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xpu-container-run

Harga belum dikonfirmasi★ 21 Star GitHubDirektori diperbarui · 14 Sep 2026agent-skill

Ringkasan

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

Lihat sumber ↗

Metadata berkas

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.

Lihat teks asli

---
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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Repositori sumber: intel/gpu-ai-skills
Lisensi: Apache-2.0
Versi: Unknown
Push GitHub terakhir: 11 Sep 2026
Direktori diperbarui: 14 Sep 2026
Jalur instruksi: plugins/intel-gpu-ai-skills/skills/xpu-container-run/SKILL.md @ 0b4fafd09c5e

Versi dilaporkan dalam metadata direktori; periksa rilis sumber.

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Dependency or permission surface needs review
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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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      "repository": "https://github.com/intel/gpu-ai-skills/tree/main/plugins/intel-gpu-ai-skills/skills/xpu-container-run",
      "install": "npx skills add intel/gpu-ai-skills --skill xpu-container-run",
      "installSafety": "standard package or runtime install path",
      "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",
    "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",
    "Quality score needs review"
  ],
  "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"
  }
}

Untuk kreator

Sumber listing

Diindeks Registry

Dapat diklaim

Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.

Kreator: intel
Sumber: intel/gpu-ai-skills
Diindeks oleh: Indeks komunitas OpenAgentSkill

Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.

Klaim skill ini

Klaim pemilik

Klaim listing skill ini

Listing Diindeks Registry ini dikaitkan dengan intel, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.

Kit berbagi

Kit backlink kreator

Tambahkan badge bukti ke README Anda

Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/intel-xpu-container-run?metric=listed&label=Listed)](https://www.openagentskill.com/skills/intel-xpu-container-run?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/intel-xpu-container-run?metric=trust&label=Trust)](https://www.openagentskill.com/skills/intel-xpu-container-run?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/intel-xpu-container-run?metric=audit&label=Audit)](https://www.openagentskill.com/skills/intel-xpu-container-run/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/intel-xpu-container-run?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/intel-xpu-container-run?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

Pratinjau badge Buka audit Kit kreator

Sinyal komunitas

Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.

Ringkasan

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

Tinjau sumber

Harga dan biaya penggunaan

Mulai dengan tugas kecil

Sumber dan catatan penggunaan

Akses agent

Skill terkait

Untuk kreator

Diindeks Registry

Klaim listing skill ini

Tambahkan badge bukti ke README Anda

Sinyal komunitas

When `--privileged` is needed

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