version: "1.0.1" name: log-analysis description: > Analyze a SLURM job log file for failure root-cause attribution and restart decisions using NVRxLogAnalyzer. Use when you have a SLURM training job log and need to determine why the job failed and whether it should be restarted. Performs per-cycle chunking, fast-path pattern matching, and LLM-based classification. compatibility: Requires LLM_API_KEY and nvidia-resiliency-ext[attribution] installed. metadata: entry-point: NVRxLogAnalyzer script: scripts/nvrx_logsage.py

Skill: log_analysis

Analyze a SLURM job log file for failure root-cause attribution and restart decisions using NVRxLogAnalyzer.

Script: `scripts/nvrx_logsage.py` → attribution/log_analyzer/nvrx_logsage.py

What it does

1. Reads the log file (UTF-8, falls back to latin-1).
2. Splits into per-cycle chunks using chunk_logs_strict (scans for profiling.py:.*Cycle:\s*N markers). Falls back to a single chunk when no markers are found.
3. For each chunk, extracts application errors via return_application_errors (logsage).
4. Classifies each chunk with fast-path pattern matching (training done, SLURM cancelled, preemption, time limit) or calls the LLM via get_proposed_solution_cat.
5. Returns one result tuple per cycle.

CLI

python scripts/nvrx_logsage.py \
    --log-path /path/to/job.log \
    [--model MODEL] \
    [--temperature 0.2] \
    [--top_p 0.7] \
    [--max_tokens 8192] \
    [--exclude_nvrx_logs] \
    [--is_per_cycle]

Programmatic API

from nvidia_resiliency_ext.attribution.log_analyzer.nvrx_logsage import NVRxLogAnalyzer
analyzer = NVRxLogAnalyzer({
    "log_path": "/path/to/job.log",
    "model": "nvidia/nemotron-3-super-120b-a12b",
    "temperature": 0.2,
    "top_p": 0.7,
    "max_tokens": 8192,
    "exclude_nvrx_logs": False,
    "is_per_cycle": False,
})
results = analyzer.run_sync({"log_path": "/path/to/job.log"})
# results: tuple[list[RawAnalysisResultItem], AttributionState]

Run-time overrides take precedence over constructor config (see base.effective_run_or_init_config).

Output

Each returned RawAnalysisResultItem keeps raw_text with five fields joined by \n, but also carries the parsed fields directly so consumers do not reparse the text:

<restart_decision>      # "RESTART IMMEDIATE" | "STOP - DONT RESTART IMMEDIATE"
<error_explanation>     # short string or ""
<attribution_text>      # "Attribution: Primary issues: [...], Secondary issues: [...]"
<additional_detail>     # extended text or ""
<checkpoint_saved>      # "True" | "False"

The serialized cycle fields are auto_resume, auto_resume_explanation, attribution_text, checkpoint_saved_flag, primary_issues, and secondary_issues, plus the parsed cycle action. The overall client decision is emitted separately as recommendation.action / recommendation.source. The runner's internal AttributionState.STOP is set only when the parsed cycle action is STOP.

Fast-path decisions (no LLM call)

Prerequisites

• LLM_API_KEY set (env var, LLM_API_KEY_FILE, or ~/.llm_api_key)
• Package installed: pip install 'nvidia-resiliency-ext[attribution]' or pip install -e '.[attribution]' from repo root