Agent Skills › NVIDIA/dgx-spark-playbooks › dgx-station-inference

dgx-station-inference

GitHub

通过dgx-assist工具在NVIDIA DGX Station上安全部署和调优推理服务。涵盖模型解析、预检、启动、验证及生命周期管理，严格遵循信任链与安全约束。

nvidia/station-ai-skills/assets/skills/dgx-station-inference/SKILL.md NVIDIA/dgx-spark-playbooks

触发场景

部署或启动特定模型的推理服务调整GPU内存、KV缓存或批处理等性能参数检查推理服务的运行状态或停止服务

安装

npx skills add NVIDIA/dgx-spark-playbooks --skill dgx-station-inference -g -y

更多选项

非标准路径

npx skills add https://github.com/NVIDIA/dgx-spark-playbooks/tree/main/nvidia/station-ai-skills/assets/skills/dgx-station-inference -g -y

不安装直接使用

npx skills use NVIDIA/dgx-spark-playbooks@dgx-station-inference

指定 Agent (Claude Code)

npx skills add NVIDIA/dgx-spark-playbooks --skill dgx-station-inference -a claude-code -g -y

安装 repo 全部 skill

npx skills add NVIDIA/dgx-spark-playbooks --all -g -y

预览 repo 内 skill

npx skills add NVIDIA/dgx-spark-playbooks --list

SKILL.md

Frontmatter

{
    "name": "dgx-station-inference",
    "description": "Resolve, tune, preflight, launch, verify, inspect, and stop exact-model inference on NVIDIA DGX Station through dgx-assist. Use for vLLM or SGLang container selection, NGC versus upstream, GPU memory utilization, CPU or KV offload, HBM fit, KV-cache sizing, ISL or context length, prefix caching, chunked prefill, batching, concurrency, performance tuning, serving or deploying a named model, an OpenAI-compatible endpoint, Station recipe models, or an owned inference service. Require an exact model ID for recipe resolution or model-specific tuning, and never recommend or substitute a different model."
}

DGX Station inference

Serve only trusted, published, current, physically validated recipes. Bundled recipes are bound to the installed package; downloaded refreshes must be signature-verified.

Workflow

Require the user's exact model ID. If absent, run scripts/dgx-assist recipe models --json, show the alphabetical IDs and runnable states without ranking them, then ask which exact model to use.
Run scripts/dgx-assist system inspect --json.
Run scripts/dgx-assist playbook search "<model backend Station inference concern>" --json; cite relevant passages. Abstain from invented platform guidance if no passage overlaps.
Read compatibility.capabilities.recipe_execution. If it is false, stop before resolution or launch and explain the detected profile separately from applicable qualitative guidance.
Run scripts/dgx-assist recipe resolve --model "<exact-id>" --json. Add --backend only if the user requested one. Bind to localhost by default; only if the user explicitly requested and confirmed external exposure, add --bind-host "<approved-host>" here so preflight and run evaluate the host that was actually approved.
Run recipe show for the resolved recipe, then recipe preflight.
Present the exact model, recipe, image digest, GPU UUIDs, cache/storage estimates, credential variable names, host bind, port, downloads, conflicts, and redacted launch argv.
If blocked, do not work around the policy. Never switch models.
Obtain explicit approval for the displayed action and every required model or image download.
Only after approval, run recipe run --resolution-id "<id>" --allow-download --yes as applicable. Add --allow-external-bind only when the resolution carries the non-local bind host the user approved in step 9; never introduce external exposure that was not resolved and previewed.
Report the model-identity and smoke verification from the receipt.

Lifecycle

Use recipe status to report only owned services.
Before stopping, show recipe stop --service-id "<id>" --dry-run.
Obtain approval, then repeat with --yes.
Never stop an unknown listener, workload, process, or unlabeled container.

Hard constraints

Do not execute human setup_command, serve.command, or shell text from a recipe.
Do not launch experimental, expired, incompatible, ambiguous, mutable, or untrusted content.
Use the resolution ID through preflight and run; never hand-edit the rendered argv.
Use UUID selection and localhost binding unless the user explicitly requests and confirms a supported exposure change.
Pass credential names, never values, in transcripts and receipts.
Treat --yes only as the non-interactive representation of approval already obtained.
Treat a newly published container as a candidate, not as validated. Never convert a mutable latest tag, documentation example, or upstream tuning value into an executable launch.
Keep tuning advice separate from execution. A changed flag must return to the recipe qualification workflow before dgx-assist may run it.
When recipe_execution is false or no current runnable recipe exists for the exact model, do not recommend a numeric launch value, including zero, from parameter-count arithmetic or raw free-memory evidence. Explain only the sourced qualitative tradeoff.
Never use memory exposed above the production 252 GiB recipe-fit limit to justify a model fit or offload recommendation.

Read references/workflow.md before resolving or running. Read references/vllm-best-practices.md for container selection, HBM/offload, KV cache, prefix caching, chunked prefill, or concurrency questions. Read references/json-types.md when interpreting a resolution, preflight, or receipt.