Billy D.
5f2d167ba0
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134 lines
5.9 KiB
Docker
134 lines
5.9 KiB
Docker
# syntax=docker/dockerfile:1.7
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# AMD Strix Halo Ray Worker for khelben (gfx1151 / RDNA 3.5)
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# Used for: vLLM (Llama 3.1 70B)
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#
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# Build:
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# docker build -t git.daviestechlabs.io/daviestechlabs/ray-worker-strixhalo:latest \
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# -f dockerfiles/Dockerfile.ray-worker-strixhalo .
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#
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# Multi-stage build: Extract ROCm 7.1 from vendor image, use Ray base for Python 3.12
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# Note: Uses TheRock gfx110X wheels due to ROCm/ROCm#5853 segfault issue
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# Note: Python 3.12 required — vLLM ROCm wheel (wheels.vllm.ai/rocm) is cp312 only
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# Stage 1: ROCm 7.1 libraries from AMD vendor image
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FROM docker.io/rocm/pytorch:rocm7.1_ubuntu24.04_py3.12_pytorch_release_2.9.1 AS rocm-source
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# Stage 2: Production image
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FROM docker.io/rayproject/ray:2.53.0-py312 AS production
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# OCI Image Spec labels
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LABEL org.opencontainers.image.title="Ray Worker - AMD Strix Halo"
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LABEL org.opencontainers.image.description="Ray Serve worker for AMD Strix Halo (vLLM LLM inference)"
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LABEL org.opencontainers.image.vendor="DaviesTechLabs"
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LABEL org.opencontainers.image.source="https://git.daviestechlabs.io/daviestechlabs/kuberay-images"
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LABEL org.opencontainers.image.licenses="MIT"
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LABEL gpu.target="amd-rocm-7.1-gfx1151"
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LABEL ray.version="2.53.0"
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WORKDIR /app
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# Copy ROCm stack from vendor image (--link makes this layer independent for better caching)
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COPY --link --from=rocm-source /opt/rocm /opt/rocm
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# ROCm environment variables - split to ensure ROCM_HOME is set first
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ENV ROCM_HOME=/opt/rocm
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ENV PATH="/opt/rocm/bin:/opt/rocm/llvm/bin:/home/ray/anaconda3/bin:/home/ray/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin" \
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LD_LIBRARY_PATH="/opt/rocm/lib:/opt/rocm/lib64:/home/ray/anaconda3/lib" \
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HSA_PATH="/opt/rocm/hsa" \
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HIP_PATH="/opt/rocm/hip" \
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# Strix Halo (gfx1151) specific settings
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HIP_VISIBLE_DEVICES=0 \
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HSA_ENABLE_SDMA=0 \
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PYTORCH_HIP_ALLOC_CONF="expandable_segments:True,max_split_size_mb:512" \
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HSA_OVERRIDE_GFX_VERSION="11.0.0" \
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ROCM_TARGET_LST="gfx1151,gfx1100"
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# Install system dependencies
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USER root
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RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
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--mount=type=cache,target=/var/lib/apt,sharing=locked \
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apt-get update && apt-get install -y --no-install-recommends \
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libelf1 \
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libnuma1 \
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libdrm2 \
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libdrm-amdgpu1 \
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kmod \
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&& rm -rf /var/lib/apt/lists/*
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# Install uv for fast Python package management (ADR-0014)
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COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv
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USER ray
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# Install vLLM ROCm build, torch ROCm 7.0, and inference dependencies.
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# IMPORTANT: vLLM ROCm wheel (0.15.1+rocm700) has C-extensions compiled against
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# the official PyTorch ROCm 7.0 ABI. We MUST use torch from the same ROCm 7.0
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# index — TheRock nightlies have an incompatible c10::hip ABI that causes
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# undefined symbol errors in vllm._C and vllm._rocm_C.
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# HSA_OVERRIDE_GFX_VERSION=11.0.0 makes gfx1151 appear as gfx1100 to this torch.
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RUN --mount=type=cache,target=/home/ray/.cache/uv,uid=1000,gid=1000 \
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uv pip install --system 'setuptools>=77.0.3,<81.0' && \
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uv pip install --system \
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--extra-index-url https://wheels.vllm.ai/rocm/ \
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--extra-index-url https://download.pytorch.org/whl/rocm7.0 \
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vllm \
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torch torchaudio torchvision \
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'transformers>=4.35.0,<5.0' \
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'accelerate>=0.25.0,<1.0' \
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'sentence-transformers>=2.3.0,<3.0' \
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'httpx>=0.27.0,<1.0' \
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'scipy>=1.11.0,<2.0'
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# FIX: Ray base image has pandas 1.5.3 which is incompatible with numpy 2.x
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# PyTorch ROCm 7.0 requires numpy 2.x, so upgrade pandas to match.
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# Pin numpy <2.3 because numba (required by vLLM for speculative decoding)
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# does not yet support numpy 2.3+.
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RUN --mount=type=cache,target=/home/ray/.cache/uv,uid=1000,gid=1000 \
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uv pip install --system 'pandas>=2.0.0,<3.0' 'numpy>=2.1.0,<2.3'
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# Install amdsmi sysfs shim LAST (required for vLLM ROCm platform detection).
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# The native amdsmi from ROCm 7.1 requires glibc 2.38 (Ubuntu 24.04),
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# but the Ray base image is Ubuntu 22.04 (glibc 2.35). This pure-Python
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# shim reads GPU info from /sys/class/drm/* instead of libamd_smi.so.
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# Must be installed after vLLM/torch to prevent PyPI amdsmi from overwriting it.
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COPY --chown=1000:100 amdsmi-shim /tmp/amdsmi-shim
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RUN --mount=type=cache,target=/home/ray/.cache/uv,uid=1000,gid=1000 \
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uv pip install --system /tmp/amdsmi-shim && rm -rf /tmp/amdsmi-shim
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# FIX: Patch torch.cuda.mem_get_info for unified memory APUs.
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# On Strix Halo, PyTorch reports GTT (128 GiB) instead of real VRAM (96 GiB)
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# from sysfs. vLLM uses mem_get_info to pre-allocate, so wrong numbers cause
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# OOM or "insufficient GPU memory" at startup. The .pth file auto-patches
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# mem_get_info on Python startup to return sysfs VRAM values.
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COPY --chown=1000:100 amdsmi-shim/strixhalo_vram_fix.py \
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/home/ray/anaconda3/lib/python3.12/site-packages/strixhalo_vram_fix.py
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RUN echo "import strixhalo_vram_fix" > \
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/home/ray/anaconda3/lib/python3.12/site-packages/strixhalo_vram_fix.pth
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# Pre-download common models for faster cold starts (optional, increases image size)
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# RUN python3 -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('BAAI/bge-large-en-v1.5')"
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# Pre-create aiter JIT build cache directory.
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# The vLLM ROCm aiter package compiles kernels on first import and needs
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# this directory writable by the ray user (uid 1000).
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USER root
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RUN mkdir -p /home/ray/.aiter && chown 1000:100 /home/ray/.aiter
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USER ray
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# Copy entrypoint script (ray-serve-apps is installed from PyPI at runtime)
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COPY --chown=1000:100 --chmod=755 dockerfiles/ray-entrypoint.sh /app/ray-entrypoint.sh
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# Environment configuration
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ENV PYTHONPATH=/app \
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PYTHONUNBUFFERED=1 \
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PYTHONDONTWRITEBYTECODE=1 \
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RAY_HEAD_SVC="ai-inference-raycluster-head-svc" \
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GPU_RESOURCE="gpu_amd_strixhalo" \
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NUM_GPUS="1"
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# Health check
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HEALTHCHECK --interval=30s --timeout=10s --start-period=120s --retries=3 \
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CMD ray status --address=localhost:6379 || exit 1
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ENTRYPOINT ["/app/ray-entrypoint.sh"]
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