debug

Run commands inside a remote Docker container via the file-based command relay (tools/debugger). Use when the user says "run in Docker", "run on GPU", "debug remotely", "run test in container", "check nvidia-smi", "run pytest in Docker", or needs to execute any command inside a Docker container that shares the repo filesystem. Requires the user to have started server.sh inside the container first.

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Dépôt: NVIDIA/Model-Optimizer
Dernière activité de la source: 12 août 2026 à 16:33
Langue détectée de SKILL.md: anglais
Étoiles: 5 493
Forks: 789

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Follow https://skillsmp.com/skill-install/prompt.md to install only the Agent Skill at https://github.com/NVIDIA/Model-Optimizer/tree/main/plugins/modelopt/skills/debug.

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npx skills add https://github.com/NVIDIA/Model-Optimizer --skill debug

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SKILL.md

Instructions source · Aperçu en lecture seule

name: debug
description: Run commands inside a remote Docker container via the file-based command relay (tools/debugger). Use when the user says "run in Docker", "run on GPU", "debug remotely", "run test in container", "check nvidia-smi", "run pytest in Docker", or needs to execute any command inside a Docker container that shares the repo filesystem. Requires the user to have started server.sh inside the container first.

# Remote Docker Debugger Execute commands inside a Docker container from the host using the file-based command relay. **Read `tools/debugger/CLAUDE.md` for full usage details** — it has the protocol and examples. ## Quick Reference ```bash # Check connection bash tools/debugger/client.sh status # Connect to server (user must start server.sh in Docker first) bash tools/debugger/client.sh handshake # Run a command bash tools/debugger/client.sh run "<command>" # Long-running command (default timeout is 600s) bash tools/debugger/client.sh --timeout 1800 run "<command>" # Cancel the currently running command bash tools/debugger/client.sh cancel # Reconnect after server restart bash tools/debugger/client.sh flush bash tools/debugger/client.sh handshake ```

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benchmark-model-kernels

Inspect Hugging Face decoder layers on meta tensors and plan or run per-rank BF16, FP8, and NVFP4 GEMM or fused-MoE microbenchmarks with the bundled scripts and a local FlashInfer checkout. Use when choosing a model, GPU, TP, EP, or M/token-concurrency sweep; deriving common fused QKV and gate/up shapes without loading checkpoint weights; or using FlashInfer benchmark utilities. Do not use for end-to-end server throughput or request latency.

2026-10-29

day0-release

Deterministic end-to-end driver for day-0 quantized-checkpoint releases — chains PTQ → evaluation → comparison with enforced gates between stages (the evaluation stage deploys the checkpoint itself), and returns a publish decision (ACCEPT / REGRESSION / ANOMALOUS / INFEASIBLE). Use when the user asks to "release a model at day-0", "quantize and validate model X is within N% of baseline and tell me if it's publishable", or "run the full day-0 workflow". Do NOT use for single-stage requests — quantizing only (use ptq), serving only (use deployment), evaluating only (use evaluation), or comparing two existing runs (use compare-results).

2026-10-29

evaluation

Evaluates accuracy of quantized or unquantized LLMs using NeMo Evaluator Launcher (NEL). Triggers on "evaluate model", "benchmark accuracy", "run MMLU", "evaluate quantized model", "run nel". Handles deployment, config generation, and evaluation execution. Always load when validating completed runs or reporting evaluation scores, including standalone launching-evals analysis. Not for quantizing models (use ptq), standalone serving (use deployment), or comparing completed baseline-vs-quantized results (use compare-results).

2026-10-28

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debug

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