Model Resource Profiler

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name: model-resource-profiler

description: Analyze model training or inference resource behavior from profiler artifacts, with focus on GPU memory (VRAM) and CPU hotspots. Uses JSON/JSON.GZ artifacts only to avoid unsafe deserialization.

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# Model Resource Profiler

Use this skill to produce a reproducible resource report from one or both inputs:

Safety Boundaries

Workflow

1. Confirm artifacts, trust boundary, and optimization objective.

2. Run deterministic analysis script.

3. Interpret with fixed rubric.

4. Deliver ranked action plan.

Commands

Run memory + CPU together:

python3 scripts/analyze_profile.py \
  --memory-json /path/to/memory_snapshot.json \
  --cpu-trace /path/to/trace.json.gz \
  --md-out /tmp/profile_report.md \
  --json-out /tmp/profile_report.json

Run CPU-only:

python3 scripts/analyze_profile.py \
  --cpu-trace /path/to/trace.json.gz \
  --md-out /tmp/cpu_report.md

Run memory-only:

python3 scripts/analyze_profile.py \
  --memory-json /path/to/memory_snapshot.json \
  --md-out /tmp/memory_report.md

Trusted environment conversion example (if user currently has pickle workflow):

import json
import torch

snapshot = torch.cuda.memory._snapshot()
with open("memory_snapshot.json", "w", encoding="utf-8") as f:
    json.dump(snapshot, f)

Output Contract

Always provide:

References