LLaMA-Factory/docker/docker-xpu

Docker Setup for Intel GPUs

This directory contains Docker configuration files for running LLaMA Factory with Intel GPU (XPU) support.

Image Details

Component	Version
Base OS	Ubuntu 24.04 LTS (x86_64)
Intel DLE base	intel/deep-learning-essentials:2026.1.0-devel-ubuntu24.04
Python	3.12
PyTorch	2.13.0+xpu
Intel GPU runtime	Bundled inside DLE (libze-intel-gpu 26.18.x, oneAPI 2026.1)

The Intel compute runtime is bundled inside the DLE base image — no GPU compute packages are needed on the host, only the kernel driver. The bundled runtime must be compatible with the host kernel driver; verify with dpkg -l libze-intel-gpu1 | grep -oP '\d+\.\d+\.\d+'.

Prerequisites

1. Docker & Docker Compose

# Ubuntu/Debian
sudo apt-get update && sudo apt-get install docker.io docker-compose-v2

See the official Docker install docs for other distros or newer versions.

2. Intel GPU Kernel Driver (host only)

# Add the Intel GPU PPA
sudo apt-get install -y gpg-agent wget
wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | \
    sudo gpg --dearmor -o /usr/share/keyrings/intel-graphics.gpg
echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] \
    https://repositories.intel.com/gpu/ubuntu noble client" | \
    sudo tee /etc/apt/sources.list.d/intel-graphics.list
sudo apt-get update

# Kernel driver only — no compute runtime packages needed on the host
sudo apt-get install -y intel-i915-dkms intel-fw-gpu
sudo reboot

After reboot, verify /dev/dri is populated: ls /dev/dri/ (expect card0, renderD128, etc).

See the Intel GPU Installation Guide for details.

Important

Enable Resizable BAR in your system BIOS before proceeding, or you may see Bus error (core dumped) or degraded performance. See Intel's guide.

3. Add your user to the GPU groups

sudo usermod -aG render,video $USER
# Log out and back in for group membership to take effect

(Optional) sanity-check the host side with sudo apt-get install -y clinfo && clinfo --list | grep Device. The check that actually matters — torch.xpu.device_count() — runs inside the container, in Usage below.

Usage

Using Docker Compose (Recommended)

cd docker/docker-xpu/
docker compose up -d
docker compose exec llamafactory bash

Verify GPU access inside the container:

python3 -c "import torch; print(torch.xpu.device_count(), 'XPU device(s) found')"

Using Docker Run

# Build the image (from the repo root)
docker build -t llamafactory:xpu -f docker/docker-xpu/Dockerfile .

# Run the container
docker run -it --rm \
    --device /dev/dri \
    -v /dev/dri/by-path:/dev/dri/by-path \
    --group-add $(getent group render | cut -d: -f3) \
    --group-add $(getent group video  | cut -d: -f3) \
    --ipc=host \
    -p 7860:7860 \
    -p 8000:8000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --name llamafactory \
    llamafactory:xpu bash

Build arguments

Argument	Default	Purpose
BASE_IMAGE	intel/deep-learning-essentials:2026.1.0-devel-ubuntu24.04	oneAPI / GPU runtime version
PIP_INDEX	https://pypi.org/simple	PyPI mirror for everything except the torch wheels
PYTORCH_INDEX	https://download.pytorch.org/whl/xpu	where the +xpu torch wheels come from
OCLOC_VERSION	26.18.38308.1	pinned intel-ocloc build — must match BASE_IMAGE's compute-runtime series

docker build -t llamafactory:xpu -f docker/docker-xpu/Dockerfile . \
    --build-arg PIP_INDEX=https://pypi.org/simple

Design notes

Why the image is built the way it is — skip to Troubleshooting if you just want to run it.

Pinned torch stack

torch, torchvision and torchaudio install together from requirements/xpu.txt as +xpu builds, via --index-url https://download.pytorch.org/whl/xpu (not --extra-index-url, which would let a plain PyPI build win). Pinning all three avoids RuntimeError: operator torchvision::nms does not exist from an ABI-mismatched pair.

No pip constraint file is needed: every other torch requirement in the dependency graph is a lower bound that 2.13.0+xpu already satisfies, so nothing later swaps it out.

If the XPU wheels ever get replaced with plain ones, torch.xpu.device_count() returns 0. Restore with:

pip install --force-reinstall -r requirements/xpu.txt \
    --index-url https://download.pytorch.org/whl/xpu

How pip is installed

The DLE base ships Python 3.12 with no pip and no ensurepip (Ubuntu strips it). pip comes from apt (python3-pip) and is left at the distro version rather than upgraded — apt's pip has no RECORD file, so pip install --upgrade pip fails with:

ERROR: Cannot uninstall pip 24.0, RECORD file not found. Hint: The package was installed by debian.

— taking setuptools/wheel/hatchling down with it in the same command.

Multi-GPU and OPTIM_TORCH

The image sets ENV OPTIM_TORCH=0. The launcher's default (OPTIM_TORCH=1) sets PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True, whose allocator path trips a Level-Zero driver bug on 2-GPU XPU runs (DDP segfault, FSDP2 hang). Disabling it costs <1% throughput. Re-enable per-run with OPTIM_TORCH=1 llamafactory-cli train ... to test it. It is tracked in intel, will be update or remove this ENV when it is resolve with PyTorch or level zero etc fixes.

Optional accelerators

deepspeed and bitsandbytes install on a best-effort basis — neither is required to train on XPU, so a build failure only prints a warning instead of failing the image:

WARNING: deepspeed install failed - deepspeed training unavailable in this image
WARNING: bitsandbytes install failed - 4-bit quantization (QLoRA) unavailable in this image

Both install and work on XPU today (verified: torch.compile, LoRA + DeepSpeed ZeRO-2 SFT, and bitsandbytes 4-bit all pass on real Arc Pro B60 hardware). deepspeed is pinned to ==0.19.6 here, separately from requirements/deepspeed.txt's shared <=0.18.4 cap: any version in that range detects XPU as the active accelerator and imports torch.utils.cpp_extension.DpcppBuildExtension — a class that used to be provided by intel_extension_for_pytorch (IPEX). IPEX is no longer part of this stack, so that import fails with ImportError: cannot import name 'DpcppBuildExtension'. deepspeed dropped the IPEX dependency and switched to the stock BuildExtension in 0.18.7, so anything from there onward works; 0.19.6 is the latest release at time of writing.

Check the build log, or verify inside the container with python3 -c "import deepspeed" / import bitsandbytes. Install manually if missing:

DS_BUILD_OPS=0 pip install "deepspeed==0.19.6"
pip install -r requirements/bitsandbytes.txt

ocloc and torch.compile

torch.compile lowers to Intel Triton, which shells out to ocloc (the Intel offline GPU compiler) — not bundled in the DLE base, so compiled paths fail with FileNotFoundError: 'ocloc' without it. The Dockerfile installs a single pinned .deb from Intel's GitHub releases (ARG OCLOC_VERSION) rather than apt-get install intel-ocloc, because the Intel graphics PPA keeps only its newest revision and would drag libze-intel-gpu1 (the host-driver-facing package) up with it.

Important

If you override ARG BASE_IMAGE, move OCLOC_VERSION to match — check docker run --rm <base-image> dpkg -l libze-intel-gpu1 | tail -1 and pick the nearest compute-runtime release.

Troubleshooting

GPU Not Detected (torch.xpu.device_count() returns 0)

1. Kernel driver too old for the DLE runtime — compare dpkg -l libze-intel-gpu1 on the host vs. docker run --rm llamafactory:xpu dpkg -l libze-intel-gpu1. Update the host driver (sudo apt-get install -y intel-i915-dkms intel-fw-gpu && sudo reboot) or use an older BASE_IMAGE.
2. Missing /dev/dri device — pass --device /dev/dri (done automatically by docker compose).
3. Missing group membership — the container process needs the render and video groups; docker compose sets these via group_add. For manual docker run, pass --group-add $(getent group render | cut -d: -f3) --group-add $(getent group video | cut -d: -f3).
4. by-path mount missing — required for multi-GPU Level-Zero IPC: -v /dev/dri/by-path:/dev/dri/by-path (included in docker-compose.yml).

fatal error: Python.h: No such file or directory

Intel Triton JIT-compiles a C driver at the first XPU kernel launch, which needs matching pythonX.Y-dev headers and a C compiler present in the image — installed at build time even though the error would happen at runtime. If you've swapped the interpreter, reinstall matching headers:

PY_MM="$(python3 -c 'import sys; print(f"{sys.version_info.major}.{sys.version_info.minor}")')"
apt-get update && apt-get install -y "python${PY_MM}-dev" build-essential

Permission Denied on /dev/dri

sudo usermod -aG render,video $USER
newgrp render   # apply without logout

SYCL Backends mismatch / libsycl.so.N: cannot open shared object file

Two SYCL runtimes exist in this image by design — torch's own intel-sycl-rt (pip) and the DLE base's oneAPI — and normally share the same libsycl.so.9 SONAME, so both coexist fine. A mismatch shows up only after changing torch or BASE_IMAGE independently. Compare versions:

pip list | grep -E "intel-sycl-rt|dpcpp-cpp-rt"
ls /opt/intel/oneapi/*/lib/libsycl.so.*

The libsycl.so.N numbers on both sides must match. ldconfig -p | grep libsycl returning nothing is expected — neither runtime is in the ldconfig cache.

Additional Notes

• The container automatically sources /opt/intel/oneapi/setvars.sh in every interactive shell (~/.bashrc). For non-interactive scripts, source it explicitly.
• For training, llamafactory-cli train dispatches automatically via torchrun for multi-GPU.