wehub-resource-sync
76 lines
2.6 KiB
YAML
76 lines
2.6 KiB
YAML
# ODS — Intel Arc GPU Overlay (oneAPI SYCL, build-from-source)
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#
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# Builds llama-server from source using Intel oneAPI Base Toolkit.
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# Use with: docker compose -f docker-compose.base.yml -f docker-compose.arc.yml up -d
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#
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# Supported hardware:
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# ARC tier — Arc A770 16 GB (and future Arc B-series ≥12 GB)
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# ARC_LITE tier — Arc A750 8 GB, A380 6 GB
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#
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# Build arguments (customise via .env or shell export):
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# LLAMA_TAG llama.cpp git tag (default: b8248)
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# ONEAPI_VERSION oneAPI Base Toolkit image tag (default: 2025.0.0-0-devel-ubuntu22.04)
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# LLAMA_ARC_IMAGE Override to a pre-built image and skip the local build entirely
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# e.g. LLAMA_ARC_IMAGE=ghcr.io/ggml-org/llama.cpp:server-intel-b8248
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#
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# First-run note:
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# The SYCL build compiles llama.cpp with Intel icx/icpx. Allow 10–20 min
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# on first `docker compose up --build`. Subsequent starts use Docker cache.
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#
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# Host prerequisites (Ubuntu/Debian):
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# apt install intel-opencl-icd intel-level-zero-gpu level-zero
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# usermod -aG video,render $USER # re-login after
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# # Verify GPU: clinfo | grep -i "intel arc"
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services:
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llama-server:
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build:
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context: ./images/llama-sycl
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dockerfile: Dockerfile
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args:
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LLAMA_TAG: ${LLAMA_TAG:-b8248}
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ONEAPI_VERSION: ${ONEAPI_VERSION:-2025.0.0-0-devel-ubuntu22.04}
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# Set LLAMA_ARC_IMAGE in .env to skip the local build and pull a pre-built image.
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image: ${LLAMA_ARC_IMAGE:-ods-llama-sycl:local}
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devices:
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- /dev/dri:/dev/dri
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group_add:
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- "${VIDEO_GID:-44}"
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- "${RENDER_GID:-992}"
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environment:
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# Level Zero selects the Intel Arc GPU; persistent cache avoids
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# recompiling SYCL kernels on every container start (~30 s savings).
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- ONEAPI_DEVICE_SELECTOR=level_zero:gpu
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- SYCL_CACHE_PERSISTENT=1
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# Enable Intel GPU System Management Interface for telemetry
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- ZES_ENABLE_SYSMAN=1
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command:
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- --model
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- /models/${GGUF_FILE:-Qwen3.5-9B-Q4_K_M.gguf}
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- --host
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- 0.0.0.0
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- --port
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- "8080"
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- --n-gpu-layers
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- "${N_GPU_LAYERS:-99}"
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- --ctx-size
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- "${CTX_SIZE:-32768}"
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- --metrics
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deploy:
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resources:
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limits:
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cpus: '${LLAMA_CPU_LIMIT:-16.0}'
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memory: ${LLAMA_SERVER_MEMORY_LIMIT:-24G}
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reservations:
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cpus: '${LLAMA_CPU_RESERVATION:-2.0}'
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memory: 4G
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dashboard-api:
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environment:
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# Hard-code sycl so the dashboard uses Intel sysfs GPU detection
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# regardless of .env state.
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- GPU_BACKEND=sycl
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volumes:
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- /sys/class/drm:/sys/class/drm:ro
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- /sys/class/hwmon:/sys/class/hwmon:ro
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