Billy D.
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95 lines
3.4 KiB
Docker
95 lines
3.4 KiB
Docker
# syntax=docker/dockerfile:1.7
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# AMD RDNA 2 Ray Worker for drizzt (Radeon 680M - gfx1035)
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# Used for: BGE Embeddings
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#
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# Build:
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# docker build -t git.daviestechlabs.io/daviestechlabs/ray-worker-rdna2:latest \
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# -f dockerfiles/Dockerfile.ray-worker-rdna2 .
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#
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# Multi-stage build: Extract ROCm from vendor image, use Ray base for Python 3.11
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# Stage 1: ROCm libraries from AMD vendor image
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FROM docker.io/rocm/pytorch:rocm6.4.4_ubuntu22.04_py3.10_pytorch_release_2.7.1 AS rocm-source
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# Stage 2: Production image
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FROM docker.io/rayproject/ray:2.53.0-py311 AS production
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# OCI Image Spec labels
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LABEL org.opencontainers.image.title="Ray Worker - AMD RDNA 2"
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LABEL org.opencontainers.image.description="Ray Serve worker for AMD RDNA 2 GPUs (BGE Embeddings)"
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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-6.4-gfx1035"
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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 (single COPY layer)
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COPY --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" \
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HSA_PATH="/opt/rocm/hsa" \
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HIP_PATH="/opt/rocm/hip" \
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# RDNA 2 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
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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 PyTorch with ROCm 6.2 wheels for Python 3.11 (uv is 10-100x faster)
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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 \
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torch==2.5.1 torchvision torchaudio \
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--index-url https://download.pytorch.org/whl/rocm6.2
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# Install inference dependencies
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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 \
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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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'numpy>=1.26.0,<2.0' \
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'scipy>=1.11.0,<2.0'
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# Pre-download embedding model for faster cold starts
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RUN python3 -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('BAAI/bge-large-en-v1.5')"
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# Copy entrypoint script (ray-serve-apps is installed from PyPI at runtime)
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COPY --chown=ray:ray --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_rdna2" \
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NUM_GPUS="1"
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# Health check
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HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --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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# Trigger rebuild
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