Huawei Cloud Modelarts Training Diagnosis

ModelArts Training Job Fault Diagnosis

Overview

This skill provides automated fault diagnosis for Huawei Cloud ModelArts training jobs. It calls ModelArts log and event APIs via hcloud CLI to collect runtime information, analyzes training job failures/timeouts/stuck jobs, and outputs diagnosis conclusions with fix suggestions and confidence levels.

Architecture

User Input
    ↓
Phase 1: Task Discovery (ListTrainingJobs)
    ↓
Phase 2: Status Assessment (ShowTrainingJobDetails)
    ↓
[Early Exit if traceback found in status.task_statuses[].message]
    ↓ (if no traceback)
Phase 3: Information Collection
    ├─ Main Path: ListTrainingJobEvents, ListTrainingJobStages
    └─ Extended Path: ShowTrainingJobLogsPreview, ShowObsUrlOfTrainingJobLogs
    ↓
Phase 4: Analysis (confidence-based inference)
    ↓
Phase 5: Output (diagnosis report + fix suggestions)

Applicable Scenarios

• Training job failure (status.phase = "Failed")
• Training job timeout (status.phase = "Timeout")
• Training job abnormal (status.phase = "Abnormal")
• Training job stuck (running long time with no progress)
• Resource shortage causing training failure

Typical Use Cases

• "My training job failed, help me diagnose"
• "Training job is stuck, no progress for hours"
• "Training job timeout, what went wrong?"
• "Scan all failed training jobs in my account"
• "Training job error code 1.015, what does it mean?"

Prerequisites

hcloud CLI Installation

• Version: 7.2.2 or higher
• Verification: hcloud version should return version >= 7.2.2
• Installation guide: See references/cli-installation-guide.md

Authentication Configuration

• AK/SK configured in ~/.hcloud/config.json
• Default region: cn-north-4
• project_id configured in profile
• skipSecureVerify=true (for WSL environment)

Verification command:

hcloud configure list

IAM Permissions

This skill requires read-only permissions for ModelArts training APIs.

Required permissions: See references/iam-policies.md

Permission failure handling:

1. If any API returns 403/401, read references/iam-policies.md
2. Display required permissions list and policy JSON to user
3. Guide user to create custom policy in IAM console
4. Pause execution until user confirms permissions are granted

KooCLI Command Format Standard

All commands follow the standard hcloud format:

hcloud ModelArts <Operation> --param1=value1 --param2=value2 --cli-region=<region>

Key conventions:

• Service name: ModelArts (PascalCase)
• Operation name: PascalCase (e.g., ShowTrainingJobDetails)
• Region parameter: --cli-region=<value> (default: cn-north-4)
• Output format: --cli-output=json (for agent processing)
• JMESPath filtering: --cli-query="<expression>" (to reduce output)

Example:

hcloud ModelArts ShowTrainingJobDetails \
  --cli-region=cn-north-4 \
  --cli-output=json \
  --training_job_id="<TRAINING_JOB_ID>"

Workflow

This skill follows a 5-phase diagnosis workflow:

Phase 1: Task Discovery

• If user provides full job ID → skip to Phase 2
• If user provides name → use ListTrainingJobs to find ID + status.phase
• If user provides nothing → scan all abnormal jobs (status.phase = "Failed"/"Timeout"/"Abnormal")

Phase 2: Status Assessment

• Call ShowTrainingJobDetails to get job status
• Check status.phase: "Failed"/"Timeout"/"Abnormal" = real fault, "Running"/"Success" = false alarm, "Initializing" = pending observation
• Extract status.task_statuses[].task_id for subsequent log APIs
• Early Exit: If status.task_statuses[].message contains full Python traceback, diagnosis can be completed at HIGH confidence without calling Phase 3 APIs

Phase 3: Information Collection (skipped if early exit applies)

Main path (always run):

1. ShowTrainingJobDetails → status.phase, status.task_statuses[].message, status.task_statuses[].task_id, status.failureAnalysisResult
2. ListTrainingJobEvents(level=Error) → error event list
3. ListTrainingJobStages → check which stage is stuck

Extended path (conditional): 4. ShowTrainingJobLogsPreview → preview logs (find traceback/error lines) 5. ShowObsUrlOfTrainingJobLogs → OBS full log download link (5min valid)

Detailed flow: See references/diagnosis-flow.md

Core Commands

Task Discovery Commands

ListTrainingJobs — Scan all abnormal training jobs

hcloud ModelArts ListTrainingJobs \
  --cli-region=cn-north-4 \
  --cli-output=json \
  --cli-query="items[?status.phase=='Failed' || status.phase=='Timeout' || status.phase=='Abnormal'].{job_id: metadata.id, name: metadata.name, phase: status.phase}"

Status Assessment Commands

ShowTrainingJobDetails — Get job status + error info

hcloud ModelArts ShowTrainingJobDetails \
  --cli-region=cn-north-4 \
  --cli-output=json \
  --training_job_id="<TRAINING_JOB_ID>"

Key response fields:

• metadata.id, metadata.name — job identifier
• status.phase — job status ("Failed", "Running", "Success", etc.)
• status.task_statuses[].message — full Python traceback or error message (primary evidence)
• status.task_statuses[].exit_code — integer exit code
• status.task_statuses[].task_id — task ID for log APIs
• status.failureAnalysisResult.analysis_results[] — platform's automatic diagnosis

Information Collection Commands

ListTrainingJobEvents — Get error events

hcloud ModelArts ListTrainingJobEvents \
  --cli-region=cn-north-4 \
  --cli-output=json \
  --level=Error \
  --limit=100 \
  --training_job_id="<TRAINING_JOB_ID>"

ListTrainingJobStages — Check stage checkpoints

hcloud ModelArts ListTrainingJobStages \
  --cli-region=cn-north-4 \
  --cli-output=json \
  --training_job_id="<TRAINING_JOB_ID>"

ShowTrainingJobLogsPreview — Preview logs (find traceback)

hcloud ModelArts ShowTrainingJobLogsPreview \
  --cli-region=cn-north-4 \
  --cli-output=json \
  --training_job_id="<TRAINING_JOB_ID>" \
  --task_id="<TASK_ID>"

ShowObsUrlOfTrainingJobLogs — Get OBS full log download link (5min valid)

hcloud ModelArts ShowObsUrlOfTrainingJobLogs \
  --cli-region=cn-north-4 \
  --cli-output=json \
  --training_job_id="<TRAINING_JOB_ID>" \
  --task_id="<TASK_ID>"

Complete command templates: See references/hcloud-command-templates.md

Parameter Confirmation

Training Diagnosis Parameters

Notes:

• task_id is required for log APIs, obtained from ShowTrainingJobDetails.status.task_statuses[].task_id
• Region can be switched (e.g., cn-north-9, cn-east-3), but project_id must be configured for that region
• Logs may contain sensitive information; only extract key error/traceback lines, do not display full logs

Output Format

This skill outputs diagnosis reports in Markdown format.

Output Template

## 诊断结论

| 项目 | 值 |
|------|-----|
| 任务 | `<metadata.name>` (`<metadata.id>`) |
| 当前状态 | `<status.phase>` |
| 故障级别 | Fault / Abnormal / 疑似异常 / 正常 |

## 根因

<一句话描述什么导致了作业失败>（置信度：HIGH/MEDIUM/LOW）

## 修复建议

### 方案 1

1. <step 1>
2. <step 2>

> 注意：以上操作涉及 [只读查询 / 需要用户确认后手动执行的变更]

## 后续步骤

[信息不足时] 当前信息不足以确定根因，建议补充以下信息：
- 调用 `<API>` 获取 `<field>`
- 或手动检查 `<environment/configuration>`

Strict constraint: Generate the report strictly following the template above. Keep analysis reasoning internal, not in the report.

Confidence Levels

• HIGH: status.task_statuses[].message contains full Python traceback with identifiable root cause, or status.failureAnalysisResult.analysis_results[].description provides explicit error description, or events contain clear error
• MEDIUM: Multiple indirect clues point to same root cause, or status.task_statuses[].exit_code non-zero but message is empty/vague
• LOW: Insufficient information; must output "information insufficient" and list what additional information is needed

Detailed confidence rules: See references/confidence-rules.md

Verification Method

This skill follows a 3-tier verification approach:

Installation Verification

hcloud version

Success criteria: Returns version >= 7.2.2

Configuration Verification

hcloud configure list

Success criteria: Displays valid AK/SK configuration with region=cn-north-4

Function Verification

hcloud ModelArts ListTrainingJobs --cli-region=cn-north-4

Success criteria: Returns HTTP 200 and training job list

Detailed verification steps: See references/verification-method.md

Best Practices

Early Exit Principle

• If ShowTrainingJobDetails returns full Python traceback in status.task_statuses[].message, skip Phase 3 APIs (ListTrainingJobEvents, ListTrainingJobStages, ShowTrainingJobLogsPreview, ShowObsUrlOfTrainingJobLogs)
• This saves API calls and speeds up diagnosis when root cause is already clear

Progressive Diagnosis

• Start with minimal information (job ID only)
• Run Phase 2 first (ShowTrainingJobDetails)
• Only run Phase 3 APIs if Phase 2 results are insufficient (no traceback in message)
• Avoid running all APIs at once; follow the 5-phase workflow

Evidence-Based Analysis

• Every inference must point to specific fields from API responses
• One evidence supports one inference; multiple evidence cross-validation increases confidence
• When return information is vague or contains no explicit errors, output "unable to determine root cause based on current information"
• Strictly prohibited: Guessing root causes without evidence

Log Handling

• Logs may contain sensitive information (IP addresses, tokens, credentials)
• Only extract key error/traceback lines for diagnosis
• Do not display full logs in output
• For complete logs, use ShowObsUrlOfTrainingJobLogs and prompt user to download (5min valid link)

Region Switching

• Default region is cn-north-4
• When switching regions, ensure project_id is configured for that region
• Use hcloud configure init or explicitly pass --project_id if region profile lacks project_id

Reference Documents

Notes

Security Constraints

1. Read-only throughout: Only call GET APIs (except ListTrainingJobs which uses POST for read-only list query); never call Create/Update/Delete/Stop
2. No credential leakage: Never print AK/SK
3. User confirmation required: If fix suggestions involve changes (restart, modify specs), must clearly prompt "requires user confirmation before manual execution"; never auto-execute
4. Sensitive information masking: Logs may contain sensitive info; mask sensitive fields before output (e.g., desensitize IPs, do not print complete tokens)

Scope Limitations

• Training job diagnosis only: Does not diagnose Notebook, inference services, or other scenarios
• No code modification: Does not modify any business code or resources
• No fabrication: Never fabricate root causes without evidence; must be based on API return fields
• No private data access: Does not directly read private data in user repositories

Known Limitations

• Some APIs may have rate limits; if throttled, wait and retry
• Log preview may be truncated; use OBS link for complete logs
• Metrics APIs removed from skill (can show state but cannot diagnose root causes)
• status.task_statuses[].message may be empty for some failures; in such cases, Phase 3 APIs become necessary