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debug

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Aktualisiert5. Juni 2026 um 23:12

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.

Installation

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Quelle

NVIDIA

NVIDIA/Model-Optimizer

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Verwandte BerufeSOC

Basierend auf der SOC-Berufsklassifikation

SoftwareentwicklerInformatik- und Mathematikberufe·SOC 15-1252

SKILL.md

readonly

name

debug

description

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

# 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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compare-results

NVIDIA/Model-Optimizer

Establish baseline-vs-candidate evaluation plans, delegate missing evaluations, compare validated results, and decide quantization feasibility. Use when the user asks to compare baseline vs quantized runs, explain an accuracy drop/regression, verify whether a quantized checkpoint is acceptable, or compare NEL/MLflow evaluation outputs. Do NOT use for generic single-model evaluation without comparison intent (use evaluation), live NEL status/debugging (use launching-evals), or generic MLflow browsing without a comparison goal (use accessing-mlflow).

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NVIDIA/Model-Optimizer

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).

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evaluation

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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. Not for quantizing models (use ptq), deploying/serving models (use deployment), or comparing completed baseline-vs-quantized results (use compare-results).

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ptq

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Use when the user asks to "quantize a model", "run PTQ", "post-training quantization", "NVFP4 quantization", "FP8 quantization", "INT8 quantization", "INT4 AWQ", "quantize LLM", "quantize MoE", "quantize VLM", or needs to produce a quantized HuggingFace checkpoint from a pretrained model using ModelOpt. Do NOT use for multi-candidate recipe exploration or optimization (use quant-recipe-search).

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quant-recipe-search

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Use when the user asks to find, search for, or optimize the best quantization recipe for a model, including direct requests like "find the best quantization recipe and generate a PTQ checkpoint." Guides the multi-candidate loop: choose compute-vs-memory success metrics, select ModelOpt recipe baselines, design AutoQuant/manual recipe deltas, interpret sensitivity, and decide next candidates. Do NOT use for a single known PTQ recipe run (use ptq), serving (use deployment), creating/running evals (use evaluation or launching-evals), monitoring jobs (use monitor), MLflow browsing (use accessing-mlflow), or comparing completed baseline-vs-candidate scores only (use compare-results).

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deployment

NVIDIA/Model-Optimizer

Serve a quantized or unquantized LLM checkpoint as an OpenAI-compatible API endpoint using vLLM, SGLang, or TRT-LLM. Use when user says "deploy model", "serve model", "start vLLM server", "launch SGLang", "TRT-LLM deploy", "AutoDeploy", "benchmark throughput", "serve checkpoint", or needs an inference endpoint from a HuggingFace or ModelOpt-quantized checkpoint. Do NOT use for quantizing models (use ptq) or evaluating accuracy (use evaluation).

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name

debug

description

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

# 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