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xpu-container-run

Precio sin confirmar★ 21 Estrellas de GitHubRegistro actualizado · 14 sept 2026agent-skill

Resumen

Launch a Docker container with Intel GPU access on Linux. Encodes the correct combination of `--device /dev/dri`, render-group access, `--ipc=host`, `ZE_AFFINITY_MASK` pinning, Hugging Face cache mount, and `--entrypoint /bin/bash` for interactive use. Use when running any Intel-XPU container (vLLM-XPU, sglang-xpu, torch-XPU, llama.cpp SYCL, etc.) and the device must be visible inside. The CUDA analogue is `docker run --gpus all` — Intel has no `--gpus` flag, you pass the Direct Rendering Manager (DRM) nodes directly.

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xpu-container-run

Intel GPUs do not plug into Docker via --gpus all. There is no nvidia-container-toolkit equivalent. Pass the kernel's Direct Rendering Manager (DRM) character devices into the container and grant the right group ownership.

CUDA → Intel cheat sheet

CUDA	Intel
`docker run --gpus all`	`--device /dev/dri --group-add "$(getent group render \| cut -d: -f3)"`
`docker run --gpus '"device=0"'`	`-e ZE_AFFINITY_MASK=0`
`--ipc=host`	same
`--shm-size=16g`	same (alternative to `--ipc=host`)
`--runtime nvidia`	nothing — `xe`/`i915` is in-kernel
`nvidia-smi` inside container	`xpu-smi discovery`

No "Intel container toolkit" needed; passing the DRM nodes is enough.

Image source

Comes from the runner skill:

vLLM serving → vllm-xpu-run (vllm/vllm-openai-xpu:latest)
SGLang → sglang-xpu-run (built from upstream docker/xpu.Dockerfile)
PyTorch / Transformers → torch-xpu-run

<image> below is whichever you picked.

Quickstart — interactive shell, one GPU

Confirm the image name with the user before running — this binds host GPU devices into the container.

docker run --rm -it \
    --device /dev/dri \
    --group-add "$(getent group render | cut -d: -f3)" \
    --ipc=host \
    -e ZE_AFFINITY_MASK=0 \
    -e HF_TOKEN="$HF_TOKEN" \
    -v "$HOME/.cache/huggingface:/root/.cache/huggingface" \
    --entrypoint /bin/bash \
    <image>

Flag	Why
`--device /dev/dri`	Pass every Intel GPU's DRM nodes. Use `--device /dev/dri/renderD128` for just the first GPU's render node (least privilege).
`--group-add "$(getent group render \| cut -d: -f3)"`	Joins the container user to the host's `render` group by GID (not name) so it works in images where a `render` group with a different GID — or no `render` group at all — exists. Required when nodes are mode 0660/0640. Skip causes `EACCES` on Level Zero init.
`--ipc=host`	vLLM and `torch.distributed` use `/dev/shm` and POSIX semaphores. `--shm-size=16g` is a private-IPC alternative.
`-e ZE_AFFINITY_MASK=0`	Pin to GPU 0. See xpu-discover for IDs. Always set explicitly.
`-v ~/.cache/huggingface:...`	Share the host model cache; avoid re-download.
`--entrypoint /bin/bash`	Override server-image autostart for interactive use.

When `--privileged` is needed

Exception, not rule. Required only for:

unitrace / VTune collectors that read PMU MSRs.
GPU firmware updates (xpu-smi updatefw).
xpu-smi diag --singletest 5 (PCIe bandwidth) and similar low-level diag tests.

For running models and most profiling, --device /dev/dri is enough. Add --privileged only when you hit a specific permission failure pointing at it.

Server mode, multi-GPU, `--net=host`

See references/server-and-multi-gpu.md for daemon-style server launches, one-process-per-GPU vs single-process TP / PP layouts, the oneCCL CCL_ZE_IPC_EXCHANGE=pidfd setting for multi-XPU TP, and when --net=host is actually needed.

Verifying the container sees the GPU

xpu-smi discovery

Symptom	Cause	Fix
`xpu-smi: command not found`	image lacks `xpu-smi`	use a different image or skip this check
empty `discovery` table	no `/dev/dri` passed	add `--device /dev/dri`
`Level Zero init failed` / `EACCES`	user not in `render` group	add `--group-add "$(getent group render
wrong GPU count	`ZE_AFFINITY_MASK` inherited from host	pass mask explicitly with `-e`
`diag` works on host, fails in container	container not privileged	add `--privileged`, or skip diag inside container

Common errors

failed to create shim task: permission denied → container runtime can't open /dev/dri/card0. Add --privileged or check host file mode.
LIBZE_LOADER: Failed to load level-zero loader → image missing libze1 / intel-level-zero-gpu. Use a different image.
RuntimeError: Cannot find any XPU devices (PyTorch) → Level Zero loaded but no device visible. Re-check ZE_AFFINITY_MASK and run xpu-smi discovery in the container.
bus error early in vLLM/PyTorch startup → shared memory too small. Use --ipc=host or raise --shm-size.

Env vars

Variable	Purpose
`ZE_AFFINITY_MASK`	Which XPU(s) visible.
`HF_TOKEN`	Hugging Face auth.
`HF_HOME`	Override in-container HF cache path.
`HUGGINGFACE_HUB_CACHE`	Older alias; some images still use it.
`OMP_NUM_THREADS`	Cap CPU threads; `1` for single-process serving.
`CCL_ZE_IPC_EXCHANGE=pidfd`	Multi-GPU-friendly oneCCL IPC mechanism.
`IGC_EnableAluBinding=1`	Battlemage matmul-codegen hint; bench both.
`ONEAPI_DEVICE_SELECTOR=level_zero:0`	Belt-and-suspenders pin alongside `ZE_AFFINITY_MASK`.

References

references/server-and-multi-gpu.md — server mode, multi-GPU, --net=host
Linux DRM device interface: https://docs.kernel.org/gpu/drm-uapi.html
Level Zero loader: https://oneapi-src.github.io/level-zero-spec/
Intel xe driver: https://docs.kernel.org/gpu/xe/index.html

Ver fuente ↗

Metadatos del archivo

name: xpu-container-run
description: Launch a Docker container with Intel GPU access on Linux. Encodes the correct combination of `--device /dev/dri`, render-group access, `--ipc=host`, `ZE_AFFINITY_MASK` pinning, Hugging Face cache mount, and `--entrypoint /bin/bash` for interactive use. Use when running any Intel-XPU container (vLLM-XPU, sglang-xpu, torch-XPU, llama.cpp SYCL, etc.) and the device must be visible inside. The CUDA analogue is `docker run --gpus all` — Intel has no `--gpus` flag, you pass the Direct Rendering Manager (DRM) nodes directly.

Ver texto original

---
name: xpu-container-run
description: Launch a Docker container with Intel GPU access on Linux. Encodes the correct combination of `--device /dev/dri`, render-group access, `--ipc=host`, `ZE_AFFINITY_MASK` pinning, Hugging Face cache mount, and `--entrypoint /bin/bash` for interactive use. Use when running any Intel-XPU container (vLLM-XPU, sglang-xpu, torch-XPU, llama.cpp SYCL, etc.) and the device must be visible inside. The CUDA analogue is `docker run --gpus all` — Intel has no `--gpus` flag, you pass the Direct Rendering Manager (DRM) nodes directly.
---

# xpu-container-run

Intel GPUs do not plug into Docker via `--gpus all`. There is no
`nvidia-container-toolkit` equivalent. Pass the kernel's Direct
Rendering Manager (DRM) character devices into the container and
grant the right group ownership.

## CUDA → Intel cheat sheet

| CUDA | Intel |
|---|---|
| `docker run --gpus all` | `--device /dev/dri --group-add "$(getent group render \| cut -d: -f3)"` |
| `docker run --gpus '"device=0"'` | `-e ZE_AFFINITY_MASK=0` |
| `--ipc=host` | same |
| `--shm-size=16g` | same (alternative to `--ipc=host`) |
| `--runtime nvidia` | nothing — `xe`/`i915` is in-kernel |
| `nvidia-smi` inside container | `xpu-smi discovery` |

No "Intel container toolkit" needed; passing the DRM nodes is enough.

## Image source

Comes from the runner skill:
- vLLM serving → **vllm-xpu-run** (`vllm/vllm-openai-xpu:latest`)
- SGLang → **sglang-xpu-run** (built from upstream `docker/xpu.Dockerfile`)
- PyTorch / Transformers → **torch-xpu-run**

`<image>` below is whichever you picked.

## Quickstart — interactive shell, one GPU

Confirm the image name with the user before running — this binds
host GPU devices into the container.

```sh
docker run --rm -it \
    --device /dev/dri \
    --group-add "$(getent group render | cut -d: -f3)" \
    --ipc=host \
    -e ZE_AFFINITY_MASK=0 \
    -e HF_TOKEN="$HF_TOKEN" \
    -v "$HOME/.cache/huggingface:/root/.cache/huggingface" \
    --entrypoint /bin/bash \
    <image>
```

| Flag | Why |
|---|---|
| `--device /dev/dri` | Pass every Intel GPU's DRM nodes. Use `--device /dev/dri/renderD128` for just the first GPU's render node (least privilege). |
| `--group-add "$(getent group render \| cut -d: -f3)"` | Joins the container user to the host's `render` group by **GID** (not name) so it works in images where a `render` group with a different GID — or no `render` group at all — exists. Required when nodes are mode 0660/0640. Skip causes `EACCES` on Level Zero init. |
| `--ipc=host` | vLLM and `torch.distributed` use `/dev/shm` and POSIX semaphores. `--shm-size=16g` is a private-IPC alternative. |
| `-e ZE_AFFINITY_MASK=0` | Pin to GPU 0. See **xpu-discover** for IDs. Always set explicitly. |
| `-v ~/.cache/huggingface:...` | Share the host model cache; avoid re-download. |
| `--entrypoint /bin/bash` | Override server-image autostart for interactive use. |

## When `--privileged` is needed

Exception, not rule. Required only for:

- `unitrace` / VTune collectors that read PMU MSRs.
- GPU firmware updates (`xpu-smi updatefw`).
- `xpu-smi diag --singletest 5` (PCIe bandwidth) and similar
  low-level diag tests.

For running models and most profiling, `--device /dev/dri` is
enough. Add `--privileged` only when you hit a specific permission
failure pointing at it.

## Server mode, multi-GPU, `--net=host`

See `references/server-and-multi-gpu.md` for daemon-style server
launches, one-process-per-GPU vs single-process TP / PP layouts,
the oneCCL `CCL_ZE_IPC_EXCHANGE=pidfd` setting for multi-XPU TP,
and when `--net=host` is actually needed.

## Verifying the container sees the GPU

```sh
xpu-smi discovery
```

| Symptom | Cause | Fix |
|---|---|---|
| `xpu-smi: command not found` | image lacks `xpu-smi` | use a different image or skip this check |
| empty `discovery` table | no `/dev/dri` passed | add `--device /dev/dri` |
| `Level Zero init failed` / `EACCES` | user not in `render` group | add `--group-add "$(getent group render | cut -d: -f3)"` |
| wrong GPU count | `ZE_AFFINITY_MASK` inherited from host | pass mask explicitly with `-e` |
| `diag` works on host, fails in container | container not privileged | add `--privileged`, or skip diag inside container |

## Common errors

- `failed to create shim task: permission denied` → container
  runtime can't open `/dev/dri/card0`. Add `--privileged` or check
  host file mode.
- `LIBZE_LOADER: Failed to load level-zero loader` → image missing
  `libze1` / `intel-level-zero-gpu`. Use a different image.
- `RuntimeError: Cannot find any XPU devices` (PyTorch) → Level
  Zero loaded but no device visible. Re-check `ZE_AFFINITY_MASK`
  and run `xpu-smi discovery` in the container.
- `bus error` early in vLLM/PyTorch startup → shared memory too
  small. Use `--ipc=host` or raise `--shm-size`.

## Env vars

| Variable | Purpose |
|---|---|
| `ZE_AFFINITY_MASK` | Which XPU(s) visible. |
| `HF_TOKEN` | Hugging Face auth. |
| `HF_HOME` | Override in-container HF cache path. |
| `HUGGINGFACE_HUB_CACHE` | Older alias; some images still use it. |
| `OMP_NUM_THREADS` | Cap CPU threads; `1` for single-process serving. |
| `CCL_ZE_IPC_EXCHANGE=pidfd` | Multi-GPU-friendly oneCCL IPC mechanism. |
| `IGC_EnableAluBinding=1` | Battlemage matmul-codegen hint; bench both. |
| `ONEAPI_DEVICE_SELECTOR=level_zero:0` | Belt-and-suspenders pin alongside `ZE_AFFINITY_MASK`. |

## References

- `references/server-and-multi-gpu.md` — server mode, multi-GPU, `--net=host`
- Linux DRM device interface: <https://docs.kernel.org/gpu/drm-uapi.html>
- Level Zero loader: <https://oneapi-src.github.io/level-zero-spec/>
- Intel `xe` driver: <https://docs.kernel.org/gpu/xe/index.html>

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GitHub adoption: 21 GitHub stars
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Repositorio fuente: intel/gpu-ai-skills
Licencia: Apache-2.0
Versión: Unknown
Último push de GitHub: 11 sept 2026
Registro actualizado: 14 sept 2026
Ruta de instrucciones: plugins/intel-gpu-ai-skills/skills/xpu-container-run/SKILL.md @ 0b4fafd09c5e

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Calidad

55/100

Prometedor

Confianza

59/100

Do not auto-install

Auditoría

71/100

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Dependency or permission surface needs review
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Low GitHub adoption signal
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GitHub adoption: 21 GitHub stars
Stars/forks activity: 21 stars, 6 forks; issue activity unavailable in current metadata
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Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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      "permissionSurface": "secrets or environment access, shell or command execution",
      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "design-creative",
      "agent-skill"
    ],
    "known_risks": [
      "AI review approval is missing",
      "Low GitHub adoption signal",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 21 GitHub stars",
      "Stars/forks activity: 21 stars, 6 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 71,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Low GitHub adoption signal",
      "AI review approval is missing",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 21 GitHub stars",
      "Stars/forks activity: 21 stars, 6 forks; issue activity unavailable in current metadata"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 55,
    "label": "Promising"
  },
  "supply": {
    "track": "Design and creative production",
    "scenario": "Design and creative",
    "maintenance": "30d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "No OpenAgentSkill engagement data yet",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "AI review approval is missing"
  ],
  "agent_contract": {
    "task_input": "Use xpu-container-run in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 67/100 Manual review",
      "Audit: 71/100 Needs review",
      "Safety: 31/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "intel-xpu-container-run (xpu-container-run)",
      "install_command": "npx skills add intel/gpu-ai-skills --skill xpu-container-run",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "intel-xpu-container-run",
      "task": "Use xpu-container-run in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/intel-xpu-container-run",
    "api": "https://www.openagentskill.com/api/agent/skills/intel-xpu-container-run",
    "audit": "https://www.openagentskill.com/skills/intel-xpu-container-run/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=intel-xpu-container-run&task=Use%20xpu-container-run%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20xpu-container-run%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20xpu-container-run%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/intel-xpu-container-run/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/intel-xpu-container-run"
  }
}

Para el creador

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Creador: intel
Fuente: intel/gpu-ai-skills
Indexado por: Índice comunitario de OpenAgentSkill

La atribución enlaza al repositorio público o al perfil del creador. Los creadores pueden reclamar la ficha para actualizar las señales de propiedad.

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Resumen

xpu-container-run

CUDA → Intel cheat sheet

Image source

Quickstart — interactive shell, one GPU

When --privileged is needed

Server mode, multi-GPU, --net=host

Verifying the container sees the GPU

Common errors

Env vars

References

Revisar el código fuente

Precio y costes de ejecución

Empieza con una tarea pequeña

Fuente y notas de uso

Acceso para agentes

Skills relacionados

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Indexado por Registry

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Añade las insignias de evidencia a tu README

Señal de comunidad

When `--privileged` is needed

Server mode, multi-GPU, `--net=host`