RHOAI tools are unavailable.

Additional cluster requirements:

• An existing InferenceService deployment to debug

When to Use This Skill

Use this skill when you need to:

• Troubleshoot an InferenceService that won't start, is stuck, or shows errors
• Diagnose slow inference latency or high error rates
• Investigate GPU scheduling failures or OOMKilled pods
• Perform root cause analysis on model deployment issues

Do NOT use this skill when:

• You want to deploy a new model (use /model-deploy)
• You want to analyze ongoing model performance (use /ai-observability)
• You need to create or fix a ServingRuntime (use /serving-runtime-config)
• You need to set up NIM credentials (use /nim-setup)

Workflow

Step 1: Identify Target InferenceService

Ask the user:

• Which InferenceService is having issues? (name or "list all")
• What namespace is it in?
• What is the symptom? (won't start / slow / errors / other)

If user says "list all" or is unsure:

MCP Tool: list_inference_services (from rhoai)

Parameters:

• namespace: user-specified namespace - REQUIRED
• verbosity: "standard" - OPTIONAL

If rhoai unavailable or returns error: Use resources_list (from openshift) with apiVersion: serving.kserve.io/v1beta1, kind: InferenceService, namespace: [namespace].

Present InferenceServices with their status:

Name	Runtime	Ready	URL	Age
[name]	[runtime]	[True/False/Unknown]	[url or "N/A"]	[age]

WAIT for user to select which InferenceService to debug.

Step 2: Status Overview

MCP Tool: get_inference_service (from rhoai)

Parameters:

• name: the InferenceService name - REQUIRED
• namespace: user-specified namespace - REQUIRED
• verbosity: "full" - REQUIRED

If rhoai unavailable or returns error: Use resources_get (from openshift) with apiVersion: serving.kserve.io/v1beta1, kind: InferenceService, name: [name], namespace: [namespace]. Extract status from .status.conditions.

Early endpoint check:

MCP Tool: get_model_endpoint (from rhoai)

• name: the InferenceService name, namespace: user-specified namespace

If rhoai unavailable or returns error: Extract endpoint from .status.url of the InferenceService obtained via resources_get (from openshift).

An empty or error URL indicates deployment issues. Report endpoint availability status.

Present status conditions:

Condition	Status	Reason	Message
Ready	[True/False/Unknown]	[reason]	[message]
PredictorReady	[True/False/Unknown]	[reason]	[message]
IngressReady	[True/False/Unknown]	[reason]	[message]

Quick Assessment: Based on conditions, provide initial assessment (e.g., "PredictorReady is False -- the model container is not running. Likely a pod-level issue.")

Ask: "Continue with deep analysis of events and pods? (yes/no)"

WAIT for user confirmation.

Step 3: Events and Pod Analysis

MCP Tool: events_list (from openshift)

Parameters:

• namespace: user-specified namespace - REQUIRED

Filter events related to the InferenceService name.

MCP Tool: pods_list (from openshift)

Parameters:

• namespace: user-specified namespace - REQUIRED
• labelSelector: "serving.kserve.io/inferenceservice=[isvc-name]" - REQUIRED

Present findings:

Events:

Time	Type	Reason	Message
[time]	[Normal/Warning]	[reason]	[message]

Predictor Pods:

Pod	Status	Restarts	Node	GPU
[pod-name]	[status]	[count]	[node]	[gpu-count]

Issues Found:

• [Issue from events or pod status]

Ask: "Continue to view pod logs? (yes/no)"

WAIT for user confirmation.

Step 4: Pod Logs Review

MCP Tool: pods_log (from openshift)

Parameters:

• namespace: user-specified namespace - REQUIRED
• name: predictor pod name from Step 3 - REQUIRED
• container: "kserve-container" - REQUIRED (main serving container)

If the container has restarted, also retrieve previous logs.

Present log analysis:

Log Analysis:

• [Error pattern identified, e.g., "CUDA out of memory", "S3 access denied", "Model not found"]
• [Relevant log line with explanation]

For NIM-specific deployments, also check:

• NGC authentication errors in logs
• TensorRT engine compilation status
• GPU compatibility messages

If the error is unrecognized -> Trigger live doc lookup:

1. Action: Read live-doc-lookup.md using the Read tool
2. Use WebFetch to look up the error message in RHOAI documentation
3. Output to user: "I looked up this error on [source]: [explanation and fix]"

Ask: "Continue to check related resources and observability? (yes/no)"

WAIT for user confirmation.

Step 5: Related Resources and Observability

Check ServingRuntime:

MCP Tool: resources_get (from openshift)

Parameters:

• apiVersion: "serving.kserve.io/v1alpha1" - REQUIRED
• kind: "ServingRuntime" - REQUIRED
• namespace: user-specified namespace - REQUIRED
• name: runtime name from the InferenceService spec - REQUIRED

Verify the runtime exists and its model format matches the InferenceService.

For NIM deployments -- Check Account CR:

MCP Tool: resources_get (from openshift)

Parameters:

• apiVersion: "nim.opendatahub.io/v1alpha1" - REQUIRED
• kind: "Account" - REQUIRED
• namespace: user-specified namespace - REQUIRED
• name: "nim-account" - REQUIRED

If ai-observability MCP is available:

• get_deployment_info: Check if the model appears in monitoring and its initialization status
• analyze_vllm: Analyze performance metrics for slow inference (latency, throughput, errors, token rates)
• chat_vllm: Ask follow-up questions about analyzed metrics (e.g., "Why is latency spiking?")
• analyze_openshift with category "GPU & Accelerators": Check GPU health and utilization
• query_tempo_tool: Trace request latency if the symptom is slow responses
• get_trace_details_tool: Drill into a specific trace ID to see span-level timing
• execute_promql: Run custom PromQL queries for deeper metric investigation (e.g., vllm:request_success:ratio, GPU memory utilization)
• korrel8r_get_correlated: Correlate signals across the inference stack -- find related logs, traces, metrics, and alerts for the failing pod/namespace (query example: k8s:Pod:{"namespace":"[ns]","name":"[pod-name]"}, goals: ["log:application", "metric:metric", "trace:span"])

If ai-observability not available: Skip with note: "Observability analysis skipped (ai-observability MCP not configured)."

Present findings:

• ServingRuntime status and compatibility
• NIM Account CR status (if applicable)
• Observability insights (if available)

Ask: "Continue to diagnosis summary? (yes/no)"

WAIT for user confirmation.

Step 6: Diagnosis Summary

Present a structured diagnosis:

## Diagnosis Summary: [isvc-name]

### Root Cause

**Primary Issue:** [Categorized root cause]

| Category | Status | Details |
|----------|--------|---------|
| ServingRuntime | [OK/FAIL] | [details] |
| Pod Scheduling | [OK/FAIL] | [details] |
| Container Start | [OK/FAIL] | [details] |
| Model Loading | [OK/FAIL] | [details] |
| GPU Access | [OK/FAIL] | [details] |
| Endpoint Health | [OK/FAIL] | [details] |

### Evidence

- [Evidence 1 from events/logs/status]
- [Evidence 2]

### Recommended Actions

1. **[Action 1]** - [description]
2. **[Action 2]** - [description]
3. **[Action 3]** - [description]

### Verification Steps

After applying fixes:
1. Check InferenceService status: `resources_get` for the InferenceService
2. Verify pod is running: `pods_list` with label selector
3. Test endpoint: curl command to the inference URL

End with options:

Would you like me to:
1. Execute a recommended fix
2. Dig deeper into a specific area
3. Debug a related resource (ServingRuntime, pod, NIM Account)
4. Invoke /serving-runtime-config to fix runtime issues
5. Exit debugging

WAIT for user to select next action.

Common Issues

For common issues (GPU scheduling, OOMKilled, image pull errors, RBAC), see common-issues.md.

Issue 1: S3 Storage Access Denied

Error: Pod logs show "Access Denied" or "NoSuchBucket" when loading model weights

Cause: S3 credentials are missing, expired, or the bucket/path is incorrect.

Solution:

1. Verify the storageUri in the InferenceService spec
2. Check that the S3 credential Secret exists in the namespace
3. Verify the Secret is referenced by the ServiceAccount or data connection
4. Test S3 access independently to confirm credentials are valid

Issue 2: NIM Authentication / GPU Incompatibility

Error: NIM pod logs show NGC authentication failure, or TensorRT engine fails to compile for the available GPU

Cause: NGC API key is invalid/expired, or the GPU type is not supported by the NIM model profile.

Solution:

1. Check Account CR status for credential errors: resources_get for accounts.nim.opendatahub.io
2. Verify NGC API key is valid at https://ngc.nvidia.com
3. Check NIM supported GPU matrix via live doc lookup against NVIDIA NIM supported models
4. Re-run /nim-setup to refresh credentials if expired

Dependencies

MCP Tools

See Prerequisites for the complete list of required and optional MCP tools.

Related Skills

• /model-deploy - Redeploy or modify the InferenceService after fixing issues
• /serving-runtime-config - Fix or create ServingRuntime if runtime is the issue
• /nim-setup - Re-run NIM platform setup if NIM credentials are the issue
• /model-monitor - Check if TrustyAI monitoring detected issues before they became failures

Reference Documentation

• known-model-profiles.md - Correct resource sizing for common models
• supported-runtimes.md - Runtime capabilities and known limitations
• live-doc-lookup.md - Protocol for looking up unrecognized errors

Critical: Human-in-the-Loop Requirements

See skill-conventions.md for general HITL and security conventions.

Skill-specific checkpoints:

• After identifying target (Step 1): confirm which InferenceService to debug
• After status overview (Step 2): confirm before deep analysis
• After events/pod analysis (Step 3): confirm before viewing logs
• After log review (Step 4): confirm before checking related resources
• After diagnosis summary (Step 6): present options, wait for user decision
• NEVER auto-delete or auto-modify InferenceService resources without user confirmation
• NEVER execute remediation actions without presenting the plan and getting explicit approval