Registry indexed
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-
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.
Source documentation, not instructions for this website. Review permissions before running any commands.
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 |
|---|---|
docker run --gpus all | --device /dev/dri --group-add "$(getent group render | cut -d: -f3)" |
docker run --gpus '"device=0"' | -e ZE_AFFINITY_MASK=0 |
--ipc=host | same |
--shm-size=16g | same (alternative to --ipc=host) |
--runtime nvidia | nothing — xe/i915 is in-kernel |
nvidia-smi inside container | xpu-smi discovery |
No "Intel container toolkit" needed; passing the DRM nodes is enough.
Comes from the runner skill:
vllm/vllm-openai-xpu:latest)docker/xpu.Dockerfile)<image> below is whichever you picked.
Confirm the image name with the user before running — this binds host GPU devices into the container.
docker run --rm -it \
--device /dev/dri \
--group-add "$(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 "$(getent group render | cut -d: -f3)" | Joins the container user to the host's render group by GID (not name) so it works in images where a render group with a different GID — or no render group at all — exists. Required when nodes are mode 0660/0640. Skip causes EACCES on Level Zero init. |
--ipc=host | vLLM and torch.distributed use /dev/shm and POSIX semaphores. --shm-size=16g is a private-IPC alternative. |
-e ZE_AFFINITY_MASK=0 | Pin to GPU 0. See xpu-discover for IDs. Always set explicitly. |
-v ~/.cache/huggingface:... | Share the host model cache; avoid re-download. |
--entrypoint /bin/bash | Override server-image autostart for interactive use. |
--privileged is neededException, not rule. Required only for:
unitrace / VTune collectors that read PMU MSRs.xpu-smi updatefw).xpu-smi diag --singletest 5 (PCIe bandwidth) and similar
low-level diag tests.For running models and most profiling, --device /dev/dri is
enough. Add --privileged only when you hit a specific permission
failure pointing at it.
--net=hostSee references/server-and-multi-gpu.md for daemon-style server
launches, one-process-per-GPU vs single-process TP / PP layouts,
the oneCCL CCL_ZE_IPC_EXCHANGE=pidfd setting for multi-XPU TP,
and when --net=host is actually needed.
xpu-smi discovery
| Symptom | Cause | Fix |
|---|---|---|
xpu-smi: command not found | image lacks xpu-smi | use a different image or skip this check |
empty discovery table | no /dev/dri passed | add --device /dev/dri |
Level Zero init failed / EACCES | user not in render group | add `--group-add "$(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 |
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.| 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/server-and-multi-gpu.md — server mode, multi-GPU, --net=hostxe driver: https://docs.kernel.org/gpu/xe/index.htmlname: 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.
---
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>
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: Apache-2.0
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
55/100
Promising
Trust
59
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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"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"
}
}Listing source
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Do not auto-install
Audit
71/100
Needs review
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.